Identify_Customer_Segments.html

<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />

<title>Identify_Customer_Segments</title>

<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script>


<style type="text/css">
    /*!
*
* Twitter Bootstrap
*
*/
/*!
 * Bootstrap v3.3.7 (http://getbootstrap.com)
 * Copyright 2011-2016 Twitter, Inc.
 * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
 */
/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */
html {
  font-family: sans-serif;
  -ms-text-size-adjust: 100%;
  -webkit-text-size-adjust: 100%;
}
body {
  margin: 0;
}
article,
aside,
details,
figcaption,
figure,
footer,
header,
hgroup,
main,
menu,
nav,
section,
summary {
  display: block;
}
audio,
canvas,
progress,
video {
  display: inline-block;
  vertical-align: baseline;
}
audio:not([controls]) {
  display: none;
  height: 0;
}
[hidden],
template {
  display: none;
}
a {
  background-color: transparent;
}
a:active,
a:hover {
  outline: 0;
}
abbr[title] {
  border-bottom: 1px dotted;
}
b,
strong {
  font-weight: bold;
}
dfn {
  font-style: italic;
}
h1 {
  font-size: 2em;
  margin: 0.67em 0;
}
mark {
  background: #ff0;
  color: #000;
}
small {
  font-size: 80%;
}
sub,
sup {
  font-size: 75%;
  line-height: 0;
  position: relative;
  vertical-align: baseline;
}
sup {
  top: -0.5em;
}
sub {
  bottom: -0.25em;
}
img {
  border: 0;
}
svg:not(:root) {
  overflow: hidden;
}
figure {
  margin: 1em 40px;
}
hr {
  box-sizing: content-box;
  height: 0;
}
pre {
  overflow: auto;
}
code,
kbd,
pre,
samp {
  font-family: monospace, monospace;
  font-size: 1em;
}
button,
input,
optgroup,
select,
textarea {
  color: inherit;
  font: inherit;
  margin: 0;
}
button {
  overflow: visible;
}
button,
select {
  text-transform: none;
}
button,
html input[type="button"],
input[type="reset"],
input[type="submit"] {
  -webkit-appearance: button;
  cursor: pointer;
}
button[disabled],
html input[disabled] {
  cursor: default;
}
button::-moz-focus-inner,
input::-moz-focus-inner {
  border: 0;
  padding: 0;
}
input {
  line-height: normal;
}
input[type="checkbox"],
input[type="radio"] {
  box-sizing: border-box;
  padding: 0;
}
input[type="number"]::-webkit-inner-spin-button,
input[type="number"]::-webkit-outer-spin-button {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: textfield;
  box-sizing: content-box;
}
input[type="search"]::-webkit-search-cancel-button,
input[type="search"]::-webkit-search-decoration {
  -webkit-appearance: none;
}
fieldset {
  border: 1px solid #c0c0c0;
  margin: 0 2px;
  padding: 0.35em 0.625em 0.75em;
}
legend {
  border: 0;
  padding: 0;
}
textarea {
  overflow: auto;
}
optgroup {
  font-weight: bold;
}
table {
  border-collapse: collapse;
  border-spacing: 0;
}
td,
th {
  padding: 0;
}
/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */
@media print {
  *,
  *:before,
  *:after {
    background: transparent !important;
    box-shadow: none !important;
    text-shadow: none !important;
  }
  a,
  a:visited {
    text-decoration: underline;
  }
  a[href]:after {
    content: " (" attr(href) ")";
  }
  abbr[title]:after {
    content: " (" attr(title) ")";
  }
  a[href^="#"]:after,
  a[href^="javascript:"]:after {
    content: "";
  }
  pre,
  blockquote {
    border: 1px solid #999;
    page-break-inside: avoid;
  }
  thead {
    display: table-header-group;
  }
  tr,
  img {
    page-break-inside: avoid;
  }
  img {
    max-width: 100% !important;
  }
  p,
  h2,
  h3 {
    orphans: 3;
    widows: 3;
  }
  h2,
  h3 {
    page-break-after: avoid;
  }
  .navbar {
    display: none;
  }
  .btn > .caret,
  .dropup > .btn > .caret {
    border-top-color: #000 !important;
  }
  .label {
    border: 1px solid #000;
  }
  .table {
    border-collapse: collapse !important;
  }
  .table td,
  .table th {
    background-color: #fff !important;
  }
  .table-bordered th,
  .table-bordered td {
    border: 1px solid #ddd !important;
  }
}
@font-face {
  font-family: 'Glyphicons Halflings';
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot');
  src: url('../components/bootstrap/fonts/glyphicons-halflings-regular.eot?#iefix') format('embedded-opentype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff2') format('woff2'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.woff') format('woff'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.ttf') format('truetype'), url('../components/bootstrap/fonts/glyphicons-halflings-regular.svg#glyphicons_halflingsregular') format('svg');
}
.glyphicon {
  position: relative;
  top: 1px;
  display: inline-block;
  font-family: 'Glyphicons Halflings';
  font-style: normal;
  font-weight: normal;
  line-height: 1;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
.glyphicon-asterisk:before {
  content: "\002a";
}
.glyphicon-plus:before {
  content: "\002b";
}
.glyphicon-euro:before,
.glyphicon-eur:before {
  content: "\20ac";
}
.glyphicon-minus:before {
  content: "\2212";
}
.glyphicon-cloud:before {
  content: "\2601";
}
.glyphicon-envelope:before {
  content: "\2709";
}
.glyphicon-pencil:before {
  content: "\270f";
}
.glyphicon-glass:before {
  content: "\e001";
}
.glyphicon-music:before {
  content: "\e002";
}
.glyphicon-search:before {
  content: "\e003";
}
.glyphicon-heart:before {
  content: "\e005";
}
.glyphicon-star:before {
  content: "\e006";
}
.glyphicon-star-empty:before {
  content: "\e007";
}
.glyphicon-user:before {
  content: "\e008";
}
.glyphicon-film:before {
  content: "\e009";
}
.glyphicon-th-large:before {
  content: "\e010";
}
.glyphicon-th:before {
  content: "\e011";
}
.glyphicon-th-list:before {
  content: "\e012";
}
.glyphicon-ok:before {
  content: "\e013";
}
.glyphicon-remove:before {
  content: "\e014";
}
.glyphicon-zoom-in:before {
  content: "\e015";
}
.glyphicon-zoom-out:before {
  content: "\e016";
}
.glyphicon-off:before {
  content: "\e017";
}
.glyphicon-signal:before {
  content: "\e018";
}
.glyphicon-cog:before {
  content: "\e019";
}
.glyphicon-trash:before {
  content: "\e020";
}
.glyphicon-home:before {
  content: "\e021";
}
.glyphicon-file:before {
  content: "\e022";
}
.glyphicon-time:before {
  content: "\e023";
}
.glyphicon-road:before {
  content: "\e024";
}
.glyphicon-download-alt:before {
  content: "\e025";
}
.glyphicon-download:before {
  content: "\e026";
}
.glyphicon-upload:before {
  content: "\e027";
}
.glyphicon-inbox:before {
  content: "\e028";
}
.glyphicon-play-circle:before {
  content: "\e029";
}
.glyphicon-repeat:before {
  content: "\e030";
}
.glyphicon-refresh:before {
  content: "\e031";
}
.glyphicon-list-alt:before {
  content: "\e032";
}
.glyphicon-lock:before {
  content: "\e033";
}
.glyphicon-flag:before {
  content: "\e034";
}
.glyphicon-headphones:before {
  content: "\e035";
}
.glyphicon-volume-off:before {
  content: "\e036";
}
.glyphicon-volume-down:before {
  content: "\e037";
}
.glyphicon-volume-up:before {
  content: "\e038";
}
.glyphicon-qrcode:before {
  content: "\e039";
}
.glyphicon-barcode:before {
  content: "\e040";
}
.glyphicon-tag:before {
  content: "\e041";
}
.glyphicon-tags:before {
  content: "\e042";
}
.glyphicon-book:before {
  content: "\e043";
}
.glyphicon-bookmark:before {
  content: "\e044";
}
.glyphicon-print:before {
  content: "\e045";
}
.glyphicon-camera:before {
  content: "\e046";
}
.glyphicon-font:before {
  content: "\e047";
}
.glyphicon-bold:before {
  content: "\e048";
}
.glyphicon-italic:before {
  content: "\e049";
}
.glyphicon-text-height:before {
  content: "\e050";
}
.glyphicon-text-width:before {
  content: "\e051";
}
.glyphicon-align-left:before {
  content: "\e052";
}
.glyphicon-align-center:before {
  content: "\e053";
}
.glyphicon-align-right:before {
  content: "\e054";
}
.glyphicon-align-justify:before {
  content: "\e055";
}
.glyphicon-list:before {
  content: "\e056";
}
.glyphicon-indent-left:before {
  content: "\e057";
}
.glyphicon-indent-right:before {
  content: "\e058";
}
.glyphicon-facetime-video:before {
  content: "\e059";
}
.glyphicon-picture:before {
  content: "\e060";
}
.glyphicon-map-marker:before {
  content: "\e062";
}
.glyphicon-adjust:before {
  content: "\e063";
}
.glyphicon-tint:before {
  content: "\e064";
}
.glyphicon-edit:before {
  content: "\e065";
}
.glyphicon-share:before {
  content: "\e066";
}
.glyphicon-check:before {
  content: "\e067";
}
.glyphicon-move:before {
  content: "\e068";
}
.glyphicon-step-backward:before {
  content: "\e069";
}
.glyphicon-fast-backward:before {
  content: "\e070";
}
.glyphicon-backward:before {
  content: "\e071";
}
.glyphicon-play:before {
  content: "\e072";
}
.glyphicon-pause:before {
  content: "\e073";
}
.glyphicon-stop:before {
  content: "\e074";
}
.glyphicon-forward:before {
  content: "\e075";
}
.glyphicon-fast-forward:before {
  content: "\e076";
}
.glyphicon-step-forward:before {
  content: "\e077";
}
.glyphicon-eject:before {
  content: "\e078";
}
.glyphicon-chevron-left:before {
  content: "\e079";
}
.glyphicon-chevron-right:before {
  content: "\e080";
}
.glyphicon-plus-sign:before {
  content: "\e081";
}
.glyphicon-minus-sign:before {
  content: "\e082";
}
.glyphicon-remove-sign:before {
  content: "\e083";
}
.glyphicon-ok-sign:before {
  content: "\e084";
}
.glyphicon-question-sign:before {
  content: "\e085";
}
.glyphicon-info-sign:before {
  content: "\e086";
}
.glyphicon-screenshot:before {
  content: "\e087";
}
.glyphicon-remove-circle:before {
  content: "\e088";
}
.glyphicon-ok-circle:before {
  content: "\e089";
}
.glyphicon-ban-circle:before {
  content: "\e090";
}
.glyphicon-arrow-left:before {
  content: "\e091";
}
.glyphicon-arrow-right:before {
  content: "\e092";
}
.glyphicon-arrow-up:before {
  content: "\e093";
}
.glyphicon-arrow-down:before {
  content: "\e094";
}
.glyphicon-share-alt:before {
  content: "\e095";
}
.glyphicon-resize-full:before {
  content: "\e096";
}
.glyphicon-resize-small:before {
  content: "\e097";
}
.glyphicon-exclamation-sign:before {
  content: "\e101";
}
.glyphicon-gift:before {
  content: "\e102";
}
.glyphicon-leaf:before {
  content: "\e103";
}
.glyphicon-fire:before {
  content: "\e104";
}
.glyphicon-eye-open:before {
  content: "\e105";
}
.glyphicon-eye-close:before {
  content: "\e106";
}
.glyphicon-warning-sign:before {
  content: "\e107";
}
.glyphicon-plane:before {
  content: "\e108";
}
.glyphicon-calendar:before {
  content: "\e109";
}
.glyphicon-random:before {
  content: "\e110";
}
.glyphicon-comment:before {
  content: "\e111";
}
.glyphicon-magnet:before {
  content: "\e112";
}
.glyphicon-chevron-up:before {
  content: "\e113";
}
.glyphicon-chevron-down:before {
  content: "\e114";
}
.glyphicon-retweet:before {
  content: "\e115";
}
.glyphicon-shopping-cart:before {
  content: "\e116";
}
.glyphicon-folder-close:before {
  content: "\e117";
}
.glyphicon-folder-open:before {
  content: "\e118";
}
.glyphicon-resize-vertical:before {
  content: "\e119";
}
.glyphicon-resize-horizontal:before {
  content: "\e120";
}
.glyphicon-hdd:before {
  content: "\e121";
}
.glyphicon-bullhorn:before {
  content: "\e122";
}
.glyphicon-bell:before {
  content: "\e123";
}
.glyphicon-certificate:before {
  content: "\e124";
}
.glyphicon-thumbs-up:before {
  content: "\e125";
}
.glyphicon-thumbs-down:before {
  content: "\e126";
}
.glyphicon-hand-right:before {
  content: "\e127";
}
.glyphicon-hand-left:before {
  content: "\e128";
}
.glyphicon-hand-up:before {
  content: "\e129";
}
.glyphicon-hand-down:before {
  content: "\e130";
}
.glyphicon-circle-arrow-right:before {
  content: "\e131";
}
.glyphicon-circle-arrow-left:before {
  content: "\e132";
}
.glyphicon-circle-arrow-up:before {
  content: "\e133";
}
.glyphicon-circle-arrow-down:before {
  content: "\e134";
}
.glyphicon-globe:before {
  content: "\e135";
}
.glyphicon-wrench:before {
  content: "\e136";
}
.glyphicon-tasks:before {
  content: "\e137";
}
.glyphicon-filter:before {
  content: "\e138";
}
.glyphicon-briefcase:before {
  content: "\e139";
}
.glyphicon-fullscreen:before {
  content: "\e140";
}
.glyphicon-dashboard:before {
  content: "\e141";
}
.glyphicon-paperclip:before {
  content: "\e142";
}
.glyphicon-heart-empty:before {
  content: "\e143";
}
.glyphicon-link:before {
  content: "\e144";
}
.glyphicon-phone:before {
  content: "\e145";
}
.glyphicon-pushpin:before {
  content: "\e146";
}
.glyphicon-usd:before {
  content: "\e148";
}
.glyphicon-gbp:before {
  content: "\e149";
}
.glyphicon-sort:before {
  content: "\e150";
}
.glyphicon-sort-by-alphabet:before {
  content: "\e151";
}
.glyphicon-sort-by-alphabet-alt:before {
  content: "\e152";
}
.glyphicon-sort-by-order:before {
  content: "\e153";
}
.glyphicon-sort-by-order-alt:before {
  content: "\e154";
}
.glyphicon-sort-by-attributes:before {
  content: "\e155";
}
.glyphicon-sort-by-attributes-alt:before {
  content: "\e156";
}
.glyphicon-unchecked:before {
  content: "\e157";
}
.glyphicon-expand:before {
  content: "\e158";
}
.glyphicon-collapse-down:before {
  content: "\e159";
}
.glyphicon-collapse-up:before {
  content: "\e160";
}
.glyphicon-log-in:before {
  content: "\e161";
}
.glyphicon-flash:before {
  content: "\e162";
}
.glyphicon-log-out:before {
  content: "\e163";
}
.glyphicon-new-window:before {
  content: "\e164";
}
.glyphicon-record:before {
  content: "\e165";
}
.glyphicon-save:before {
  content: "\e166";
}
.glyphicon-open:before {
  content: "\e167";
}
.glyphicon-saved:before {
  content: "\e168";
}
.glyphicon-import:before {
  content: "\e169";
}
.glyphicon-export:before {
  content: "\e170";
}
.glyphicon-send:before {
  content: "\e171";
}
.glyphicon-floppy-disk:before {
  content: "\e172";
}
.glyphicon-floppy-saved:before {
  content: "\e173";
}
.glyphicon-floppy-remove:before {
  content: "\e174";
}
.glyphicon-floppy-save:before {
  content: "\e175";
}
.glyphicon-floppy-open:before {
  content: "\e176";
}
.glyphicon-credit-card:before {
  content: "\e177";
}
.glyphicon-transfer:before {
  content: "\e178";
}
.glyphicon-cutlery:before {
  content: "\e179";
}
.glyphicon-header:before {
  content: "\e180";
}
.glyphicon-compressed:before {
  content: "\e181";
}
.glyphicon-earphone:before {
  content: "\e182";
}
.glyphicon-phone-alt:before {
  content: "\e183";
}
.glyphicon-tower:before {
  content: "\e184";
}
.glyphicon-stats:before {
  content: "\e185";
}
.glyphicon-sd-video:before {
  content: "\e186";
}
.glyphicon-hd-video:before {
  content: "\e187";
}
.glyphicon-subtitles:before {
  content: "\e188";
}
.glyphicon-sound-stereo:before {
  content: "\e189";
}
.glyphicon-sound-dolby:before {
  content: "\e190";
}
.glyphicon-sound-5-1:before {
  content: "\e191";
}
.glyphicon-sound-6-1:before {
  content: "\e192";
}
.glyphicon-sound-7-1:before {
  content: "\e193";
}
.glyphicon-copyright-mark:before {
  content: "\e194";
}
.glyphicon-registration-mark:before {
  content: "\e195";
}
.glyphicon-cloud-download:before {
  content: "\e197";
}
.glyphicon-cloud-upload:before {
  content: "\e198";
}
.glyphicon-tree-conifer:before {
  content: "\e199";
}
.glyphicon-tree-deciduous:before {
  content: "\e200";
}
.glyphicon-cd:before {
  content: "\e201";
}
.glyphicon-save-file:before {
  content: "\e202";
}
.glyphicon-open-file:before {
  content: "\e203";
}
.glyphicon-level-up:before {
  content: "\e204";
}
.glyphicon-copy:before {
  content: "\e205";
}
.glyphicon-paste:before {
  content: "\e206";
}
.glyphicon-alert:before {
  content: "\e209";
}
.glyphicon-equalizer:before {
  content: "\e210";
}
.glyphicon-king:before {
  content: "\e211";
}
.glyphicon-queen:before {
  content: "\e212";
}
.glyphicon-pawn:before {
  content: "\e213";
}
.glyphicon-bishop:before {
  content: "\e214";
}
.glyphicon-knight:before {
  content: "\e215";
}
.glyphicon-baby-formula:before {
  content: "\e216";
}
.glyphicon-tent:before {
  content: "\26fa";
}
.glyphicon-blackboard:before {
  content: "\e218";
}
.glyphicon-bed:before {
  content: "\e219";
}
.glyphicon-apple:before {
  content: "\f8ff";
}
.glyphicon-erase:before {
  content: "\e221";
}
.glyphicon-hourglass:before {
  content: "\231b";
}
.glyphicon-lamp:before {
  content: "\e223";
}
.glyphicon-duplicate:before {
  content: "\e224";
}
.glyphicon-piggy-bank:before {
  content: "\e225";
}
.glyphicon-scissors:before {
  content: "\e226";
}
.glyphicon-bitcoin:before {
  content: "\e227";
}
.glyphicon-btc:before {
  content: "\e227";
}
.glyphicon-xbt:before {
  content: "\e227";
}
.glyphicon-yen:before {
  content: "\00a5";
}
.glyphicon-jpy:before {
  content: "\00a5";
}
.glyphicon-ruble:before {
  content: "\20bd";
}
.glyphicon-rub:before {
  content: "\20bd";
}
.glyphicon-scale:before {
  content: "\e230";
}
.glyphicon-ice-lolly:before {
  content: "\e231";
}
.glyphicon-ice-lolly-tasted:before {
  content: "\e232";
}
.glyphicon-education:before {
  content: "\e233";
}
.glyphicon-option-horizontal:before {
  content: "\e234";
}
.glyphicon-option-vertical:before {
  content: "\e235";
}
.glyphicon-menu-hamburger:before {
  content: "\e236";
}
.glyphicon-modal-window:before {
  content: "\e237";
}
.glyphicon-oil:before {
  content: "\e238";
}
.glyphicon-grain:before {
  content: "\e239";
}
.glyphicon-sunglasses:before {
  content: "\e240";
}
.glyphicon-text-size:before {
  content: "\e241";
}
.glyphicon-text-color:before {
  content: "\e242";
}
.glyphicon-text-background:before {
  content: "\e243";
}
.glyphicon-object-align-top:before {
  content: "\e244";
}
.glyphicon-object-align-bottom:before {
  content: "\e245";
}
.glyphicon-object-align-horizontal:before {
  content: "\e246";
}
.glyphicon-object-align-left:before {
  content: "\e247";
}
.glyphicon-object-align-vertical:before {
  content: "\e248";
}
.glyphicon-object-align-right:before {
  content: "\e249";
}
.glyphicon-triangle-right:before {
  content: "\e250";
}
.glyphicon-triangle-left:before {
  content: "\e251";
}
.glyphicon-triangle-bottom:before {
  content: "\e252";
}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
    </style>
<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
.highlight .mh { color: #666666 } /* Literal.Number.Hex */
.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
.highlight .vm { color: #19177C } /* Name.Variable.Magic */
.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
    </style>
<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

    </style>


<style type="text/css">
/* Overrides of notebook CSS for static HTML export */
body {
  overflow: visible;
  padding: 8px;
}

div#notebook {
  overflow: visible;
  border-top: none;
}@media print {
  div.cell {
    display: block;
    page-break-inside: avoid;
  } 
  div.output_wrapper { 
    display: block;
    page-break-inside: avoid; 
  }
  div.output { 
    display: block;
    page-break-inside: avoid; 
  }
}
</style>

<!-- Custom stylesheet, it must be in the same directory as the html file -->
<link rel="stylesheet" href="custom.css">

<!-- Loading mathjax macro -->
<!-- Load mathjax -->
    <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS_HTML"></script>
    <!-- MathJax configuration -->
    <script type="text/x-mathjax-config">
    MathJax.Hub.Config({
        tex2jax: {
            inlineMath: [ ['$','$'], ["\\(","\\)"] ],
            displayMath: [ ['$$','$$'], ["\\[","\\]"] ],
            processEscapes: true,
            processEnvironments: true
        },
        // Center justify equations in code and markdown cells. Elsewhere
        // we use CSS to left justify single line equations in code cells.
        displayAlign: 'center',
        "HTML-CSS": {
            styles: {'.MathJax_Display': {"margin": 0}},
            linebreaks: { automatic: true }
        }
    });
    </script>
    <!-- End of mathjax configuration --></head>
<body>
  <div tabindex="-1" id="notebook" class="border-box-sizing">
    <div class="container" id="notebook-container">

<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h1 id="Identify-Customer-Segments">Identify Customer Segments<a class="anchor-link" href="#Identify-Customer-Segments">&#182;</a></h1><p>In this project, I apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that I
use has been provided by Bertelsmann Arvato Analytics, and represents a real-life data science task.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[1]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import libraries here; add more as necessary</span>
<span class="kn">from</span> <span class="nn">lib.workspace_utils</span> <span class="k">import</span> <span class="n">active_session</span>
<span class="kn">import</span> <span class="nn">time</span> 

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>

<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">Imputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>

<span class="c1"># magic word for producing visualizations in notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-0:-Load-the-Data">Step 0: Load the Data<a class="anchor-link" href="#Step-0:-Load-the-Data">&#182;</a></h3><p>There are four files associated with this project (not including this one):</p>
<ul>
<li><code>Udacity_AZDIAS_Subset.csv</code>: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).</li>
<li><code>Udacity_CUSTOMERS_Subset.csv</code>: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).</li>
<li><code>Data_Dictionary.md</code>: Detailed information file about the features in the provided datasets.</li>
<li><code>AZDIAS_Feature_Summary.csv</code>: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns</li>
</ul>
<p>All of the <code>.csv</code> data files in this project: they're semicolon (<code>;</code>) delimited, so I need an additional argument in  <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html"><code>read_csv()</code></a> call to read in the data properly.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[2]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the general demographics data.</span>
<span class="n">azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_AZDIAS_Subset.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>

<span class="c1"># Load in the feature summary file.</span>
<span class="n">feat_info</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;AZDIAS_Feature_Summary.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[3]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check the structure of the data after it&#39;s loaded (e.g. print the number of</span>
<span class="c1"># rows and columns, print the first few rows).</span>
<span class="n">azdias</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[3]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-1</td>
      <td>2</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1</td>
      <td>1</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-1</td>
      <td>3</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-1</td>
      <td>3</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 85 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[4]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[4]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(891221, 85)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[5]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[5]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>count</th>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>886367.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>891221.000000</td>
      <td>...</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>774706.000000</td>
      <td>794005.000000</td>
      <td>794005.000000</td>
      <td>794005.00000</td>
    </tr>
    <tr>
      <th>mean</th>
      <td>-0.358435</td>
      <td>2.777398</td>
      <td>1.522098</td>
      <td>3.632838</td>
      <td>3.074528</td>
      <td>2.821039</td>
      <td>3.401106</td>
      <td>3.033328</td>
      <td>2.874167</td>
      <td>3.075121</td>
      <td>...</td>
      <td>2.253330</td>
      <td>2.801858</td>
      <td>1.595426</td>
      <td>0.699166</td>
      <td>1.943913</td>
      <td>3.612821</td>
      <td>3.381087</td>
      <td>3.167854</td>
      <td>5.293002</td>
      <td>3.07222</td>
    </tr>
    <tr>
      <th>std</th>
      <td>1.198724</td>
      <td>1.068775</td>
      <td>0.499512</td>
      <td>1.595021</td>
      <td>1.321055</td>
      <td>1.464749</td>
      <td>1.322134</td>
      <td>1.529603</td>
      <td>1.486731</td>
      <td>1.353248</td>
      <td>...</td>
      <td>0.972008</td>
      <td>0.920309</td>
      <td>0.986736</td>
      <td>0.727137</td>
      <td>1.459654</td>
      <td>0.973967</td>
      <td>1.111598</td>
      <td>1.002376</td>
      <td>2.303739</td>
      <td>1.36298</td>
    </tr>
    <tr>
      <th>min</th>
      <td>-1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>...</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>1.00000</td>
    </tr>
    <tr>
      <th>25%</th>
      <td>-1.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>2.000000</td>
      <td>...</td>
      <td>1.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>0.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>4.000000</td>
      <td>2.00000</td>
    </tr>
    <tr>
      <th>50%</th>
      <td>-1.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>...</td>
      <td>2.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>1.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>5.000000</td>
      <td>3.00000</td>
    </tr>
    <tr>
      <th>75%</th>
      <td>-1.000000</td>
      <td>4.000000</td>
      <td>2.000000</td>
      <td>5.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>...</td>
      <td>3.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>1.000000</td>
      <td>3.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>7.000000</td>
      <td>4.00000</td>
    </tr>
    <tr>
      <th>max</th>
      <td>3.000000</td>
      <td>9.000000</td>
      <td>2.000000</td>
      <td>6.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>...</td>
      <td>4.000000</td>
      <td>4.000000</td>
      <td>3.000000</td>
      <td>2.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>5.000000</td>
      <td>9.000000</td>
      <td>9.000000</td>
      <td>9.00000</td>
    </tr>
  </tbody>
</table>
<p>8 rows × 81 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[6]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[6]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>attribute</th>
      <th>information_level</th>
      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1,0,9]</td>
    </tr>
    <tr>
      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[0]</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[7]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking the unique types of how missing values were encoded</span>
<span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[7]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([&#39;[-1,0]&#39;, &#39;[-1,0,9]&#39;, &#39;[0]&#39;, &#39;[-1]&#39;, &#39;[]&#39;, &#39;[-1,9]&#39;, &#39;[-1,X]&#39;,
       &#39;[XX]&#39;, &#39;[-1,XX]&#39;], dtype=object)</pre>
</div>

</div>

</div>
</div>

</div>Summary: we see that there are missing values that have been encoded with "X" or "XX". Let's have a closer look as to what columns have these values.
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[8]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#which columns have strange encodings with &#39;X&#39; values?</span>
<span class="nb">print</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;[-1,X]&#39;</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;[XX]&#39;</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;[-1,XX]&#39;</span><span class="p">])</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>57    CAMEO_DEUG_2015
Name: attribute, dtype: object
58    CAMEO_DEU_2015
Name: attribute, dtype: object
59    CAMEO_INTL_2015
Name: attribute, dtype: object
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[9]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># how many values were encoded with X or XX?</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;X&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>(373, 85)
(373, 85)
(373, 85)
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[10]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#are these the same rows that miss data for the three columns with &#39;X&#39; encodings?</span>
<span class="nb">print</span><span class="p">((</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;X&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
<span class="nb">print</span><span class="p">((</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
<span class="nb">print</span><span class="p">((</span><span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;X&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span> <span class="o">==</span> <span class="n">azdias</span><span class="p">[</span><span class="n">azdias</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span> <span class="o">==</span><span class="s1">&#39;XX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>373
373
373
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Summary: it seems like there are 373 rows that have missing values encoded with either 'X' or 'XX' and all these values come from just three columns 'CAMEO_DEUG_2015', 'CAMEO_DEU_2015' and 'CAMEO_INTL_2015'.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[11]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#NaNs in the original dataset</span>
<span class="n">nans_origin</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">nans_origin</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[11]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>4896838</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[12]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[12]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>False    838727
True      52494
dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Step-1:-Preprocessing">Step 1: Preprocessing<a class="anchor-link" href="#Step-1:-Preprocessing">&#182;</a></h2><h3 id="Step-1.1:-Assess-Missing-Data">Step 1.1: Assess Missing Data<a class="anchor-link" href="#Step-1.1:-Assess-Missing-Data">&#182;</a></h3><h4 id="Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">Step 1.1.1: Convert Missing Value Codes to NaNs<a class="anchor-link" href="#Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">&#182;</a></h4><p>The fourth column of the feature attributes summary (loaded in above as <code>feat_info</code>) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. <code>[-1,0]</code>), this will get read in as a string object.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>From initial look at the data, we know that the missing/unknown values were encoded either with digits -1, 0, 9 or with letters 'X' or 'XX'. In the following, I first convert X/XX values to NaNs. Then I convert digits to NaNs.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[13]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_clean</span> <span class="o">=</span> <span class="n">azdias</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;XX&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">})</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[14]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">transform_missing_in_featinfo</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
    <span class="n">col</span> <span class="o">=</span> <span class="n">col</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;[-1]&#39;</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;[-1,X]&#39;</span><span class="p">,</span> <span class="s1">&#39;[XX]&#39;</span><span class="p">,</span> <span class="s1">&#39;[-1,XX]&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="n">x</span><span class="p">)</span>
    <span class="n">col</span> <span class="o">=</span> <span class="p">[</span><span class="nb">eval</span><span class="p">(</span><span class="n">col</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">col</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
    <span class="k">return</span> <span class="n">col</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[15]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">transform_missing_in_featinfo</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">])</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[16]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[16]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0       [-1, 0]
1    [-1, 0, 9]
2       [-1, 0]
3           [0]
4          [-1]
Name: missing_transform, dtype: object</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: we cannot just replace all -1, 0 or 9 values with NaNs. What if they are meaningful in another column? So we need to go through each column and replace values that were encoded as missing specifically for this column:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[17]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Identify missing or unknown data values and convert them to NaNs</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]):</span>
    <span class="n">azdias_clean</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">azdias_clean</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">]),</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[18]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># alternative way:</span>
<span class="c1">#finding all digits in a string object &#39;[-1, 0, 9]&#39; and converting it to a list </span>

<span class="c1">#import re</span>
<span class="c1">#for i in feat_info[&#39;missing_or_unknown&#39;].index:</span>
<span class="c1">#    feat_info[&#39;missing_or_unknown&#39;][i] = [int(s) for s in re.findall(r&#39;-?\d+&#39;, feat_info[&#39;missing_or_unknown&#39;][i])]</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[19]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#number of NaNs after conversion:</span>
<span class="n">azdias_clean</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[19]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>8373929</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: In the original dataset we had 4896838 naturally missing values. After conversion we have now 8373929 NaNs. So, there are 3477091 values that were encoded as 'missing' or 'unknown'. <br>
Is it a lot? Well, given that the original dataset has 891221 x 85 = 75753785 datapoints, the final prop of missing values after conversion is about 11%.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[20]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># checking the NaNs in the data frame</span>
<span class="n">col_missing_prop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">azdias_clean</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">col_missing_prop</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">,</span> <span class="s1">&#39;missing_prop&#39;</span><span class="p">]</span>
<span class="n">col_missing_prop</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[20]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>features</th>
      <th>missing_prop</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>0.769554</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>0.003233</td>
    </tr>
    <tr>
      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>0.005446</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>5</th>
      <td>FINANZ_SPARER</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>6</th>
      <td>FINANZ_VORSORGER</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>7</th>
      <td>FINANZ_ANLEGER</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>8</th>
      <td>FINANZ_UNAUFFAELLIGER</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>9</th>
      <td>FINANZ_HAUSBAUER</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>10</th>
      <td>FINANZTYP</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>11</th>
      <td>GEBURTSJAHR</td>
      <td>0.440203</td>
    </tr>
    <tr>
      <th>12</th>
      <td>GFK_URLAUBERTYP</td>
      <td>0.005446</td>
    </tr>
    <tr>
      <th>13</th>
      <td>GREEN_AVANTGARDE</td>
      <td>0.000000</td>
    </tr>
    <tr>
      <th>14</th>
      <td>HEALTH_TYP</td>
      <td>0.124768</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.1.2:-Assess-Missing-Data-in-Each-Column">Step 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Step-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html"><code>hist()</code></a> function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)</p>
<p>For the remaining features, are there any patterns in which columns have, or share, missing data?</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[21]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Perform an assessment of how much missing data there is in each column of the dataset.</span>
<span class="n">col_missing_prop_sorted</span> <span class="o">=</span> <span class="n">col_missing_prop</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;missing_prop&#39;</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">col_missing_prop_sorted</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">15</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[21]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>features</th>
      <th>missing_prop</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>40</th>
      <td>TITEL_KZ</td>
      <td>0.997576</td>
    </tr>
    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>0.769554</td>
    </tr>
    <tr>
      <th>47</th>
      <td>KK_KUNDENTYP</td>
      <td>0.655967</td>
    </tr>
    <tr>
      <th>64</th>
      <td>KBA05_BAUMAX</td>
      <td>0.534687</td>
    </tr>
    <tr>
      <th>11</th>
      <td>GEBURTSJAHR</td>
      <td>0.440203</td>
    </tr>
    <tr>
      <th>43</th>
      <td>ALTER_HH</td>
      <td>0.348137</td>
    </tr>
    <tr>
      <th>70</th>
      <td>KKK</td>
      <td>0.177357</td>
    </tr>
    <tr>
      <th>73</th>
      <td>REGIOTYP</td>
      <td>0.177357</td>
    </tr>
    <tr>
      <th>48</th>
      <td>W_KEIT_KIND_HH</td>
      <td>0.166051</td>
    </tr>
    <tr>
      <th>60</th>
      <td>KBA05_ANTG1</td>
      <td>0.149597</td>
    </tr>
    <tr>
      <th>61</th>
      <td>KBA05_ANTG2</td>
      <td>0.149597</td>
    </tr>
    <tr>
      <th>62</th>
      <td>KBA05_ANTG3</td>
      <td>0.149597</td>
    </tr>
    <tr>
      <th>63</th>
      <td>KBA05_ANTG4</td>
      <td>0.149597</td>
    </tr>
    <tr>
      <th>65</th>
      <td>KBA05_GBZ</td>
      <td>0.149597</td>
    </tr>
    <tr>
      <th>71</th>
      <td>MOBI_REGIO</td>
      <td>0.149597</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[22]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate patterns in the amount of missing data in each column.</span>
<span class="n">col_missing_values</span> <span class="o">=</span> <span class="n">azdias_clean</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">col_missing_values</span><span class="p">);</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Summary: the majority of columns seem to have less than 200000 missing values.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[23]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">col_del</span> <span class="o">=</span> <span class="n">col_missing_prop_sorted</span><span class="p">[</span><span class="s1">&#39;features&#39;</span><span class="p">][:</span><span class="mi">6</span><span class="p">]</span>
<span class="n">col_del</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[23]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>40        TITEL_KZ
0         AGER_TYP
47    KK_KUNDENTYP
64    KBA05_BAUMAX
11     GEBURTSJAHR
43        ALTER_HH
Name: features, dtype: object</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[24]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove the outlier columns from the dataset. (You&#39;ll perform other data engineering tasks such as re-encoding and imputation later.)</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_clean</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="n">col_del</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[25]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[25]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(891221, 79)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">Discussion 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>The analysis of missing values revealed that we have 6 columns with missing values for more than 200000 rows or in other words more than 30% of data in these columns is missing. These columns are:</p>
<ul>
<li>TITEL_KZ</li>
<li>AGER_TYP</li>
<li>KK_KUNDENTYP</li>
<li>KBA05_BAUMAX</li>
<li>GEBURTSJAHR</li>
<li>ALTER_HH 
<br><br>
Because of large amount of missing values, we cannot use imputation methods (or it will eventually skew the results) for these columns and hence it was decided to remove them from further analysis completely. </li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.1.3:-Assess-Missing-Data-in-Each-Row">Step 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Step-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>NOTE: I decided to look at missing values in each row on the dataset resulting from the previous step (not the original one) - i.e. the one with deleted 6 columns. I find that in this way I could retain more rows - e.g. when dividing into a subset with below 5 missing values in a row I get 719624 rows on the dataset with 79 columns vs. 599243 rows in the original dataset with 85 columns.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[26]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How much data is missing in each row of the dataset?</span>
<span class="n">row_missing_prop</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">azdias_fin</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">row_missing_prop</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;rows_num&#39;</span><span class="p">,</span> <span class="s1">&#39;missing_prop&#39;</span><span class="p">]</span>
<span class="n">row_missing_prop_sorted</span> <span class="o">=</span> <span class="n">row_missing_prop</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;missing_prop&#39;</span><span class="p">],</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">row_missing_prop_sorted</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[26]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>rows_num</th>
      <th>missing_prop</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>732775</th>
      <td>732775</td>
      <td>0.620253</td>
    </tr>
    <tr>
      <th>643174</th>
      <td>643174</td>
      <td>0.620253</td>
    </tr>
    <tr>
      <th>472919</th>
      <td>472919</td>
      <td>0.607595</td>
    </tr>
    <tr>
      <th>345274</th>
      <td>345274</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>299868</th>
      <td>299868</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>582092</th>
      <td>582092</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>681255</th>
      <td>681255</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>818343</th>
      <td>818343</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>582109</th>
      <td>582109</td>
      <td>0.594937</td>
    </tr>
    <tr>
      <th>439981</th>
      <td>439981</td>
      <td>0.594937</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[27]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">row_missing_values</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">row_missing_values</span><span class="p">);</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Summary</strong>: based on the histogram, it seems like there are two different patterns for row missing values. About 800000 or something like 90% of rows have missing values in less than 20 columns, while the rest has much more  missing values - in more than 20 columns.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[28]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Write code to divide the data into two subsets based on the number of missing</span>
<span class="c1"># values in each row.</span>
<span class="n">subset_less_missing</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="n">row_missing_values</span> <span class="o">&lt;=</span> <span class="mi">20</span><span class="p">]</span>
<span class="n">subset_more_missing</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="n">row_missing_values</span> <span class="o">&gt;</span> <span class="mi">20</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;subset_less_missing shape:&quot;</span><span class="p">,</span> <span class="n">subset_less_missing</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;subset_more_missing shape:&quot;</span><span class="p">,</span> <span class="n">subset_more_missing</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;prop of missing values in subset_less_missing relative to all datapoints: </span><span class="si">{:.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="n">subset_less_missing</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">subset_less_missing</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;prop of missing values in subset_less_missing relative to all datapoints: </span><span class="si">{:.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="n">subset_more_missing</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">/</span><span class="n">subset_less_missing</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>subset_less_missing shape: (797426, 79)
subset_more_missing shape: (93795, 79)
prop of missing values in subset_less_missing relative to all datapoints: 1.59%
prop of missing values in subset_less_missing relative to all datapoints: 6.41%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[29]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># let&#39;s create a list of top 20 columns that has the least of missing values (aka non-missing features)</span>
<span class="n">col_nomissing</span> <span class="o">=</span> <span class="n">azdias_clean</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">ascending</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">20</span><span class="p">)</span><span class="o">.</span><span class="n">index</span>
<span class="n">col_nomissing</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[29]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>Index([&#39;ZABEOTYP&#39;, &#39;SEMIO_TRADV&#39;, &#39;SEMIO_PFLICHT&#39;, &#39;SEMIO_KAEM&#39;, &#39;SEMIO_DOM&#39;,
       &#39;SEMIO_KRIT&#39;, &#39;SEMIO_RAT&#39;, &#39;SEMIO_KULT&#39;, &#39;SEMIO_ERL&#39;, &#39;SEMIO_LUST&#39;,
       &#39;SEMIO_VERT&#39;, &#39;SEMIO_MAT&#39;, &#39;SEMIO_REL&#39;, &#39;SEMIO_SOZ&#39;, &#39;SEMIO_FAM&#39;,
       &#39;FINANZ_VORSORGER&#39;, &#39;ANREDE_KZ&#39;, &#39;FINANZ_MINIMALIST&#39;, &#39;FINANZ_SPARER&#39;,
       &#39;FINANZ_ANLEGER&#39;],
      dtype=&#39;object&#39;)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[30]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the distribution of values for at least five columns where there are</span>
<span class="c1"># no or few missing values, between the two subsets.</span>
<span class="k">def</span> <span class="nf">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
    <span class="n">base_color</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">color_palette</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">wspace</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">subset_more_missing</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">col</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="n">base_color</span><span class="p">);</span>
    <span class="c1">#plt.hist(subset_more_missing[col])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;subset_more_missing&#39;</span><span class="p">)</span>
    
    <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">subset_less_missing</span><span class="p">,</span> <span class="n">x</span> <span class="o">=</span> <span class="n">col</span><span class="p">,</span> <span class="n">color</span> <span class="o">=</span> <span class="n">base_color</span><span class="p">);</span>
    <span class="c1">#plt.hist(subset_less_missing[col])</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="n">col</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;subset_less_missing&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[31]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># change the values of col_nomissing[i] between 0 and 19  </span>
<span class="c1"># to see the quality of distributions for these columns for the two subsets </span>
<span class="n">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col_nomissing</span><span class="p">[</span><span class="mi">0</span><span class="p">]);</span>
<span class="n">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col_nomissing</span><span class="p">[</span><span class="mi">4</span><span class="p">]);</span>
<span class="n">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col_nomissing</span><span class="p">[</span><span class="mi">8</span><span class="p">]);</span>
<span class="n">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col_nomissing</span><span class="p">[</span><span class="mi">10</span><span class="p">]);</span>
<span class="n">plot_subset_countplot_by_column</span><span class="p">(</span><span class="n">col_nomissing</span><span class="p">[</span><span class="mi">12</span><span class="p">]);</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[32]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">subset_less_missing</span>

<span class="c1">#checking the size of the new dataset without 6 columns</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;number of columns:&quot;</span><span class="p">,</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;number of rows in the original dataset:&quot;</span><span class="p">,</span> <span class="n">azdias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;number of rows in the final dataset:&quot;</span><span class="p">,</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;prop of final dataset with no missing data: </span><span class="si">{0:.2f}</span><span class="s2">%&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="n">azdias_fin</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">/</span><span class="n">azdias</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>number of columns: 79
number of rows in the original dataset: 891221
number of rows in the final dataset: 797426
prop of final dataset with no missing data: 89.48%
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-1.2:-Select-and-Re-Encode-Features">Step 1.2: Select and Re-Encode Features<a class="anchor-link" href="#Step-1.2:-Select-and-Re-Encode-Features">&#182;</a></h3><p>Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, I need to make a few encoding changes. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (<code>feat_info</code>) for a summary of types of measurement.</p>
<ul>
<li>For numeric and interval data, these features can be kept without changes.</li>
<li>Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).</li>
<li>Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[33]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How many features are there of each data type?</span>
<span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[33]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>ordinal        49
categorical    21
mixed           7
numeric         7
interval        1
Name: type, dtype: int64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.1:-Re-Encode-Categorical-Features">Step 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Step-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>For categorical data, I need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:</p>
<ul>
<li>For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.</li>
<li>There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.</li>
<li>For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html">OneHotEncoder</a>), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[34]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Assess categorical variables: which are binary, which are multi-level, and</span>
<span class="c1"># which one needs to be re-encoded?</span>
<span class="n">categorical_col</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;categorical&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">attribute</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">categorical_col</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[34]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>attribute</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>AGER_TYP</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>ANREDE_KZ</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>CJT_GESAMTTYP</td>
    </tr>
    <tr>
      <th>3</th>
      <td>10</td>
      <td>FINANZTYP</td>
    </tr>
    <tr>
      <th>4</th>
      <td>12</td>
      <td>GFK_URLAUBERTYP</td>
    </tr>
    <tr>
      <th>5</th>
      <td>13</td>
      <td>GREEN_AVANTGARDE</td>
    </tr>
    <tr>
      <th>6</th>
      <td>17</td>
      <td>LP_FAMILIE_FEIN</td>
    </tr>
    <tr>
      <th>7</th>
      <td>18</td>
      <td>LP_FAMILIE_GROB</td>
    </tr>
    <tr>
      <th>8</th>
      <td>19</td>
      <td>LP_STATUS_FEIN</td>
    </tr>
    <tr>
      <th>9</th>
      <td>20</td>
      <td>LP_STATUS_GROB</td>
    </tr>
    <tr>
      <th>10</th>
      <td>21</td>
      <td>NATIONALITAET_KZ</td>
    </tr>
    <tr>
      <th>11</th>
      <td>38</td>
      <td>SHOPPER_TYP</td>
    </tr>
    <tr>
      <th>12</th>
      <td>39</td>
      <td>SOHO_KZ</td>
    </tr>
    <tr>
      <th>13</th>
      <td>40</td>
      <td>TITEL_KZ</td>
    </tr>
    <tr>
      <th>14</th>
      <td>41</td>
      <td>VERS_TYP</td>
    </tr>
    <tr>
      <th>15</th>
      <td>42</td>
      <td>ZABEOTYP</td>
    </tr>
    <tr>
      <th>16</th>
      <td>47</td>
      <td>KK_KUNDENTYP</td>
    </tr>
    <tr>
      <th>17</th>
      <td>52</td>
      <td>GEBAEUDETYP</td>
    </tr>
    <tr>
      <th>18</th>
      <td>55</td>
      <td>OST_WEST_KZ</td>
    </tr>
    <tr>
      <th>19</th>
      <td>57</td>
      <td>CAMEO_DEUG_2015</td>
    </tr>
    <tr>
      <th>20</th>
      <td>58</td>
      <td>CAMEO_DEU_2015</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[35]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;unique&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">azdias_clean</span><span class="p">[</span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">categorical_col</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[36]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;num_categories&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">azdias_clean</span><span class="p">[</span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">categorical_col</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[37]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">categorical_col</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[37]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>attribute</th>
      <th>unique</th>
      <th>num_categories</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>AGER_TYP</td>
      <td>[nan, 2.0, 3.0, 1.0]</td>
      <td>4</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>ANREDE_KZ</td>
      <td>[1.0, 2.0]</td>
      <td>2</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>CJT_GESAMTTYP</td>
      <td>[2.0, 5.0, 3.0, 4.0, 1.0, 6.0, nan]</td>
      <td>7</td>
    </tr>
    <tr>
      <th>3</th>
      <td>10</td>
      <td>FINANZTYP</td>
      <td>[4.0, 1.0, 6.0, 5.0, 2.0, 3.0]</td>
      <td>6</td>
    </tr>
    <tr>
      <th>4</th>
      <td>12</td>
      <td>GFK_URLAUBERTYP</td>
      <td>[10.0, 1.0, 5.0, 12.0, 9.0, 3.0, 8.0, 11.0, 4....</td>
      <td>13</td>
    </tr>
    <tr>
      <th>5</th>
      <td>13</td>
      <td>GREEN_AVANTGARDE</td>
      <td>[0.0, 1.0]</td>
      <td>2</td>
    </tr>
    <tr>
      <th>6</th>
      <td>17</td>
      <td>LP_FAMILIE_FEIN</td>
      <td>[2.0, 5.0, 1.0, nan, 10.0, 7.0, 11.0, 3.0, 8.0...</td>
      <td>12</td>
    </tr>
    <tr>
      <th>7</th>
      <td>18</td>
      <td>LP_FAMILIE_GROB</td>
      <td>[2.0, 3.0, 1.0, nan, 5.0, 4.0]</td>
      <td>6</td>
    </tr>
    <tr>
      <th>8</th>
      <td>19</td>
      <td>LP_STATUS_FEIN</td>
      <td>[1.0, 2.0, 3.0, 9.0, 4.0, 10.0, 5.0, 8.0, 6.0,...</td>
      <td>11</td>
    </tr>
    <tr>
      <th>9</th>
      <td>20</td>
      <td>LP_STATUS_GROB</td>
      <td>[1.0, 2.0, 4.0, 5.0, 3.0, nan]</td>
      <td>6</td>
    </tr>
    <tr>
      <th>10</th>
      <td>21</td>
      <td>NATIONALITAET_KZ</td>
      <td>[nan, 1.0, 3.0, 2.0]</td>
      <td>4</td>
    </tr>
    <tr>
      <th>11</th>
      <td>38</td>
      <td>SHOPPER_TYP</td>
      <td>[nan, 3.0, 2.0, 1.0, 0.0]</td>
      <td>5</td>
    </tr>
    <tr>
      <th>12</th>
      <td>39</td>
      <td>SOHO_KZ</td>
      <td>[nan, 1.0, 0.0]</td>
      <td>3</td>
    </tr>
    <tr>
      <th>13</th>
      <td>40</td>
      <td>TITEL_KZ</td>
      <td>[nan, 4.0, 1.0, 3.0, 5.0, 2.0]</td>
      <td>6</td>
    </tr>
    <tr>
      <th>14</th>
      <td>41</td>
      <td>VERS_TYP</td>
      <td>[nan, 2.0, 1.0]</td>
      <td>3</td>
    </tr>
    <tr>
      <th>15</th>
      <td>42</td>
      <td>ZABEOTYP</td>
      <td>[3.0, 5.0, 4.0, 1.0, 6.0, 2.0]</td>
      <td>6</td>
    </tr>
    <tr>
      <th>16</th>
      <td>47</td>
      <td>KK_KUNDENTYP</td>
      <td>[nan, 1.0, 3.0, 6.0, 4.0, 2.0, 5.0]</td>
      <td>7</td>
    </tr>
    <tr>
      <th>17</th>
      <td>52</td>
      <td>GEBAEUDETYP</td>
      <td>[nan, 8.0, 1.0, 3.0, 2.0, 6.0, 4.0, 5.0]</td>
      <td>8</td>
    </tr>
    <tr>
      <th>18</th>
      <td>55</td>
      <td>OST_WEST_KZ</td>
      <td>[nan, W, O]</td>
      <td>3</td>
    </tr>
    <tr>
      <th>19</th>
      <td>57</td>
      <td>CAMEO_DEUG_2015</td>
      <td>[nan, 8, 4, 2, 6, 1, 9, 5, 7, 3]</td>
      <td>10</td>
    </tr>
    <tr>
      <th>20</th>
      <td>58</td>
      <td>CAMEO_DEU_2015</td>
      <td>[nan, 8A, 4C, 2A, 6B, 8C, 4A, 2D, 1A, 1E, 9D, ...</td>
      <td>45</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[38]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># because we have NaNs, those binary categorical values don&#39;t need to be reencoded that have 3 unique values</span>
<span class="n">categorical_col</span><span class="p">[</span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;num_categories&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">]</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[38]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>attribute</th>
      <th>unique</th>
      <th>num_categories</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>12</th>
      <td>39</td>
      <td>SOHO_KZ</td>
      <td>[nan, 1.0, 0.0]</td>
      <td>3</td>
    </tr>
    <tr>
      <th>14</th>
      <td>41</td>
      <td>VERS_TYP</td>
      <td>[nan, 2.0, 1.0]</td>
      <td>3</td>
    </tr>
    <tr>
      <th>18</th>
      <td>55</td>
      <td>OST_WEST_KZ</td>
      <td>[nan, W, O]</td>
      <td>3</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[39]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># let&#39;s deal first with &#39;OST_WEST_KZ&#39;</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">],</span> <span class="n">drop_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[40]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># choosing multi-categorical features and dropping OST_WEST_KZ column because it was reencoded separately</span>
<span class="n">cat_var_reencode</span> <span class="o">=</span> <span class="n">categorical_col</span><span class="p">[(</span><span class="n">categorical_col</span><span class="p">[</span><span class="s1">&#39;num_categories&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">)]</span><span class="o">.</span><span class="n">attribute</span>
<span class="n">cat_var_reencode</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[40]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0             AGER_TYP
2        CJT_GESAMTTYP
3            FINANZTYP
4      GFK_URLAUBERTYP
6      LP_FAMILIE_FEIN
7      LP_FAMILIE_GROB
8       LP_STATUS_FEIN
9       LP_STATUS_GROB
10    NATIONALITAET_KZ
11         SHOPPER_TYP
13            TITEL_KZ
15            ZABEOTYP
16        KK_KUNDENTYP
17         GEBAEUDETYP
19     CAMEO_DEUG_2015
20      CAMEO_DEU_2015
Name: attribute, dtype: object</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[41]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the last feature &#39;CAMEO_DEU_2015&#39; has 45 values to reencode, will be dropped here:</span>
<span class="n">cat_var_reencode</span> <span class="o">=</span> <span class="n">cat_var_reencode</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">cat_var_reencode</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[41]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0             AGER_TYP
2        CJT_GESAMTTYP
3            FINANZTYP
4      GFK_URLAUBERTYP
6      LP_FAMILIE_FEIN
7      LP_FAMILIE_GROB
8       LP_STATUS_FEIN
9       LP_STATUS_GROB
10    NATIONALITAET_KZ
11         SHOPPER_TYP
13            TITEL_KZ
15            ZABEOTYP
16        KK_KUNDENTYP
17         GEBAEUDETYP
19     CAMEO_DEUG_2015
Name: attribute, dtype: object</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[42]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># since all features with &#39;GROB&#39; are aggregations of the equivalent &#39;FEIN&#39; features, we can drop &#39;FEIN&#39;</span>
<span class="n">cat_var_reencode</span> <span class="o">=</span> <span class="n">cat_var_reencode</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">labels</span><span class="o">=</span><span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
<span class="n">cat_var_reencode</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[42]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0             AGER_TYP
2        CJT_GESAMTTYP
3            FINANZTYP
4      GFK_URLAUBERTYP
7      LP_FAMILIE_GROB
9       LP_STATUS_GROB
10    NATIONALITAET_KZ
11         SHOPPER_TYP
13            TITEL_KZ
15            ZABEOTYP
16        KK_KUNDENTYP
17         GEBAEUDETYP
19     CAMEO_DEUG_2015
Name: attribute, dtype: object</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[43]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#updating azdias_fin</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[44]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># updating azdias_fin</span>
<span class="n">col_fein</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">]</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">col_fein</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[45]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># now we need to filter columns that are in the final azdias dataset (because we removed some columns from the analysis when handling missing data)</span>
<span class="n">cat_var_reencode_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">cat_var_reencode</span><span class="p">)]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[46]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">cat_var_reencode_fin</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cat_var_reencode_fin</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>[&#39;CJT_GESAMTTYP&#39;, &#39;FINANZTYP&#39;, &#39;GFK_URLAUBERTYP&#39;, &#39;LP_FAMILIE_GROB&#39;, &#39;LP_STATUS_GROB&#39;, &#39;NATIONALITAET_KZ&#39;, &#39;SHOPPER_TYP&#39;, &#39;ZABEOTYP&#39;, &#39;GEBAEUDETYP&#39;, &#39;CAMEO_DEUG_2015&#39;]
10
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[47]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">cat_var_reencode_fin</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[48]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking new number of columns after reencoding </span>
<span class="nb">len</span><span class="p">(</span><span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[48]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>129</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[49]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking new features that were added after reencoding. Should start from new version of &#39;OST_WEST_KZ&#39;</span>
<span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">65</span><span class="p">:]</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[49]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>Index([&#39;OST_WEST_KZ_W&#39;, &#39;CJT_GESAMTTYP_1.0&#39;, &#39;CJT_GESAMTTYP_2.0&#39;,
       &#39;CJT_GESAMTTYP_3.0&#39;, &#39;CJT_GESAMTTYP_4.0&#39;, &#39;CJT_GESAMTTYP_5.0&#39;,
       &#39;CJT_GESAMTTYP_6.0&#39;, &#39;FINANZTYP_1.0&#39;, &#39;FINANZTYP_2.0&#39;, &#39;FINANZTYP_3.0&#39;,
       &#39;FINANZTYP_4.0&#39;, &#39;FINANZTYP_5.0&#39;, &#39;FINANZTYP_6.0&#39;,
       &#39;GFK_URLAUBERTYP_1.0&#39;, &#39;GFK_URLAUBERTYP_2.0&#39;, &#39;GFK_URLAUBERTYP_3.0&#39;,
       &#39;GFK_URLAUBERTYP_4.0&#39;, &#39;GFK_URLAUBERTYP_5.0&#39;, &#39;GFK_URLAUBERTYP_6.0&#39;,
       &#39;GFK_URLAUBERTYP_7.0&#39;, &#39;GFK_URLAUBERTYP_8.0&#39;, &#39;GFK_URLAUBERTYP_9.0&#39;,
       &#39;GFK_URLAUBERTYP_10.0&#39;, &#39;GFK_URLAUBERTYP_11.0&#39;, &#39;GFK_URLAUBERTYP_12.0&#39;,
       &#39;LP_FAMILIE_GROB_1.0&#39;, &#39;LP_FAMILIE_GROB_2.0&#39;, &#39;LP_FAMILIE_GROB_3.0&#39;,
       &#39;LP_FAMILIE_GROB_4.0&#39;, &#39;LP_FAMILIE_GROB_5.0&#39;, &#39;LP_STATUS_GROB_1.0&#39;,
       &#39;LP_STATUS_GROB_2.0&#39;, &#39;LP_STATUS_GROB_3.0&#39;, &#39;LP_STATUS_GROB_4.0&#39;,
       &#39;LP_STATUS_GROB_5.0&#39;, &#39;NATIONALITAET_KZ_1.0&#39;, &#39;NATIONALITAET_KZ_2.0&#39;,
       &#39;NATIONALITAET_KZ_3.0&#39;, &#39;SHOPPER_TYP_0.0&#39;, &#39;SHOPPER_TYP_1.0&#39;,
       &#39;SHOPPER_TYP_2.0&#39;, &#39;SHOPPER_TYP_3.0&#39;, &#39;ZABEOTYP_1.0&#39;, &#39;ZABEOTYP_2.0&#39;,
       &#39;ZABEOTYP_3.0&#39;, &#39;ZABEOTYP_4.0&#39;, &#39;ZABEOTYP_5.0&#39;, &#39;ZABEOTYP_6.0&#39;,
       &#39;GEBAEUDETYP_1.0&#39;, &#39;GEBAEUDETYP_2.0&#39;, &#39;GEBAEUDETYP_3.0&#39;,
       &#39;GEBAEUDETYP_4.0&#39;, &#39;GEBAEUDETYP_5.0&#39;, &#39;GEBAEUDETYP_6.0&#39;,
       &#39;GEBAEUDETYP_8.0&#39;, &#39;CAMEO_DEUG_2015_1&#39;, &#39;CAMEO_DEUG_2015_2&#39;,
       &#39;CAMEO_DEUG_2015_3&#39;, &#39;CAMEO_DEUG_2015_4&#39;, &#39;CAMEO_DEUG_2015_5&#39;,
       &#39;CAMEO_DEUG_2015_6&#39;, &#39;CAMEO_DEUG_2015_7&#39;, &#39;CAMEO_DEUG_2015_8&#39;,
       &#39;CAMEO_DEUG_2015_9&#39;],
      dtype=&#39;object&#39;)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: we reencoded 11 columns and dropped 3 new columns (in addition to 6 done previously). The resulting dataset has 129 columns. So this means that we have (85-11-3-6)=65 old columns. The rest 64 are our new binary reencoded features starting from OST_WEST_KZ_W.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.2.1:-Re-Encode-Categorical-Features">Discussion 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Discussion-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>There are 21 categorical features in the dataset.
Among these, we have 4 features with binary categories - ANREDE_KZ, GREEN_AVANTGARDE, SOHO_KZ and VERS_TYP. These can be left unchanged.
One binary variable that takes on non-numeric values - OST_WEST_KZ - needs to be reencoded with dummy variable.
All other categorical features need to be reencoded/dropped. So here's my approach:</p>
<p>I first reencoded 'OST_WEST_KZ' with get_dummies, dropping the first column.</p>
<p>I dropped 'CAMEO_DEU_2015' because it has too many values.</p>
<p>I also excluded from the list of categorical features (ANREDE_KZ, GREEN_AVANTGARDE, SOHO_KZ and VERS_TYP) because they are binary and don't need to be reencoded.</p>
<p>Then I dropped features LP_FAMILIE_FEIN and LP_STATUS_FEIN because they are highly correlated with LP_FAMILIE_GROB and LP_STATUS_GROB (which are basically aggregates of 'FEIN' features). <br></p>
<p>Finally, I checked what other categorical variables were dropped in the previous step when looking for missing values. It turned out that 3 columns were already removed (AGER_TYP, TITEL_KZ, KK_KUNDENTYP)</p>
<p>After all, from 21 categorical features only 10 were left.</p>
<p><em>Reencoding</em>: I applied onehotencoding on 10 features that were left from the previous step. This created 64 binary vectors. Together with reencoded earlier OST_WEST_KZ, this means we added 64 new features that are dummies for their earlier versions.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.2:-Engineer-Mixed-Type-Features">Step 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Step-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:</p>
<ul>
<li>"PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.</li>
<li>"CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).</li>
<li>If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.</li>
</ul>
<p>Be sure to check <code>Data_Dictionary.md</code> for the details needed to finish these tasks.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[50]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking which columns where defined as &#39;mixed&#39;</span>
<span class="n">mixed_col</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;mixed&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">attribute</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="n">mixed_col</span><span class="p">[</span><span class="s1">&#39;num_categories&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">mixed_col</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span><span class="o">.</span><span class="n">unique</span><span class="p">())</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">mixed_col</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
<span class="n">mixed_col</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[50]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>index</th>
      <th>attribute</th>
      <th>num_categories</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>15</td>
      <td>LP_LEBENSPHASE_FEIN</td>
      <td>42</td>
    </tr>
    <tr>
      <th>1</th>
      <td>16</td>
      <td>LP_LEBENSPHASE_GROB</td>
      <td>14</td>
    </tr>
    <tr>
      <th>2</th>
      <td>22</td>
      <td>PRAEGENDE_JUGENDJAHRE</td>
      <td>16</td>
    </tr>
    <tr>
      <th>3</th>
      <td>56</td>
      <td>WOHNLAGE</td>
      <td>9</td>
    </tr>
    <tr>
      <th>4</th>
      <td>59</td>
      <td>CAMEO_INTL_2015</td>
      <td>23</td>
    </tr>
    <tr>
      <th>5</th>
      <td>64</td>
      <td>KBA05_BAUMAX</td>
      <td>7</td>
    </tr>
    <tr>
      <th>6</th>
      <td>79</td>
      <td>PLZ8_BAUMAX</td>
      <td>6</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Summary</strong>: there are 7 features marked as 'mixed' in the original dataset. 'KBA05_BAUMAX' was already removed from the dataset due to the missing values.LP_LEBENSPHASE_FEIN is basically the same as LP_LEBENSPHASE_GROB and will be dropped. Let's check if LP_LEBENSPHASE_GROB is highly correlated with categorical features 'LP_FAMILIE_FEIN'/'LP_STATUS_FEIN' that we already reencoded:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[51]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">lp_col</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;LP_LEBENSPHASE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_LEBENSPHASE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span>
       <span class="s1">&#39;LP_FAMILIE_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_GROB&#39;</span><span class="p">]</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">azdias</span><span class="p">[</span><span class="n">lp_col</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">(),</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;rocket_r&#39;</span><span class="p">,</span> <span class="n">annot</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">fmt</span> <span class="o">=</span> <span class="s1">&#39;.2f&#39;</span><span class="p">);</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Summary: LP_LEBENSPHASE_GROB is highly correlated with categorical feature 'LP_FAMILIE_FEIN' and so can be also dropped from further analysis.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[52]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">([</span><span class="s1">&#39;LP_LEBENSPHASE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_LEBENSPHASE_GROB&#39;</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>So, in the following we can concentrate on 4 'mixed' features - PRAEGENDE_JUGENDJAHRE, WOHNLAGE,  CAMEO_INTL_2015, PLZ8_BAUMAX.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Dealing with PRAEGENDE_JUGENDJAHRE</strong></p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[53]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from readme file we know that </span>
<span class="c1"># values in the list [1,3,5,8,10,12,14] correspond to &#39;mainstream&#39;, will be encoded with 1</span>
<span class="c1"># values in the list [2,4,6,7,9,11,13,15] correspond to &#39;avantgarde&#39;, will be encoded with 0</span>

<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;movement&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">5</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="mi">12</span><span class="p">,</span><span class="mi">14</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">,</span><span class="mi">9</span><span class="p">,</span><span class="mi">11</span><span class="p">,</span><span class="mi">13</span><span class="p">,</span><span class="mi">15</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[54]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;movement&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[54]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1     1.0
2     0.0
3     1.0
4     1.0
5     1.0
6     1.0
7     1.0
8     0.0
9     0.0
10    1.0
Name: movement, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[55]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking if the newly engineered feature &#39;mainstream&#39; is correlated with feature &#39;GREEN_AVANTGARDE&#39;</span>
<span class="p">(</span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;GREEN_AVANTGARDE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">))</span><span class="o">.</span><span class="n">corr</span><span class="p">(</span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;movement&#39;</span><span class="p">])</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[55]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>-0.9999999999999999</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[56]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;movement&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Commentary: since we have already the feature 'GREEN_AVANTGARDE' in our dataset, there is no need to create a new feature 'mainstream' that is the opposite of 'GREEN_AVANTGARDE. Hence, it will be dropped.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[57]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># from readme file we can construct codes as dict </span>
<span class="n">codes</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">8</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span> <span class="mi">9</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">12</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">13</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">14</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span><span class="mi">90</span><span class="p">}</span>
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;decade&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">codes</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[58]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;decade&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[58]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    90.0
2    90.0
3    70.0
4    70.0
5    50.0
Name: decade, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[59]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#we are done reengineering &#39;PRAEGENDE_JUGENDJAHRE&#39; and can now drop it from further analysis</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Dealing with CAMEO_INTL_2015</strong>:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[60]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;CAMEO_INTL_2015&quot; and engineer two new variables.    </span>
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;life_stage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">%</span><span class="k">10</span>
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;life_stage&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[60]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    1.0
2    4.0
3    2.0
4    3.0
5    4.0
Name: life_stage, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[61]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;wealth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span><span class="p">)</span><span class="o">-</span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">%</span><span class="k">10</span>/10
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;wealth&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[61]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    5.0
2    2.0
3    1.0
4    4.0
5    5.0
Name: wealth, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[62]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#we are done reengineering &#39;CAMEO_INTL_2015&#39; and can now drop it from further analysis</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Dealing with WOHNLAGE</strong>:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Feature 'WOHNLAGE' is constructed from two different variables - one based on quality of the neighborhood and another one is flag for rural neighborhood (values 7 and 8). We cannot disaggregate info on quality of neigborhood. So this info will be gone (alternative - create dummy var on WOHNLAGE, but we have some information quality of neighborhood at the region level, so I decided not to create a dummy). <br>
Let's create a new feature solely as flag for rural neighborhood:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[63]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;rural&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;rural&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[63]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    0.0
2    0.0
3    1.0
4    0.0
5    1.0
Name: rural, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[64]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#we are done reengineering &#39;WOHNLAGE&#39; and can now drop it from further analysis</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Dealing with PLZ8_BAUMAX</strong>:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p>Feature PLZ8_BAUMAX is constructed from variables that refer to either family homes or business buildings. Since we have the information on family homes in other features (PLZ8_ANTG1 through PLZ8_ANTG4) we can ignore this information in PLZ8_BAUMAX and create a new feature that would only flag business buildings:</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[65]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;business_building&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">azdias_fin</span><span class="p">[</span><span class="s1">&#39;business_building&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[65]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>1    0.0
2    0.0
3    0.0
4    0.0
5    0.0
Name: business_building, dtype: float64</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[66]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#we are done reengineering &#39;PLZ8_BAUMAX&#39; and can now drop it from further analysis</span>
<span class="n">azdias_fin</span> <span class="o">=</span> <span class="n">azdias_fin</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[67]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking how many features left after this step</span>
<span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[67]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>Index([&#39;ALTERSKATEGORIE_GROB&#39;, &#39;ANREDE_KZ&#39;, &#39;FINANZ_MINIMALIST&#39;,
       &#39;FINANZ_SPARER&#39;, &#39;FINANZ_VORSORGER&#39;, &#39;FINANZ_ANLEGER&#39;,
       &#39;FINANZ_UNAUFFAELLIGER&#39;, &#39;FINANZ_HAUSBAUER&#39;, &#39;GREEN_AVANTGARDE&#39;,
       &#39;HEALTH_TYP&#39;,
       ...
       &#39;CAMEO_DEUG_2015_5&#39;, &#39;CAMEO_DEUG_2015_6&#39;, &#39;CAMEO_DEUG_2015_7&#39;,
       &#39;CAMEO_DEUG_2015_8&#39;, &#39;CAMEO_DEUG_2015_9&#39;, &#39;decade&#39;, &#39;life_stage&#39;,
       &#39;wealth&#39;, &#39;rural&#39;, &#39;business_building&#39;],
      dtype=&#39;object&#39;, length=128)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: from the previous step, I have 129 features. In this step, I dropped 2 features ('LP_LEBENSPHASE_FEIN', 'LP_LEBENSPHASE_GROB') and engineered 5 new features from 4 features that were then dropped (PRAEGENDE_JUGENDJAHRE, CAMEO_INTL_2015, WOHNLAGE, PLZ8_BAUMAX). So at the end we have 129-2+5-4=128 features.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Discussion-1.2.2:-Engineer-Mixed-Type-Features">Discussion 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Discussion-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>There are 7 features marked as 'mixed' in the dataset.</p>
<ol>
<li>'LP_LEBENSPHASE_FEIN': I decided to drop it because it has to many categories (41) and we haves similar feature 'LP_LEBENSPHASE_GROB' which presents aggregated data. </li>
</ol>
<ol>
<li>'LP_LEBENSPHASE_GROB':  it turned out that 'LP_LEBENSPHASE_GROB' is highly correlated with the categorical variable 'LP_FAMILIE_FEIN' so that I decided to drop it too.</li>
</ol>
<ol>
<li>'PRAEGENDE_JUGENDJAHRE': Based on 'PRAEGENDE_JUGENDJAHRE' we created two new features - 'mainstream' which is a binary variable for movement and 'decade' which is interval-type variable for a decade.  However, the newly engineered feature 'mainstream' is fully negatively correlated with another feature in our dataset 'GREEN_AVANTGARDE'. So there is no need to duplicate this information. The feature 'mainstream' will be excluded. </li>
</ol>
<ol>
<li>'CAMEO_INTL_2015': Based on 'CAMEO_INTL_2015' we created two new ordinal features 'wealth' and 'life_stage' which do not need being reencoded. </li>
</ol>
<ol>
<li>'KBA05_BAUMAX': this feature was already dropped in earlier stages when discovered that it has lots of missing values</li>
</ol>
<ol>
<li>'WOHNLAGE': I created a new feature 'rural' that is dummy for rural neighborhood</li>
</ol>
<ol>
<li>'PLZ8_BAUMAX': I created new feature 'business_building' that is dummy for business building </li>
</ol>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h4 id="Step-1.2.3:-Complete-Feature-Selection">Step 1.2.3: Complete Feature Selection<a class="anchor-link" href="#Step-1.2.3:-Complete-Feature-Selection">&#182;</a></h4><p>The data frame should only have the columns that I want to keep. To summarize, it should consist of the following:</p>
<ul>
<li>All numeric, interval, and ordinal type columns from the original dataset.</li>
<li>Binary categorical features (all numerically-encoded).</li>
<li>Engineered features from other multi-level categorical features and mixed features.</li>
</ul>
<p>Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep "PRAEGENDE_JUGENDJAHRE", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from <strong>the subset with few or no missing values</strong>.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[68]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If there are other re-engineering tasks you need to perform, make sure you</span>
<span class="c1"># take care of them here. (Dealing with missing data will come in step 2.1.)</span>
<span class="n">azdias_fin</span><span class="o">.</span><span class="n">columns</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[68]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>Index([&#39;ALTERSKATEGORIE_GROB&#39;, &#39;ANREDE_KZ&#39;, &#39;FINANZ_MINIMALIST&#39;,
       &#39;FINANZ_SPARER&#39;, &#39;FINANZ_VORSORGER&#39;, &#39;FINANZ_ANLEGER&#39;,
       &#39;FINANZ_UNAUFFAELLIGER&#39;, &#39;FINANZ_HAUSBAUER&#39;, &#39;GREEN_AVANTGARDE&#39;,
       &#39;HEALTH_TYP&#39;,
       ...
       &#39;CAMEO_DEUG_2015_5&#39;, &#39;CAMEO_DEUG_2015_6&#39;, &#39;CAMEO_DEUG_2015_7&#39;,
       &#39;CAMEO_DEUG_2015_8&#39;, &#39;CAMEO_DEUG_2015_9&#39;, &#39;decade&#39;, &#39;life_stage&#39;,
       &#39;wealth&#39;, &#39;rural&#39;, &#39;business_building&#39;],
      dtype=&#39;object&#39;, length=128)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[69]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Do whatever you need to in order to ensure that the dataframe only contains</span>
<span class="c1"># the columns that should be passed to the algorithm functions.</span>
<span class="n">azdias_fin</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[69]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(797426, 128)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="NOTE:">NOTE:<a class="anchor-link" href="#NOTE:">&#182;</a></h2><h4 id="I've-integrated-this-step-in-steps-1.2.1.-and-1.2.2.-I-found-it-easier-to-follow-transformations-while-they-occur.-My-final-data-frame-azdias_fin-contains-only-the-features-I-want-to-keep-for-further-analysis.">I've integrated this step in steps 1.2.1. and 1.2.2. I found it easier to follow transformations while they occur. My final data frame azdias_fin contains only the features I want to keep for further analysis.<a class="anchor-link" href="#I've-integrated-this-step-in-steps-1.2.1.-and-1.2.2.-I-found-it-easier-to-follow-transformations-while-they-occur.-My-final-data-frame-azdias_fin-contains-only-the-features-I-want-to-keep-for-further-analysis.">&#182;</a></h4>
</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-1.3:-Create-a-Cleaning-Function">Step 1.3: Create a Cleaning Function<a class="anchor-link" href="#Step-1.3:-Create-a-Cleaning-Function">&#182;</a></h3>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[70]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">clean_data</span><span class="p">(</span><span class="n">df</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Perform feature trimming, re-encoding, and engineering for demographics</span>
<span class="sd">    data</span>
<span class="sd">    </span>
<span class="sd">    INPUT: Demographics DataFrame</span>
<span class="sd">    OUTPUT: Trimmed and cleaned demographics DataFrame</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="c1"># Put in code here to execute all main cleaning steps:</span>
    <span class="c1"># convert missing value codes into NaNs, ...</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Start step 1: dealing with missing values&quot;</span><span class="p">)</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">replace</span><span class="p">({</span><span class="s1">&#39;X&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;XX&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">})</span>
    <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_or_unknown&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">&#39;[-1]&#39;</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;[-1,X]&#39;</span><span class="p">,</span> <span class="s1">&#39;[XX]&#39;</span><span class="p">,</span> <span class="s1">&#39;[-1,XX]&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="n">x</span><span class="p">)</span>
    <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">eval</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>

    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;attribute&#39;</span><span class="p">]):</span>
        <span class="n">df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">(</span><span class="n">feat_info</span><span class="p">[</span><span class="s1">&#39;missing_transform&#39;</span><span class="p">][</span><span class="n">i</span><span class="p">]),</span> <span class="n">col</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span> 
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Checking number of missing values after step 1:&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
        
    <span class="c1"># remove selected columns and rows, ...</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Start step 2: removing selected columns and rows&quot;</span><span class="p">)</span>
    <span class="n">col_del</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;TITEL_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;AGER_TYP&#39;</span><span class="p">,</span> <span class="s1">&#39;KK_KUNDENTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;KBA05_BAUMAX&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBURTSJAHR&#39;</span><span class="p">,</span> <span class="s1">&#39;ALTER_HH&#39;</span><span class="p">,</span>
               <span class="s1">&#39;LP_LEBENSPHASE_FEIN&#39;</span><span class="p">,</span><span class="s1">&#39;LP_FAMILIE_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_STATUS_FEIN&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEU_2015&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_LEBENSPHASE_GROB&#39;</span><span class="p">]</span>

    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col_del</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">row_missing_values</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">row_missing_values</span> <span class="o">&lt;=</span> <span class="mi">20</span><span class="p">]</span>   
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Checking the new shape of the data frame after step 2:&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
    
    <span class="c1"># select, re-encode, and engineer column values.</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Start step 3.1: reencoding with dummies&quot;</span><span class="p">)</span> 
    <span class="n">cat_var_reencode</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;CJT_GESAMTTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;FINANZTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;GFK_URLAUBERTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;LP_FAMILIE_GROB&#39;</span><span class="p">,</span>
                        <span class="s1">&#39;LP_STATUS_GROB&#39;</span><span class="p">,</span> <span class="s1">&#39;NATIONALITAET_KZ&#39;</span><span class="p">,</span> <span class="s1">&#39;SHOPPER_TYP&#39;</span><span class="p">,</span>
                        <span class="s1">&#39;ZABEOTYP&#39;</span><span class="p">,</span> <span class="s1">&#39;GEBAEUDETYP&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_DEUG_2015&#39;</span><span class="p">]</span>
    
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">cat_var_reencode</span><span class="p">)</span>    
    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">df</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;OST_WEST_KZ&#39;</span><span class="p">],</span> <span class="n">drop_first</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>   
    
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Start step 3.2: reengineering features&quot;</span><span class="p">)</span>
    <span class="n">codes</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">2</span><span class="p">:</span><span class="mi">40</span><span class="p">,</span> <span class="mi">3</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">4</span><span class="p">:</span><span class="mi">50</span><span class="p">,</span> <span class="mi">5</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">6</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">7</span><span class="p">:</span><span class="mi">60</span><span class="p">,</span> <span class="mi">8</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span> <span class="mi">9</span><span class="p">:</span><span class="mi">70</span><span class="p">,</span> <span class="mi">10</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">11</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">12</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">13</span><span class="p">:</span><span class="mi">80</span><span class="p">,</span> <span class="mi">14</span><span class="p">:</span><span class="mi">90</span><span class="p">,</span> <span class="mi">15</span><span class="p">:</span><span class="mi">90</span><span class="p">}</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;decade&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">codes</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;life_stage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">%</span><span class="k">10</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;wealth&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">/</span><span class="mi">10</span><span class="p">)</span><span class="o">-</span><span class="n">df</span><span class="p">[</span><span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">float</span><span class="p">)</span><span class="o">%</span><span class="k">10</span>/10
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;rural&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">,</span><span class="mi">5</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">7</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
    <span class="n">df</span><span class="p">[</span><span class="s1">&#39;business_building&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">4</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="n">col_reengineered</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;PRAEGENDE_JUGENDJAHRE&#39;</span><span class="p">,</span> <span class="s1">&#39;CAMEO_INTL_2015&#39;</span><span class="p">,</span> <span class="s1">&#39;WOHNLAGE&#39;</span><span class="p">,</span> <span class="s1">&#39;PLZ8_BAUMAX&#39;</span><span class="p">]</span>
    <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col_reengineered</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Checking the new shape of the data frame after step 3:&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Checking the number of missing values in the resulting data frame:&quot;</span><span class="p">,</span> <span class="n">df</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">())</span>
        
    <span class="c1"># Return the cleaned dataframe.</span>
    <span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">(</span><span class="n">drop</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[71]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_features</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">azdias</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Start step 1: dealing with missing values
Checking number of missing values after step 1: 8373929

Start step 2: removing selected columns and rows
Checking the new shape of the data frame after step 2: (797754, 74)

Start step 3.1: reencoding with dummies
Start step 3.2: reengineering features
Checking the new shape of the data frame after step 3: (797754, 128)
Checking the number of missing values in the resulting data frame: 751844
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: The number of missing values after step 1 is the same as when the step was performed without function call. The number of features after transformation via function is also the same. However, we retained more rows in the cleaned via function dataset compared to what we did earlier step by step (797754 vs 797426). The reason is  because in the clean_data function I have clumped together all 11 columns I decided to delete before removing rows with missing values in step 2. While earlier, I removed only 6 of them at first and the decision to remove the remaining 5 came later. <br></p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Step-2:-Feature-Transformation">Step 2: Feature Transformation<a class="anchor-link" href="#Step-2:-Feature-Transformation">&#182;</a></h2><h3 id="Step-2.1:-Apply-Feature-Scaling">Step 2.1: Apply Feature Scaling<a class="anchor-link" href="#Step-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the <a href="http://scikit-learn.org/stable/modules/classes.html">API reference page for sklearn</a> to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:</p>
<ul>
<li>sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html">Imputer</a> to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.</li>
<li>For the actual scaling function, a <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> instance is suggested, scaling each feature to mean 0 and standard deviation 1.</li>
<li>For these classes, you can make use of the <code>.fit_transform()</code> method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[72]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If you&#39;ve not yet cleaned the dataset of all NaN values, then investigate and</span>
<span class="c1"># do that now.</span>
<span class="n">imputer</span> <span class="o">=</span> <span class="n">Imputer</span><span class="p">(</span><span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;most_frequent&#39;</span><span class="p">)</span>
<span class="n">azdias_features_imputed</span> <span class="o">=</span> <span class="n">imputer</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">azdias_features</span><span class="p">)</span>

<span class="n">azdias_features_imputed</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">azdias_features_imputed</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">azdias_features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
<span class="n">azdias_features_imputed</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[72]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>CAMEO_DEUG_2015_6</th>
      <th>CAMEO_DEUG_2015_7</th>
      <th>CAMEO_DEUG_2015_8</th>
      <th>CAMEO_DEUG_2015_9</th>
      <th>OST_WEST_KZ_W</th>
      <th>decade</th>
      <th>life_stage</th>
      <th>wealth</th>
      <th>rural</th>
      <th>business_building</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>90.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>90.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>70.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>3.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>70.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>50.0</td>
      <td>4.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 128 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: since many columns in this dataset are ordinal, I decided to impute the most frequent values.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[73]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking if the result of imputation</span>
<span class="n">azdias_features_imputed</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[73]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>0</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[74]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply feature scaling to the general population demographics data.</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_features_imputed</span><span class="p">)</span> 
<span class="n">azdias_features_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">azdias_features_imputed</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[75]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking if scaler performed well on 10 first features</span>
<span class="n">azdias_features_scaled</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[75]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([ 8.03392459e-18, -6.37013622e-17,  4.62618673e-17,  1.12047418e-16,
       -7.59036645e-17, -3.71591279e-17, -1.53953865e-16, -1.20669191e-16,
        1.39373450e-16, -2.91799801e-16])</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[76]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#checking if scaler performed well on 10 first features</span>
<span class="n">azdias_features_scaled</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[76]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: scaler works as expected - all features were transformed correctly and have mean 0 and std 1.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.1:-Apply-Feature-Scaling">Discussion 2.1: Apply Feature Scaling<a class="anchor-link" href="#Discussion-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>Since I have decided to remove data with missing points earlier in this project, I didn't have to clean the dataset any further. 
The decision to remove these points was driven by another decision - namely to reencode 12 categorical featuers with their dummies. If I kept missing values at that stage, I could have ended up with additional 12 columns for NaNs. <br>
Alternatively, if I decided to delete the 12 categorical features all together, I could have kept the certain level of missing data in rows (say up to 5) and later impute these values.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-2.2:-Perform-Dimensionality-Reduction">Step 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Step-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html">PCA</a> class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).</li>
<li>Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html"><code>plot()</code></a> function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.</li>
<li>Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[77]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply PCA to the data.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="n">X_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">azdias_features_scaled</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[78]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the variance accounted for by each principal component.</span>
<span class="k">def</span> <span class="nf">scree_plot</span><span class="p">(</span><span class="n">pca</span><span class="p">):</span>
    <span class="sd">&#39;&#39;&#39;</span>
<span class="sd">    Creates a scree plot associated with the principal components </span>
<span class="sd">    </span>
<span class="sd">    INPUT: pca - the result of instantian of PCA in scikit learn</span>
<span class="sd">            </span>
<span class="sd">    OUTPUT:</span>
<span class="sd">            None</span>
<span class="sd">    &#39;&#39;&#39;</span>
    <span class="n">num_components</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
    <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_components</span><span class="p">)</span>
    <span class="n">vals</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span>
 
    <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
    <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
    <span class="n">cumvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">vals</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">vals</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">cumvals</span><span class="p">)</span>
 
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Principal Component&quot;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Variance Explained (%)&quot;</span><span class="p">)</span>
    <span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Explained Variance Per Principal Component&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[79]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scree_plot</span><span class="p">(</span><span class="n">pca</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[80]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># how many components together explain about 80% of variance?</span>
<span class="n">cum_expl_var_ratio</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="nb">len</span><span class="p">(</span><span class="n">cum_expl_var_ratio</span><span class="p">[</span><span class="n">cum_expl_var_ratio</span> <span class="o">&lt;=</span> <span class="mf">0.805</span><span class="p">])</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[80]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>55</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[81]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-apply PCA to the data while selecting for number of components to retain.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="mi">55</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_features_scaled</span><span class="p">)</span>
<span class="n">azdias_features_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">azdias_features_scaled</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[82]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[82]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>array([0.1068349 , 0.18904279, 0.24116579, 0.27267736, 0.29625962,
       0.31635506, 0.33476651, 0.35233056, 0.36839587, 0.38360877,
       0.39790406, 0.41175416, 0.42452159, 0.43675519, 0.44847467,
       0.45984398, 0.47116332, 0.48177354, 0.49211878, 0.50240449,
       0.51260587, 0.52251093, 0.53222582, 0.54189901, 0.55148549,
       0.56084479, 0.5701031 , 0.57918112, 0.58823775, 0.59722533,
       0.60604118, 0.61484042, 0.62356428, 0.63221521, 0.64078305,
       0.6493133 , 0.65777359, 0.66607647, 0.67434415, 0.68247155,
       0.69058794, 0.69861201, 0.70659242, 0.71456863, 0.72244326,
       0.73029756, 0.73810932, 0.74590137, 0.75366262, 0.76133828,
       0.76897082, 0.77650509, 0.78401029, 0.79134442, 0.79849783])</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.2:-Perform-Dimensionality-Reduction">Discussion 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Discussion-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>As a rule of thumb, one should keep as many components that together they would explain about 80% of variance. From the scree plot we can conclude that about 55 components explain 80% of variance. So I decided to keep 55 PCA components for the clustering part of the project. <br>
Moreover, the first three components add the most to the explanation of the variance (together explain about 25%), while the rest of the components' weights are rather small (~1-2%).</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-2.3:-Interpret-Principal-Components">Step 2.3: Interpret Principal Components<a class="anchor-link" href="#Step-2.3:-Interpret-Principal-Components">&#182;</a></h3><p>Let's check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.</p>
<p>As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.</p>
<ul>
<li>To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.</li>
<li>Investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the <em>i</em>-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.</li>
</ul>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[83]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the first principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="c1"># HINT: Try defining a function here or in a new cell that you can reuse in the</span>
<span class="c1"># other cells.</span>

<span class="k">def</span> <span class="nf">map_pca</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">df_scaled</span><span class="p">):</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;PCA_&quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)],</span> <span class="n">index</span> <span class="o">=</span> <span class="n">df_scaled</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
    <span class="n">weights</span> <span class="o">=</span> <span class="n">weights</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="n">by</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;PCA_&quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)])</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;positive top5 weights:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">tail</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">-----------------------------</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;negative top5 weights:</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[84]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">map_pca</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">azdias_features</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>positive top5 weights:
                        PCA_1
HH_EINKOMMEN_SCORE  0.181879
wealth              0.182751
PLZ8_ANTG4          0.186437
PLZ8_ANTG3          0.192128
LP_STATUS_GROB_1.0  0.198213

-----------------------------

negative top5 weights:
                       PCA_1
FINANZ_MINIMALIST -0.204375
MOBI_REGIO        -0.193761
PLZ8_ANTG1        -0.192182
KBA05_GBZ         -0.186407
KBA05_ANTG1       -0.184530
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[85]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the second principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="n">map_pca</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">azdias_features</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>positive top5 weights:
                          PCA_2
SEMIO_LUST            0.161946
SEMIO_ERL             0.187227
ZABEOTYP_3.0          0.203869
FINANZ_VORSORGER      0.214276
ALTERSKATEGORIE_GROB  0.229072

-----------------------------

negative top5 weights:
                           PCA_2
decade                -0.224657
FINANZ_SPARER         -0.216684
SEMIO_REL             -0.216012
FINANZ_UNAUFFAELLIGER -0.212395
SEMIO_TRADV           -0.206713
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[86]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the third principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="n">map_pca</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">azdias_features</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>positive top5 weights:
                   PCA_3
FINANZTYP_5.0  0.136707
SEMIO_KULT     0.248099
SEMIO_SOZ      0.257945
SEMIO_FAM      0.258574
SEMIO_VERT     0.327127

-----------------------------

negative top5 weights:
                PCA_3
ANREDE_KZ  -0.353231
SEMIO_KAEM -0.325678
SEMIO_DOM  -0.297417
SEMIO_KRIT -0.271625
SEMIO_ERL  -0.199927
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-2.3:-Interpret-Principal-Components">Discussion 2.3: Interpret Principal Components<a class="anchor-link" href="#Discussion-2.3:-Interpret-Principal-Components">&#182;</a></h3><ol>
<li><strong>PCA_1 can be called "low income earners segment"</strong>: <br>
The first principal component PCA_1 has the following top5 features with the highest/lowest weights:</li>
</ol>
<p><em>negative weights</em>:
MOBI_REGIO, FINANZ_MINIMALIST, KBA05_ANTG1, PLZ8_ANTG1, KBA05_GBZ <br></p>
<p><em>positive weights</em>: HH_EINKOMMEN_SCORE, wealth, PLZ8_ANTG4, PLZ8_ANTG3, LP_STATUS_GROB_1.0 <br></p>
<p>The PCA_1 is mainly about financial wealth and the number of buildings in the region/microcell where the person lives. Thus, positive weights here point out to 'low-income earners', 'poor households', 'very low income' and to region with 'high share of 6 or 10+ family homes' (which is logical given that poorer families tend to live in the densely populated areas which are usually cheaper). The negative weights are associated with such features as mobility, low financial interest, share of 1-2 family homes. From the way that these features are constructed, we can say that the relationship between the negatively weighted features and positively weighted features are as follows:
the more low-income earners, the higher the share of 6 or 10+ family homes in the microcell, poorer households and more low income families is associated with higher mobility, higher 'low financial interest', lower share of 1-2 family homes.</p>
<ol>
<li><strong>PCA_2 can be called "conservative seniors segment"</strong>: <br>
The second principal component PCA_2 has the following top5 features with the highest/lowest weights:</li>
</ol>
<p><em>negative weights</em>: decade, SEMIO_REL, FINANZ_SPARER, FINANZ_UNAUFFAELLIGER, SEMIO_TRADV <br></p>
<p><em>positive weights</em>: SEMIO_LUST, SEMIO_ERL, ZABEOTYP_3.0, FINANZ_VORSORGER, ALTERSKATEGORIE_GROB</p>
<p>The PCA_2 revolves around such topics as money saving, conservatism/religiosity, old age. Thus, positive weights point out to the fact that the persons in this segment are the least 'sensual-minded', 'low event-oriented', believe their energy is ‘fair supplied’, they also have low financial provisions/savings and are seniors (&gt;50 years). When considering the negatively weighted features and their relationship with the positively weighted features we get fuller picture of the individuals in this segment. In addition to the said above, they are born in earlier decade (probably 1940s/50s ), they have the highest affinity to religion, they are financially modest, more active money-savers and are the most tradional-minded.</p>
<ol>
<li><strong>PCA_3 can be called "practical male segment"</strong>: <br>
The third principal component PCA_3 has the following top5 features with the highest/lowest weights:</li>
</ol>
<p><em>negative weights</em>: ANREDE_KZ, SEMIO_KAEM, SEMIO_DOM, SEMIO_KRIT, SEMIO_ERL <br></p>
<p><em>positive weights</em>: FINANZ_MINIMALIST, SEMIO_KULT, SEMIO_FAM, SEMIO_SOZ, SEMIO_VERT</p>
<p>The PCA_3 creates a distinctive segment consiting of mostly male that are self-confident, less family oriented and mostly event-oriented. So, the positively weighted components are associated here with such characteristics as 'the least dreamful', 'the least socially-minded', 'the least family-minded', 'the least cultural-minded', the least 'low financially interested'. While negatively weighted components correspond to features such as 'male', highly ‘combative attitude’,  'very dominant-minded', 'the most critical-minded', 'the most event-oriented'.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h2 id="Step-3:-Clustering">Step 3: Clustering<a class="anchor-link" href="#Step-3:-Clustering">&#182;</a></h2><h3 id="Step-3.1:-Apply-Clustering-to-General-Population">Step 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Step-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>I've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, I apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[87]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Over a number of different cluster counts...</span>
<span class="n">range_n_clusters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">25</span><span class="p">]</span>
<span class="n">sum_of_squared_distances</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">since</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>

<span class="k">with</span> <span class="n">active_session</span><span class="p">():</span>
    <span class="k">for</span> <span class="n">n_clusters</span> <span class="ow">in</span> <span class="n">range_n_clusters</span><span class="p">:</span>
        <span class="n">cluster_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;calculating </span><span class="si">{}</span><span class="s2"> clusters&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">n_clusters</span><span class="p">))</span>
        <span class="c1"># run k-means clustering on the data and...</span>
        <span class="n">clusterer</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">n_clusters</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">120</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_features_scaled</span><span class="p">)</span>

        <span class="c1"># compute the average within-cluster distances.</span>
        <span class="n">sum_of_squared_distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">clusterer</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;sum of squared distances:&quot;</span><span class="p">,</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;time for this cluster:&quot;</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">cluster_start</span><span class="p">)</span>

<span class="n">time_elapsed</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">since</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Clustering complete in </span><span class="si">{:.0f}</span><span class="s1">m </span><span class="si">{:.0f}</span><span class="s1">s&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time_elapsed</span> <span class="o">//</span> <span class="mi">60</span><span class="p">,</span> <span class="n">time_elapsed</span> <span class="o">%</span> <span class="mi">60</span><span class="p">))</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>calculating 5 clusters
sum of squared distances: 85420965.92336746
time for this cluster: 215.34582018852234
calculating 10 clusters
sum of squared distances: 80123262.11216384
time for this cluster: 428.9132068157196
calculating 15 clusters
sum of squared distances: 77357465.88423884
time for this cluster: 837.2431218624115
calculating 20 clusters
sum of squared distances: 75472896.78918467
time for this cluster: 999.5252838134766
calculating 25 clusters
sum of squared distances: 72981948.56508066
time for this cluster: 1267.9483151435852
Clustering complete in 62m 30s
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[88]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the change in within-cluster distance across number of clusters.</span>
<span class="c1"># HINT: Use matplotlib&#39;s plot function to visualize this relationship.</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">range_n_clusters</span><span class="p">,</span> <span class="n">sum_of_squared_distances</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Number of Clusters&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(</span><span class="n">range_n_clusters</span><span class="p">,</span> <span class="n">range_n_clusters</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Sum of Squared Distances&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Elbow Method for Optimal k&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">();</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[89]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-fit the k-means model with the selected number of clusters and obtain</span>
<span class="c1"># cluster predictions for the general population demographics data.</span>
<span class="n">clusterer</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">random_state</span> <span class="o">=</span> <span class="mi">123</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">azdias_features_pca</span><span class="p">)</span>
<span class="n">azdias_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">azdias_features_pca</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[90]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">azdias_preds</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[90]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>797754</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-3.1:-Apply-Clustering-to-General-Population">Discussion 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Discussion-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>We can see from the plot above that the line has a slight "elbow" at 10 clusters, meaning that each additional cluster reduces the distances between classes much less (marginal gain drops). I also tested some other combinations of cluster numbers earlier. But none gives a nice elbow. So after all I decided to cluster the data into 10 segments.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-3.2:-Apply-All-Steps-to-the-Customer-Data">Step 3.2: Apply All Steps to the Customer Data<a class="anchor-link" href="#Step-3.2:-Apply-All-Steps-to-the-Customer-Data">&#182;</a></h3><p>Now that I have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters.</p>
<p>I first load the customers data and apply the same feature wrangling, selection, and engineering steps to the customer demographics using the <code>clean_data()</code> function I created earlier.</p>
<p>Then I use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, I don't use a <code>.fit()</code> or <code>.fit_transform()</code> method to re-fit the old objects.</p>
<p>Then I carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[91]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the customer demographics data.</span>
<span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_CUSTOMERS_Subset.csv&#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[92]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply preprocessing, feature transformation, and clustering from the general</span>
<span class="c1"># demographics onto the customer data, obtaining cluster predictions for the</span>
<span class="c1"># customer demographics data.</span>
<span class="n">customers_clean</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">customers</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>Start step 1: dealing with missing values
Checking number of missing values after step 1: 3112918

Start step 2: removing selected columns and rows
Checking the new shape of the data frame after step 2: (141689, 74)

Start step 3.1: reencoding with dummies
Start step 3.2: reengineering features
Checking the new shape of the data frame after step 3: (141689, 127)
Checking the number of missing values in the resulting data frame: 105205
</pre>
</div>
</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[93]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">set</span><span class="p">(</span><span class="n">azdias_features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span><span class="o">-</span><span class="nb">set</span><span class="p">(</span><span class="n">customers_clean</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[93]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>{&#39;GEBAEUDETYP_5.0&#39;}</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[94]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">idx_col</span> <span class="o">=</span> <span class="n">azdias_features</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="s1">&#39;GEBAEUDETYP_5.0&#39;</span><span class="p">)</span>
<span class="n">new_col</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">customers_clean</span><span class="p">[</span><span class="s1">&#39;GEBAEUDETYP_4.0&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<p><strong>Commentary</strong>: It turned out that the customers dataset doesn't have values for 'GEBAEUDETYP'=5. So after getting dummies for the categorial variables, we have 127 features in the customers dataset instead of 128 features as in the azdias dataset. That's why I added a new column to customers_clean dataset called 'GEBAEUDETYP_5.0' filled with 0s.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[95]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers_clean</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">idx_col</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="s1">&#39;GEBAEUDETYP_5.0&#39;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="n">new_col</span><span class="p">)</span>
<span class="n">customers_clean</span><span class="o">.</span><span class="n">shape</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[95]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>(141689, 128)</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[96]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># checking if GEBAEUDETYP_5.0 was correctly inserted</span>
<span class="n">idx_col</span> <span class="o">==</span> <span class="n">customers_clean</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">get_loc</span><span class="p">(</span><span class="s1">&#39;GEBAEUDETYP_5.0&#39;</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[96]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>True</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[97]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers_clean_imputed</span> <span class="o">=</span> <span class="n">imputer</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">customers_clean</span><span class="p">)</span>

<span class="n">customers_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">customers_clean_imputed</span><span class="p">)</span>  

<span class="n">customers_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">customers_scaled</span><span class="p">)</span>

<span class="n">customer_preds</span> <span class="o">=</span> <span class="n">clusterer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">customers_pca</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[98]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">len</span><span class="p">(</span><span class="n">customer_preds</span><span class="p">)</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[98]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>141689</pre>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Step-3.3:-Compare-Customer-Data-to-Demographics-Data">Step 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Step-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>At this point, I have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, I compare the two cluster distributions to see where the strongest customer base for the company is.</p>
<p>Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.</p>

</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[99]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the proportion of data in each cluster for the customer data to the</span>
<span class="c1"># proportion of data in each cluster for the general population.</span>
<span class="n">customers_preds_prop</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">customer_preds</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">/</span><span class="n">customer_preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">azdias_preds_prop</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="mi">100</span><span class="o">*</span><span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">azdias_preds</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span><span class="o">/</span><span class="n">azdias_preds</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">preds</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">&#39;customers_count&#39;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">customer_preds</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(),</span> 
                      <span class="s1">&#39;population_count&#39;</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">azdias_preds</span><span class="p">)</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(),</span> 
                      <span class="s1">&#39;customers_prop&#39;</span><span class="p">:</span> <span class="n">customers_preds_prop</span><span class="p">,</span> <span class="s1">&#39;population_prop&#39;</span><span class="p">:</span> <span class="n">azdias_preds_prop</span><span class="p">})</span>
<span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">preds</span><span class="o">.</span><span class="n">customers_prop</span><span class="p">[</span><span class="n">x</span><span class="p">]</span><span class="o">-</span><span class="n">preds</span><span class="o">.</span><span class="n">population_prop</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">preds</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
<span class="n">preds</span><span class="p">[</span><span class="s1">&#39;overrepresented&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;target&#39;</span> <span class="k">if</span> <span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;-&#39;</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">preds</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
<span class="n">preds</span><span class="p">[</span><span class="s1">&#39;underrepresented&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;not a target&#39;</span> <span class="k">if</span> <span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">][</span><span class="n">x</span><span class="p">]</span> <span class="o">&lt;</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="s1">&#39;-&#39;</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">preds</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
<span class="n">preds</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">column</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="n">preds</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
<span class="n">preds</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt output_prompt">Out[99]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>cluster</th>
      <th>customers_count</th>
      <th>population_count</th>
      <th>customers_prop</th>
      <th>population_prop</th>
      <th>diff</th>
      <th>overrepresented</th>
      <th>underrepresented</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>4695</td>
      <td>39139</td>
      <td>3.31</td>
      <td>4.91</td>
      <td>-1.60</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>1162</td>
      <td>65789</td>
      <td>0.82</td>
      <td>8.25</td>
      <td>-7.43</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
    <tr>
      <th>2</th>
      <td>2</td>
      <td>14335</td>
      <td>85160</td>
      <td>10.12</td>
      <td>10.67</td>
      <td>-0.55</td>
      <td>-</td>
      <td>-</td>
    </tr>
    <tr>
      <th>3</th>
      <td>3</td>
      <td>2038</td>
      <td>83417</td>
      <td>1.44</td>
      <td>10.46</td>
      <td>-9.02</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
    <tr>
      <th>4</th>
      <td>4</td>
      <td>1486</td>
      <td>89844</td>
      <td>1.05</td>
      <td>11.26</td>
      <td>-10.21</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
    <tr>
      <th>5</th>
      <td>5</td>
      <td>55745</td>
      <td>84540</td>
      <td>39.34</td>
      <td>10.60</td>
      <td>28.74</td>
      <td>target</td>
      <td>-</td>
    </tr>
    <tr>
      <th>6</th>
      <td>6</td>
      <td>14447</td>
      <td>100340</td>
      <td>10.20</td>
      <td>12.58</td>
      <td>-2.38</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
    <tr>
      <th>7</th>
      <td>7</td>
      <td>17705</td>
      <td>107107</td>
      <td>12.50</td>
      <td>13.43</td>
      <td>-0.93</td>
      <td>-</td>
      <td>-</td>
    </tr>
    <tr>
      <th>8</th>
      <td>8</td>
      <td>27395</td>
      <td>84016</td>
      <td>19.33</td>
      <td>10.53</td>
      <td>8.80</td>
      <td>target</td>
      <td>-</td>
    </tr>
    <tr>
      <th>9</th>
      <td>9</td>
      <td>2681</td>
      <td>58402</td>
      <td>1.89</td>
      <td>7.32</td>
      <td>-5.43</td>
      <td>-</td>
      <td>not a target</td>
    </tr>
  </tbody>
</table>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[100]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
<span class="n">tidy</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">melt</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">id_vars</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;cluster&#39;</span><span class="p">],</span> <span class="n">value_vars</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;customers_prop&#39;</span><span class="p">,</span> <span class="s1">&#39;population_prop&#39;</span><span class="p">],</span> <span class="n">var_name</span><span class="o">=</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="n">value_name</span><span class="o">=</span><span class="s1">&#39;%&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">barplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">&#39;%&#39;</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">&#39;dataset&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tidy</span><span class="p">);</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Cluster ratios for customers and population datasets&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">);</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_png output_subarea ">
<img src="data:image/png;base64,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
"
>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[101]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">interpret_clusters</span><span class="p">(</span><span class="n">cluster_num</span><span class="p">):</span>
    <span class="n">pca_inversed</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">clusterer</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="n">cluster_num</span><span class="p">,</span> <span class="p">:])</span>
    <span class="n">scaler_inversed</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">pca_inversed</span><span class="p">),</span> <span class="n">decimals</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">results_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scaler_inversed</span><span class="p">,</span> <span class="n">azdias_features</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">results_df</span>
</pre></div>

    </div>
</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[102]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is overrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;target cluster number:&quot;</span><span class="p">,</span> <span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">argmax</span><span class="p">())</span>
<span class="n">target_cluster</span> <span class="o">=</span> <span class="n">interpret_clusters</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">argmax</span><span class="p">())</span>
<span class="n">target_cluster</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>target cluster number: 5
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[102]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ALTERSKATEGORIE_GROB</th>
      <td>3.37</td>
    </tr>
    <tr>
      <th>ANREDE_KZ</th>
      <td>1.45</td>
    </tr>
    <tr>
      <th>FINANZ_MINIMALIST</th>
      <td>4.60</td>
    </tr>
    <tr>
      <th>FINANZ_SPARER</th>
      <td>1.54</td>
    </tr>
    <tr>
      <th>FINANZ_VORSORGER</th>
      <td>4.33</td>
    </tr>
    <tr>
      <th>FINANZ_ANLEGER</th>
      <td>1.65</td>
    </tr>
    <tr>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <td>2.04</td>
    </tr>
    <tr>
      <th>FINANZ_HAUSBAUER</th>
      <td>2.13</td>
    </tr>
    <tr>
      <th>GREEN_AVANTGARDE</th>
      <td>0.97</td>
    </tr>
    <tr>
      <th>HEALTH_TYP</th>
      <td>2.08</td>
    </tr>
    <tr>
      <th>RETOURTYP_BK_S</th>
      <td>3.70</td>
    </tr>
    <tr>
      <th>SEMIO_SOZ</th>
      <td>4.25</td>
    </tr>
    <tr>
      <th>SEMIO_FAM</th>
      <td>3.55</td>
    </tr>
    <tr>
      <th>SEMIO_REL</th>
      <td>2.96</td>
    </tr>
    <tr>
      <th>SEMIO_MAT</th>
      <td>3.15</td>
    </tr>
    <tr>
      <th>SEMIO_VERT</th>
      <td>4.88</td>
    </tr>
    <tr>
      <th>SEMIO_LUST</th>
      <td>5.32</td>
    </tr>
    <tr>
      <th>SEMIO_ERL</th>
      <td>5.11</td>
    </tr>
    <tr>
      <th>SEMIO_KULT</th>
      <td>3.61</td>
    </tr>
    <tr>
      <th>SEMIO_RAT</th>
      <td>3.06</td>
    </tr>
    <tr>
      <th>SEMIO_KRIT</th>
      <td>4.20</td>
    </tr>
    <tr>
      <th>SEMIO_DOM</th>
      <td>4.66</td>
    </tr>
    <tr>
      <th>SEMIO_KAEM</th>
      <td>4.12</td>
    </tr>
    <tr>
      <th>SEMIO_PFLICHT</th>
      <td>3.12</td>
    </tr>
    <tr>
      <th>SEMIO_TRADV</th>
      <td>3.08</td>
    </tr>
    <tr>
      <th>SOHO_KZ</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>VERS_TYP</th>
      <td>1.52</td>
    </tr>
    <tr>
      <th>ANZ_PERSONEN</th>
      <td>2.31</td>
    </tr>
    <tr>
      <th>ANZ_TITEL</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>HH_EINKOMMEN_SCORE</th>
      <td>2.23</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_2.0</th>
      <td>0.23</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_3.0</th>
      <td>0.35</td>
    </tr>
    <tr>
      <th>ZABEOTYP_1.0</th>
      <td>0.47</td>
    </tr>
    <tr>
      <th>ZABEOTYP_2.0</th>
      <td>0.13</td>
    </tr>
    <tr>
      <th>ZABEOTYP_3.0</th>
      <td>0.33</td>
    </tr>
    <tr>
      <th>ZABEOTYP_4.0</th>
      <td>0.08</td>
    </tr>
    <tr>
      <th>ZABEOTYP_5.0</th>
      <td>-0.03</td>
    </tr>
    <tr>
      <th>ZABEOTYP_6.0</th>
      <td>0.03</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_1.0</th>
      <td>0.77</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_2.0</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_3.0</th>
      <td>0.09</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_4.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_5.0</th>
      <td>-0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_6.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_8.0</th>
      <td>0.12</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_1</th>
      <td>0.18</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_2</th>
      <td>0.29</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_3</th>
      <td>0.14</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_4</th>
      <td>0.16</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_5</th>
      <td>0.07</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_6</th>
      <td>0.09</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_7</th>
      <td>0.04</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_8</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_9</th>
      <td>0.02</td>
    </tr>
    <tr>
      <th>OST_WEST_KZ_W</th>
      <td>0.91</td>
    </tr>
    <tr>
      <th>decade</th>
      <td>64.91</td>
    </tr>
    <tr>
      <th>life_stage</th>
      <td>3.69</td>
    </tr>
    <tr>
      <th>wealth</th>
      <td>1.92</td>
    </tr>
    <tr>
      <th>rural</th>
      <td>0.09</td>
    </tr>
    <tr>
      <th>business_building</th>
      <td>0.01</td>
    </tr>
  </tbody>
</table>
<p>128 rows × 1 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[103]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is underrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;underrepresented cluster number:&quot;</span><span class="p">,</span> <span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">argmin</span><span class="p">())</span>
<span class="n">underrepres_cluster</span> <span class="o">=</span> <span class="n">interpret_clusters</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">argmin</span><span class="p">())</span>
<span class="n">underrepres_cluster</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>underrepresented cluster number: 4
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[103]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ALTERSKATEGORIE_GROB</th>
      <td>1.79</td>
    </tr>
    <tr>
      <th>ANREDE_KZ</th>
      <td>1.98</td>
    </tr>
    <tr>
      <th>FINANZ_MINIMALIST</th>
      <td>1.12</td>
    </tr>
    <tr>
      <th>FINANZ_SPARER</th>
      <td>4.35</td>
    </tr>
    <tr>
      <th>FINANZ_VORSORGER</th>
      <td>2.26</td>
    </tr>
    <tr>
      <th>FINANZ_ANLEGER</th>
      <td>4.18</td>
    </tr>
    <tr>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <td>3.75</td>
    </tr>
    <tr>
      <th>FINANZ_HAUSBAUER</th>
      <td>4.21</td>
    </tr>
    <tr>
      <th>GREEN_AVANTGARDE</th>
      <td>0.02</td>
    </tr>
    <tr>
      <th>HEALTH_TYP</th>
      <td>2.36</td>
    </tr>
    <tr>
      <th>RETOURTYP_BK_S</th>
      <td>2.70</td>
    </tr>
    <tr>
      <th>SEMIO_SOZ</th>
      <td>3.32</td>
    </tr>
    <tr>
      <th>SEMIO_FAM</th>
      <td>3.92</td>
    </tr>
    <tr>
      <th>SEMIO_REL</th>
      <td>5.27</td>
    </tr>
    <tr>
      <th>SEMIO_MAT</th>
      <td>4.71</td>
    </tr>
    <tr>
      <th>SEMIO_VERT</th>
      <td>2.29</td>
    </tr>
    <tr>
      <th>SEMIO_LUST</th>
      <td>3.15</td>
    </tr>
    <tr>
      <th>SEMIO_ERL</th>
      <td>4.39</td>
    </tr>
    <tr>
      <th>SEMIO_KULT</th>
      <td>4.06</td>
    </tr>
    <tr>
      <th>SEMIO_RAT</th>
      <td>5.94</td>
    </tr>
    <tr>
      <th>SEMIO_KRIT</th>
      <td>5.48</td>
    </tr>
    <tr>
      <th>SEMIO_DOM</th>
      <td>6.11</td>
    </tr>
    <tr>
      <th>SEMIO_KAEM</th>
      <td>6.09</td>
    </tr>
    <tr>
      <th>SEMIO_PFLICHT</th>
      <td>6.05</td>
    </tr>
    <tr>
      <th>SEMIO_TRADV</th>
      <td>5.34</td>
    </tr>
    <tr>
      <th>SOHO_KZ</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>VERS_TYP</th>
      <td>1.53</td>
    </tr>
    <tr>
      <th>ANZ_PERSONEN</th>
      <td>1.46</td>
    </tr>
    <tr>
      <th>ANZ_TITEL</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>HH_EINKOMMEN_SCORE</th>
      <td>5.62</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_2.0</th>
      <td>0.46</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_3.0</th>
      <td>0.16</td>
    </tr>
    <tr>
      <th>ZABEOTYP_1.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>ZABEOTYP_2.0</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>ZABEOTYP_3.0</th>
      <td>0.13</td>
    </tr>
    <tr>
      <th>ZABEOTYP_4.0</th>
      <td>0.47</td>
    </tr>
    <tr>
      <th>ZABEOTYP_5.0</th>
      <td>0.29</td>
    </tr>
    <tr>
      <th>ZABEOTYP_6.0</th>
      <td>0.10</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_1.0</th>
      <td>0.41</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_2.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_3.0</th>
      <td>0.32</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_4.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_5.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_6.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_8.0</th>
      <td>0.26</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_1</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_2</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_3</th>
      <td>0.02</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_4</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_5</th>
      <td>0.06</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_6</th>
      <td>0.11</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_7</th>
      <td>0.14</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_8</th>
      <td>0.32</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_9</th>
      <td>0.32</td>
    </tr>
    <tr>
      <th>OST_WEST_KZ_W</th>
      <td>0.76</td>
    </tr>
    <tr>
      <th>decade</th>
      <td>86.77</td>
    </tr>
    <tr>
      <th>life_stage</th>
      <td>2.02</td>
    </tr>
    <tr>
      <th>wealth</th>
      <td>4.39</td>
    </tr>
    <tr>
      <th>rural</th>
      <td>-0.01</td>
    </tr>
    <tr>
      <th>business_building</th>
      <td>0.29</td>
    </tr>
  </tbody>
</table>
<p>128 rows × 1 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In&nbsp;[104]:</div>
<div class="inner_cell">
    <div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">&quot;exploring the second target cluster:&quot;</span><span class="p">,</span> <span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
<span class="n">interpret_clusters</span><span class="p">(</span><span class="n">preds</span><span class="p">[</span><span class="s1">&#39;diff&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">])</span>
</pre></div>

    </div>
</div>
</div>

<div class="output_wrapper">
<div class="output">


<div class="output_area">

    <div class="prompt"></div>


<div class="output_subarea output_stream output_stdout output_text">
<pre>exploring the second target cluster: 8
</pre>
</div>
</div>

<div class="output_area">

    <div class="prompt output_prompt">Out[104]:</div>


<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>ALTERSKATEGORIE_GROB</th>
      <td>3.28</td>
    </tr>
    <tr>
      <th>ANREDE_KZ</th>
      <td>1.11</td>
    </tr>
    <tr>
      <th>FINANZ_MINIMALIST</th>
      <td>4.50</td>
    </tr>
    <tr>
      <th>FINANZ_SPARER</th>
      <td>1.67</td>
    </tr>
    <tr>
      <th>FINANZ_VORSORGER</th>
      <td>4.30</td>
    </tr>
    <tr>
      <th>FINANZ_ANLEGER</th>
      <td>2.20</td>
    </tr>
    <tr>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <td>1.74</td>
    </tr>
    <tr>
      <th>FINANZ_HAUSBAUER</th>
      <td>2.29</td>
    </tr>
    <tr>
      <th>GREEN_AVANTGARDE</th>
      <td>0.05</td>
    </tr>
    <tr>
      <th>HEALTH_TYP</th>
      <td>2.31</td>
    </tr>
    <tr>
      <th>RETOURTYP_BK_S</th>
      <td>3.85</td>
    </tr>
    <tr>
      <th>SEMIO_SOZ</th>
      <td>4.82</td>
    </tr>
    <tr>
      <th>SEMIO_FAM</th>
      <td>4.66</td>
    </tr>
    <tr>
      <th>SEMIO_REL</th>
      <td>3.72</td>
    </tr>
    <tr>
      <th>SEMIO_MAT</th>
      <td>3.35</td>
    </tr>
    <tr>
      <th>SEMIO_VERT</th>
      <td>5.76</td>
    </tr>
    <tr>
      <th>SEMIO_LUST</th>
      <td>4.79</td>
    </tr>
    <tr>
      <th>SEMIO_ERL</th>
      <td>4.30</td>
    </tr>
    <tr>
      <th>SEMIO_KULT</th>
      <td>4.82</td>
    </tr>
    <tr>
      <th>SEMIO_RAT</th>
      <td>2.67</td>
    </tr>
    <tr>
      <th>SEMIO_KRIT</th>
      <td>3.59</td>
    </tr>
    <tr>
      <th>SEMIO_DOM</th>
      <td>3.14</td>
    </tr>
    <tr>
      <th>SEMIO_KAEM</th>
      <td>2.65</td>
    </tr>
    <tr>
      <th>SEMIO_PFLICHT</th>
      <td>3.38</td>
    </tr>
    <tr>
      <th>SEMIO_TRADV</th>
      <td>2.94</td>
    </tr>
    <tr>
      <th>SOHO_KZ</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>VERS_TYP</th>
      <td>1.56</td>
    </tr>
    <tr>
      <th>ANZ_PERSONEN</th>
      <td>1.83</td>
    </tr>
    <tr>
      <th>ANZ_TITEL</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>HH_EINKOMMEN_SCORE</th>
      <td>3.89</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_2.0</th>
      <td>0.21</td>
    </tr>
    <tr>
      <th>SHOPPER_TYP_3.0</th>
      <td>0.18</td>
    </tr>
    <tr>
      <th>ZABEOTYP_1.0</th>
      <td>0.27</td>
    </tr>
    <tr>
      <th>ZABEOTYP_2.0</th>
      <td>0.01</td>
    </tr>
    <tr>
      <th>ZABEOTYP_3.0</th>
      <td>0.43</td>
    </tr>
    <tr>
      <th>ZABEOTYP_4.0</th>
      <td>0.21</td>
    </tr>
    <tr>
      <th>ZABEOTYP_5.0</th>
      <td>-0.02</td>
    </tr>
    <tr>
      <th>ZABEOTYP_6.0</th>
      <td>0.09</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_1.0</th>
      <td>0.71</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_2.0</th>
      <td>0.02</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_3.0</th>
      <td>0.14</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_4.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_5.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_6.0</th>
      <td>0.00</td>
    </tr>
    <tr>
      <th>GEBAEUDETYP_8.0</th>
      <td>0.12</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_1</th>
      <td>0.04</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_2</th>
      <td>0.15</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_3</th>
      <td>0.19</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_4</th>
      <td>0.23</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_5</th>
      <td>0.08</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_6</th>
      <td>0.14</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_7</th>
      <td>0.07</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_8</th>
      <td>0.05</td>
    </tr>
    <tr>
      <th>CAMEO_DEUG_2015_9</th>
      <td>0.03</td>
    </tr>
    <tr>
      <th>OST_WEST_KZ_W</th>
      <td>0.78</td>
    </tr>
    <tr>
      <th>decade</th>
      <td>65.18</td>
    </tr>
    <tr>
      <th>life_stage</th>
      <td>3.26</td>
    </tr>
    <tr>
      <th>wealth</th>
      <td>2.57</td>
    </tr>
    <tr>
      <th>rural</th>
      <td>0.58</td>
    </tr>
    <tr>
      <th>business_building</th>
      <td>0.01</td>
    </tr>
  </tbody>
</table>
<p>128 rows × 1 columns</p>
</div>
</div>

</div>

</div>
</div>

</div>
<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
</div><div class="inner_cell">
<div class="text_cell_render border-box-sizing rendered_html">
<h3 id="Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">Discussion 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>The cluster analysis of customer data and democraphics data shows that there are primarily 2 segments of the population that are interested in the company's products. The target audiences for the company are thus clusters 5 and 8, because these segments are overrepresented in the customer data.</p>
<p>Almost 40% of customers are in Cluster 5, followed by almost 20% in cluster 8.</p>
<p>TARGET GROUPS: <br></p>
<p><strong>Cluster 5 - "conservative prosperous middle-aged green avangarde"</strong>. This segment is comprised of predominantly  prosperous middle-aged Germans (from middle class to upper class) located in West Germany born in the 1960s. They prefer 'green energy' and can be classified as "green avantgarde". These are older families with kids probably already grown-up (but also single/couples). Conservative, likely religious, rational, materialistic and dutiful.</p>
<p><strong>Cluster 8 - "dominant-minded middle-aged men with average income"</strong>: the second target group consists of mostly dominant-minded and combative men of the same age as the first target group. However, they are less affluent than the first group and belong to established/consumption oriented middleclass with average income. They are unlikely to have kids and live a bit further away from the city center than the first target group.</p>
<p>LEAST LIKELY CUSTOMER GROUP:</p>
<p><strong>Cluster 4 - "single less affluent women in their 20s-30s"</strong>: The least likely customers are women born in the 1980s that come from working class and are less affluent. They are probably single or are young couples with children. Not religious, dreamful, irrational and not conservative. Price-driven with regards to energy consumption.</p>

</div>
</div>
</div>
    </div>
  </div>
</body>

 
</html>