SIT719_Week2_Class_10-minutes-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "daffadeb-f0c8-4a27-b62c-aea950edc588",
    "_uuid": "e0e294bdfc0df9aa8f58fb8b6ae828c7ae062ccd"
   },
   "source": [
    "This guide comes directly from the [10 Minute Guide to Pandas](https://pandas.pydata.org/pandas-docs/stable/10min.html) on the Pandas official website. I ported it into this kernel to make the code more accessible. This guide is geared towards beginners who haven't used Pandas before. It has great examples on how powerful Pandas can be. Enjoy!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "da21b90d-98fc-483f-bf58-bd1dacf7027c",
    "_kg_hide-input": true,
    "_uuid": "90fb4599b093efccc95cacb6db2da95bcd695545"
   },
   "source": [
    "### This is a short introduction to pandas, geared mainly for new users. You can see more complex recipes in the [Cookbook](https://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "f26dff57-ef68-4cbf-86f1-b027cc75d6c1",
    "_uuid": "e0a227d03ea6a2d1995268f8cea078357eb596d4",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0c4de500-e940-428a-a624-d72cf69055ae",
    "_uuid": "666bd202e06457942b464ff348f49256eb40cb10"
   },
   "source": [
    "# Object Creation\n",
    "See the [Data Structure Intro section](Data Structure Intro section). Creating a Series by passing a list of values, letting pandas create a default integer index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "684d2ff9-f491-4384-b51d-d89a545f40a6",
    "_uuid": "3d527ebba36697ffcf0b5d46ef717172edcb0539",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    3.0\n",
       "2    5.0\n",
       "3    NaN\n",
       "4    6.0\n",
       "5    8.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a34354da-ed7a-4b6c-b29a-371b98778256",
    "_uuid": "762a9ec540f47f1aeba21b5a7deb1cf51a0994eb"
   },
   "source": [
    "Creating a [DataFrame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html#pandas.DataFrame) by passing a numpy array, with a datetime index and labeled columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "1f57494c-5ec2-49a4-adb4-4843d5c39c62",
    "_uuid": "50b786a38ed5b11ec29c308d29599623c26f0f23",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-11-01', '2014-11-02', '2014-11-03', '2014-11-04',\n",
       "               '2014-11-05', '2014-11-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20141101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "e29399aa-3818-4dd8-bc3b-634aa5c3fe78",
    "_uuid": "b21c8f9e3992c18eb839851aaa6ad057654e9082",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index=dates,columns=['one','two','three','four'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6622566b-e889-4589-88db-9e6ac53bda43",
    "_uuid": "886f7623a8e6ef893a83174796656aa7417c72be"
   },
   "source": [
    "Creating a DataFrame by passing a dict of objects that can be converted to series-like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "a36d23bb-aa78-4964-984f-1c0fef73c85b",
    "_uuid": "91c969a5e0dede5ed222df08fda0a19e7ecb6ba9",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2013-01-02  1.0  3   test  foo\n",
       "1  1.0 2013-01-02  1.0  3  train  foo\n",
       "2  1.0 2013-01-02  1.0  3   test  foo\n",
       "3  1.0 2013-01-02  1.0  3  train  foo"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({ 'A' : 1.,\n",
    "   ....:                      'B' : pd.Timestamp('20130102'),\n",
    "   ....:                      'C' : pd.Series(1,index=list(range(4)),dtype='float32'),\n",
    "   ....:                      'D' : np.array([3] * 4,dtype='int32'),\n",
    "   ....:                      'E' : pd.Categorical([\"test\",\"train\",\"test\",\"train\"]),\n",
    "   ....:                      'F' : 'foo' })\n",
    "   ....: \n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "10d03951-7cbd-4ee0-9302-1a77697a74f2",
    "_uuid": "05e30726935db33f49e624f496fc8c953be46383"
   },
   "source": [
    "Having specific [dtypes](https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-dtypes):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "e862ec95-5893-4f46-ac7d-4d8a638ffc1f",
    "_uuid": "9e7f0e3693cf1f63718fce13feed81c93ed22d58",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "860dc5f5-ce12-47b0-b0b1-09cc4aba5f73",
    "_uuid": "886b98db6af991c2ec742c18085b0b8a7b88d31f"
   },
   "source": [
    "\n",
    "# Viewing Data\n",
    "See the [Basics section](https://pandas.pydata.org/pandas-docs/stable/basics.html#basics)\n",
    "See the top & bottom rows of the frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "b1b5ca0e-4b43-4ec9-80ee-65b807d234d2",
    "_uuid": "c1d9e77ee2cfdc5b81af10b29565a9166ca01994",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "3428b24c-520e-4d23-b439-9c9e2b8d353d",
    "_uuid": "74c7c24d7adc0382cdc6055a88643ea7cbe2c111",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "efec648c-6c39-4b09-a87e-cf3dc723ff0b",
    "_uuid": "5c4372dac3fba6ebf7fc16e222ab135529dfb3fe"
   },
   "source": [
    "Display the index, columns, and the underlying numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "53a240a3-ecb2-4f38-8862-da1d00aedf7c",
    "_uuid": "056ab69a40592e47b9e07cb2514784eb50d81ade",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-11-01', '2014-11-02', '2014-11-03', '2014-11-04',\n",
       "               '2014-11-05', '2014-11-06'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "a9cae030-681e-4b3d-993f-0e2cbaaa10a9",
    "_uuid": "b86456edbcc2249100502525c1fd38dd4235d0f3",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['one', 'two', 'three', 'four'], dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "0d442ada-0d41-4fe4-a843-48592068306d",
    "_uuid": "842fe1b27b0714ee2155a657e3eec4f3719a40b0",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.08101876, -0.85651746,  0.61240146, -1.67474707],\n",
       "       [-0.84527813,  1.98330603,  0.1919533 ,  0.13448792],\n",
       "       [-0.60531364, -0.63618384,  0.7078619 ,  0.49423983],\n",
       "       [ 0.62221713,  2.82817154, -0.22267165, -0.17031828],\n",
       "       [-0.03034599,  0.8171015 , -2.49251603,  1.75647502],\n",
       "       [ 0.95711216,  0.14861098, -0.79510819, -0.90633501]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ab9e7b9e-f32b-4c0f-9df6-32d4464a4cb2",
    "_uuid": "7ef425f14c048accd98f18f3a5e0974fa9fe8a78"
   },
   "source": [
    "Describe shows a quick statistic summary of your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "43393dfd-989c-43a6-834e-550c8d3ccbd4",
    "_uuid": "1f1dbb5dd09ffa4c2ee0c3c289328de4c070ad2b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.330438</td>\n",
       "      <td>0.714081</td>\n",
       "      <td>-0.333013</td>\n",
       "      <td>-0.061033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.100958</td>\n",
       "      <td>1.462920</td>\n",
       "      <td>1.195155</td>\n",
       "      <td>1.181562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.785287</td>\n",
       "      <td>-0.439985</td>\n",
       "      <td>-0.651999</td>\n",
       "      <td>-0.722331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.317830</td>\n",
       "      <td>0.482856</td>\n",
       "      <td>-0.015359</td>\n",
       "      <td>-0.017915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.459076</td>\n",
       "      <td>1.691755</td>\n",
       "      <td>0.507289</td>\n",
       "      <td>0.404302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            one       two     three      four\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.330438  0.714081 -0.333013 -0.061033\n",
       "std    1.100958  1.462920  1.195155  1.181562\n",
       "min   -2.081019 -0.856517 -2.492516 -1.674747\n",
       "25%   -0.785287 -0.439985 -0.651999 -0.722331\n",
       "50%   -0.317830  0.482856 -0.015359 -0.017915\n",
       "75%    0.459076  1.691755  0.507289  0.404302\n",
       "max    0.957112  2.828172  0.707862  1.756475"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "21ba544f-8736-4382-8462-20f02c353328",
    "_uuid": "e5fb874c26019da50f1b68fbea8bcf0e7940809f"
   },
   "source": [
    "Transposing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "13e0e285-eac4-48fc-8a82-23491e8ebaf0",
    "_uuid": "ee5d4078cd8d5a545be55483208e73b9c9e939f4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <th>2014-11-02 00:00:00</th>\n",
       "      <th>2014-11-03 00:00:00</th>\n",
       "      <th>2014-11-04 00:00:00</th>\n",
       "      <th>2014-11-05 00:00:00</th>\n",
       "      <th>2014-11-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.845278</td>\n",
       "      <td>-0.605314</td>\n",
       "      <td>0.622217</td>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.957112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.856517</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>0.148611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>0.612401</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>-0.795108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>-1.674747</td>\n",
       "      <td>0.134488</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>-0.170318</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       2014-11-01  2014-11-02  2014-11-03  2014-11-04  2014-11-05  2014-11-06\n",
       "one     -2.081019   -0.845278   -0.605314    0.622217   -0.030346    0.957112\n",
       "two     -0.856517    1.983306   -0.636184    2.828172    0.817101    0.148611\n",
       "three    0.612401    0.191953    0.707862   -0.222672   -2.492516   -0.795108\n",
       "four    -1.674747    0.134488    0.494240   -0.170318    1.756475   -0.906335"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d92b9eac-9dc6-446e-b87e-d50ef52630a8",
    "_uuid": "b526362d5e5f451a460ca3add042b633538055dc"
   },
   "source": [
    "Sorting by an axis (In this case, sorting the columns in reverse alphabetical order):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "1dfd42f4-5231-4508-900f-1ddc37cdd250",
    "_uuid": "397ac00b05aa5867ba188f537ae7ac814275352c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>one</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>-0.845278</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>-0.605314</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>0.622217</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>-0.030346</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>0.957112</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 two     three       one      four\n",
       "2014-11-01 -0.856517  0.612401 -2.081019 -1.674747\n",
       "2014-11-02  1.983306  0.191953 -0.845278  0.134488\n",
       "2014-11-03 -0.636184  0.707862 -0.605314  0.494240\n",
       "2014-11-04  2.828172 -0.222672  0.622217 -0.170318\n",
       "2014-11-05  0.817101 -2.492516 -0.030346  1.756475\n",
       "2014-11-06  0.148611 -0.795108  0.957112 -0.906335"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis=1, ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9898c02b-57bd-48d6-884f-3fe8eb53b34a",
    "_uuid": "e3c6b93d9352a0660f989b63e09f03e05c9e1feb"
   },
   "source": [
    "Sorting by values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "389c1344-4f60-4628-b6c1-de736d429d3a",
    "_uuid": "df1ac4e8bc643bbcc49f0769c52778ca19761a9e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='two')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6e80dc85-7f33-4803-928c-8f327da5c15a",
    "_uuid": "5202ddc8b46f9ce7333977c7dc4c919304fbfd4b"
   },
   "source": [
    "# Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cbf1a871-cf24-46ae-8384-72d99f6b3c9a",
    "_uuid": "dd007c4689288bd481c99133d13cb05e204ca1a8"
   },
   "source": [
    "Note While standard Python / Numpy expressions for selecting and setting are intuitive and come in handy for interactive work, for production code, we recommend the optimized pandas data access methods, .at, .iat, .loc, .iloc and .ix.\n",
    "See the indexing documentation [Indexing and Selecting Data](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing) and [MultiIndex / Advanced Indexing](https://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "002668d8-3da6-4f78-bb84-0c1d3d42c81c",
    "_uuid": "446149fabdb526d11b6d42aa58a7219043a63210"
   },
   "source": [
    "## Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "04f82f76-6390-4f0e-9a64-67619cc85e42",
    "_uuid": "8f34cc17657fc7f8b77f35c5beefdca76343994e"
   },
   "source": [
    "Selecting a single column, which yields a Series, equivalent to df.one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "4be8e75e-64f3-453e-b3e5-b788b1c967a5",
    "_uuid": "9bd8927534a077d8d9c3d74747ea6ab3f5070d68",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01   -2.081019\n",
       "2014-11-02   -0.845278\n",
       "2014-11-03   -0.605314\n",
       "2014-11-04    0.622217\n",
       "2014-11-05   -0.030346\n",
       "2014-11-06    0.957112\n",
       "Freq: D, Name: one, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "c0092d95-8fd7-4059-8938-580a05c70f6b",
    "_uuid": "9678ff7f4faaffc358ad10bb176d96c7d45ac236",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01   -2.081019\n",
       "2014-11-02   -0.845278\n",
       "2014-11-03   -0.605314\n",
       "2014-11-04    0.622217\n",
       "2014-11-05   -0.030346\n",
       "2014-11-06    0.957112\n",
       "Freq: D, Name: one, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a9160b5a-2a8d-42f7-a1a0-cf59481bbbe5",
    "_uuid": "724ce079707e2d58e3e6ec122a128fb280f85010"
   },
   "source": [
    "Selecting via [], which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "f5909120-c5ee-498a-8ed1-b4316b51226e",
    "_uuid": "dbcd1c56866336f25d10ebfb1dbd457b1c5c415e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "0b5c50db-08a3-487e-8f9b-3fac98f0c336",
    "_uuid": "1bc96862bd0c4fde57487e71bce38dc272763a36",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20141102':'20141104']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "47120656-c870-4ccb-87d6-cdf0cb10628b",
    "_uuid": "50e9f44238b630e0f854dffa849f4a26f2b7abfd"
   },
   "source": [
    "## Selecting by Label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5eb8a10b-100c-4949-bf57-fc1b0b709023",
    "_uuid": "6c5b006e3dabd7dc506fb4c840b003667cdcb019"
   },
   "source": [
    "See more in[ Selection by Label](https://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label)\n",
    "\n",
    "For getting a cross section using a label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "07d52142-94b9-42fb-a649-63f2f56bd3af",
    "_uuid": "34c5205092732b67a2de0667418af4f3e73fa18f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one     -2.081019\n",
       "two     -0.856517\n",
       "three    0.612401\n",
       "four    -1.674747\n",
       "Name: 2014-11-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6a647eb5-c61b-4a01-a9a9-cc9de6284338",
    "_uuid": "847a4c224d66434bc572cdd3106c99bce8925607"
   },
   "source": [
    "Selecting on a multi-axis by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "7001a024-168a-4c7f-bf36-d813fb798bfb",
    "_uuid": "f46aa94ce3199632c14f66aea3f9155fc57c8d3e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two\n",
       "2014-11-01 -2.081019 -0.856517\n",
       "2014-11-02 -0.845278  1.983306\n",
       "2014-11-03 -0.605314 -0.636184\n",
       "2014-11-04  0.622217  2.828172\n",
       "2014-11-05 -0.030346  0.817101\n",
       "2014-11-06  0.957112  0.148611"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['one','two']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e28a81bd-14eb-4931-a839-98d0ecaecd3c",
    "_uuid": "9a6ae941b46b104db1cd8def0545981d4733bfe9"
   },
   "source": [
    "Showing label slicing, both endpoints are included"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "96ab0649-01d3-48f2-b519-dd78c2186336",
    "_uuid": "e02bd76d6a3169255570d96c2cb84d1d19baafe9",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two\n",
       "2014-11-02 -0.845278  1.983306\n",
       "2014-11-03 -0.605314 -0.636184\n",
       "2014-11-04  0.622217  2.828172"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20141102':'20141104',['one','two']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1da0f850-8b42-4221-ae51-223924b40266",
    "_uuid": "74e06e2a77ac27d580b88b90068a795052f00e8e"
   },
   "source": [
    "Reduction in the dimensions of the returned object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "e7b0cb7d-70cc-41f6-8b55-32824b804241",
    "_uuid": "6477a1db5cf42f8260bc8c2cee54c2745fb255a1",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one   -0.845278\n",
       "two    1.983306\n",
       "Name: 2014-11-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20141102',['one','two']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3d623580-97fc-456e-924f-5bab3121d87d",
    "_uuid": "9f4dd3322a60f1b105d9a21783287edb27c8ea0c"
   },
   "source": [
    "For getting a scalar value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "5097b9ad-5170-41d8-857f-97142be556f5",
    "_uuid": "9f1d7120553ca8bb9a6b658c2f04e3bfe67f97a6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.081018759354865"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],'one']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "68f229fd-5117-4207-856e-5c6e4b58c135",
    "_uuid": "3586bfb5875d760a36ecf78e62dd0ca38a67b54b"
   },
   "source": [
    "For getting fast access to a scalar (equiv to the prior method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "39271947-9c6d-456a-aaf3-307e55d24442",
    "_uuid": "c30b411195fff3849b54bcdc3b658fbcc96cecbb",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.081018759354865"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'one']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7dd7fef8-149a-420f-a45b-af912c3f2369",
    "_uuid": "16f6dc5101bf7f1cef79a016c9b557ee910930ad"
   },
   "source": [
    "## Selection by Position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "428d1a82-86e1-41d0-a416-52906afc8995",
    "_uuid": "a789524d4da761f0fa9b77b459acf52e8a6291b6"
   },
   "source": [
    "See more in [Selection by Position](Selection by Position)\n",
    "\n",
    "Select via the position of the passed integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "fe438107-8d37-4714-8da7-d1c5b3afc846",
    "_uuid": "62558a81897d581168ee6c2b41f76267b9c91124",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      0.622217\n",
       "two      2.828172\n",
       "three   -0.222672\n",
       "four    -0.170318\n",
       "Name: 2014-11-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cd04525c-ada2-4260-b5f7-747d4e5df884",
    "_uuid": "4709ff3e512e7d238ed456980113d4934499b5d6"
   },
   "source": [
    "By integer slices, acting similar to numpy/python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "728cc20e-ba9d-413d-9ee5-e76eb2735c8a",
    "_uuid": "602b716b2b8d28bede8043ce95f3247233d3b94c"
   },
   "source": [
    "By lists of integer position locations, similar to the numpy/python style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "0af7f46d-7da7-4b36-bcfd-02a9fdb8dbe5",
    "_uuid": "83f0a59d8beabd0bb6192467ebe9b287a3216776",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>0.191953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>0.707862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>-2.492516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one     three\n",
       "2014-11-02 -0.845278  0.191953\n",
       "2014-11-03 -0.605314  0.707862\n",
       "2014-11-05 -0.030346 -2.492516"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e9f6b749-ad67-4d89-aaed-4024b8530936",
    "_uuid": "61639e9a7523b05fb1a03d464ffa1f1eb1688efb"
   },
   "source": [
    "For slicing rows explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "467719ff-d23e-45c2-9657-0b7bcaff6655",
    "_uuid": "8d69d0090825b1644d037ecb051afdb9633628d3",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "38c01dfe-f13a-4cef-bfed-b49e77eef480",
    "_uuid": "aa3ccb6ed055424381bdb3e41b97ea2f881d19db"
   },
   "source": [
    "For slicing columns explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "7a0bdd04-6670-4821-8328-3090827a13ed",
    "_uuid": "c14695bf9699073d68e0aeb63973a58bee51c57c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 two     three\n",
       "2014-11-01 -0.856517  0.612401\n",
       "2014-11-02  1.983306  0.191953\n",
       "2014-11-03 -0.636184  0.707862\n",
       "2014-11-04  2.828172 -0.222672\n",
       "2014-11-05  0.817101 -2.492516\n",
       "2014-11-06  0.148611 -0.795108"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c7526fcd-5d0f-41c0-946f-726fadd3d8cf",
    "_uuid": "728d45f365a6e58c4fe60caaec27170c956b47e5"
   },
   "source": [
    "For getting a value explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_cell_guid": "32c6bb98-903a-45b1-b430-08d1b898e813",
    "_uuid": "8c824f1f3d44d8ed41b7b1328354863861ee9ef2",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9833060275110046"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "24a9bd7b-8362-4722-8aa2-0c2484163586",
    "_uuid": "9cc662005289530c6cf43b87dabd314a313ffde1"
   },
   "source": [
    "For getting fast access to a scalar (equiv to the prior method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "2080b00d-b540-4bba-b8bc-682b80356290",
    "_uuid": "dfedfb75ff53703c78c0558f2822b01a0b8af49e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.081018759354865"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4205069b-9b81-4015-8113-80e5997ed02a",
    "_uuid": "1f65376861105ccf80d8230b7518ec631e869383"
   },
   "source": [
    "## Boolean Indexing\n",
    "Using a single column’s values to select data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "b7d6f231-8fca-4b94-b4da-7e1077bfc1c6",
    "_uuid": "0897ab764824072dc3d6d45bc03c70f1f29c6833",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.one > 0.5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "001075c2-f7be-4218-9683-ffa1f5d9240e",
    "_uuid": "4b70f057a80db995d082b8d30c54ba603fe2cb62"
   },
   "source": [
    "Selecting values from a DataFrame where a boolean condition is met."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "01955ea3-17c6-4b0d-8d2e-cc5d654bb6cf",
    "_uuid": "8a6a6d699089df278c1436c28b9ff50c18e424ec",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.756475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four\n",
       "2014-11-01       NaN       NaN  0.612401       NaN\n",
       "2014-11-02       NaN  1.983306  0.191953  0.134488\n",
       "2014-11-03       NaN       NaN  0.707862  0.494240\n",
       "2014-11-04  0.622217  2.828172       NaN       NaN\n",
       "2014-11-05       NaN  0.817101       NaN  1.756475\n",
       "2014-11-06  0.957112  0.148611       NaN       NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0d9b0104-071e-4b67-a28c-af0a3b5298c5",
    "_uuid": "152e38c5f8c9b1623f719b3d733d4a7dd83d37bd"
   },
   "source": [
    "Using the isin method for filtering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "81efdb26-8d6d-4d30-8095-0212e2068bd7",
    "_uuid": "ac6deb8efac6fea270befc2c5a444825de86a270",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four   five\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747    one\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488    one\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240    two\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318  three\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475   four\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335  three"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['five'] = ['one', 'one','two','three','four','three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "b8a6507a-ab97-4884-ad6b-86ce08b562b1",
    "_uuid": "1057b0c34eae282d8b234583497a6e9d53a254f5",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four  five\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240   two\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475  four"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['five'].isin(['two','four'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b6eff184-f774-4668-a5fa-2eb792a8ee19",
    "_uuid": "36dc62173e385467dca8e0b12398ce00cd3bc7c9"
   },
   "source": [
    "## Setting\n",
    "Setting a new column automatically aligns the data by the indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_cell_guid": "4d61f140-af66-4b4c-9296-17e243bf4991",
    "_uuid": "c0f9338b687ee3fb7b535f772955de1cb232e10e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01    1\n",
       "2014-11-02    2\n",
       "2014-11-03    3\n",
       "2014-11-04    4\n",
       "2014-11-05    5\n",
       "2014-11-06    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6], index=pd.date_range('20141101', periods=6))\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_cell_guid": "ef2e599f-68db-48c8-b750-e8f5143fe407",
    "_uuid": "6f01ca50121e0d9289c675ea6de02a716ae66dfb",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>-2.081019</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four  six\n",
       "2014-11-01 -2.081019 -0.856517  0.612401 -1.674747    1\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488    2\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240    3\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318    4\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475    5\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335    6"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['six'] = s1\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "360096be-309b-4cc7-8ecb-d23721cb14a2",
    "_uuid": "ae8946ddaffc0e88adab4a082e2ffd2252700078"
   },
   "source": [
    "Setting values by label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "a3acd34a-8443-4d9a-83e7-8b98dc75ba63",
    "_uuid": "190aff49aae8c1396239b406e9909e62de82c0b3",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.856517</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four  six\n",
       "2014-11-01  0.000000 -0.856517  0.612401 -1.674747    1\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488    2\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240    3\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318    4\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475    5\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335    6"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0],'one'] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0da27b58-8d50-424c-85a5-a13606a93e8a",
    "_uuid": "2b4a495f12e347c3d5b6db4951766e7390dc516f"
   },
   "source": [
    "Setting values by position:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "a6891c06-b41e-440a-a15e-8e80bedef5fe",
    "_uuid": "1500650883a2caeaf0d0cb3c1895bb305fa58eb7",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>-1.674747</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>0.134488</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>0.494240</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-0.170318</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>1.756475</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-0.906335</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three      four  six\n",
       "2014-11-01  0.000000  0.000000  0.612401 -1.674747    1\n",
       "2014-11-02 -0.845278  1.983306  0.191953  0.134488    2\n",
       "2014-11-03 -0.605314 -0.636184  0.707862  0.494240    3\n",
       "2014-11-04  0.622217  2.828172 -0.222672 -0.170318    4\n",
       "2014-11-05 -0.030346  0.817101 -2.492516  1.756475    5\n",
       "2014-11-06  0.957112  0.148611 -0.795108 -0.906335    6"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[0,1] = 0\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "1e8225cf-4459-4cb4-98ef-a32d09c382e2",
    "_uuid": "6993b6c151f135cc7a965730c4c572775cbd8a01",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.957112</td>\n",
       "      <td>0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six\n",
       "2014-11-01  0.000000  0.000000  0.612401     5    1\n",
       "2014-11-02 -0.845278  1.983306  0.191953     5    2\n",
       "2014-11-03 -0.605314 -0.636184  0.707862     5    3\n",
       "2014-11-04  0.622217  2.828172 -0.222672     5    4\n",
       "2014-11-05 -0.030346  0.817101 -2.492516     5    5\n",
       "2014-11-06  0.957112  0.148611 -0.795108     5    6"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,'four'] = np.array([5] * len(df))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5ac1ed59-30d2-41c3-883c-bb96634f4830",
    "_uuid": "1f0389c283d54453415ca78a3eafc8d127ba2893"
   },
   "source": [
    "A where operation with setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_cell_guid": "7108c375-08f9-4402-9f39-7e68f3ac929f",
    "_uuid": "783d37539e38075c324ebd1c29689211afb66a03",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.612401</td>\n",
       "      <td>-5</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>-1.983306</td>\n",
       "      <td>-0.191953</td>\n",
       "      <td>-5</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>-0.707862</td>\n",
       "      <td>-5</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>-0.622217</td>\n",
       "      <td>-2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>-5</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.030346</td>\n",
       "      <td>-0.817101</td>\n",
       "      <td>-2.492516</td>\n",
       "      <td>-5</td>\n",
       "      <td>-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>-0.957112</td>\n",
       "      <td>-0.148611</td>\n",
       "      <td>-0.795108</td>\n",
       "      <td>-5</td>\n",
       "      <td>-6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six\n",
       "2014-11-01  0.000000  0.000000 -0.612401    -5   -1\n",
       "2014-11-02 -0.845278 -1.983306 -0.191953    -5   -2\n",
       "2014-11-03 -0.605314 -0.636184 -0.707862    -5   -3\n",
       "2014-11-04 -0.622217 -2.828172 -0.222672    -5   -4\n",
       "2014-11-05 -0.030346 -0.817101 -2.492516    -5   -5\n",
       "2014-11-06 -0.957112 -0.148611 -0.795108    -5   -6"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2[df2 > 0] = -df2\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4bab0a44-3ea1-4817-ae0e-233d159e74e5",
    "_uuid": "5481ab3311350209e065b43c612af4b85bee6a1d",
    "collapsed": true
   },
   "source": [
    "## Missing Data\n",
    "pandas primarily uses the value np.nan to represent missing data. It is by default not included in computations. See the [Missing Data section](https://pandas.pydata.org/pandas-docs/stable/missing_data.html#missing-data)\n",
    "\n",
    "Reindexing allows you to change/add/delete the index on a specified axis. This returns a copy of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_cell_guid": "8ea992cf-a44c-48ba-8c29-772a76a62e25",
    "_uuid": "b34287e42a0e574b9ada0dd0660e7610bde2a4a2",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six    E\n",
       "2014-11-01  0.000000  0.000000  0.612401     5    1  1.0\n",
       "2014-11-02 -0.845278  1.983306  0.191953     5    2  1.0\n",
       "2014-11-03 -0.605314 -0.636184  0.707862     5    3  NaN\n",
       "2014-11-04  0.622217  2.828172 -0.222672     5    4  NaN"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ['E'])\n",
    "df1.loc[dates[0]:dates[1],'E'] = 1\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7e0319e8-fc11-4556-a0e6-4bc520b2179f",
    "_uuid": "46eaf209fd3122be95d2ef0804dc8bd665794205"
   },
   "source": [
    "To drop any rows that have missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "9c118c95-34b0-4a03-9ae6-4a4e2fb3ab52",
    "_uuid": "be778687edde438a54f9378d59f532c9fe9d87da",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six    E\n",
       "2014-11-01  0.000000  0.000000  0.612401     5    1  1.0\n",
       "2014-11-02 -0.845278  1.983306  0.191953     5    2  1.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b833f8bd-684d-42d6-babb-6a2d02257757",
    "_uuid": "8a107a35dfb6b1f888a3760b1ddc1a2fc8ef1eae"
   },
   "source": [
    "Filling missing data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "f2521f7e-c916-4de2-abb5-c9c5ebebc600",
    "_uuid": "a5089cfe6c94a390800d4ebc997f143f5c000e8e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.191953</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-0.605314</td>\n",
       "      <td>-0.636184</td>\n",
       "      <td>0.707862</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>0.622217</td>\n",
       "      <td>2.828172</td>\n",
       "      <td>-0.222672</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six    E\n",
       "2014-11-01  0.000000  0.000000  0.612401     5    1  1.0\n",
       "2014-11-02 -0.845278  1.983306  0.191953     5    2  1.0\n",
       "2014-11-03 -0.605314 -0.636184  0.707862     5    3  5.0\n",
       "2014-11-04  0.622217  2.828172 -0.222672     5    4  5.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "02f262f9-27dd-4e4e-9c6d-030c23123e90",
    "_uuid": "3f1f384adbd5d3081028c45a797f6ab1f0174490"
   },
   "source": [
    "## Operations\n",
    "See the [Basic section on Binary Ops](https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-binop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a599044b-97a5-4ff1-ac07-d2205c9bd275",
    "_uuid": "7dd84063efa95105e26e900a85b3b593c5f79406"
   },
   "source": [
    "### Stats\n",
    "Operations in general *exclude* missing data.\n",
    "\n",
    "Performing a descriptive statistic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "873b0539-d75a-4d8f-81e2-42c1ee70f945",
    "_uuid": "18a53af2b3fcb793e6c52ddfabc3bf4b01edc3ae",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      0.016399\n",
       "two      0.856834\n",
       "three   -0.333013\n",
       "four     5.000000\n",
       "six      3.500000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "20530588-c864-428f-aad0-4ad48a3df1e5",
    "_uuid": "f066371c33bd242c801bb35fb61caed24cff26f6"
   },
   "source": [
    "Same operation on the other axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "88cdbfa8-728c-4803-a400-5b348828e03e",
    "_uuid": "25a698388c134cb27fc648f76ecef27c12471fce",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01    1.322480\n",
       "2014-11-02    1.665996\n",
       "2014-11-03    1.493273\n",
       "2014-11-04    2.445543\n",
       "2014-11-05    1.658848\n",
       "2014-11-06    2.262123\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1fffe4d6-a49d-42e5-8b89-e44724ad03db",
    "_uuid": "a10e0ecc601023ea17c043b422ae7774a718e97e"
   },
   "source": [
    "Operating with objects that have different dimensionality and need alignment. In addition, pandas automatically broadcasts along the specified dimension. The following code also shifts the data vertically, leaving NaN values in the unoccupied spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "e461e77a-cc2d-4186-9a6f-00f7b898328d",
    "_uuid": "7eda48639b578d0a9fb2e6e3a3f4ba76fbde780c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014-11-01    NaN\n",
       "2014-11-02    NaN\n",
       "2014-11-03    1.0\n",
       "2014-11-04    3.0\n",
       "2014-11-05    5.0\n",
       "2014-11-06    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1,3,5,np.nan,6,8], index=dates).shift(2)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_cell_guid": "950ca30f-af5b-4184-9556-f98187afe6d5",
    "_uuid": "b88c2ac00262f1176bc919dc4bfa9e37bafa3d07",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-1.605314</td>\n",
       "      <td>-1.636184</td>\n",
       "      <td>-0.292138</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>-2.377783</td>\n",
       "      <td>-0.171828</td>\n",
       "      <td>-3.222672</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-5.030346</td>\n",
       "      <td>-4.182899</td>\n",
       "      <td>-7.492516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six\n",
       "2014-11-01       NaN       NaN       NaN   NaN  NaN\n",
       "2014-11-02       NaN       NaN       NaN   NaN  NaN\n",
       "2014-11-03 -1.605314 -1.636184 -0.292138   4.0  2.0\n",
       "2014-11-04 -2.377783 -0.171828 -3.222672   2.0  1.0\n",
       "2014-11-05 -5.030346 -4.182899 -7.492516   0.0  0.0\n",
       "2014-11-06       NaN       NaN       NaN   NaN  NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8a609a42-405f-4145-a258-2b2a1a836fa6",
    "_uuid": "a82c28ab38951a030fc5aaffc9229091af226cf8"
   },
   "source": [
    "### Apply\n",
    "Applying functions to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "ed25e76b-00c8-4f75-b8f2-a6b2669e264f",
    "_uuid": "0fdf92e9b753e997301f3d7c9e88941f87e086b1",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>six</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.612401</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-02</th>\n",
       "      <td>-0.845278</td>\n",
       "      <td>1.983306</td>\n",
       "      <td>0.804355</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-03</th>\n",
       "      <td>-1.450592</td>\n",
       "      <td>1.347122</td>\n",
       "      <td>1.512217</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-04</th>\n",
       "      <td>-0.828375</td>\n",
       "      <td>4.175294</td>\n",
       "      <td>1.289545</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-05</th>\n",
       "      <td>-0.858721</td>\n",
       "      <td>4.992395</td>\n",
       "      <td>-1.202971</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-06</th>\n",
       "      <td>0.098392</td>\n",
       "      <td>5.141006</td>\n",
       "      <td>-1.998079</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 one       two     three  four  six\n",
       "2014-11-01  0.000000  0.000000  0.612401     5    1\n",
       "2014-11-02 -0.845278  1.983306  0.804355    10    3\n",
       "2014-11-03 -1.450592  1.347122  1.512217    15    6\n",
       "2014-11-04 -0.828375  4.175294  1.289545    20   10\n",
       "2014-11-05 -0.858721  4.992395 -1.202971    25   15\n",
       "2014-11-06  0.098392  5.141006 -1.998079    30   21"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c29ce7af-9539-4857-9f41-0453d788992f",
    "_uuid": "f016b295830e32f680784dbe2f41b568215d9efb"
   },
   "source": [
    "### Histogramming \n",
    "See more at [Histogramming and Discretization](https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-discretization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "765ec7fe-83b0-4180-acdc-540a28422a85",
    "_uuid": "8cc61eeda690d11eefecf2116e28136020ca0003",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    3\n",
       "2    4\n",
       "3    1\n",
       "4    5\n",
       "5    1\n",
       "6    6\n",
       "7    3\n",
       "8    5\n",
       "9    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "cd3bda59-12a7-434c-9195-23c4dcba5b8b",
    "_uuid": "77963d7b3d63b8e939652cc2c114a0731baa359e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    3\n",
       "3    2\n",
       "1    2\n",
       "6    1\n",
       "4    1\n",
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cb8c8038-acc7-4b09-8e70-3354521439e6",
    "_uuid": "e74ee3f403a6358e6658dcfae2f2909128d64130"
   },
   "source": [
    "### String Methods\n",
    "Series is equipped with a set of string processing methods in the str attribute that make it easy to operate on each element of the array, as in the code snippet below. Note that pattern-matching in str generally uses [regular expressions](https://docs.python.org/2/library/re.html) by default (and in some cases always uses them). See more at [Vectorized String Methods](https://pandas.pydata.org/pandas-docs/stable/text.html#text-string-methods)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "d6eb8ddf-f32a-406d-8d9e-bd0c0a76bdc9",
    "_uuid": "b74596be6dc4925c11bbc9552f52e0ab8a401b19",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = pd.Series(['A', 'B', 'C', 'Aaba', 'Baca', np.nan, 'CABA', 'dog', 'cat'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "ea018f68-1d1e-487a-9d8d-e985f2c1d08f",
    "_uuid": "fb5a35db50d449608f76c79aaacf3c27f79ac398",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       a\n",
       "1       b\n",
       "2       c\n",
       "3    aaba\n",
       "4    baca\n",
       "5     NaN\n",
       "6    caba\n",
       "7     dog\n",
       "8     cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bbeb9a06-d07c-4d38-96f6-fd96f8109a36",
    "_uuid": "9ecce4d43cdb73c250c231bd1f6ced47238ee848"
   },
   "source": [
    "# Merge\n",
    "### Concat\n",
    "pandas provides various facilities for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.\n",
    "\n",
    "See the [Merging section](https://pandas.pydata.org/pandas-docs/stable/merging.html#merging)\n",
    "\n",
    "Concatenating pandas objects together with [concat()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html#pandas.concat):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "35b5371c-d03f-4014-b43e-0ea171cc2af5",
    "_uuid": "0e198af891bd5bd91c825d3e3f825211106140a6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.436112</td>\n",
       "      <td>0.302169</td>\n",
       "      <td>0.067651</td>\n",
       "      <td>0.967016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.077582</td>\n",
       "      <td>0.491488</td>\n",
       "      <td>0.325616</td>\n",
       "      <td>-1.142436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.431596</td>\n",
       "      <td>0.137711</td>\n",
       "      <td>0.139401</td>\n",
       "      <td>0.928530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.595253</td>\n",
       "      <td>0.152700</td>\n",
       "      <td>0.332961</td>\n",
       "      <td>0.827073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.705462</td>\n",
       "      <td>-1.262584</td>\n",
       "      <td>-0.177256</td>\n",
       "      <td>1.388932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.004146</td>\n",
       "      <td>0.399645</td>\n",
       "      <td>-0.439888</td>\n",
       "      <td>0.625605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.017679</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.364786</td>\n",
       "      <td>0.333770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.665226</td>\n",
       "      <td>-0.487820</td>\n",
       "      <td>0.125152</td>\n",
       "      <td>0.743315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.544550</td>\n",
       "      <td>0.456231</td>\n",
       "      <td>-0.972469</td>\n",
       "      <td>-0.176727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.799549</td>\n",
       "      <td>0.198477</td>\n",
       "      <td>1.500031</td>\n",
       "      <td>-0.340852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.436112  0.302169  0.067651  0.967016\n",
       "1 -0.077582  0.491488  0.325616 -1.142436\n",
       "2  1.431596  0.137711  0.139401  0.928530\n",
       "3 -0.595253  0.152700  0.332961  0.827073\n",
       "4  0.705462 -1.262584 -0.177256  1.388932\n",
       "5 -1.004146  0.399645 -0.439888  0.625605\n",
       "6  0.017679  0.493244  0.364786  0.333770\n",
       "7  0.665226 -0.487820  0.125152  0.743315\n",
       "8  0.544550  0.456231 -0.972469 -0.176727\n",
       "9 -0.799549  0.198477  1.500031 -0.340852"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "295ac685-5727-4a04-b910-34810572e984",
    "_uuid": "e1b0e0b7a541f0347586622dbd355ca42b7778cd",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0 -0.436112  0.302169  0.067651  0.967016\n",
       " 1 -0.077582  0.491488  0.325616 -1.142436\n",
       " 2  1.431596  0.137711  0.139401  0.928530,\n",
       "           0         1         2         3\n",
       " 3 -0.595253  0.152700  0.332961  0.827073\n",
       " 4  0.705462 -1.262584 -0.177256  1.388932\n",
       " 5 -1.004146  0.399645 -0.439888  0.625605\n",
       " 6  0.017679  0.493244  0.364786  0.333770,\n",
       "           0         1         2         3\n",
       " 7  0.665226 -0.487820  0.125152  0.743315\n",
       " 8  0.544550  0.456231 -0.972469 -0.176727\n",
       " 9 -0.799549  0.198477  1.500031 -0.340852]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#break it into pieces:\n",
    "pieces = [df[:3], df[3:7], df[7:]]\n",
    "pieces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "aa28f68e-a314-4bbe-a71b-568238c3be23",
    "_uuid": "ec51477795c9b3f0bd0366d130175b226cc9232b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.436112</td>\n",
       "      <td>0.302169</td>\n",
       "      <td>0.067651</td>\n",
       "      <td>0.967016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.077582</td>\n",
       "      <td>0.491488</td>\n",
       "      <td>0.325616</td>\n",
       "      <td>-1.142436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.431596</td>\n",
       "      <td>0.137711</td>\n",
       "      <td>0.139401</td>\n",
       "      <td>0.928530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.595253</td>\n",
       "      <td>0.152700</td>\n",
       "      <td>0.332961</td>\n",
       "      <td>0.827073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.705462</td>\n",
       "      <td>-1.262584</td>\n",
       "      <td>-0.177256</td>\n",
       "      <td>1.388932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.004146</td>\n",
       "      <td>0.399645</td>\n",
       "      <td>-0.439888</td>\n",
       "      <td>0.625605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.017679</td>\n",
       "      <td>0.493244</td>\n",
       "      <td>0.364786</td>\n",
       "      <td>0.333770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.665226</td>\n",
       "      <td>-0.487820</td>\n",
       "      <td>0.125152</td>\n",
       "      <td>0.743315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.544550</td>\n",
       "      <td>0.456231</td>\n",
       "      <td>-0.972469</td>\n",
       "      <td>-0.176727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.799549</td>\n",
       "      <td>0.198477</td>\n",
       "      <td>1.500031</td>\n",
       "      <td>-0.340852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.436112  0.302169  0.067651  0.967016\n",
       "1 -0.077582  0.491488  0.325616 -1.142436\n",
       "2  1.431596  0.137711  0.139401  0.928530\n",
       "3 -0.595253  0.152700  0.332961  0.827073\n",
       "4  0.705462 -1.262584 -0.177256  1.388932\n",
       "5 -1.004146  0.399645 -0.439888  0.625605\n",
       "6  0.017679  0.493244  0.364786  0.333770\n",
       "7  0.665226 -0.487820  0.125152  0.743315\n",
       "8  0.544550  0.456231 -0.972469 -0.176727\n",
       "9 -0.799549  0.198477  1.500031 -0.340852"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "62030eae-f3ff-4d73-816b-a7c9b16d557d",
    "_uuid": "bd71a71d0715857037ccb116bec2084ac65f0a17"
   },
   "source": [
    "### Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1fc997a6-5359-4fcd-bd1a-b0348f71b819",
    "_uuid": "2e5ea2b06b8349ad6e39af887988652edb8fbb73"
   },
   "source": [
    "SQL style merges. See the [Database style joining](https://pandas.pydata.org/pandas-docs/stable/merging.html#merging-join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "_cell_guid": "e4564981-808b-4154-8e35-6e94c2883821",
    "_uuid": "ae963f9acd041abc13954a8b57425a162550106f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "_cell_guid": "4fc7aac1-cf73-4026-9f1a-765b5a8177b3",
    "_uuid": "4c66f190e7c6b22d22629bba2da2b253ebc0258d",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "d5208a6c-f5d4-4e03-9ae0-bbfb8c119e36",
    "_uuid": "72654ecbb57e52b793ee3be15f82cade614c6158",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right,on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1bac3a7a-793c-4af0-a6a4-7a52ee72058d",
    "_uuid": "c1d886d9d9ebee002af9f789a1b7acf9ede81959"
   },
   "source": [
    "Another example that can be given is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "ec2e4826-c7f0-44e1-90aa-cd981d8deb4f",
    "_uuid": "bc2f4621a6c78f60b886b35ec9a6aceaf4f7f1a6",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1, 2]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_cell_guid": "e5c24cf6-23e1-4949-9fee-bdedd1ad4130",
    "_uuid": "337fd14edbbaeedb42b598f0a185e235f71d673a",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  bar     5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'key': ['foo', 'bar'], 'rval': [4, 5]})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_cell_guid": "475c1acd-7405-4994-a861-28c7a8416b54",
    "_uuid": "acdb46712ef690150987d134784361bbc32eb90b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  bar     2     5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='key')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dae9ef46-214d-48e2-ba5c-7e8191f33320",
    "_uuid": "52dfbaeb0fc5416d2e15972f3f544b689f32b701"
   },
   "source": [
    "### Append\n",
    "Append rows to a dataframe. See the [Appending](https://pandas.pydata.org/pandas-docs/stable/merging.html#merging-concatenation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_cell_guid": "df5cdd4b-3313-4a96-82cc-873ad0c67565",
    "_uuid": "4475724eba1b82530cfd5c7d32d46f4d3c2be1a5",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.456646</td>\n",
       "      <td>1.421271</td>\n",
       "      <td>0.977136</td>\n",
       "      <td>-1.999726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.223248</td>\n",
       "      <td>1.143462</td>\n",
       "      <td>0.078680</td>\n",
       "      <td>0.304016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.283058</td>\n",
       "      <td>-0.533155</td>\n",
       "      <td>0.112845</td>\n",
       "      <td>1.722456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003237</td>\n",
       "      <td>1.082237</td>\n",
       "      <td>0.676849</td>\n",
       "      <td>-0.141792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.226915</td>\n",
       "      <td>1.328215</td>\n",
       "      <td>0.253559</td>\n",
       "      <td>-1.376209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.858082</td>\n",
       "      <td>-1.005122</td>\n",
       "      <td>-0.922258</td>\n",
       "      <td>0.541143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.568793</td>\n",
       "      <td>0.402721</td>\n",
       "      <td>-0.347052</td>\n",
       "      <td>0.672379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.118566</td>\n",
       "      <td>-0.045804</td>\n",
       "      <td>0.023041</td>\n",
       "      <td>0.221892</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.456646  1.421271  0.977136 -1.999726\n",
       "1 -0.223248  1.143462  0.078680  0.304016\n",
       "2 -1.283058 -0.533155  0.112845  1.722456\n",
       "3  0.003237  1.082237  0.676849 -0.141792\n",
       "4 -2.226915  1.328215  0.253559 -1.376209\n",
       "5  0.858082 -1.005122 -0.922258  0.541143\n",
       "6  0.568793  0.402721 -0.347052  0.672379\n",
       "7  1.118566 -0.045804  0.023041  0.221892"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "b15aafac-1acf-4dff-87fc-3e43b9e0ae68",
    "_uuid": "963ccfe458e2b84996e688c787dc9daf00ddfa2f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.003237\n",
       "B    1.082237\n",
       "C    0.676849\n",
       "D   -0.141792\n",
       "Name: 3, dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.iloc[3]\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_cell_guid": "8e8e9c59-b72f-423a-888b-adb66ff31f0d",
    "_uuid": "5de0b99314a3e8b7d347050a5d09f729dd54ed52",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.456646</td>\n",
       "      <td>1.421271</td>\n",
       "      <td>0.977136</td>\n",
       "      <td>-1.999726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.223248</td>\n",
       "      <td>1.143462</td>\n",
       "      <td>0.078680</td>\n",
       "      <td>0.304016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.283058</td>\n",
       "      <td>-0.533155</td>\n",
       "      <td>0.112845</td>\n",
       "      <td>1.722456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003237</td>\n",
       "      <td>1.082237</td>\n",
       "      <td>0.676849</td>\n",
       "      <td>-0.141792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.226915</td>\n",
       "      <td>1.328215</td>\n",
       "      <td>0.253559</td>\n",
       "      <td>-1.376209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.858082</td>\n",
       "      <td>-1.005122</td>\n",
       "      <td>-0.922258</td>\n",
       "      <td>0.541143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.568793</td>\n",
       "      <td>0.402721</td>\n",
       "      <td>-0.347052</td>\n",
       "      <td>0.672379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.118566</td>\n",
       "      <td>-0.045804</td>\n",
       "      <td>0.023041</td>\n",
       "      <td>0.221892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.003237</td>\n",
       "      <td>1.082237</td>\n",
       "      <td>0.676849</td>\n",
       "      <td>-0.141792</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.456646  1.421271  0.977136 -1.999726\n",
       "1 -0.223248  1.143462  0.078680  0.304016\n",
       "2 -1.283058 -0.533155  0.112845  1.722456\n",
       "3  0.003237  1.082237  0.676849 -0.141792\n",
       "4 -2.226915  1.328215  0.253559 -1.376209\n",
       "5  0.858082 -1.005122 -0.922258  0.541143\n",
       "6  0.568793  0.402721 -0.347052  0.672379\n",
       "7  1.118566 -0.045804  0.023041  0.221892\n",
       "8  0.003237  1.082237  0.676849 -0.141792"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(s,ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dbf8666d-7af7-4fd0-b40a-5dd3a93cc0cb",
    "_uuid": "9f29392de68aceec0cadc01ccdfc2c124e6821f2"
   },
   "source": [
    "# Grouping\n",
    "By “group by” we are referring to a process involving one or more of the following steps\n",
    "\n",
    "Splitting the data into groups based on some criteria\n",
    "Applying a function to each group independently\n",
    "Combining the results into a data structure\n",
    "\n",
    "See the [Grouping section](https://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "_cell_guid": "23331b94-daca-4454-9f8c-f442169e5062",
    "_uuid": "4934d8285f1ed9fc748cb571d8bf212d6f4880ed",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.671250</td>\n",
       "      <td>-0.331143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>-0.495065</td>\n",
       "      <td>1.478643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.439880</td>\n",
       "      <td>0.703543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.481897</td>\n",
       "      <td>-0.126245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>1.507422</td>\n",
       "      <td>0.078659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>0.894381</td>\n",
       "      <td>1.346379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.741226</td>\n",
       "      <td>0.988908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.010205</td>\n",
       "      <td>-0.642624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo    one -0.671250 -0.331143\n",
       "1  bar    one -0.495065  1.478643\n",
       "2  foo    two  1.439880  0.703543\n",
       "3  bar  three -0.481897 -0.126245\n",
       "4  foo    two  1.507422  0.078659\n",
       "5  bar    two  0.894381  1.346379\n",
       "6  foo    one  0.741226  0.988908\n",
       "7  foo  three -0.010205 -0.642624"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',\n",
    "   ....:                           'foo', 'bar', 'foo', 'foo'],\n",
    "   ....:                    'B' : ['one', 'one', 'two', 'three',\n",
    "   ....:                           'two', 'two', 'one', 'three'],\n",
    "   ....:                    'C' : np.random.randn(8),\n",
    "   ....:                    'D' : np.random.randn(8)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6ba9b996-c9dc-4ea0-901b-32ee9251529c",
    "_uuid": "c58620f714db8772c05d14a5945c69db2e2f8e62"
   },
   "source": [
    "Grouping and then applying a function sum to the resulting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_cell_guid": "ea8abe75-1945-402c-9a22-7d19393853ba",
    "_uuid": "98178d2cec0567107285d68e1dcb9aa411b74ad4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>-0.082580</td>\n",
       "      <td>2.698776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>3.007072</td>\n",
       "      <td>0.797343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar -0.082580  2.698776\n",
       "foo  3.007072  0.797343"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8d5181e7-9196-4575-979a-5167e1347f99",
    "_uuid": "4e11d0fa50d0c5f4ba267e7f13af361eb78715fa"
   },
   "source": [
    "Grouping by multiple columns forms a hierarchical index, which we then apply the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_cell_guid": "d4fd100b-cd69-4f81-afae-f1930e1d66c2",
    "_uuid": "f66bb1fb16b537febc7839743a177a222f4946e5",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.495065</td>\n",
       "      <td>1.478643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.481897</td>\n",
       "      <td>-0.126245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.894381</td>\n",
       "      <td>1.346379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>0.069976</td>\n",
       "      <td>0.657765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>-0.010205</td>\n",
       "      <td>-0.642624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2.947301</td>\n",
       "      <td>0.782203</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar one   -0.495065  1.478643\n",
       "    three -0.481897 -0.126245\n",
       "    two    0.894381  1.346379\n",
       "foo one    0.069976  0.657765\n",
       "    three -0.010205 -0.642624\n",
       "    two    2.947301  0.782203"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A','B']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d0c596d2-7b4a-4b22-80c9-fad65f090aba",
    "_uuid": "15911cf2e4cddee344569d63d9ed362efca7f919"
   },
   "source": [
    "# Reshaping\n",
    "See the sections on [Hierarchical Indexing](https://pandas.pydata.org/pandas-docs/stable/advanced.html#advanced-hierarchical) and [Reshaping](https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-stacking).\n",
    "\n",
    "### Stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "266aea3e-433a-436a-83df-08551438f6df",
    "_uuid": "5d1deec928f77a06ad5d090b9a72c04168cda5c2",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('bar', 'one'),\n",
       " ('bar', 'two'),\n",
       " ('baz', 'one'),\n",
       " ('baz', 'two'),\n",
       " ('foo', 'one'),\n",
       " ('foo', 'two'),\n",
       " ('qux', 'one'),\n",
       " ('qux', 'two')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuples = list(zip(*[['bar', 'bar', 'baz', 'baz',\n",
    "   ....:                      'foo', 'foo', 'qux', 'qux'],\n",
    "   ....:                     ['one', 'two', 'one', 'two',\n",
    "   ....:                      'one', 'two', 'one', 'two']]))\n",
    "tuples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "084d1096-800c-4bc4-aa44-cea9599a65dd",
    "_uuid": "4568a8525a65f30a40a3c6cbae4b8c53eb403b97",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['bar', 'baz', 'foo', 'qux'], ['one', 'two']],\n",
       "           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],\n",
       "           names=['first', 'second'])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "0764158a-666f-46d9-a981-c97df7b39b34",
    "_uuid": "a05b99f0ea1fa20c13aaad0e738045f14c97e8fb",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.427081</td>\n",
       "      <td>-0.448652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.348324</td>\n",
       "      <td>0.224204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>0.961195</td>\n",
       "      <td>-0.171636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.261235</td>\n",
       "      <td>-1.143567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.422736</td>\n",
       "      <td>-0.042326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.050748</td>\n",
       "      <td>-2.078037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>one</th>\n",
       "      <td>0.465950</td>\n",
       "      <td>0.799862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.476400</td>\n",
       "      <td>-0.280527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.427081 -0.448652\n",
       "      two     1.348324  0.224204\n",
       "baz   one     0.961195 -0.171636\n",
       "      two    -1.261235 -1.143567\n",
       "foo   one    -0.422736 -0.042326\n",
       "      two     0.050748 -2.078037\n",
       "qux   one     0.465950  0.799862\n",
       "      two     0.476400 -0.280527"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index=index, columns=['A', 'B'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "35cca8ca-6aed-4f7e-9d52-14f5ec8e4e42",
    "_uuid": "3987868916766e10a30f5bdc3d1759ae29aec4b0"
   },
   "source": [
    "The [stack()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.stack.html#pandas.DataFrame.stack) method “compresses” a level in the DataFrame’s columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "ba300ff7-e5a1-4169-9b00-6c5a13e4d557",
    "_uuid": "a05087c82e0941531337c2166c6027ce8b8e9624",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.427081\n",
       "               B   -0.448652\n",
       "       two     A    1.348324\n",
       "               B    0.224204\n",
       "baz    one     A    0.961195\n",
       "               B   -0.171636\n",
       "       two     A   -1.261235\n",
       "               B   -1.143567\n",
       "foo    one     A   -0.422736\n",
       "               B   -0.042326\n",
       "       two     A    0.050748\n",
       "               B   -2.078037\n",
       "qux    one     A    0.465950\n",
       "               B    0.799862\n",
       "       two     A    0.476400\n",
       "               B   -0.280527\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked = df.stack()\n",
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e15acb89-1dd2-4ff4-90ca-796d6dc510b3",
    "_uuid": "b8d507f4029c20f98bb3a782845bc8ca13637a82"
   },
   "source": [
    "With a “stacked” DataFrame or Series (having a MultiIndex as the index), the inverse operation of [stack()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.stack.html#pandas.DataFrame.stack) is [unstack()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html#pandas.DataFrame.unstack), which by default unstacks the last level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_cell_guid": "73d256c6-dd54-418d-b3c0-ce432f397763",
    "_uuid": "7ac035cd3392fdb6e9bc8bdf49b69c279309c018",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.427081</td>\n",
       "      <td>-0.448652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.348324</td>\n",
       "      <td>0.224204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>one</th>\n",
       "      <td>0.961195</td>\n",
       "      <td>-0.171636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.261235</td>\n",
       "      <td>-1.143567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.422736</td>\n",
       "      <td>-0.042326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.050748</td>\n",
       "      <td>-2.078037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>one</th>\n",
       "      <td>0.465950</td>\n",
       "      <td>0.799862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>0.476400</td>\n",
       "      <td>-0.280527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.427081 -0.448652\n",
       "      two     1.348324  0.224204\n",
       "baz   one     0.961195 -0.171636\n",
       "      two    -1.261235 -1.143567\n",
       "foo   one    -0.422736 -0.042326\n",
       "      two     0.050748 -2.078037\n",
       "qux   one     0.465950  0.799862\n",
       "      two     0.476400 -0.280527"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_cell_guid": "cd7ae481-efc0-41cb-aacf-a7388c4111c0",
    "_uuid": "904cdcfbabb4492ab2a1a573c2cbd57f72bfe4bb",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>second</th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.427081</td>\n",
       "      <td>1.348324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.448652</td>\n",
       "      <td>0.224204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>0.961195</td>\n",
       "      <td>-1.261235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.171636</td>\n",
       "      <td>-1.143567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.422736</td>\n",
       "      <td>0.050748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.042326</td>\n",
       "      <td>-2.078037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">qux</th>\n",
       "      <th>A</th>\n",
       "      <td>0.465950</td>\n",
       "      <td>0.476400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.799862</td>\n",
       "      <td>-0.280527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.427081  1.348324\n",
       "      B -0.448652  0.224204\n",
       "baz   A  0.961195 -1.261235\n",
       "      B -0.171636 -1.143567\n",
       "foo   A -0.422736  0.050748\n",
       "      B -0.042326 -2.078037\n",
       "qux   A  0.465950  0.476400\n",
       "      B  0.799862 -0.280527"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_cell_guid": "63d4143c-2488-40a7-9d6a-8751ed8f5bec",
    "_uuid": "21092205bbd06eab81ab2b54fc709110c8ebedee",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "      <th>foo</th>\n",
       "      <th>qux</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.427081</td>\n",
       "      <td>0.961195</td>\n",
       "      <td>-0.422736</td>\n",
       "      <td>0.465950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.448652</td>\n",
       "      <td>-0.171636</td>\n",
       "      <td>-0.042326</td>\n",
       "      <td>0.799862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>1.348324</td>\n",
       "      <td>-1.261235</td>\n",
       "      <td>0.050748</td>\n",
       "      <td>0.476400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.224204</td>\n",
       "      <td>-1.143567</td>\n",
       "      <td>-2.078037</td>\n",
       "      <td>-0.280527</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz       foo       qux\n",
       "second                                          \n",
       "one    A -0.427081  0.961195 -0.422736  0.465950\n",
       "       B -0.448652 -0.171636 -0.042326  0.799862\n",
       "two    A  1.348324 -1.261235  0.050748  0.476400\n",
       "       B  0.224204 -1.143567 -2.078037 -0.280527"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2aeae2c4-7559-4fa7-a837-4500980c823c",
    "_uuid": "6108ff3064ecc74f8d264309548f6feb125ed9be"
   },
   "source": [
    "### Pivot Tables\n",
    "See the section on [Pivot Tables](https://pandas.pydata.org/pandas-docs/stable/reshaping.html#reshaping-pivot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "1a188e29-26e1-4ffc-acd1-5e8fb7c99465",
    "_uuid": "0acd04af17bb429101737d38d9f603571fb3006f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.713789</td>\n",
       "      <td>-0.514429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-2.088411</td>\n",
       "      <td>1.047928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.037998</td>\n",
       "      <td>1.741442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.656743</td>\n",
       "      <td>-0.930621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.688656</td>\n",
       "      <td>-0.542723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.503639</td>\n",
       "      <td>1.723579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>2.235146</td>\n",
       "      <td>0.361746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.177621</td>\n",
       "      <td>-1.140500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.254126</td>\n",
       "      <td>0.288719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.028623</td>\n",
       "      <td>0.891349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-2.723264</td>\n",
       "      <td>0.726894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.686261</td>\n",
       "      <td>1.800097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo -0.713789 -0.514429\n",
       "1     one  B  foo -2.088411  1.047928\n",
       "2     two  C  foo -1.037998  1.741442\n",
       "3   three  A  bar  1.656743 -0.930621\n",
       "4     one  B  bar -0.688656 -0.542723\n",
       "5     one  C  bar  0.503639  1.723579\n",
       "6     two  A  foo  2.235146  0.361746\n",
       "7   three  B  foo -0.177621 -1.140500\n",
       "8     one  C  foo -1.254126  0.288719\n",
       "9     one  A  bar  1.028623  0.891349\n",
       "10    two  B  bar -2.723264  0.726894\n",
       "11  three  C  bar  0.686261  1.800097"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['one', 'one', 'two', 'three'] * 3,\n",
    "   .....:                    'B' : ['A', 'B', 'C'] * 4,\n",
    "   .....:                    'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 2,\n",
    "   .....:                    'D' : np.random.randn(12),\n",
    "   .....:                    'E' : np.random.randn(12)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2b2e7f69-e135-4cdb-9053-15446689a343",
    "_uuid": "8a59aa522a9c86d054fc8f1917117ff7131ce9d7"
   },
   "source": [
    "We can produce pivot tables from this data very easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_cell_guid": "236b385e-a44c-4526-b7dd-826b858aec30",
    "_uuid": "24977494d59f97c71999811110baf456f25d9e1c",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>1.028623</td>\n",
       "      <td>-0.713789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.688656</td>\n",
       "      <td>-2.088411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.503639</td>\n",
       "      <td>-1.254126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>1.656743</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.177621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.686261</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.235146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-2.723264</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.037998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A  1.028623 -0.713789\n",
       "      B -0.688656 -2.088411\n",
       "      C  0.503639 -1.254126\n",
       "three A  1.656743       NaN\n",
       "      B       NaN -0.177621\n",
       "      C  0.686261       NaN\n",
       "two   A       NaN  2.235146\n",
       "      B -2.723264       NaN\n",
       "      C       NaN -1.037998"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d2a048eb-a0a2-49f1-8322-cec52e82f686",
    "_uuid": "1e9fb4493ac63b9741daf82d345cf4996fb534ae"
   },
   "source": [
    "# Time Series\n",
    "pandas has simple, powerful, and efficient functionality for performing resampling operations during frequency conversion (e.g., converting secondly data into 5-minutely data). This is extremely common in, but not limited to, financial applications. See the [Time Series section](https://pandas.pydata.org/pandas-docs/stable/timeseries.html#timeseries)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_cell_guid": "8e241bb8-4365-46e1-af4e-e212c17ab73c",
    "_uuid": "0dce2c62fa3fe83d54a964e6be8edfc70f256167",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-01-01    24600\n",
       "Freq: 5T, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2017', periods=100, freq='S')\n",
    "ts = pd.Series(np.random.randint(0, 500, len(rng)), index=rng)\n",
    "ts.resample('5Min').sum()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0daa2a55-6a1a-468f-84a0-593aa67ce25e",
    "_uuid": "81d2d75a0ce40e06025deccd8aa025de20b03b75"
   },
   "source": [
    "Time zone representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_cell_guid": "c5014c81-5303-432f-bc1f-bd4f7e667c30",
    "_uuid": "43018b7fc38618006b1ee7c3f755ff8d28c4662b",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-03-06   -1.062920\n",
       "2017-03-07   -0.108798\n",
       "2017-03-08   -0.894970\n",
       "2017-03-09    0.486812\n",
       "2017-03-10    0.567902\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('3/6/2017 00:00', periods=5, freq='D')\n",
    "ts = pd.Series(np.random.randn(len(rng)), rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_cell_guid": "641dbfaf-d904-4110-ac33-8fd6d51f35ae",
    "_uuid": "7f698edf03e66b1064830336ecfcf039d638629a",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-03-06 00:00:00+00:00   -1.062920\n",
       "2017-03-07 00:00:00+00:00   -0.108798\n",
       "2017-03-08 00:00:00+00:00   -0.894970\n",
       "2017-03-09 00:00:00+00:00    0.486812\n",
       "2017-03-10 00:00:00+00:00    0.567902\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc = ts.tz_localize('UTC')\n",
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1426cdde-ca90-4601-a103-5f36eab1738f",
    "_uuid": "91157d3eb394f4e4588bc97e4d45f2537e33dcca"
   },
   "source": [
    "Convert to another time zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_cell_guid": "7e2e2c12-6154-418a-acbb-8d1a67825b63",
    "_uuid": "910dcb7ee20644396fb426839cf6803cb9e7bbf1",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-03-05 19:00:00-05:00   -1.062920\n",
       "2017-03-06 19:00:00-05:00   -0.108798\n",
       "2017-03-07 19:00:00-05:00   -0.894970\n",
       "2017-03-08 19:00:00-05:00    0.486812\n",
       "2017-03-09 19:00:00-05:00    0.567902\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert('US/Eastern')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0c0f6eec-5035-48dc-a2d4-8cc84a257ab9",
    "_uuid": "aeb2afaeacdd39a558b63e59cf98c4c27e194083"
   },
   "source": [
    "Converting between time span representations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_cell_guid": "489d710f-c382-45f4-a368-fc78ce0819fb",
    "_uuid": "618d89ae23db202f5551fcca5f3db769843bc3a8",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31   -1.381079\n",
       "2012-02-29    0.732092\n",
       "2012-03-31   -0.378388\n",
       "2012-04-30   -0.117352\n",
       "2012-05-31   -1.046533\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = pd.date_range('1/1/2012', periods=5, freq='M')\n",
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_cell_guid": "46e4915f-17a5-40eb-8506-49e8e5e434a8",
    "_uuid": "31c57985582c3ef27f84933dfab9321a6e0285f8",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01   -1.381079\n",
       "2012-02    0.732092\n",
       "2012-03   -0.378388\n",
       "2012-04   -0.117352\n",
       "2012-05   -1.046533\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = ts.to_period('M')\n",
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_cell_guid": "3a8066ba-79d4-4975-86a2-804c82fe1dd6",
    "_uuid": "1589235a92ca818bc2be2cbb0a426d529335f0b2",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01   -1.381079\n",
       "2012-02-01    0.732092\n",
       "2012-03-01   -0.378388\n",
       "2012-04-01   -0.117352\n",
       "2012-05-01   -1.046533\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d1e644da-5215-4870-aaf3-d1719c46588e",
    "_uuid": "1dd44d9eff96202caddabee5740aa912e9530223"
   },
   "source": [
    "Converting between period and timestamp enables some convenient arithmetic functions to be used. In the following example, we convert a quarterly frequency with year ending in November to 9am of the end of the month following the quarter end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "328ed5ad-d841-4b7a-8217-5e58b876daa6",
    "_uuid": "43d477d81ad7c6a7cc7dd4156eba62a794820a3e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00   -1.756576\n",
       "1990-06-01 09:00   -0.176688\n",
       "1990-09-01 09:00    0.104222\n",
       "1990-12-01 09:00   -0.391258\n",
       "1991-03-01 09:00   -0.831849\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prng = pd.period_range('1990Q1', '2000Q4', freq='Q-NOV')\n",
    "ts = pd.Series(np.random.randn(len(prng)), prng)\n",
    "ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9\n",
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d10de9eb-33fc-4713-993b-b1acc275f8a7",
    "_uuid": "930ca52af7bf3d56dfe637764cf53dd6c91b8edf"
   },
   "source": [
    "# Categoricals\n",
    "pandas can include categorical data in a DataFrame. For full docs, see the [categorical introduction](https://pandas.pydata.org/pandas-docs/stable/categorical.html#categorical) and the [API documentation](https://pandas.pydata.org/pandas-docs/stable/api.html#api-categorical)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_cell_guid": "ab9789c4-b2e0-4012-8c7b-2923437a8223",
    "_uuid": "186d5dd0c91b786139eb600d599853e301e08c92",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         a\n",
       "5   6         e"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"id\":[1,2,3,4,5,6], \"raw_grade\":['a', 'b', 'b', 'a', 'a', 'e']})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0d1cb293-571d-4c98-a17a-b15ead76786b",
    "_uuid": "ae51724d3f551ece6efffc7b39a325ab334631e1"
   },
   "source": [
    "Convert the raw grades to a categorical data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "0fec7445-afdd-42cd-ad78-deab6ea43832",
    "_uuid": "db91d55922cbee6333b0b60b2eaa0066b1c682d8",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): [a, b, e]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "21e5d042-6245-4971-803f-e527357a654e",
    "_uuid": "b2e1342efaa7495d1a001e3ec9bb53a2d3c0a7e0"
   },
   "source": [
    "Rename the categories to more meaningful names (assigning to Series.cat.categories is inplace!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_cell_guid": "8e187065-c04e-4df2-b297-834d4452b5af",
    "_uuid": "1fde92f0bb025c22106fd9480bf879087b20d98b",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79ff2b61-ce4f-47bf-b400-44cce807b0a3",
    "_uuid": "a3edf0571ae751050f46690aa93bff03902ba7bb"
   },
   "source": [
    "Reorder the categories and simultaneously add the missing categories (methods under Series .cat return a new Series per default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_cell_guid": "17680b48-9fb9-4373-89c8-22197cd0b551",
    "_uuid": "07effd8ab8f0fcf8d202551d8c7b27d7c44676d7",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): [very bad, bad, medium, good, very good]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories([\"very bad\", \"bad\", \"medium\", \"good\", \"very good\"])\n",
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "_cell_guid": "d081f4d8-b9df-427a-abdd-af50ac3e1cb6",
    "_uuid": "6f39477d1629a4dfd818f58c8f1ad54e12cb1ccc"
   },
   "source": [
    "Sorting is per order in the categories, not lexical order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_cell_guid": "32408bba-dbf0-44ed-b65d-03f66671e120",
    "_uuid": "cc7d213cf05e72e3eeb71438c457a0d520bd718f",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good\n",
       "4   5         a  very good"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=\"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c36e4653-564c-4366-bd3e-9fcd0807ee45",
    "_uuid": "35364a872dd0c030ec3b09901b464762a4000ef9"
   },
   "source": [
    "Grouping by a categorical column shows also empty categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "_cell_guid": "b7eb5c6c-8857-42bc-86ce-8c13dbc4044c",
    "_uuid": "300a83bdb3f4e92697b0fbf2b51ced032cdb507e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f7329129-1fc9-434a-ac45-e79986ed395c",
    "_uuid": "4fad41dc8d59a36e31eee33e2e4ceadaef91ef5c"
   },
   "source": [
    "# Plotting\n",
    "[Plotting](https://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization) docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_cell_guid": "161bb7ae-199a-4afb-a96e-cd44ce1ad5ba",
    "_uuid": "d7773d5d125e65665d1fa5742a5d96a4c4f66869",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa0340db898>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0340dbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))\n",
    "ts = ts.cumsum()\n",
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0176b40e-ada1-4bcd-8e12-d3ff15e1b0f7",
    "_uuid": "7b6fa4a5c292aeadb392e26b6b8c22de265340e6"
   },
   "source": [
    "On DataFrame, plot() is a convenience to plot all of the columns with labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_cell_guid": "486b1a48-5629-43a9-a421-ab9659b3b5c2",
    "_uuid": "dd719ceca935f661779a4ae7a9d0bdb456707afd",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa030cd4400>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa030cc9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAEHCAYAAACtAv3IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsnXd4VNXWh98zNb33BEICBBCUjigg\nRayooFiwX7tXvXavYrmCvWGvYPdTRFRELCAdAQXpNZSQQHpvk0yf8/1xpmYmfUISOO/z8OSUfc7Z\nEyZr77P2Wr8liKKIjIyMjMyJj6KzOyAjIyMjc3yQDb6MjIzMSYJs8GVkZGROEmSDLyMjI3OSIBt8\nGRkZmZME2eDLyMjInCTIBl9GRkbmJEE2+DIyMjInCbLBl5GRkTlJUHV2B9yJiYkRe/Xq1dndkJGR\nkelWbN26tUwUxdjm2nUpg9+rVy+2bNnS2d2QkZGR6VYIgnC0Je1kl46MjIzMSYJs8GVkZGROEmSD\nLyMjI3OS0KV8+DIyMjLHG7PZTF5eHgaDobO70iwBAQGkpKSgVqvbdL1s8GVkZE5q8vLyCA0NpVev\nXgiC0NndaRRRFCkvLycvL4+0tLQ23UN26cjIyJzUGAwGoqOju7SxBxAEgejo6Ha9ifjN4AuCoBQE\nYbsgCL/Y99MEQdgkCMJhQRAWCIKg8dezZE4MRKsV0WbzPCZXYJPpBLq6sXfQ3n76c4Z/H7Dfbf9l\n4A1RFPsAlcAtfnyWzAlA5uAh5N19j3PfnJ9P5oBTqF25shN7JSNz4uIXgy8IQgowBfjYvi8Ak4Dv\n7U2+AKb541kyJwaiKILFgm71auf+4bMnA1D65lud2TUZmU7hp59+QhAEMjMzO+wZ/prhvwn8F3C8\nn0cDVaIoWuz7eUCyrwsFQbhdEIQtgiBsKS0t9VN3ZLoq5oIC6jZuxFpe7jxW9cMPmLKzXY0U8tKS\nzMnH/PnzGTt2LPPnz++wZ7Q7SkcQhIuAElEUtwqCMKG114uiOBeYCzBixAjZgXuCk33ZdKxVVYRf\neqnzWOETT5Ly/vvOfUVQUGd0TUaG2Uv2sq+gxq/3PCUpjKcvHthkG51Ox/r161m9ejUXX3wxs2fP\n9msfHPhjKjUGuEQQhBzgWyRXzltAhCAIjgElBcj3w7NkujGiyYS1qgqA6kWLnMdVsbGYCwtc7WzW\n4943GZnOZPHixZx//vlkZGQQHR3N1q1bO+Q57Z7hi6I4E5gJYJ/hPyyK4rWCICwELkcaBG4EFrf3\nWTLdm7q//vLYD5k0CXVSElULF2I8cBBBrSZkwgSM2Uc6qYcyJzvNzcQ7ivnz53PfffcBMGPGDObP\nn8/w4cP9/pyOTLx6FPhWEITngO3AJx34LJlugDHL05AnPP00ppwcKv/v/6j68UfUSUkoQkKw1dV3\nUg9lZI4/FRUVrFq1it27dyMIAlarFUEQePXVV/0eLurX1TFRFNeIoniRffuIKIqjRFHsI4riFaIo\nGv35LJnuh37XLpTh4c59VUw02oy+0o7FgjoxEUVQELZ62eDLnDx8//33XH/99Rw9epScnBxyc3NJ\nS0vjzz//9Puz5HAImQ7FcPAgNUuXUfT8C9QuXUrIhPEAKCMjEZRKlBERCBopJ0+dmCjN8HU6RKvs\nx5c5OZg/fz6XugUxAEyfPr1DonVkLR2ZDiX7kqke+6r4BPqsXoUiNAyQMgcVwcFYTSY06ekoIyPA\naqX0rbeJuevfKAICmry/bu1aCmfPJuWNNwgcPLjDPoeMTEex2p6L4s69997bIc+SZ/gyHYLNaMSU\nm+t9QhBQJyaiDAl2tdXpAND2TkeTmgpA+dy5lL37rs971/zxB4fPOw/RYqF2zRosBYXod+7y/4eQ\nkTnBkGf4Mh1C3l13U7dhg/eJBto5AKLZDIAmLR2F20Bgqaj0ee+i2c9gLS/HXFQEdu0d0SQvEcnI\nNIc8w5fpEHwae0CTnu51LHDoUOlcjxRUsa46zHV//4VosXi0PXbzzc4sXXNurvO8vNArI9M8ssGX\n6RCUEREe+4qwMFK/+ZrwaVO92vb48APSFv2IoFYjCAJhF14IgKWgkJqlyzza1m10xfIb9u6l+vsf\nAORQThmZFiC7dGT8jmizeejh9PzsU9Q9eqJJ8SmnhDI83CNcM/n1Odjq6tCtXYvxQCZcNMXnde5+\ne3mGLyPTPLLBl/E7+h07sVZUEPfIIwSfeQYBAwa0+h49PvqQrCkXYcrJAcBaXY0xK8ujjbnAJccg\nG3wZmeaRXToyfkU0m526OMHjxrbJ2DtQRUVRu3wFVl0dJXNe5+g113qcdwwGALa6ujY/R0ams1Eq\nlQwZMoTBgwczbNgwNm7c2CHPkQ2+jF/JPPU0Ch56GPD247cW/S7JZVP2ztsYj7hm9wmzZhE4YrjT\nyKuTktCtWUPp2+/IFbNkuiWBgYHs2LGDnTt38uKLLzJz5swOeY7s0pHxG9baWo99VTsNvqP8oSkv\nH5ObDk/gsKHo9+xGv0VSFBS0WgDK3n+fiBlXoY6La9dzZU5ifn8Minb7954Jp8IFL7W4eU1NDZGR\nkf7tgx3Z4Mv4DXNennNbERTklExoKz0+/IDcW25F16DkYUBGBuEXXUTtsj8IGTcWw35XhSBLaals\n8GW6HXq9niFDhmAwGCgsLGTVqlUd8hzZ4Mv4DXO+W8kDpbLd9wsZM4bgceOo8yEiFTx6NP3+2QxA\nzoyrncctctU0mfbQipm4P3G4dAD++usvbrjhBvbs2dO11TJlTm5M9hm+OjmZ2Afu98s9HWqa6pQU\nEl96kR4ffejVJvmN14m+9Rag+xv8ym+/pe7vTZ3dDZlO5IwzzqCsrIyOKPkqG3yZZjEcOEDWRRdh\nqfQtdeBAv3MniuBgeq9YTtQ11/jl2QH9+gFgraoiYto0QsaP92qjTkwk5t//BsBWXe2X53YGhgMH\nKZo1m6JZszq7KzKdSGZmJlarlejoaL/fW3bpyDSJbt06yj74ENPhLOo2biR8iu8kKJteT+0fywm/\n+GK/voYGnHIKAKqE+CbbCUFBoFZj7cYG33hAWouwuBV4d2AuKUG/bRth559/vLslcxxw+PABRFHk\niy++QOkHt2hDZIMv4xNRFLHpdOTefofrYBMa9YbMTLBaCT1nsl/7oe3Th8Tnn0fTs0eT7QRBQBke\njrXavwWojyeOzGFFSIjXucLHn6Bu/Xqqxi8idOIkImdcdby7J9OBWI9T/QfZ4Mv4JPeWWzx0awDM\nxcWNtjfs3QdAwKBBfu9LxPTLWtROMviuGX7VTz9J10+b5vc+dQS69dLidEO3lOnYMerWrwegbu06\n6tauI/jMM9D07Hnc+yjTvZF9+DI+aWjsASyFhV7HRFFEt3Ytxc89ByoVqk4MiWxo8Asfm0nhYx2T\nwNKQ2hUrKHnjzXbdw1paBkgyETa9Hv3OnYgWC1ULv/dqm3Xuee16lszJiWzwZbywGQzO7YCBA53b\nld/Mx3DggEdb3cqV5N5xp7Rjsfg9jKw1KMPDqf/7b/b3H4BNr3cePx5RL3n3/Ifyjz7yzPS1WmDr\n51CdD/lbG73WlJMj9bm+HnWqNGuv/HYBOVfNoGrhQqw1vt1UolttAf3evYgmk18+i8yJi2zwZbyw\nlEkzTVVSIqlffuFxTtegHJt77H3iSy92fOeawF1x03TMVW0r97bbjlsfPNwxu76FJffBG6fAvEmg\n9x3l5C4BrbSXfix5+WUArNU1WCvK0fbtQ5/Vq4i6+WbXs+wDgSkvj5zpl1P8yqv+/jgyJxiywZcB\npLBHm32GaCmR4n8TZ89GERxM/717nO0aunosFZUgCPTfu6fTfeXK8DDntvHgQdcJlarDNHZMR49i\n1bmE28zubi99lWfjPT/6vIe7Cmj4pZ6/Q9FiQbdhI8rIKNSJicT/9xGib7sVAKu9NKS1ogKA+i1b\n2vw5ZE4OZIMvg81k4tD4CeTMmAG4kpdU9jhgQakkYfZsAAz79nkYT0tRIarEBIQOCCFrLebiEue2\nYf9+AEImTEDU67FWVTV2WZsRTSayzjuf/AcecB6rXvyzq0FNvucFh1f4vI/x8GGCx41jQOZ+Iq+6\nipi77yb9lyUAlL37LmJ9PerERGf7QHv4nmOG71i3cJSKlJFpDNngy2CtqkI0GjHuk4yk6ehRANQ9\nXKGQkVddSdxjj2LT6aS3Ab2egsdmUr34ZwRF5xt7kProoOLTTwEIGjUKAPOxY35/nmNQcZd+qFq4\n0NWgIhtiMkATKu0f+A12LvC4h2i1YjpyBG3v3gAIKhWx/7kHbZ8+Hu0ir7/eua0Ile5nrZHE6hxx\n+6asLLkuQDemqKiIGTNm0Lt3b4YPH86FF17IQfc3VT8gG3wZbG4qlzW//07p66+jio9HaTcsDhxG\nqfi558m7+x6q7WGP7qJpnUnwGWfQb9fOBsdGA65BzJ8YD0uuGCEoCABNWhq2ujqXamhlNkT3hZm5\ncPta6djmuR73MOfnIxqNaPt6GngAbf/+zm11cpJzWxkmua6stfYZvluxdw89I5lugyiKXHrppUyY\nMIGsrCy2bt3Kiy++SHETodBtQY7Dl/Ew+PkPPAhI2jUNCRo+HICaX3/1OB581rgO7F3rULgpdEbf\ncYczVl23dh0lr79Bwv+eInTSJL88y7B3r7Rhd6Vo+/XDlJ2NubAQZXAwVOZAn8kgCJA0BE69gupV\nGzE8cS/xz74JCoVz0HAMpu70WvAtR6+/AU2PHh61BRwDsc0+w7dWuDJzzUVFaPv29cvnOxl5efPL\nZFZkNt+wFfSP6s+jox5tss3q1atRq9XceeedzmODBw/2az9ANvgyeOvYg8t/744iKAhlbIwzXhyg\n9/I/UEVFdWj/Wkuvhd+BQkHgwIHO9QbHIKX78892G3xbfT25d91N/d9/Ay7feUD/ftQuXUr2JVPp\n/8XDCBYDRKU7rzOLsRSsFIHlRI57H83591C9aBEAGh8GX6HVkvbdAu/j9hm+paSYii+/ovrX31zP\n8JErIdP12bNnD8PtE6qORDb4MlirfOjPiDbvY0DPuXMpmjUb/U7JdaLp0bTkQWcQeOqpzu2GeQH1\nf/2N8fBhLx95a9CtWeM09qEXnE/t70tRBAUSYlqJQ9+w/vPHCI4Hol2GvHxNtuseW/ahDdtI7fLl\nAF7us6ZQBAcDUPrW285j2owMjIcOYSkqauOnkgGanYl3d2Qfvgy6tWtRhIY6Z44AAQN9SyQEDBhA\n6rfzAYi569/HpX/+xJSTw5GLLm7Ttfrdu8kcOoy6TZIOf/ovS9CkpgIQNjINbdkfzrYWvRLRBlZN\ngvOYsVhHQLQJdYiFuv351Cz7g7YgKLz/bJXRUahiYzEXyga/OzJw4EC2bm08Oc9fyAZfBnNeHoGn\nDqLnZ5+iio8n9v77iPrXjY22FwSBAZn7ib333uPYy/aT8v77TZ4XrVbK5s7DeCTb5/nyufMQ9Xqq\nFi5EnZSE9uBcyJIS0VRlmxAUEJomtbUYFZTsDOPg5Esx7N+PaDJhPFqMNtRCUIwJQ1Y+VQu83TVt\nxmpDnZSEMSsL0WLx331ljguTJk3CaDQyd65rUX/Xrl386aP4T3uQDb4M1spKlBERBA4cSN+1a4i5\n804UAQGd3S2/0XvZUtJ//43QSRMlcTe12mc73Zo1lL7+OuVz5/o879TpsdkQbHrY/BFi7jYABKW0\nVpA8qgBBIWIN6kd1oaQrVLVoEfrdu7HW6Ai55CrUwVYsVa5krcBhw9r82RyZt6LVSvCZZ2LYtYtD\nY8a2+X4ynYMgCCxatIgVK1bQu3dvBg4cyMyZM0lISGj+4lYg+/BlsFRVoYzomKLJXQGH2wUgZPx4\nDHv2IFqtXslijpBGX4vYNoOB+s2bnftK+3ioDbdH6ERIPwUB1MEWjOp+WHXS7Mx89BiWMimSRjPp\nZizr57v61rs3qV983urP1GPePBQBWhAEKefAYkHrKBbTjWsCnMwkJSXx3XffdegzZIPfCNaaGkSL\npctFoPgb0WLBVl2NMvLENfjuOBY8qxZ+76UpbymXJApEk0lyi4gigv1toG7jRo+2yTeOhAN7Ce+l\nJyDCTMCos+GUSyBnA9piE7Xr1oNd3MxcUIC1Urq3MjIKRZDrDSP6pn85n9EaQsZJs3hHVnT4pZei\njpeLt8s0jWzwGyHr3POwVlUxIHN/Z3elQ3FozmhSTw5t9ZCzxlHyyiteiVj127Y5QyT127dz9Lrr\nsen1pC+Wksvcq1BpevVCbZL8/IIAAZEWuPILUAfC0OsI1fxK7WppgFDFx2MuKMBS4TD4EaAOBiQ/\ne1gjFcRaiio2lv67d4FKhbXMFS4rimKnKpfKdE1kH74PrHb5AKDDRLe6CnV/SWJoQcchBrgroO3T\nB1V8vJe2ztFrrsVSImnx2HQ69Dt2YDxwAJNdksGhW9Pjk49J/XwuHN0IZ94LI2+FiJ6SsbcT4JYh\nGzx6NLa6OsrefgeQEsOElKEARN9+G4pA13VtRVCrEQQBVWwskTdIEgw1S5a0+74yJx6ywfdB8fMv\nOLetPuqL2kwman77zWft0e6Erb6ekldfA0CVlNRM6xMHVUyMUwLa61xCAsFjxhBz112A9KZnq6+X\nchXUaoLPPBNVwRqwmaHvuTBlDty/2+MeSjc3YIhbklfkNVcDEHb2GOKHVhNzw+X+/WBAtH0R13TU\n/9pBMt0f2eD7wFzkylas37aNus2bnQthlvJySue8Tv6DD3FozFhK5rzeWd1sMVadziml68Bw4CAH\nhrlm9SfT678qNrZRgx80YgQ9P/mYmHvudh7T796DtboaZXi49Hs6uBQie0Ev39Ew7rr8gYNPAyB8\n+mXEz5SqbwlhiUT1q0Nh9q2P3x7UCQkowsI6RB1Upvsj+/CboW7DRqoWLCDgtNOIvOpKCp940uN8\n+bx5xD30YCf1rmUcuehiUAj0XbXKeazg4Yec2z0//aQzutVpqGJj0Ltp/JtyXcVSHMJkgkJB5A3X\nU/nlVxTOnIkmLQ1VpF3PpuqYpILZyCApKBRo+/UjdPJk1AkJZGzZgkKjdi3OhtpD7WqLIdHnLdr3\n+SIjsVb6fzCR6f60e4YvCEIPQRBWC4KwTxCEvYIg3Gc/HiUIwnJBEA7Zf3aLMBD93r3U/+MqJOFI\nfTfs2uVl7B0Ys7OlRb/Fi49LH1uDpbQUS1ERloJCDG5Sq9ZqV9m84DPP7IyudRrKmBis5RWIVisA\n1Yt+cp5TBAc5t6NvugmQomzqNmxAnWKXkag6BuFNS0qkL/6J2P/cIz0vJBjBTdTNafB1HZMVq4yO\nxpST44zgqV7yC1U/+C6+ItM1UCqVDBkyhIEDBzJ48GDmzJmDzeZb3qQ9+GOGbwEeEkVxmyAIocBW\nQRCWA/8CVoqi+JIgCI8BjwFdXqgiZ7rkV1XGxBA0dKjT4DfFkQsudG6HT53aYX1rC44/eoDsS6Y6\no44cLiplbEyn9KszUcXEgM2GtaJCWlB1y8ANGjnSua1sICCnTkkBixEMVRDajql5SLz0c8Nbknxy\n6hltv5cPgseOoeztdzg07ixi7v2Pc8E4Yvplfn2OjP8IDAxkx44dAJSUlHDNNddQU1PDbHvhIX/R\nboMvimIhUGjfrhUEYT+QDEwFJtibfQGsoRsYfAeq2FiUMd6KkQ4EjYaen3/O0Wuu8TguWiwIqq7j\nKfPlqzYePoxoNHZCb7oGqthYQPrdHP3XTc7jPeZ+RMhZZzn3FRoNSXNeo+ChhwEIHDQQ6u0L9cGN\nfzea74AWAiOh/DB8dj7MaiRRymKEda/BqNshJLbFt4+57TYEtZrSOa87jb1Myyh64QWM+/0rj6wd\n0J+Exx9vcfu4uDjmzp3LyJEjmTVrll/X1/y6aCsIQi9gKLAJiLcPBgBFQHwj19wuCMIWQRC2lLrN\nRjsDk1shD0GlckY8uGuR9176OyBJ4gYNG+oRkQG0yneq370bc1ER9du3O90LTbXVrVuH2MrXPEeG\nJ/asUlEUKZjp+vKFT7moVfc7EVDFSMbTmHXEWXRcCAryMPYOwqdMoefnnxEycSIhEya4DH5QOwx+\nQ355EGaFOxO1nOz8Fta9An++1qrbCWo1Mbfd5iGGB1K2sEz3ID09HavVSklJSfONW4HfpqKCIIQA\nPwD3i6JY4z4qiaIoCoLgM6BdFMW5wFyAESNGdGrQe/Fzzzu3k197FU2PHgzI3I+lrIxDY8cRdMZo\n1PbwRXVPyYfrKCDtwFJe7pxBNkfOFa6SfGEXX0zyq6802zbplZcJv+SSFifWmPPyQKEg7sEHKHn1\nNayVlc6Ik17fLfBZ6ORER2V3YxU8/LDzWFO/yeDRowkeLVXOwvHGFNROV5jBtYbCFvuiefkh2DwP\nxj8qzehz7VIODWvjtpCG5Q5NR48SYJdfkPFNa2bi3RG/zPAFQVAjGfuvRVF0rA4VC4KQaD+fCPh3\nqPIzoig6i0eok5M99FdUMTGkfPgBya+8gqDRkPLuO6R+/rnH9fGPSyF3zhl1M9jq6jz2a5YsaTTJ\nyz3Erm7TJgpmPs6hM8c0+1ZgOnaMsvffR52c7KxPay4oxFJURPjl09GkpnaJ4uPHG1WMD2PtQ3LY\nJxVShSrC2zlQij7+7/54Cv6ZB3/OkfaLdkk/a9tW5i75Dc+QYf2uXd7dsNkoee01Z31ema7BkSNH\nUCqVxMX5Vy7DH1E6AvAJsF8URfdv2M+AQ2P3RqDrhbC4UTpnDsYDBwAIu+B8r/OhEyY4Z+6hkyej\nTpQW7Rwx1w53gKWsebeUKS+PA8NHeB13z+61mUzO44YDruia6h9+pHrRImmmXlPjdQ93il96GQCb\nQY/arrqn374d0WRCnZzcbD9PVBSBgU4D70iGipg+vWUXZ/4KAeFSdm17GHGL9HPaB65jh5ZJP7Uh\n0k+9faCva9tcKeycc+i3cwf99+1FCAjAlHXEq03Nr79R/vEnzu+KTOdTWlrKnXfeyT333OP3/Bh/\nzPDHANcDkwRB2GH/dyHwEnCOIAiHgMn2/S6JaLFQ/rErFj32gQdafG36r7+Q/vtvKO2zRl+ZuQ6K\nX36Fw2dPRrdypc/zlhJpsKj44gsOnDYYs716kfGAtIjkEP5yYK1sOrnGUUUp/uGHUcVLBr/4eclt\nFTBgQJPXnvDY/eUhk84m45/NxP33keavsRghaxUMmt5oDH6LufA1eLIEBl/t46T93iZ7slxtMbRR\n4kOh1SIoFGhSUzFmexp80WKh6Lnn2nTfroIoinyz6RgVdabmG3dh9Hq9Myxz8uTJnHvuuTz99NN+\nf44/onTW07gL9Oz23v94oFvnKjKg7dunVW4OVUwMqpgYyaeu1ToVF31R8dlngGcMvDuWokLEtF6U\n2Gdb9Vu3Ej5lCvrdexACA50Lr9q+fTAeOoyltBRteprHPay1teTMuJqk559DFG2oU1IInzrVy13k\nrvdyMhMydkzLG9fa4+aT2q5f70ShAIVW2r5hMfz2CJTZ3+T09u+QSQchCVK8fvY6SB/f5scFDhtK\n9Q8/OtdvQArNdSxam3Jy2nzvzmRzdgWPL9rNtmOVTDk1kfu+3c6fj04iPLD1CqSdibUZ96y/kKUV\nAHOupDsS++CD9Gik+EVzCIKAMiwMW61vY+7ub6/+9RfndvillzpuQM3y5WSe5qpUb62uxnDwoOTf\n1+sJyMgAcKboH7vRuypV3YaNmLKyKP3gA6xl5c5i5IIgkPbzYpRRUYSed55fRLu6Mz0+/pjkd95u\nvqE7tfags/bE4PsifQKc4pa/oSuG+gqwmmCAPYqq7KCvK73J2eCswuVO+IUXIppM1G/b5jzmmHho\nMzKwFBf7rAPQ1dl6TIqKUwjw3urD1Bgs/JXlWzZDRpZWAOxJSIJA9K23+KwX2lIUoaFYa3U+zx0+\n5xzntvnoMVTx8SS/PofAoUNJfO5Zcq64kurvf/C4xlZdjTnPFaGR/M7bGA8cRJvRt9E+6NatA0DT\nM5X6f/5xRhUBBGRk0HfD+pNKN6cxWjWzd+A0+P6tQgRIsfbZf4KxFvK3wyv2N7cIe/BAfQuF+r6+\nHMz18N9sCHKFDAeccgog5WCETpxI1fffo9uwQTp36iCMBw9izs9H2Y3e/OqMFj5dnwPAd1tcIdXf\nbcnjvIEJfL4xh/MHJZAYfvwnN7UGM7UGC0kRXWtiJc/wkRZLlWFh7TL2AIrQEKeMrjuizYaloNDj\nWMSVVxA0fDiCQoGgVBJx1VVe15W+9TaiyZUgpYqMJHj06aiiooi68QYErdYjLl+0WNBv3w5Iei7W\n8nJU0Z55ArKxbwc19v/DsA5QFg2Jg1uWwbgHoSbP7YTdFbfjm+bvIYqSsQco9UweEoKCQKXCVqvD\nqquj8MmnqP19KQCa1F4A3U5w7YEFOyjTeScQ5lbUk1epZ/aSfcyY+zdnvbKa1ZlNL3y3VQa91mCm\npMY7vyG7rI4ynRGrzb+R5u2Va5cNPmCtqvZIrmorxkOHqdu40Vkqz4H7IBB67rkAXusEjgpG4KpT\nCqBbvx6AmLv+7dFek5aGaDQ6NdwBcu+4E1O2VJij5vffsVRWoozyY4LQyU5tASjtWbIdxakNJJMd\n0UBVR8HcTOJUnZsrI3eTxylBEFBoNJTPncvBEZ4RYpq0XkDrkgY7G4PZypqDpVxzume0VHyYlkMl\nOnblSWsTR8vrOVZRz6cbfBemBwgICKC8vLxNxjS7rI6iGgM2UcRqEzlSqqOy3rWAbLT4zzcviiLl\n5eUEtKPetOzSAaxVlSgiwptv2AyiPdHFsH+/R9ijo9pR0quvEDR8OOaiIsIvucTjWlW8KxE59v77\nCOjfj4L/Pup080TfdptHe01P6YtuOnrMGXJZZ39FB5wDgSqqW2jWdW1qi+GLi6FMCtttd4ROc0z7\nEDa+A9f9ILmP+kyGwyvAUA3qJv7Y690M/v4lMNYz2qxhIpaDoCFDAMh/4EG0/fujTUvz2a4rkVWq\nw2SxMaZ3DBMyYrn9q60Ea5SkRgVTXGPk7m+2ebQP1vg2dUaLleTkZPLz82lLpn9xpV76eQzCAlTU\nGCxoVAqsVhtWEUxlaoK1/jOzAQEBpLQjWVI2+IC5pMQvX/KoG2+g4osvPZJ4rFVVFD71P0AqjKFO\nSiLtuwVe1wpKJYGDB2OprESm0Q5IAAAgAElEQVSh0RB+ySUU/FeSHhK0Wq9FVkeGrLmgoMk+nczx\n9n5j2eMuY388GHK19M/B4KtdBj/Up0KJROFO6Wd0H0mnRxQbHZwChw8n+pZbCBwy2CkJDZIQYHco\n63m0XBq8esUEMTApnJyXpmCy2PhgTRabc7wj5crrvF0/z/6yj0/WSzP/mBAtW56c3OLnl9QaKKs1\ncdsXf3qdOyM9mmMV9eRX6bltXBpPTOk6IdCySwewFJegimviD6mFRFw1A4DK//s/Z83Uwqdnod+6\nFWg+9j312/n0/v03537ym29K1w0c6NXW4YKq+OpLQPLfO0h8wVWxyz1jWKaN6NwyXW9pXj3V7wTY\n3z53fA22JlwEi+6QfiaPkAaHet8hwsFnnkHcQw8ROmkiqqgoBJWK+C4iKbDuYClma/N6UXmVksHv\nEeWSs9aoFNw1sbdz/8zeLndmQZW3O8xh7AHKdEZu/vwfLC149vdb8xj1/EoufFsy9sN6RjAwyTVo\nFtcayK+SZv4Hin0HcXQWJ73Bt9XXY6ut9XCptBVHYlTdxr/IOu986jZuRG+XPAVQNRBaa4ggCB6+\nfa09DDPy2mu82ipCpGxM4779WKuqnFm38Y/PJOKyS0n96kuCx42TDb4/MLipWfYYdfyfr7Ubkw1v\nwjNRTRt9gGR7noBDBsJOj3nzSH7zTXp++ilBw4Z6nIu43JVpXLtmTXt73CbWHyrjhk838/7qrGbb\nlutMaFQKQhu4S9RKBeMzYhmQGMbzl54KgEohUFRjcC6gVtSZyCmrQ9Hg5WdVZgmZRbX27WKKawz8\nsbfIabwBTBYbDy/c6XHdjWf24tKhrjfp0/76ncsPrSY5IpB9BTUtGkSOFye8wTcXFpJz3XWNaoU4\nXCJqP9R0bZgJW/7551iKixHU6jZVldKmp9Fvm5R81RD3iCJzcYlzwc2xSBs0ciQ95831LLwh03qs\nZpemTUj7JwVtQhPkuV9b6N3G6DaTTLMnaG35zKNJyLixhJ1/ns9HKIKCiH9KKvBT+uZbbe5qY+RW\n1HPq08vILGpcDmS7Pab+cGnzs+LyOhPRwRqfUWdf3DyK3+4dS1pMMDkvTWHWJQOx2kTunb+dzzZk\nM/qFlUx4bQ02EfrGhXjd12SxcfPnW5jy9npu/2orV8/923n+SJl331IigzjnlHjSYoJJi9Ryx56f\nuWXvr1w2JJEynZGNWV2n9vUJb/APT5yEfstWCv/nO03ZEVGjTvaDwQ/y9LPX2TN4ey//o81VpRRB\nQc22sRQXOUPqlJHtjzaScWPbF9LPsx6Be/7pnD7ED/Lcr8qF1S/Cpo+kf2tegsoc6dzln0FMX+mt\noGB7qx4TccUVgLTuI5rNfui4i593FlBrtPDt5txG2yzaIf0tmloQ2VJRZyIquPHJjPtAkGyPhf91\ndyGzl+zD5DbjvmmMtHanVkrty3VGSu2hno6Qz2MVrsXuA/Y3gBkjXRXPekQGkhodzOqHJzD7F5cm\n0cREqX/ubwidzQlv8B0otFqfx01Og9/+xc3G4vhbKpfcWjS9JX+luajINcP3Q3jpSU/BDpgzQDKq\ny5+G2P4w8QmXL/14IwhwgZt0dtUxWPsS/P5f6d+aFyHPPhhFpoJCCUOvlwaBVoQaKjQaNGlp6Fau\npPDJp/z6Ef7xsZDqjs0mkmePeKnWNz3YiKJIdlkdcaG+/6YbkhjReGTT5AGSGuXAJOn/tlxnorhB\nXL1G5fq7PlBUi0ohkBLpmtzFhGipXbUKQ2YmsaWuHIqk/dsQBCiq9h1OW1xjoNxHHkFHckIb/Opf\nfnVuC8G+Z8rmvHwErdZvRrn/7l3037eXXj9873p2B0kQp/+0CICi/z1N/sOS+JcqUg7DbDdHN0ox\n92tfkvRsLpvX8aGYzXH6HfCEXcunYJv3+b/fB4UKotKl/ag0sOhd+j8txGaUjFP14sXtTvJxUFRt\nYM0BuzBgIyJnDlcKQLXe4rONg4JqA9lldYzPaNnfbHIT2a5xYQF8fevpfHKjlJvw/G/72Vfg6XYy\nWWwU2GfpB4tr6R0b4hwgBiaFIVgt5N11N9nTJJmUHTF9pOtWLic2REt+lR6d0cKDC3ZQWusy8Fd9\n9BfDn1vht99zS+iWBt9cUIClogLDwYMYDhzkoe92Mned50KPOT/fo8BF3dp11G/Z0vBWmPPzUScl\n+S0DVVCrERQKlA38+R2BoHYJRIn2akYN67DKtBJRhCNrPI8lnNopXfFCHQjBcbDpQ+9zZQeh3wWu\npLDIXtJPh6vHnU0fwcpnfT5CrHe5H3Sr17S5q6LVyqEJEyn/+GOe+3UfCsERp+579u5wlUQHa6hp\nZoZfaR80ElsoWxAaoGbFg2dx4Dlv2XOAMX1iiA5xvS28usw7BPepn/bw6Pe7yCyqJSMhlIn94/h7\n5tn8eu84jEfcVEhVKnq9/irFI8dj2LOHQTFa/j5SzoJ/cvlxe77TTomiSI49tLTeZGVHbhVfbzrq\nHPQ6im5p8A9POptDZ40n54oryZ46lZjP3+OF3zxTyc1uGajxTzwBeKpiAtQsX07tsmWoEvy/GNdw\nAbejUKe6Mg1T3n+/UdeVTAvZ8omkS586FibMhPGPdf7s3p1eTWgAJbiE94i055VUumWY1hTCqucl\nN1AjZRPjHnHJROvWrm3V7NNSWUnBzMexVldTv3MnlqIiSl6bwy+7CrGJcFpKRKPGfHN2OUqFwOQB\n8c0afMf5sICWK2L2iQtFq1Jy7yRp9v3x9cNZepun6um3t0tVzRwupTvGp/P1racDsDKzhAVbcsmr\n1NMvXlroTQiXXEXu2e7atF6cMao/w26YjrWykinWAvIq9SzcIq1dFFYb+GVXgcebztqDpUx7bwNP\nLNrD0r2teyNrLd3S4ANgsTgLcU878qfXa5ujeHfajz8Qdf11aHr3xnjE9RYgms3k/+deAAL6+V8w\n6ngZ/N5LlxJx5ZVEzLiK0EkTj8szT2h2LZR89td9DxMeg4kzO7tHnlw2D+7ZAo/7SLhLc6vJG5kK\nSg2UuEWnLf+fVCPXgY9KWhHTL6P/3j1oMzKoWrCAzAGneFRns+p0lL7zLjajt++54tPPqF60iKof\nfuTTxdKagk0lhU2+cOmphAVKmai+yKvSEx+qJSUykFqjhXpT424dh0FuiwTyfZMz+PXesYzI3Ig4\nZZKHLPTo9GhuGtPLuT/zggGM6RPDJYM9Azr6JTSoFexevU4pfV6Hsu3EBA0BaoUz3POXXYXc8812\nhj+3wnnJXV+7XHRHyzwr4fmb7mvwGxCk9JyJOAqRKKOlwiTa9HRMh10G36FRE3n99cTe+x+/90c4\nTvLDgiCQ+MxsEmfNOi7PO6GxWqBotyRXrO5aKodOlGopCkcTDPfthAf2waSnpDq4PU/3bBc3wBVS\nCq5KWg52+hZkE5RKj+xb/U5X3Hnl/31N2XvvUTl/PlWLfqLw6VnUb92KfudOyufNAyQDmLlfSjxU\nWCz00Nq45vSeFFUbOFyiI6tB2OXegmp+3JZPQngA/ROl5+4vbDx8s9C+CBoe1HqDr1QIDEwKp3a5\nlEBXu2KFx/kJ/bxLCj598Ske+/0TQj323Q2+MVPyNDiDJ2prMJhb7qaZs/wgNj8LrrnT7Qy+r5kF\nQGl+KbvyXGp/pmO5CBoNqhjJp63pnY4pNxfDgYPY6uspfEKKOY66/roWhT62FseaQMCpXcT/K9M8\n+VvBXAc9R3d2T1pGZC8IT4azHoaJPjJlYzKk9YhZ4fDDbdKM353tX8P8a6RF6gYkzXmN4DPPAEC/\nZ6/zuCOj23wsl8KZM6lasICj115Hjj3LHKQSnuEmlxE8/6hUjD3G7iffeNhTr/6KD/8CICpYQ4bd\nXTL9g7+46+utlNQYqDGYOe+NdcxbJ/nKl+yS3m6igtqeYyIESu6YktfmeBz39dYQFawhIcwV6dPQ\nm+Bu8GPuuguQpNJRKLC0UIH0sQv60ztW8goU17qietYeLGXbMf+J2nU/g+9WpOHtIZfz0ohrAQgz\n1fHuqsPOc8asw2jS050RMtrevcFqJXvqVA4MG461ogJldLRThKwj6LN6Famff9Z8Q5mugSNuvWfb\ncia6HMluqpi7v/Ne7C0/BAd+hc8ugMX3eJxSx8fTY948UKup/OorRJuNur//ds7iDQd9awsdi0jC\nVFJCtKGGak0Q1QGhnLdTkmF+/SpJpK2q3tNHX2+S4u6VCoFYt1DL33YXMeqFlfyTXcGB4lqe/20/\nVpvI/sIarhieQqCm+eg3Y3Y2xsOHvY6LBt8TR18GXxAE/n78bD65cQTPThuEokGKrrvBd3gLBIUC\nZViYh+T0w+dKbp5TEsOYOkRyEzncRbEhWp6cIr1JXPyOJIJ402ebufHTzVz2vveA3Fa6ncE35UqL\nH8qYGNb0OZMxw6RY9FBTPZFuI77pcJZk5O1o0tO97hV23rkd2ld1YuJx8+XL+IHSTAiIkLTpTwRG\n3tr4uagGfw/bv/JqIiiVRN90E5bSUopmzebYv25yrpvpt0j6ULEPPkjoea7s3bzAKAybNjEhbzu5\nofEs6zGCYL0O0WQiRKsiOlhDQSNx6UqFQFADVUu11cIPv7mi6/Ir9RjMNkb2alqmxMGRCy7kyEUX\nS33eu5fsK67EqqvzmDi6J5mFBTSuJ3n2gHiuH+0tVWKrq0NQq+m/Z7fHcVVcHJbiEiYPkIJCEuyF\nWPrEhfDaFYPZ/tQ5PDttEPdP7svE/nHO2P4ynRGjxcrqAy71Tn+5ebqdwa/7U/K9pyxejN5iQ2uX\n/x1cdphgtTTy2urrMefno+nt+lJrfRh8RUio1zGZk5jqPGmxsytF5bQHZRNiuOn2BX61mzvT4h0j\nH3LWOACqvvvO520iLp9Oyltv0n/vHi65+CWKgqLAZiPIYiQ3JI6SIOnv0+HaCFArmb/5GLn27FX3\nKKBRPoz4Tft+5T/zHibcKBnoV5ZJPvK4sNZHo5W8+hqG3bvRb9+O1W1Wbil3SR+E2Wf4DTV6msJS\nXoEiNBRB5XmNIiwU3apVvHNJH7Y+ORmb/bMGa5WolQoigzWEB6q5f3IGUcEa+saHkhgeQO/YYPbk\nS2sYGqVkohsLZ20t3c7gW2tqUISGUmv/omoSJS346zP/IGnRl/ywNQ/jESkUTdu7j/O6hvLCypgY\ngkZ6FoKQOckx1nZeNu3x5Mz/wHnPw5VfQb8LXcerjno19fVm7I6jIDoKBWjULMg4mxU9hrMjpg/L\nhpxPtUZ6w3VkgjtkBj5Ym4XFauOqjySdmodSTFydLA20P9/jCj09rUwKtBhSKrllftkl6QjFhLTO\n4FtraxG0kgfAWlGOpbDQmbPiHqmjVip4dtogFt3dMreefu9eqhct8ilS6Fi4NSxbRnSIlotOS+Ty\n4Sk8eE6/Ru83pk8MWaV1LN8nRVA9cI7kBqptJLqptXQ7g2+rrUURGsKSndLCTUq6SxIhad9WHlq4\nk2/ul5JKtH16e1wbfYckH6tJTSVj/Z+EjBt3nHot0y0w1riUKU8UrvgcJjwOp/8brvkOZnwD5z4n\nRSGdcglc+CoMu1Fq++4IsHj6tpWRkQSPGeM0jn8lDOTWs//rPO9YI1u8owCzVSQqKZY5w69m5tg7\nuffa8U6DX/HpZ2RfdRX3Zy0lxFTPN5uO0eeJ39mcU0G0vprJ7z5O/n33A9Brz98MLj0EgNruL7+2\n7gD9K3Kcz22twS998y3q1kr1ngsefQxrVRVBw6Q4/KrvFlL2wQfOBenrR6fSJ65lb/86u7Jo7H/u\n8TqX9PzzABj27gEgSKPitSsGe6xTNMQh6/Dh2ixSo4NIi5F+f83JTbSUblcAxarToQwJZU9+NbGh\nWsb2icGRchVkNpCoK+PMPCmMrOGoG/fA/cTef1+r9EVkTiKMtSeewR94adPng6LgkrddInGHV0B/\nlzqrIAj0/ORjAOYs+IsPt5RgVqr498SHSDVVYb+K5fuLCQtQMW1oMm+uOMS/zuzFlNMSGTFzKhUX\nfUj14sUAnAfsUUezoudI5zMmVEnG3SFkmH//A7wELLzsfpJVkhHusX8Lb+zfwgXTpISxmJDWRehU\nfv211zFLaSnK8HBqfpNqUASNHEnQiNa99ZtyjqJOTvYpjqgMD0fbrx+WipZH2Tw/7VTOenU1AMNT\nIwkLlEz0ST7DD+VQiY4BiWEIgkD49MsAUIlW4vXSL/eV8bd5+dTArjnvJnKWW1HP2JdX8ej3u7za\nypxkGGog4AQz+K1l8zz47gZY552JWx8cjtm+LpATnsja2AGuDNIqPQOTwplyaiIAV46Q1CTjeqdK\nBdTdGJXg6V6dGGyXBQkP9yjkc8WPb6Is9U4OG9MnGpWyedPVXJZw8NixqBITnfuGfS2v9CXabJiO\nHqVmyZJGS0eC9Jms1S0vDt8zOogLT5Xc1KenRTmzif01w+92Bt+qq0UREkJWqc6pZZ04ezbGyRcQ\nbajhxQ0fAVAQFON17cbDZV4xwD9uyyevUs/6BsdlTjJE0e7SOUkX8q+1i/0dWQ37FsMqb62dvMp6\n0mODyXz2fJY/IGX1/mH3NRdVG0iMCKBvfCg5L03hFHsFKEEQCJ3omQE+TJfvsT8kUhpEzPn5XvIn\nDZmYHs5TF53SZBsHDn0p7BO/hGefcZ7rtfA7Yu76NwGDXNXkzMUtlzU4cuEUss6TtHmaKv6uDA/H\nVl3d6Hlf/O+igbxw6alMH5ZCXKgWhQCfbcj2i8hatzL4fx4qpfJYIcbQCAxmG33sBl9Qqci4/V8e\nbXMVwc7Zh4NrPt7ENR9vAqTFo5k/7mLxTunL15x+h8wJjrEWEE88l05L6XsODLZXVouzG8EGJRIP\nFuvIiAslQK2kb3wolw1NZt3BUoprDBTXGkkM9y1D7KjY1uNjyTUUvG45W/qV8oKQyXszBiPoXREz\n1T//3GQ3507tS/+Elv0fOeLj42c+RsbffxFx+eXEPfII4ZdPJ2DQIASFgsSnn6b3ihUoY2Kw1dQ2\nc0cJ0Wr1WOhtCmVEONaq1hn8hPAArjm9JyqlgriwAB4+rx+bsitIm/kb7632ziloDd3K4N/60XqC\ndFUUB0sLSO7VagIbZLTWq7Q88v0up7JeQ8a8tIr5m3M5Uip9KWqNFgzm5gsvyJyg2F2BBLUsvvuE\n5JzZUqGXc+0z4axVzlMGs5Wc8jr6uckKTB2ajNFi4/QXVmK1iSSG+5ajCBo2jIx/NhMydgxxjz0K\nQOnLLzN00ceMr83BWqtD21eKqKvbKCUZqRIkt0bvZUuJf+pJQs+Vcmaamk03xOFqUQQHo4yIQBAE\nom+5maTnnnNmwgtqNZqUZClJqqZxOQd3dOvWeeynfPB+o22VUdFYSks5NH5Ci/vdkGtHudYiX112\noF3unW5l8PtXSmFjO0XpS9fHzeB7yRvb96/4SErbdhdjsjZIYhiRKsUKHy2vp7refFz1qWW6CAa7\nnzXgJC4gExIHk56E9EkQkQo/3AK/PQLvn8GholpE0VNHxiEF4KCxGT6AMlS6Lvpf/yLodJfmj7mg\nAOOhQyhjYhACArDV1KBOSaHHRx8Sc9ddqHv2JOraa4m8VsqoL3njDSwVTRdTceCY4bck+VEZFoat\ntobyTz7BcOBgk23z/n2Xx35Dl5U7mh4pAFiKixFNviefzdFQMyinHQJr3crgT8zdTq06kLfr4xnZ\nK5KIBloavb6d79x2fDEPl0hCTQVuZcayG9SlvGO8FL75+cYcRr2wgscX7aaiztRopRqZExDHDD9Q\nLiCDQgHDbpC2N8+Fkn1UHZLS/dNjXZOshuGFcaENDL6u1GeZRSHAdV3R009jKSpCUKtR2ov3BAzo\nT0C/fsTe+x/nRM4R0163dh2l77zT7Ecw7NvHsZtvkT5OC7SyFGGhmPLzKXn1NbKnTqXg0cecbxtN\nkTDLd+lUB+oUVylESyveThpy9SjXfbJPBoNvqa7mzMLdbI3rh1GlYXiq96t34JAhpLz3Lj2/+IKX\np5/mce5FN738nDLpVW/2JQOZc8VgJg+IIzxQzfzNxzBabMzfnMuwZ5cz+sWVHfuhZLoOssH3ZMTN\nHrsH10iTqQS3WbxW5allE+0eKll6AJY9Dp9fDDZPV6kqxjugQhke4TTq2v7ecuWani6Dpwhs3oAX\nPf+Cq+xnC2b4qqhozEePOferFy92DhgO9Lv3UPbBB879tEU/EjljBk0ROGyoc9sh2d4WXrzsNPbO\nliQsimraPhHtNga/aO7HhJr1LOwrvT6515R0J/Tsswk+fZQzRRqkEmUrM11FCo7a07pTIgOZPjwF\nQRAa9YvdO791haBlmmfbsUo+XJuFzSay+kAJFmvHVvlpEXq7SyfwJHbpuNNgLSNBlKJxGmrNZL3g\nytT1KCr+3ihJsM1U61V1K/bee+kx9yPnfuDQocTPfMypfRU0YiQNUQQG0m+H9LfoK9zaHauuDv12\n199tS1w67m8d7tT/4ypcn3PFFZS+9TYAIZPPJmDAgGbvq9BonJ4Hh3RyWwnWqgjSKD3KJLaWbmPw\ndT8tYkPiII5ESJm1F56a2GT7tJhgZ5ba3gJpldyRrOHQ8XAfFF6/cjC++HlnAUfLO7YowcnGjI/+\n5qXfM3l5WSY3ffZPh1f5aRHyDL9RDtqS6SFIQl4N18qUbsqRAWr7jN/U4O9l32KpsIwddXw8IWe5\nirWkfvM1qqgo4h55hOS33yL49FE++6EICABBoHrJkib7ayksAJtrEqEICWmitYStVufzuOlYrs/j\nrZFUDxg0CG3fPlR+M7/5xs0QolXxyfpsr5oCLaXLGnzRasV4JBtrbS3mkhLE8nJ2x/QmUK3krgm9\nPWcTjZBkr1Z/qV1e9OFzJQ2LY3aD7y6FetmwFM5IjyY9xns2MP7VNRRW672Oy7QNEWlR/KO1kr55\nQ7ncTsFQBaqALlf4pM5o8ajzcLzIrahnbvSjbOxxG2ttgxko5LD/Ud9ZqJ/+a4RT+heAas84e1bO\nhh9vhTzPmtIJzz5Dj3nznIOIOj6OsHObUbAVRSyFhRj27Wu0iXt5UwBVfPMlTKPvuN3n8aqFC7FU\nVnLorPHN3qMxBJWK8KlTMezdy8ExYzEXF3sodLYGhyLwd//4Hoiao8tKK5S8/joVn3yKtn9/SQ4B\nOByezII7RnNaSsteu3VGT9/hoGRJ6GnrUWk2F9Fg9fub207HJsJl729gZ55n7GxBlaHRsDOZ1qFW\nKjBbXf83Zbq2v6K2mczfwGqCgdOkfX1ll4zQuebjTezMrSI+TMtD5/QjMljDOae0rgaz0WJFo1R4\nR7I1wUXvrKdaL731jlfs5DbVbwS+lQH9L4Kr/s9DUXRS/3gm9XfrU02e75sWbIcU16ARecUVrfoc\n7tRv307AKb4TsOrWSwvMEVdeiTIq0iOzvjECMjIYkLkfS0UFtvp6NCkpHLvlVuo2bKD4xRc96tYC\nRNmjhlpK0CjprcVaXs5he4hm//37WvV/ArDgjtEMeWa5lyZ/S+myM3zdCmnB1JSTg2HfPkRB4Eh4\nknckQBP0aODn7x0bQkSQmmq9mRGpkV73EgQBpULgp7vH8PC5GbxpL9gAUFXftpAqGW8sDcJi31xx\niDeWH8RkaaMvv6YAtnzWco0kiwm+vRoW3gjf3wL7f5GSjLqg/35nrjS7L64x8t8fdnHbl1uaucLF\npiPl7C2opt+TS3l/TZbHOatN5MZPN7P6QInXdbvyqjzWtP60ueW4ZP7iWSfXFw1n+A7KDrW4743R\nZ+1aAIqffc5n3Ly5qIiKz6SiQwlPPUnc/fe36v6qqCg0KVIoZeKLLwAu7X+A8KmX0H//PgIH+3YB\nN4avwcmZCdwKIoI0xIVqnSJr32w6xop93vITjdG1DL7VSsGjj2IpLXWmQ1stVsrefgddTCJGTUCr\nRJNevMwzGStQo3S+Et12VuOyr4IgcM+kvkwbmszaRyYA8PxvLdfZkGmclfuLfRr2t1YeIuPJ31md\n6W2AmmXx3fDL/VBxpGXt9W5x3Hu+hwXXQk0+hCU1fk0bMVtt6IxSDojFauP//j6K0dKyBD9zI4vZ\nbyw/yOcbspu8tqLOxFVz/2bK21L9iFeXSRWq1hwooddjv7K3oJq1B0u56TNpUbK01shLv2dSUKXn\nknc3eNzrs5tHwyg3l0dpcwbfPsPvfxGMe9h1vKzp+PaWoI53Fac5OOp0rO6FTCwWDp8juYQCBp+G\noG59zVuPZ8XFEXzmmZgLXAXjFcEhrZ6Vg+TWSfnwA49j1lZKLjioMZj5cVs+H6zJ4vFFu7nrm23N\nX2SnSxl807Fcqhf/zKFxZ2HKkmYkCos009gUkIhNpEWiSQ5CA9R8cO0wj2MfXT+cJ6cMcFahaY6I\nQGmAOFJa55WwJdN6bvmi6Rnqb7sLW39Tq302uuZFV7RNUxh8/KEVbJeSjfzMXV9vY9DTyzj3jbW8\nveowT/60h/dXZzV/ITQaLPDWykPMWtK4Dzu/Su9cp2rIl39JyYs/bnPNwq02kScW7ebDtVm8sdzb\nKPeJC4HTrnIdKPVd3tCJrhiCYmDG11IilwM/GPyGWIpcC/6mY8fA7hvvNb/9C6QAmgYS68qotmdi\nh06Y4LGeYK1uWWZvQ6YNkQJXXl4qRf205s24Sxl8m95zYVQXJ32wKk0w7w2+jLMyYlt9z8mnxDMq\nLYrb7TP6jPhQbh2X7hFd0BShbmFoOU1E6yzdU9iuhIiTgZZEFrTJn+8w1LsXwh9PSi4bqxkW/RuK\n93q392XwAaL7+D7eDhyFLA4W63h7peTSOFDUvGbLl3/lMPn1dU22ySrVMeePAx6Z4cU1Bsa8tIpp\n723weY0j2MFdLHDk8ys4Wi4NEAu3uvzvU4ckceC586Wi3SkjYFa1VCf3wO9Nd95Y61IdFQS4byeM\nfUB6i9K3vyB3pJv/vHL+t85th7xyyIQJLfLbt4SQ8Z6Ltcqw9onrpX71JRH2tYvWqGi6c+VIKSdh\ntGIfI4XWhXp2uMEXBJ+CITwAACAASURBVOF8QRAOCIJwWBCEx5psbLM5NTWO9h/OLxFSAsaalGFc\nfHpvvrjJOz63OSoMpbx3fR8ev7D5mFlfKBQC39wqpYJnl9Yxb90RnzP9O/9vGxNfW9OmZ5wMmK02\nzp4j+V/H9XUl3jQcxNcdKmt9/U5zHYTYZ07bv4Il90l+5p3fwI93uNod2wQ5Gxo3+IOmt+65bWTp\n3qIm/a7VejP/W+waqI68cCHPTB3IhH6ev6uz56zlnVWHKXTLCG9qMDGYrU5tKUcGOkjunwPF3tdN\nG5LslVxF2llQtAtWPuPV3omx1lN1NLIX9Borbf/zcePXtZDwaVOd25XffIOlshLRaqXg4UcASJg9\nq93PcNCwNGp7a1RrevYk8jppwLKUljbT2jfpMcEoFQLfap5jobaJ/wcfdKjBFwRBCbwHXACcAlwt\nCEKT2qaHr7yD/949jzv7X82mBKnp7ph0Xr1icKt9Z1ablcnfT+byJZcDkFubyxtb38Amtm5xMMae\nQv7w9zt5/rf9rDvo+R/l7pN1+GtPNtYfKmsydPWfbJff/KYxvZzbc68fzkPnuEL6rDaR2tb+DrP/\nhFC3vIw930Ot3TVkcwt/+/Rc+PxCDLVSXyyi9PX/Q3M23LcLwprO7fAnt365hU1Hyn2e+2bTMY99\nhULghjN68flNo3wW2S6xJ+KIosisn10DxQuXSmtYjrqoJTVGZ4nB5njqolMY7+uNOsb+f/XnHLvC\nqA98FZLpNQ7CkmHfz6BrwzqNG86yinYsxcXo1q51+sRVcf4rQu9wwYRMmkTM3XcTfvHF7b6nJi0N\ngOIXXmzT9RFBGsb0cU2aUoNb/vfS0TP8UcBhURSPiKJoAr4FpjbW2CbAM7m/sjtfetXJjEplxgWz\neOjZOxq7pEkK6qTFlgpDBf8U/cNjfz7Gp3s+5XBV6yRGo+2vwY548Sq9Z8ROvVv4Z0te109Ervtk\nk3MG74tSu6vmq1tGMal/PO9dM4yLBycRoFY6tdMd6qfVrYnLr8iG+jLPYtxWU5ORJOXFUgzzNqQ3\nyAWcLxUv7wAcyX9vzRjidW5PQQ3L9hZ5Lc66++4bGvgvbh5FarRn0s+09zZQVS/N0o/Y3Yp7Zp/H\nNaf35NvbR/P21VJ6/2M/7vJ4G2hM7CwjPoRbxqb5Dv3LOA8U9j4t/JfP671m+AAqLZx2pfR28Frf\n5t1CTaDu0YPYBx4g+c03ACh7/wPy7robgB4ffdimRdXGEBQKMv7ZTMrbbxH7n3sQNK2rtOULhUaD\nOrUn1vJy74Xbxt4+GxDo9uI1I7jrLNomA+4ZAnn2Y04EQbhdEIQtgiBsKYqE6uT1nK+QNOvfu2YY\ny5+ZyvDUtmU/Vhpc/sJtxdswWKQvu87Uuiy1yAYibc//6mlM6tyUODOL2rYQ051xuLjqTdZGI1Ac\n6eCn2nMhppyWyDt2QzSpfxyf3TSSh8+TEuMqWxMCm2dPfZ/4uOfxI2ukn9V5kpaL0fV/nrzpWepF\nLfG3/8AfqQ+zVpeMzSb6VEk1WqztUk/Vm6xcMVxK6gO4eHASW56cTIhWxdebjnLHV1t5ZOFOft1V\n6DT8hdUGBiaFceMZqTwzdZDH/Yb2jOSPB87yes6I51awy547Eh2sIUQrGeXR6dGk21UtN2aVU603\nM6aP1JezB/ieCf/xQBNJRkFR8KD9+39kDZh8LA6bfBh8gHCXHk57FnAFQSDmjtsJGCS9wdT+8Yfz\nXEOfuz9QhoY2K+fQWuIfk7zbxiP2yLKiPVLY6ks9Ycc3zV+vdk0KLu3bcjPe6Yu2oijOFUVxhCiK\nI/R2OYuXA94lZ/Z4ppyW2Kq4+4Z8uudT53ZRfRFKQRoWV+eubtV9FAqBQLVrSC3TmRg8+w/nTLTe\n5DJyDn39kwm9Wx2BU5/+w2eb8joTKoXgkd3sQBAEJvaLc75Jtcrg52+VZvc9z/A8Xm9flDTppIXb\nas9koCDBSEpiIgUZ12GxSYvFN3y6mcs/cCkkWm0io19YyYPf7Wx5f+wYzFZeW3aAohoDUSEa4sIC\n+PO/E3n+0kHEhGgZ2jPC+V35aUcBd3+zjXvnb8dqEymuMZAUEcjsqYOYNjTZ695alZIHJmd4HLPY\nRJ78SSqWveGxSR7nEhrM5B+YnMGXN4/ikXP7s+nxs53HZ118ikfuSaOExMGNv4DNAi8kwvc3uwZU\nUYT6SggI977OPc9hzw/NP6cZVLGeImwNXT1dGU3PngCY8wukqKcPx0hF5AF2f9/0xVYL/6v+n3M3\nocT3Ar0vOtrg5wNuwzop9mO+O2OfSM0PC4HNH8GPt8P6N10NvrkKlrQ8kWLlMSl5K0wTxtLspU6D\nvySraS0OX/z56ESW3DPWGdtfrTezx67R4+63b6zgyolMvdvnN9lnqX8eKuVYeb1zdlxWayQ6RNPk\n67bDMLXUzyw13gaJQ0DZYAZWWww9RtvbbJUiRNzQixqUCoERvaQwu2//yeXPQ2VsOep6Kyyo0lNZ\nb2bR9ka/so2yObuCd+3ViWKCpZlMj6ggZ43SsX28FSN/31PE6swSCqsNJIQ1PdHpGy+5v9JjXZpR\njvC8ALXnQmtYgJqvb3Vp0A/rGclZGbGEB6k9BuAbz+zlc4DxSdo46G0fWPb8ANu+lLbrysBYDVE+\n8lx6neWqplW400tFs7UotC7Bs6DRo+kxb2677nc8cYR3WisrXetNDmzN+ORL9qEqdqvBnbupxc/t\naIP/D9BXEIQ0QRA0wAyg0RpmiVbJOLwXGQGCEnYtgBVPw6xw2PMjHFwKW1uWUelw3wDEBcWhM+vY\nUy7NgAxWQ6tf02NCtJyaEs5lw5KZNkRK0HGURfxsQ46z3V+NLMSdyLi/4YA06F3/yWbOenU1j/4g\nfTHL60zEhPhWJHSQHBFIsEbJoeIWutz2/Qx5myHWPtt1D6usK4G4/oAg/UE1MPhKjZSFPSg5nAn9\nYnndLf7cMeC4h9m2NgfDPVM1JtTb7+sw2A15Z9UhqvVmL635hpw/MIFXpp/Gon+P4fLhKc3254z0\naK4e1YMl94z18M0HqJW8edUQ5l4/vPW+70s/ctux/34y7ZOp2H7e7UNi4S43jfkW+qtbQo8PPyDw\ntNOab9hFUIaFgeL/2TvvMCmqrA+/t3tyzgmGHIYcJQmiBMmiopgI5jWtn2vAsO4a113DqquiAqKi\nmMCsKCggigpIZkgShgFmgMk5h/r+uF3V1WmmJzGBep9nnqmuuhW6q/vUveee8zsmqnKy5UNSz7Gf\n4ZfnXe+c50K6wg2a1OArilIJ3AWsAQ4AKxRFcRIYLfHXfUlKis7weEQYmWqi1dd/tTYsqF1dMTk/\nGYDnxz7PjX2t2t5mYaaoooj04vpFCnh7mLUQz8yiclJyivlmtzUT73ReKRsP1y/cqrWiGnx/L9mz\n/EX3/ldsS+GtjUmcyi2p1eALIYgM9Obd35O11PEaUX2dvS16ODeshom6MDXfUPALl4lAdun+XpOt\nRbofnmIbsnvTu3JeQJ930fufqzl4Jt/tPAG9wY8McOytd4+y9XG/bkkQVDWcaksTMZkEs8+LJ9jP\nkwFuaEuZTIJ/X96ffu0d3R6XDmrHxX1iaj2GA/76KB4hO2Lrn5a9+85u+NKLG945in74IaIfeVgq\nabYihNkM1dVkvbUU5deXHAc765+GChcj3RxLlvWCY3DLeudtXNDkPnxFUb5TFKWHoihdFUX5V01t\nPU2e3DFQlg+77fRaPgsM4IUwy4StPgojw5JsUJQFh9c6PVZSrpwM6RLchRldZzC6nYwDntFVhlVN\n+HQCP52omy9fJdTia84oKGNzkmO5tftX7nY5gqiL0WgtqOUj1YSQvam2PbenVx3g4JkC2wIZLki2\nJAC9saGWbNSiTDhkifToaikxFxApw/9UvAMhIAqlMJ2U43Y6LkPma4v6Oq169D38sspqJr+8kaFP\nr2Xdgdq1S1SD/+o1gxjZNdxhe7sQq85T0jNTmdovllA/W/eKuwzt1EySzkJYffWluXBikzTisQPA\nZHa930xLDdhGMPhh8+cTNm9eg49TJ0pyGxxaqqKUl5O26iR/roxD6THVdqOrJLWcZBn26hsK7YZA\nVI2R7jY0+6StPaHe8su7Q0ijeFpN/CjSfcAb/g3Pd4Pnu8AHs5x+MEfzjmISJjoFdQIgPlAaozm9\n5mht7v7pbvot60dBed1CKT3NJjqF+/HmhqNsS5YG/6mZfVh771j8vcyk5ZexfPNx7l2xizs/3KFl\nWAJMfnkjM179tU7nUzmWWcTjX+/jrY1uasacJQ6clpFJcRY1UdVox4fZitdF1tLD1+NscteGXEus\nepCdz1nf6/QOotonFPHnd7RP/owD1XKiLLHn3S4PO7yz9K1mFpZxKK3Apm6ySm3yECD1Trw8TMwY\nEOc0q9tkEjw6rRfv3HCe5mJRk5z+Mb03gT7u68D4eJq5Zlg8XSP9eXPOELf3axT+zzKhXZxlLXQy\n5r6a94m2+PHtXRmtAUWRtueF7pDRMKmI6AelzlDOETkHUzXldfinrgNZ7KJ2b06yDCNWXXD+7isQ\ntDyD72PbW9nh48OL9voVJ7dAkc5t4kTb41jeMeID4/Eyy17lPYPv4d3J79IzrCfTu0y3aZtSUHef\nWLtQX8qrqvl460ku6hnJ3JGd6BYVwN4nJhHs68nOk7l8viOVVXtO8+KPhygqq6TIMrl5up61cm98\ndyvv/p7M06sOtKgEr63JOXh7mBjTQ05EqnHkP9qF97nTw5/URya6BDpJMLJBFUC74h3b9SHx0q3T\n+QLoO4s/TlrnA9ZWD2bpuG0kzH7C5WH7tgvm4JkChj69lt+OZDG4QwhH/jWl1uu2J7uw3KbH7oyb\nx3Thop7W0MhhlodN37ggV7u45N+X92fdfRcyuW89XDMNwTdUhltufQtypE6P0wlbPf6WCetG6OGf\ndQ6usibzLb6wfscoL4KTWzFvfclmdVVmphwZXW3RAXLVwy9Ms000vOZj5+2c0PIMvrfj8PSd4ABS\nPMyM6Nie3UGO0Q3OVBJPFpykQ2AH7bWfpx9DomXv599j/s0zo5/Rtv3nj//UOfv23onW+QZ91psQ\ngoqqahtxKoDdKbkNLqKSpXMFZbUgt1BhWSXdowMIt0SjJGcV4+dlxsfTbNNDrs2HD/CSJSywrDZB\nKEvvJ4cAR+nq8/8P5n8DfmE8VGod7ncbdz03XdAdzxoE+PRGWgiYPTQeD7OJD2+RUS5qRMymo1kc\ndiJHoHImv/ZIG3v+Pq0Xb80byvAuji6gFk2eJdXm5/+AdzB41SI/4GvpwLVGg//FbdblinqGYK+6\nH5ZOwFRh6xaqzLJ0YkIsgY0l2bBkPKyYb7u/fSazl/vVt1qcwW8f6DziYMXw6ygymVjRzRJeljAd\nJlh6apYf/7G8Y7y5+00URSG7JJsIXycPBwthPtZRw470HRzNdU/BUEWtpgU4VN+yj1oB+PlQhlZ5\nC2TY4vubkpn0Us0CWSq5xeXkl1p79ZmFLSf8s6C0gkBvT61XXl5ZTbTF2H1+xyitnTs5Fb6eZjxM\nQhsNOVCaL0XRLNWTLno9kSFPO5/HAUhWYulRuowD075gyriLXLbb8sh4Ni64iKuHdeCOC7uy658T\nOfbvaVrY5qiuEST/Zxo3WmQhrlmymYkv/cKr62znBhRFYW9qHkkZRQ7x77URHeTDhDoWN2lxdBhe\nexsvPzknl3NMqpS2JvS1fuMGu27niszDUuMJMHvZzvNVZlgeAGqZzYI0SN0G+7+0PYazTGY3aXEG\nP9ZfDlX6hvflpr7WqvG7KuVEYIliMQTCxLZu5/NaSDDKD38HReGWH25h4a6FZJRkkF2abWPU7Qmx\nq250Iv+Ei5bO0fdWQ+0Mvl7kakB8CJ3C/Vj0cxIFOoM9d+kf/OOrffyZVkC6GxEpt74vizCoWZN7\nUnIblAHaGFRVK/x8KIOC0koCfTxs4r/VmPAgH0/euf485ozooLksakIIga+X2elDE4DNr8sfzB8y\nJDAPf6qqFafa8WoopcnTm55DXBt7kMY2PsyPiABvFkxOIMTPufupV6ytu+W/Px7iX6usUsV//3Iv\n01/9ldTcEtqFuN/zatXoM2hj+rlupycgSsbuL74QqlqOe7JGKkrlaGbsg9BtAtTRKwDYhAd7BVRi\nCvAj4g4ZqFKRYnEtqyOgJF1QyVFdNI5ejbSOtDiDL4Rgw+wNLJ20lLsH382r414FYGe67AlsKzzB\nGyFBpHl6c8OaG1kUGkyBScCJTaQVy+iJdSfWUalU1mjwg7xsP7CDOXWTGdW7BcLsjMM711tVPW8a\n3dnG0G1c4Gh4nl5Ve3GVPyziY6rMwxPf7OfV9XXTBGooBaUVvP3rMSqrqnl/83H++tEO5r/9BwfP\nFDhMMl6UEKlbjuLpS/vh5WGS+iv/6Sh76i7w9/LQIn8cL8Iakruw8hIUy1d4/ynr8SqqqjmSXki2\nJQnuockJ9S4JZ8/QTmEORn/JxmOs3nuayqpqPtHVGh3lJDqnTXLLT9ZMZ1839eITdPNo9olHLZWs\nw9LIR/aUOQSnd0Ga67oEGmf2Qr7lPeqkKDx8q+nx/UdE3v1XPKKirAXTPX3B7G2rN/T+ZTIqsbIc\nKkvbTg8fINw3HD9PP0zCRNdg2wIEOZVFvB4awiftranlSZ6erDtized6Zov0z6uROc6I8Y9hVNwo\n3pn0Dv0j+/Pm7jdZmri0XtcbF2IbjSKEYOOCi3h0Wi+m9o2x0QKKDfbhqzvPtwnL+3r3KTYddc+f\nOTDeOjL5sh4ZoA3h398f5Mlv97Nyewr/+HIv3yVaja/9JOsTl/S131325PZ9IUP4ti5xeZ4AHw+b\n0dDe1Dwq1R68Lunk+cqrrW1OWUNB//nVPia8+LMmwtc+tHF72vp7oHLb8h18tiPFJkHLWThmmyQg\nUmY7AyhuZs/qJ3YbkEh0Vkm3dAoje8GguXL5jZHS9eIKRZGyCUssHT1VYVRI0ysson0eUVFUZlmi\nloSwuI4UCO0Mgy3zUM93gSM/ymV7NVI3aZEGX4/ep+9jtvpElxx4X1ueGxfDPSmrbPYzCzODo137\n2DxNniyauIihMUPpHCTlSl/e8TKphXU3os7KLsaH+XHzmC54mE2M6mqdS/AwmxgQH8J/Z9vWxLxm\nyeYaz9EhzA9Ps2DBZOtkcWP1Wt3liCUD9oMtx23WRwR4M72/dMV9dMsIHp/R23mBGX22q9rDL0iD\n10fCV3fJwuJAVKC3Jvm7eu9ppr/6q1XeIC8F/MJ5LPYNwv29OPyvKZgEnNFFPn27RybCrdkrf4gd\nwhvX4Lsqs/nWRpkQ89SlfVk0dwj+3o0ruNWiaWf5rYV1rbmdiqeuk/T9Avd6ys1N+n6pABDe1SaP\ng8SVrvdRJ7QLTkNhhlQLAPjLRpj7pVQRReoA2ShnqplY3SbAFF3WrVqHIKB+cz0t/hupT/fuENSB\nQznuxb5+fenXBHu7J6YU428NZUsvTqddgHt6IrMGt+f3o5m1pqTHh/nxwKSeNhmRI7qEc/V58Xia\nTby/+TjRQa4jWBRF4Ux+KTee39mmIEVyZhGVVdV1KvvYEJIypcHfm2rrjtn26ARteWTXcNc9WzVO\nG6wFrRNXyB9S+n5ZvOTxPKKDfDQX1pp90mhrPv2idLI7TuGjPaGM7RmKp9lEZKC3jcFXQ1Y/2SZ/\nbHWdPK2NOy/qRri/F1uP57Bqj9UdcdhSVGRcQpTNCO6coN+VUis/zg3xNYA+l8Px3+U9P7MHvvgL\n3Laxaa+xoez9TD7YLEaaqz+Cj6+x5oQ4I1cnFvyCTvojvBvEWEfB5uBgqw8frCOBPpeCp+77qyad\nxtatiLpKi+/hA3QLkR9UZokc8qg9/XuH3OvQ9v0p7/Pr1b/SIaiDwzZXqBPF+nO4w39nD2DTw+Nr\nb4g0EqO720YN/WdWf566tC9T+sZoolrOyCoqp7yyWjNcy24cxlVD46msVjj/2fW8vPaQ1eXRRGQW\nljmNDKpLUXnN4McOlLLGVZVO08djgn1ILyiltKJKM95V1YpsX5zNe4nFlFdVa0Y1LsTXZQ1XgMBG\n7mn7eJq5/vzO+NhXg1Kvv47hmG0CIdw39iCN2MzXrK89WvhnVlEKuceh+yTruoSp8iFX0xyEqyIx\nnrbv1xwSQlWuruShatCdZdEGx8sqYvWgVRj8JRcvYfHExTw+8nF6h/dm87WbSZyfyA19b9DaDC+V\nyRC9wnu53bNX6RFqnQ/IKD77OjjhAV5kFJa5jLo5nSt7r7GWTNaxPSJ5dLrUf0nLL+PltYf58I8T\nJKbkNVnkziFLYRf9YObLO89n7b1uaKZUVcovftYRWTxj4HUyczppg20CneW+DescRkWVwuakLC08\ns7i80iJ5rJCpyHZqZM7A+BB2p+RSomn62Br4xiyIocfLQ/58nrikj80Izd16yQbA5Gfl/6qWk1fi\nFPV7GmBXQyAg2vY7bI8zg99tgsMqc0gIVfn5KJYi7Fy1XI4g1DDQBcfgEssDsv15tj/EOtAqDH6E\nbwQj40ZyUYeL+GT6J5id6HQ8mJnJ7pmr8Da7n76vkhCWwOAo6YPccHJDQy+3zvSMDiS3uMJlL/WU\nJWFLH/sf6OPJAN3k4T+/2seM137la52QW2Oi+tRV+QSQhtZV+KIN6x6Hf7eXw/eOo6CXpUxc7nGZ\nNagSICN7RnYJx9fTzE8H07VIm6LyKk2/JMNi8K8ZJkdx0/rFUlpRzfLNx6msqqawrJL/G9+9IW/X\nLeaP6oifl5kJvaP57u4xte9g4MiI26DfbClTUFNnJW0fZNUtV6ZRUaVdHAx+lO132J4yi1/+klfh\nH5lw0aNw5bsOzTzj40FRKDtqeY8BkXIEoeIXBgOvhQmPw+T6lUaEVmLw3SGsqgpT9rF67etp9mTZ\nlGXM7z2fbWnbNGnl/PKzU71KzdS97i3nutaqfzo22NYvnFng2Cuqr2xDbaiulYemyLKAL/U/CRtf\ndG/n7Rat9JIciBskfyTCDPmnpBHvNAaGXK/J5fp4mkmIDWTLsWytuHZxWaX2o8tUglk0dwh9LdWz\nhnYKo1+7YNYeSNP86BGB3oT6eWpFVZqChJgg9j85mXYhvoRb8jLUTFyDOnB6N1SWwMFvnW+vroI3\nRsGrg92SRm8SVN0fe90a/yg5GesKtYff9wowe8LYB5yGVHp3kYEjpx/5u+tjmcww+m8QWH/5jFZv\n8P930f8YFNaL0OrqmidP3CAhPIGK6gpOFZ1ixZ8rOP+j8+sVtVNXVCORklNCtRPd9VN5JXiZTQ7G\na4xlTuCz263VnuyrRTk7Xn1QXSvje0WR/J9pXHboQVj3hHs/QH3qt2+o/OJ6+sLGF+SP3T9ShpmV\n5svj7fuC+CAPDp4p0A5fXF4FO5cDkEmww2fRIcyPjMIy1u6Xva1JfaLZ/Mh4fn/YtvpTU7L6njF8\nfvuo2hsa2NLZUrLRVaROpi6b+bDzimpNiqLAh7Plsn0lr4AoWdLRWalHkAZfmG2jkpzg008mrGk9\n/Cai1Rv8cR3G8d7FS+UbKXGhLucmId7SRZJfls9HB6WAUWpB0xt8IQT3TZTzCAVOJAVO55YSHezt\nEIb5xMw+bFxwkU30z/e62Pg/zxTQ+7HVrNlXe/2A2igsq8QksCn1CMhiDTWRf8p2UktNG1frClcU\nS4lXn2Dpxz3wNay8niuLbQWhCsssMfzIHr69nEWQrydJGUWs/zOddiG+RAX64O1htolqamoSYoIc\nsq4N3GDKc/K/q4Io+o6cE6HEJuf0Luuyffy7Gh6Zut35vgVpUiyuFp+7MJmIeuB+lLIy28nbRqbV\nG3wAvALA5OlaTtRNgr3k0zu/PF9z5/yS4p7WTUOJtUScqHVy9ZzOK3Fw54CU040P87MJyzyRXay5\nX37cf4bSimr+8v52dp2s5UukWKJgTv4BwE9/ptPpoVWkF1gKv5dV4u/lISdAj2+y7vfezJqPa1+s\n2lkmZsdR1p6TRXI2Guu97BLhT1quFKrKUgIpwtfB4JdYMnN3nsh1WU3KoIViMkF0XxmeaU9lGXxk\nTbBrFsG1rbqETPsefqDF4C+zVeDVyDoM4e7NJ3l3k9GIWmHzJqBtGHw1M62BPfwgy9M7ryyPSktd\nyWX7lzX48twhxKL/7qyA96ncUuJqiSV/dJq1apOqHnlGp9Hzip3IF0h3j6Io8N0CeCIEngqHpRMh\n4xCvWWQb1HKDRWWVBPh4yAfDO5Pdf2NqcWtVu0jNsOyik5iI6GH9IVmKj/v4WCff3yy5n88z5UTv\nC5WzCff3ctDLf0hXtcpZvViDFk63CZC8Ed6dDkd0YnhZR63ZuwHR8NvL8P1DZ/fa9HODduGUmqQE\nyNGspcPE6yPhx8ekOyqiG+7g1dVi8I80nWRK2zD4AH4RsqB1AyZ11B7+I78+Qnap9eFRWVtRYXse\nD4YfHq3TLqH+0oCtSjzNU99KX+aLPx7iy52ppOWXaiMAV9w8pgtvWMrkncgqJre4nDN51kldZ8lH\nVy/ZLIt5/LHIdkN5gVZiUA19/DOtUOrZ13UUVW6RkL18MVy5zJpscvWHsjTheTeDd4B1qLzlTQD8\nLCXrPKikR5X1YfV91TDeueE8h1DLmGAfQizSxn3i6haWa9ACCJOTliRvhOWzrN8z1c0z5zNZrhJg\nyxuuj3PgW1nruLHIPQHHayhY5OUPYy0PoFeHyg7T5jdlIuFvL8tOqJs9fM+4WISvL+U6P37Z0aOU\npzSe9ETbMfgJ0yBtryzEUE+C7PxzEzrIeNkzRTX4wBUF/h0PmxbK12pK9O+v1uncqstm8S9JLP31\nGOkFpbyy7jD3fLKLymqF6FqKWgOagNm1b21h4JM/sjsll4m9o+ka6U9Oke3IITW3BNPxXxlx5CWH\n45QU5pGSI0NBd5/Mo7S8kqyUQ4xPiLbWHogfIXXnRS0+ctVXHztAZg2qePnB7GUw7b/ytZ36X6jl\n+TQ21FoEYld1+p9UwAAAIABJREFUV3IJdBkJM62fTKDrYbh0Wh+BcbavD62R/1WD7xtmq79TXuR8\novST62DF3Ma7rmSdsVcnl+1R3TqqPv7qB223R7hn8IXJhHeXLpQdPoJSVUXxjp0kTZvO0Ul1GFHX\nQtsx+B1GyP/f3V/vQ5iE7cdxdYL0Hc7+djZXfnOl4w5PR0tXSFk+rHlErnM18VQL0XbZmT8dtC2O\nMLhj7XVLg3xtE44yCsro3y6Y9qF+HLIr1rHxUAYfez3NrR62GkQAj3xs1e1/ae0hin9fwq/e99BX\nJEG2pfdxyasyvEypkuGWeonbgjPw0TUylE01+F61GGE7bRBzWT6/PHARL4219uR3VUudFlfl/x6b\n0Yd1943VQiQNWhFBdgZfnehXf08+wTL+PNQyEvhPB3ipj+vjOVQFd0JRlnUE6gr1/AuOyaI6zqhN\nyMxdyWikH7/o99/JWryY49deK1dWuSlI5wZtx+C7qy2x7R347gGXGtzvTJIl80K9Q+kYJJXsCsoL\nOJhtJ59cXixlSlXCLX660jrMsJ/eI68HmZ2p75m+8IN1snNYpzD6t3dUaLTHmSG8oEckY3tEcjSj\niGRdUe78bZ/YNgzvBkFSqM5klx1YeFhOXLevOiGzZYVJ1tT0sLiZnu0kh68gRzz/7Ql/fge7PrT+\noDxrETDTp4rHDoSSXDpUnSBo3cMAZE1ayHOVVzNnhGvJDC8PE10jjd59qyQywbrsHWRNZtIb/JAO\nMOlf8nV1pXSXVJRAlWOggyZaZiEpN8n2N1xZDv/tAW9NdH1Nhemw2uKu8anh91fTNrAtR1gL3j2l\nOGLOJyvc3qcutB2D7x8h/cE1ffjV1fDtPfDHYq3qjD1DY4aya+4u1s9eT5RfFGady8JGtsB+glhN\npnBVh9IZS8bJ67EMTX/421hW3iYngTJ0SVWDOtRu7MFWw+XCnpH8dVw3+rcPZqKlitLaA/JHdDyr\niFvTn7bdues4im+URRYChHTnvGwpN5hZLN93fOpq+OV5qQnu4W0bW2yJkedXXTJWcSZsel0um2r5\nqpnMMPw2mP6yvJelufDOFDlM9vQnfOQcfnpkGk/NdCK7bND6MXvA7Pfgxh9kYpEqmVyULjsY6qS+\nvYbM/q/hqQjpu9eTZTvxOfOrmVz5zZVUqT3/wjT50EjfB9/cI92yBWfkw0NVct2hC9io6fvrTNcm\n2vI9HXlXnWQQQq68AoDKM1Y3sjlYvveqwiKUyoYVi2k7Bh9k1ltprnx6O6NAJzvw9V9dTvCaTWY8\nTB6YhIkqnb73p4c/tTbST14OnGP9giZ+Zl2f+CmstzOsetRiyOnWhJMB7UO48yJbidm7xrk3y+/r\nZX04vXvDMO67uCdCCOLD/EiICeTDLSeorKrm+73WL9Pu6i6sGP09XPwvjubL/YMp4v34b7h4y3w8\nqCQpWz58Ak6ssz2h3uCXWX4kqTus6377n0xKcXf0NeVZGHqD/HGf2ml9qFpGUtFBPk2mi2PQAug9\nU5ZIjO5rLX2YfkBKLpsto9eIHrb7fHGr/H/8N9mhUzmynorqCoeAizd2WyZ8dbkh1dvf4XB1iawq\n9VQE/CdeRgf5WaK9+jlx5+oJ66Lp22tc+wlE9JSd0DpgCrCOUL06dybijtupysvj5G23c2joUFIf\neKBOx3M4foP2bmlYtFhcihnZa15kHHTeTkefcKuf8MlNT7ImeQ05pTm2PfyoBHnOjS/C5oXW9Z/d\nJHvEJbmw9gnZk9+6FN4cbflyWoyXOkGFdEs8MCmB9feNZd19Y1l192iXPmtnfHrbSL6+63yH9bOH\nxpOUWcTGw5ks+tkaBRAgSkmqCAMPL/adKSZP8eNqj/WMyfgIvzNbGW/ayUTstPpVf7ze4BdnSUXB\ngjPQ5ULoONq67fI6TqQnbbB97W5RDYO2QfwwWTshL0V2hqJ1ipFmT7jvEEx9wXYfD2+t05FrMpF9\n9AdmfDGDy766zOZBsGjPIt7f9x6sf0pb90lgAJe3j2XbsR8pEoIqkOetsEwKq4EFrjCZ4M6tcLt1\n7ovg9nDXH9boIzfRd2iEpyd+Q4cCULhhAwAF36+u0/EcLrVBe7c0/C3CRr/9z7lKXaHtRKhNQQ4X\nLLl4Cd9cap2suf/n+7n/5/utpfZmLrT6H9c94fwgX90pXR2JK2HVvXAmURYnNlkmWdMdU8q7RAbQ\nNTKgziGGQ134++PDpA/9hne3kldsdRftrO7Gmz8f5ds9p/jlcAYleBMnrA+zRV4vESzsoiFukIVK\nbGqZgtQ6KTgDATFwlbVADUHu+zAB6GEXlTDq7rrtb9C6ibcUQl91v4yBt5cIDoyGYbfYJkGV5Gpa\nPBd0aM/YgDJSC1NJzk+GJ20DHp7b9jxP5yeiAHgHcchLdqi2pO9gRKd4nooIkx01NRCjtoADkLH2\n0TVMItcFi/tIqajAb+RIh83VJY6S4m4fut57tkRUJbs/FsHez63rc47Di71hj2UiZMLj1vW1EOjh\nT6eiHOL8ZRRB7/De7M3ci5K2T2b39r9K1risCbU25c/PWtf9/qrVpVPQcOmD2lBj1AHCsDwMB8/n\n7xU3AnDXhzvZeDiTGOFiDmLI9dZl1UXTfqhtm/xUyE+Rk2t+uozautbfnP6SNdzz0Qy4+Kma2xu0\nLeIGyYCAQ98DCkT1ct5O75EtzYWtS8mL6o1i5/UrduIG/CQokERvLyovXcgmf2nQD1bJiLLPAgOk\nxpOKE3Vel0x9AW5oWC+8/UKLDLLJhBAC7+7d8IiJod3LMjCi/Fj9RCKhrRn8IF2lKv3k6Y73pDHa\nZ3kIDL8dzF61i62VFcLLfWHxhbx73qN8fsnnTOs8jeLKYvL2fS4LPpg9tegWjYHX2b5WXRL6EcUB\nXXJIwWn5ADj6E01FqM7gv3eZfDBeVX6YyP7WIhQFpZVkBPeXL/zCZejlsFvhnznWMmuRdj++ma87\n9sDVB8Idm6Wud13x8IZ79sCDyeBhaNOccwgBXXWZ2M6KgIDmEQWkYmX6fu4IcXR/HvOUI+kAs69N\nNbtq4I+gCFLN8kB7va3hvPV2Ig67heoOw+uerKnDZEk6VCdrO61cSdc1q/HuKvMQSg+5V/XP6bHr\nvWdLJLgd3LVd9rz1Bl9v2H3DZHp0cLzUYwc5QZPh5EPc9aFmpGOrFbqHdteqb22qzIbzbpHtTCb4\nq26ycvA8mRn4sBOX0W12WXsRPeU5fnhUun5ccXit/KsnYf7eBJMLVBCryBHF/qIU8irSbbTj90xa\nKaNlrlsp38fU5+X78/CC6z6DeV/aHnjQdbIHfsXbMvys35XQzVIFLKqXVfu+rgS3twqtGZx7qHLE\nYI29tyfCMrKOHyGzYStLOVntKA++31t2GpZHjuW7LvO09YVdxvJNknTXDgnoSKZOaO/f4fX77mWW\nZDLgvQEMen9QvfYH8OnVC3NICFF/uweQDwCTtzdeXbpgjoig6BfbUpB537iQlXZCi69pW2ciull0\ndXKktkVgrO3krJrgE9ZFqi9aFBgBeNwuaSpTp8xXmA6LL2LEqR34de7ELm9vpuhdGuG6yJrwbjK0\nEGRMcZlOVz+ko+05ek6xnqeyBi37D2bJ/w8eB1/3wjT1hJmKGdPxMXZ6BBNYeiXHPay3PkKXxRsd\n7CujZZzR3bFSj0bfWfLPwKAxUAMs7tomQzadMfs9me9RlAEnN3PU04OcqhLOizmPrWe2as0+CgrE\nQ1HosGkxJuCJ0AgeC/EjdcCVfLvlaaL9oukU2Y/thVYX7ydBgTwaNVoWZwG+OfoNfSP60jm45knY\nT/605rdUVFXgaXY/4ELFHBJCj82bHNYLsxmfHj0oT7WVWshcuNChrSvaVg9fxTdUiha92EtmwupV\n+NSYWXv/M8jJVD1p+61hYFlH4NQOTEBseSnpHmZbF5IeVfMDHJMuvAOh20QZyvnQCemvVHGVnKRP\nLDm63nmbWig+vYuf/XzJ9ypnV2Yiz0Zbiyi0C7G6TToZBTwMWgKXL5HzRuGuQ5KVwBje8/Nktb8v\n90ZFcHk762/twa5XckdOLh0qKjjs5UWHikpU0zs6X47+v0+Wc2v3D72fiED5W+4VKgMwfD18pTuy\n9yVUK9U88usjXPXtVS6vJc9S2eporjUCri71sd3FHBpKVY41ubNo82bKk5Pd3r9tGvyAKDjxu/Nt\no+UwiZ5THbe9qQslVBRZVq3j+TKaRvcwiKqqIs1sdlTOu/VnKRCmnyRSCzuPfQguWyy3zfkULl0o\nowz0E1J5J+GQpcBDVQUcWSczgvUuqfVP1V0grjSP3A8v117uLThOtS6kMirE6m8MaOSC3wYG9aLD\ncJjxvxqTlg5mH+T5bc/zwP63+NHfj2pL25KKEub0vIrbc/P5V0YWI6OH8WCeNbIlyBKmuT1tO2E+\nYVzc6WKKLDo44ztO4JZ+t1BeVU61ItsVlMsgh5JK59ExezL2MPrj0dzz0z1sPbOVMB8ZsJBeku60\nfUOQBt/qrk5/7nk84+Jq2MOWtmnwTU6GUTNfhzv/sGruxPaH8+8Br0CYtdSx/S8vyHqUMX1lRaa0\nvdqm6Moq0nyd6GfEDbQVCAOY9qIs1DzmPhjgpIcQ2RMeOSUnKP3CYdcHMnFsyUWw/HKpDKgmofS7\nUoqX1UW+ASD3BIW6xJDs0hxKPKxunGpzDn+b0INVd492treBQYvkSK5zGeEZXWdopQgHdpnM4slL\nGRVsdbn6KAqjQ+VEcK+wXpiEiasTrub8uPO5OuFqwn3DqVKqyC2TvzP1vytOFMgO2boT68gty2VO\nrzkIBL+nuuh0NgBzSAjVBQUoFRXkr15N6f79hMyuJTFMR9s0+Pqq8OMfk/8TpjmGT058Ah5JgX5X\nwAUPyGw5NfX6J0uGbERP+eXJSdZ2iwrvRSaOWXxO8Q6QhZprijbx8pduqIie0h/564vWEcWJzTKi\nxycEulomQ+3zCXQk5Sbx4rYXrSnkAHmpFOmqZZ2pLuEkFfSPkBE5JwtO8n8Tuhuywgativ1Z1vyV\nWd1nMa/3PD6c+iHXJFwj5/FuXidlucE68TvqrzDjf4ztLjtmZkvIZcegjrw58U2CvYMJ95UuWdUl\nk1Nas1zKwxsftnk9o+sMBkUN4qeTjR91Zw6V83cF69aRes/fAOrUw2+b4/fht8kK795Bckg46q/W\n1GxXBERLjZj9X8E3/2ddHz/cOtHbaQxc8zHRx1ZRveVpskqyiPaPdn68+hAQKVPJs45If37cIJlQ\nkp0kJ3dVRcHCdJex/6/sfIV1J9YxLLwPo8sV6DkZvrufQp0WyPcB/lBVwuVxIzmQfYDEzESmdZnW\neO/DwOAscDjnMP0j+vPBtA+cN9DP06mFvzuMgoSpTCjJZNn+97ik6yUOu4X7SIN/74Z7OZ5/nDHt\nxri8Br10+oPnPciezD3E+McwJHoISxKXsCZ5DZM6Tar7m3OBR6iMHlKNPYApwP08l7bZwzeZZCSL\nySQNvjsz5Wqi0Kp7rVE1Q2+SPfPBc6UB7ncleAcQEyAnh9KK01wcrJ74R8rohNyTEDdY9uqLs+S6\noDjrg8deIkJHUKlMHkn64ib46Co5Qsg7ye4Yy2SULs08LiCO4bHDWfHnCrJKmqF0nIFBA8gqzSLK\nL8q9xmMXwKVvyo4TEOEbwfezvndqjNUe/vF8GbWzMdUaBrkqaZU2eq5Wqlm0x1o86Lpe1/HcBbI+\nb6SfdCnd/3P95dqdYQ51DBc1B7qvENs2DX59UFU2S3UhlGqPuvdM6WcfMh9A+5Idy6t/xptTYvpJ\nOdiTm2W2qm8oZByQiVuBsVKSGKRGj6KQX57P3O/m8v2x77VDKBaBuCy1zu12qfi3yFIj9j+h52lt\ng72CmdNrDhXVFVy44kJy6zo3YGBwlimuKOanEz9RVlVGQXkBgV5u9m59gmHgNW4pV6o9fGc8tPEh\nLfRy25ltfHrIKqio18FpH2BNxswsyeSOtXdwutAq2FZfnBl8U+C53sOvD2psu1IlQzev+kDqdajo\nbma0n+xpP/pb3coY1krvSxnVoT0PRIZDdB9uKN7HK6EWv3pUb02s7NJ2Mfz95wVsOrWJXRm7WPDL\nAu0Q+ZVS9ybTbEki2f2hTQb64Mkva8tB3kH0jbDKDSfnJzfu+zEwqCfVSrWtHDnS2A//cDh3/3Q3\n7+9/n4LyAgLc0bmpI0Fe1oCM9gHteXLUk4yIHaGtS8qTVd/U38vzFzzPpmts4+bHtB9DtF803UO7\nszRxKRtTN7Ly0MoGZeACeERbRvlmM+ZImetj8nc/lLpBBl8I8bwQ4qAQYo8Q4gshRIhu28NCiCNC\niD+FEI3nxGoq9Dr6g+ZCr+mOFeothHjXPfHJLXxDKDCbWB3gT3pEV7ZVZLEkJFi6dzpJBczygddy\n1MuLr4+v5vM/V2q75pfLkUmWJbwsy8f6Q8i3+O/vH3o/Ibr3GeQVRLB3MBe0l6XbDLeOQUvgdOFp\nxq8cz3Nbn7NZfyD7gLb8S8ovFFcWu9/DrwNCCNZesZYZXWbw2SWfcVn3y/jPmP9o2/MtLt9Thafw\nEB5M7DjR6YNndLvR5JTmaK7fJYlLmPL5lAZdm0doKO1fX0iHpUsJv/EmwHmv3xUN7eH/CPRVFKU/\ncAh4GEAI0Ru4GugDTAZeF6K24qfNjN7gu6pdaUEIwa39pQ53RbWTajv1RN+jOehrjZOv1pX/O97T\nWqFnV9o2YvzlZNSu9F2cKTrDbmS2bnaUdVI30+LeUUcmc3vLmp/B3vKB9sQoqfLZ6HMSdqQUpFCo\nljw0MHDBW4lvkVmSyfIDy8kry6O4oph+y/px/errtTZqhI6+N96YRPtH88yYZ/CzJEOqsfVg7Vxl\nlGQQ4RehRfrYE+oTSm5prk0y1pmiM1qSVn0JHDcO/xHDCbt+Pgn792EOOEs+fEVRflAURR2jbAZU\nx9VM4GNFUcoURTkGHAGGNeRcTY5etyVucK3NI33lpExtN29n+k4+OviRW5egJn8AHMu3pnkXdLJK\npGZ5WR8ExUoVQwNluNmR9N3sWyiTvLqZ/ckqzYFrPoZBc8m++EnA+qW9b8h9fDXzK+1hEeYThq+H\nLymFtinbjc2Uz6cw6bOWP9gzaD5KKktYe8KqGXX3+rvZkb7Dps1tA26jrEpKfKu/w6ZGCMGOuTsY\nGTtSM/j55fk1PnBCvUOpVCo1F5DKifxaRBvrcE2itkpydjSmD/9GQJ09bAfoi0qmWNa1XEwmuPQN\nqQrpSrtDhzoLv+nUJpbtW8YtP9zitN287+fxzJZnHPyRzlC/SGDb287qbRUgyxbVNvtUHVlLmFcQ\nJzL3s9ui9jfCL570knQKOo/mt6HXcOP+NwGrwTebzHQJ6aIdwyRMdArq5PDFbAr079HAwJ6juUfJ\nLrXWY9iVscsm3j7EO4RrE67VXveLcL9AeEPxNHkS7B2sdfJqmzQO9bF2ImP9rbIPZ4qbXg7dFbVa\nNiHEWiDGyaa/K4rylaXN34FKwEVAbI3HvxW4FaBDB9cFqs8KA6+tvY2FMe3G4GnyZFf6LlYckjr7\nR3OP0jWkq9P229O2MzRmKGlFaaQWpjI42nEUoTeG+hn9nDJr4kdWqW0t3faVlZzKz2B72SaSQ2Rv\no3todyg4yKiPRtm0jfJ3HcLWPrA9h3MOu9xeH/Zn7SfKL4qXtr9E9xCrIufy/cuZ03tOo57LoG2g\nziN5mjypqK6gWqnmt9TftO3tAtoR6hPKhtkb2JW+i9iAOhbXaSDxgfH8ePxHMoozOJp7lEFRrlUx\n9Qb/9gG388/f/wnQrG7NWg2+oig1SCSCEOJ6YDowXrF2Y1MBfTmk9pZ1zo6/GFgMMHTo0DqKxDQf\nXmYvOgZ15NskqzTpoZxDLg3+DWtuAMDb7E1ZVRlrr1hLtH80pwpPEeAVQJBXkM2k6abT1ll/fY8n\npcDqdulRVs7tOXmkRZj52seaWXvZBU/yxAerNC0QgKfPf7rG4WeUXxSbTjkq9NWXaqXapdjUs1uf\nZXbP2XiZDa17A1vU7Nb3p77PxpSNLNy1kB3pO5jYcSIewoPbB94OyFj58R3Hn/XrGxw9mCWJSxi3\nchxAjT383uG96Rnak4SwBC7rfhmDowcz/YvpzTrKbWiUzmRgAXCJoij6OnhfA1cLIbyFEJ2B7sAf\nDTlXS+RI7hGKK61vWy/JqhLhG2HzWvU9bk/bDsCkzyZx1TdX8UPyD/yc8rPWrqiiiE5BnQCZ2v36\nrte5Y+0dbEzdSPfQ7lwVNYJX0zPwnPAEnaMHavuFewUhzB70DLXNxJ3ZbWaN7yXCN4LCikKKK4pr\nbOcuqYU1l48csnxIo/kyDdoOGSWyHnW3kG7M623Vrh8UNYjnxj5XqzxxU6MGPqj4mH1ctJQu1E8v\n+ZSnR0uZlvjAeARCE2NrDhrqw38NCAR+FELsEkK8CaAoyj5gBbAfWA3cqShtrxL1k6Oe1JZj/WP5\n+eTPNtvTi9Nteud6Xtn5CqUW/fuUwhTu+/k+bXI30FP2GtRJ3N0Zu3lj9xtsTN3IyYKTzOgyg0en\nLCHu0WwYfQ+XdLcqYX47SxZEVydkb+53Mx9P/7jW96JOfjWWpOuRHFthq4viL3JoM+0LQ87BwJak\nvCRi/WPxNntrETIgjWVLwL4D1zGoo4uWjpiEiQCvgNZr8BVF6aYoSryiKAMtf7fptv1LUZSuiqL0\nVBTl+5qO01q5rPtl2vKwmGEOQ7Vd6buoVqp5bdxrWk1cldTCVN7e+7bDMQO9AimokF+IWT1mEeAZ\nwNdHv7ZpM6GDrZctKtSqGa7GA9839D5u7X8rdw28iz7htRdXVkM0nQ03f0n5hX7L+tXaa9dzNO+o\nzevnxz7PP0b8g7cnvc1l3eTndrYiLAxaD4dzDtMjtIfD+pqyX88m6u8E4M6Bd0qhtjoQ5BVEXnnD\nwjIbgpFp20i0C2xHaVUp5VXl2jo1zHFw9GDWXLFGW//elPcAeGP3Gw7H6R7SXU66Ajf1vYnCCscJ\nnrgAO3W84HYsPZ3G8krrj6JjUEf+OuivLmOE7VG/yNescvwCL02U8tGJGYkO21yRnJesLa+ZtQZv\nszeze87mvJjzePL8J5naeSoV1RUs2r3IiM03AGSFqOS8ZO37r0fVt2luTDqZ8b/0/0udK1pF+UWR\nXtz4OvnuYhj8RiLUW87I63vIKQUphHiHaBM7q2etZvHExQyKGkRCWILT43QO7syiCYtYNHERPh4+\nWlKUHgcjHtKBYVd9yoC539X7+vUTuqqBtz/fyYKTuMuZojO0D2jPx9M/dnxAIX2huWW5vLbrNTaf\n3lzPqzZoSyTlJVGpVNpEdKmj2ZZi8AFmdJlBpG+kjXaOu8T4xdgobJ5tDIPfQFRfuWrU7Q2+XkSp\nXUA7RsbJJKre4b2dHi/aL5pIv0hGxcmQypldrZOtI2NHMqeXi3DGzheAl4sSiW6gN/gv73jZZluF\npcTi6SL3xZ9OF52mb0Rfl+4kfcjaZ4c/Y3fG7rpcrkEb5HCuDAvW9/CfveBZvrvsO7zN3q52O+s8\nM+YZ1s+uX6nRGP8Y0orS3MrLaQoMg99AvrjkC9ZfuV7r4WeX6EIoC1NoH9je6X7qJJRahETFz66u\nrb43v/jixTw47MFGuW577PWB/sy2FnBXfY7uSi/847d/cKLghE2ySU3n+zX1V+Z8Z8Tln+scyzum\nJQGqeJm9iA9qGRO2jUG0fzTl1eU2uTVnE8PgN5AArwAi/SJpZymCrE5sJuUlcbLgpMuevK+HlEjo\nHtqdHXN28M6kdwDXmYNNHY7mafZkx9wdmp73Fd9cQXJeMgezD2pJYL+k/MKrO1+t8Thv7n6TL498\nCVizkZ3h7+mo8NdcvR6DlkFWSRZhPmF19ou3JmL8pEegudw6hsFvJOL84zAJk1bfUg1LVF0z9qjx\nvAlhCXiaPRkaM5Ttc7Y7zcDdfO1mVkxf0URXbsXT5MnFHS/mwvgLAfjXln9x5TdXUlpVqrmuFu9Z\n7HL/L498ycJdC7XXNmUW7XBWuMJVkWiDc4OskqwWE43TVKiZwc2Vg2IY/EbC0+xJQlgCf5yW+WVZ\npTJr1tVk0/gO41k8cTGze87W1rnKPPX39MfHw3WCR2NiNpl5dsyzADaTqa+Ne01bdhZVU1ldyT9+\n+wcg3TVDoocwvet0l+cZGDWQ7y77jqHR1jJ0+iQ2g3OLdcfXsSFlQ5Po27ckuod2J8AzgD/ONE8e\nqmHwG5EBkQO0+PPs0mwEwqV2vhCCkXEjbcK8Wgp+nn5M72I11iNjR9IzrCf/HftfwHkWrX6IOjBq\nIO9OftchScWe+KB43p70Ns+MfgawVQs1OLd4YpOMRhsXP66Zr6Rp8TR50jeiL3sz9zbL+VuetWnF\nRPpGUlBewO+pv5NVkkWIdwgeptZZJ16vCfTmRKm2qc5TqHo+JZUlPLLxEfov628jK6FOYLuDEEKb\nqDYM/rnJ6uTV2iTmvD7zamnd+ukV1osjuUeczlklZiTW6AptKIbBb0TUHu1f1v6FU4WnzrqSX2My\nv898Xr7wZX6a/ZM2ClFDTLelbQPg3b3v8k3SNygoWp1PoEYFQWeoE7jNZfBXHlrJqcJTzXJuA/jp\nxE/NfQlnlUi/SCqqKxyy2n9P/Z1rv7uWxXsW8+rOV7n0y0upqq7iZMHJRqs3bRj8RiTa3yqs9Nup\n32xi8FsbniZPxnccb+OWCfIKokdoD1YlrUJRFF7f/bq2bV/WPgB2zt1pIznhDv4e0uA3lnBbXVh5\naCVPbnqSB3554Kyf+1xl25ltPL/1ea2Huz9rP14mLz6Z/kkte7YN1LoU9jpbql//9d2vs3jPYo7m\nHeVgzkGmfj6VhzY+1CjnNgx+I6LPEAToFNypeS6kiRBCcE3CNeSU5fBnzp9O29THhaXv4acVpfHf\nbf9tcBk4d3lykxTAa6welEHtPLf1Od7b/x7b07ajKAppxWlclXCVyxDmtoZq8P/M/pP88nx+S/2N\n+zbc5zR+4W8+AAAXUElEQVTPZWfaTkB2IPVUK9X1Cu1snQ7mFor9JKW+Mk9boUOgLFKzJ2MPIKMO\nHhv5GKuPrWZ8h/rpk6s+/KS8JB7cKBPL3t33LgvHL9QKrDcVYT5hZJdmt/nokJaEWgf60d8e5e1J\nb1NSWXJOCempyZgP/PIAYT5hlFSWOIQkD4sZxh9n/uDZrc9q63JLcwmx1N5+avNTfHroUzZds6lO\n312jh9+ICCG4se+N2mt94eO2gvqeDmYfBOCh8x5iQOQAHhz2IENjhta0q0vUHv6iPYts1t+57s4G\nXGntKIqi/dDqohNk0DBUV05qYSr/3vJvwLGz1JaJD4xnZKyUWMkuzdYegHqcdZ6e2fKMtvzpoU8B\nWSujLhgGv5GZ23suAKPbja6XuFJLJ8zX1uA7S6CqK34e9dcAagjb0rZRUllClG8UBeUFZ82NdC5T\nXFFso8m0IWUDcHZr07YEJnaaqC1XVlc6bO8e2p3hMcNt1p0uOk1WSZZNlb2pX0y1kUGpDcPgNzIR\nvhEkzk/kjQmO0sdtgWCvYDxMHiRmSqnkxjD47ko4NyaKonDjGjkaGxs/FkArQGNQM4qiMOyDYbyy\n4xV2pO1wWxIjKTeJBb8scEiw8zB5tLn5rtrQG/NLul6idXrUkOauIV15YewLWpthMcMorCjk3g33\n8vDGh22OZS92WBOGwTeoE2aTmat7Xq29thd7a2z0dXkbE/0EmZpktnDXQk0Z1MCRiuoKHvv9MeZ9\nP4+SyhKWJC5h/ur5bmeNLt27VCvj+f6U9/nq0q/oGtxVy+w+l+gQ1IHd83bz5KgneXTEo7wy7hWm\ndp7Kx9M/5pnRzxDmE6b560FW1Msvzyc5P9nhWHXJezEmbQ3qzNUJV7P8wHI8TY0nchXlG0V6iSwM\n8dXMr3j0t0dJzEzkVOEpl4qj9SWzJJOJn8oh9TuT3rGJDtl8ejM9w3o2ysilLaEoCq/seIXPD3/u\nsC2rJMutY+hzHfpF9MNsMvPlpV822jW2NkzCpIUwD48dzvBY2evX14/430X/w8fDh40pGyksL3Qq\nv/JN0jfun7OB12xwDtIxqCP/GPEPt2rlusu62eu05S4hXVhw3gIAjuYedbVLvaiqruKiFdb6ur3D\ne+Pj4cPH0+R7uWPdHYxfWb9oo7bMmuQ1vLvvXafbMkoyeH3X65qOlCtSClO4sP2FfDL9k2Zx47VG\nxnUYx6i4UZiEieLKYnLLrOHDa2atqbMUhWHwDerF7J6zndYebSiqimi3EFmn9671dzWqbLK+vNzy\nqcs1l1Tv8N74mM+OQF1rJKMkA7CdYB/bXs597Mvaxxu733AaVaUoivaXXZJN5+DO50y8fWMyInaE\nzeuHhj1EXECcTSEhdzBcOgYthvVXrtdUQfWxxSWVJY02V6D67mP9Y20MjxACXw9fSqtKATl30BKF\n7ZqLX1N/BWDtlWv56OBHnCo8xeOjHmfq51P5/tj3AE7jwW9ccyNFFUUsuXgJ5dXlNn5pA/cZ034M\ng6MGsyN9B9cmXMt1va4D4LYBt9EluAvzme/WcYxvtEGLIdIvUisVCTCr+yyARguXPJZ3jE2nNgHw\n6rhXHeYgzos5T1uua3xzW+R04Wme3PQkxRXF/H7qd0CW8ry1/608PupxwHbCMLMk06Z4vaIobEvb\nxoHsAyzZs8ShvUHd8PWURZP0hYVi/GPqJDhnGHyDFsvodqMBa4nFhnLJl5do+j9qQRc9T57/JFM6\nTQHgu2P1LwjfWqiorqDfsn4ORetVblt7GysPrWT4h8OdbgeoVGxjyK/69iptWe9vXrZ/GdCyipG3\nNsoqy4CGhUIbBt+gxRLsHQw0Tg9f77sH26LtKv6e/jx7wbOE+YRpiWVtGXUU81biW063qzLYKmo9\nBD0TOkywea2PsVc/8+v7XK+tGxI9pF7XamAVW+scVP9yp4YP36DFohr8xnCvqH5mFVdZ0EIIuod0\n51jesQafs6EUlBeQlJfEgMgBjXbMbWe2UalUMiJ2hPa5Ovss3tj1BuXV5drrbiHdGBE3wqHdzf1u\nxsfDh88Pf86R3CNarWaQNZABruhxBf0i+hHtH+20lrGBe1ze/XJe2PZCg+pbGz18gxaL6nbRp+LX\nF32i1SVdL6mxbf/I/iRmJrJs3zLKqsrqdJ6HNz5Mv2WNIxPwlx//wpzv5jjVWqkPFVUV3LDmBm75\n4RYA7t1wLyAfLGrRDUVReGjjQzbS1wBfzPzC6ahICMHc3nP5YuYXDIoaRJy/NYb866NfMzxmOB2D\nOnJxp4sb9cF1LjKv9zx2zt3ZIKE/w+AbtFiCvIII9Ap0WlKxrqQVpdEpqBOJ8xP51+h/1dh2Vg85\nWfzCthdcujtcodc5aSiqfEVj1QlQq0qpnCiwFtJWz5VblsuqpFWADP0TCG7oe4Nbx+8T3oejeUc5\nkX+CyupKThacZECUYeQbCyFEgyvoGQbfoEXTLqBdo1SjSitO02L83Tmnys70nTbbkvKSnBrgzJJM\nblpzk/a6sXrlAIUVjkXj60N+mbXCkn3GrJrgNu97a8RHuG84u+ft5m+D/+bW8ef1noeH8ODzw5+T\nVZJFlVLl9mducHYwDL5Bi6ZdQDtSC1Opqq6iqrqqXhO4ldWV0uD719345Jflk16czr7MfWxM2cjM\nL2dy8w83O7RLzEi00ZRpzOpdheX1M/hFFUU2+355xCpj8NjvjwHwwFBZ6evxTY/z7B/P2mi1CARC\nCLdVX2MDYukV3ovEzETNhWZIVLQsjElbgxZNXEAc606sY+D7A7V1O+bucFvH57uk77SiKvXpbZ4o\nOMHkzybb9NgTMxOprK60GV7bh44WlBdok871QV/I+p+//5OPp31cZ7ntWV/PIrUwlcT5iWw6tUkL\njdTTObgzEzpMYO2JtSw/sByQOjfZpdkMixlW5+tOCEtgdfJqjucfB6T2u0HLwejhG7Ro9O4VlYLy\nArf333Byg7ZcF4O/cPxCeoT2oKiiyMbYq1EoqkFT0btLoOFuGL2/fX/W/nqFiapzH6/tfI1dGbuc\ntukf2Z9nL3iWx0c+Dshe/YfTPmT1rNV1TtsHafALygvYfHozAmEY/BaGYfANWjTOKiHZG1dXVFVX\n8X2yNRyzLi6dC9pfwD2D73FYryaD2ceo55fnIxDc0k9GwDQ0dyCzJNPm9eeHP6+3ptCiPYscHlAA\nN/S5gWDvYLzMXlze/XLm9p7L8qnL63UOlYSwBEBG6MQFxDlVdzRoPgyDb9Ci6RjU0WGduz18+2Sr\nutZNdSbL3CusFwCnimwnkk8WnCTQK1CTu23oRLP9/h//+THfJn3LifwTbhev1sfErz2+FrAaZMAm\nTFIIwYLzFtA/sn9DLpvuod21ZWf3zqB5MXz4Bi2ahLAEVl22itTCVJLzk3lmyzPkl9few39779sc\nyTkCSMNzPP+4U/dQTeh1yVW6hHTB0+TpkBuw4eQGhsUOI8Y/BpMwNTiUdOWhlQA8d8FzvL33bQ5m\nH+RwzmEe+fURABLnJ9a4f1lVGSWVJbQPaE9KYQplVWU8NvIxruhxBYkZiXQN6dokxWt8PXzx9/Sn\nqKKIYK/6z2EYNA1GD9+gxdMhqAMj40ZqZeHccZe8tP0lrTDEoyMeZfe83XVWavQ2e/POpHeY0nmK\nts7L5EWId4iNW6m4opjiymIGRg7E0+RJiHcIi/YsqjVDWFEUp9E8uzN2a+qUkztN5sNpHwJQUGEd\n2RRVFLk8bllVGRd9IjX/9b1sNXGqX2S/Jq1Udu8QmdAVH2T471sahsE3aDWoIX5nimt2aegjXEAa\nuvpKHQ+NGcpzFzzHyhkr6RXWi/6R/Qn2Duazw59pBT/UCdYwH1ngfVKnSQD8ePzHGo/9VuJbDP9w\nOHsz99qs17uihBB4mjzpGtyVjSkbtfW/pf7m8ribT23WHg4dgjpo64O8HTNlm4JLul7CHQPu4Ka+\nN9Xe2OCsYhh8g1ZDgFcAwd7BDgbSHvsJT73kcn1JCEtgxYwVBHsHU14lNWZu+kEaNLUnr0a1PDzs\nYaJ8o/jjzB8UVxS79LlvSNkA4KDbo06wLptsDaPsG9HXRh5CdfnYsyZ5DXetv0t7re/hN8bn4A4+\nHj7cPvD2Jq93bFB3DINv0KpICE3gx+M/aun/zlBVBVXqOllbG3pJgtTCVG2CVdUpF0JwXux57Ezb\nyS0/3qLVz918ejM3r7lZewCoBdPtXT9rktfQPbQ7g6MHa+v0wmWXd7+cfZn7HKJ2qpVqTXdexZlL\nx+DcpVEMvhDiPiGEIoSIsLwWQohXhBBHhBB7hBCDazuGgYE7PD36aQBWJ69mxZ8r6LesH1vPbLVx\n43zy5ycAmIWZtye9rVXRaiweGvaQtnw8/zg703fi6+FLjxBrycf2Ae1JL0lnT8YeQGbivrz9Zbac\n2aIVE1H98HrdeJAjlP4RttEyQ6KsssL9I/pTUFHAyYKTNm2O5x/nz5w/bdZ1CuqkLTurAWBwbtHg\nKB0hRDxwMXBCt3oK0N3yNxx4w/LfwKBBxPjHcGH7CzmSc0RLqrpxzY1c2P5CXh3/KgCfHf4MgC9n\nfkmn4E6Nfg3X9bqOkbEjmfnVTHJLc9metp3+Ef3xNFuzf1V/vsq1312rLf+S8gv7s/ZrIwV7g19Y\nXujgftHnEKilGfdl7bPx0dvH2o9uN5q4gDgmdpxIcWWx29nJBm2XxujhvwQsAPTjy5nAe4pkMxAi\nhIhthHMZGBDtH01KoW3i04aUDZzIP2Gzril1XFTZhNyyXI7mHqVXeC+b7WG+Yc52A2DdiXXaKEQ9\nhkp5VTmlVaUEeNpK4OonndUC7wt+WWAzX7E3cy8Coblx/j7875iEiRcvfJE3J7xZ17do0AZpkMEX\nQswEUhVF2W23qR2gH2+mWNY5O8atQohtQohtGRkZDbkcg3MEVxIJP538CUVR8DB5cH2f65t00lA1\n+GeKz8ji3N62IZ8RPo4ZwvbcN+Q+BkcNtvHhq6MTZ4VCFk1cxAdTP8DT7KklVa0/sV7bvuX0FgZE\nDmDpxUv525C/1TnvwKDtU6vBF0KsFULsdfI3E3gE+GdDLkBRlMWKogxVFGVoZGTjTq4ZtE1Ud8nY\n9mO5tf+t2vrEzETyy/OprK50KsnQmHiYPAjzCeOdve8AOAil6V1JD573IF2Du9psn9p5Ktf3vZ5Q\nn1CbHv4zW54BHCeeAUbFjdIyYX+a/RNgG8KZUZJBu8B2RPtHc2PfG+sstmbQ9qnV4CuKMkFRlL72\nf0AS0BnYLYRIBtoDO4QQMUAqoM+6aG9ZZ2DQYNTedJVSxV8H/ZXd83YzMnYkKQUpmryvM1mExqZa\nqdaW7SNgwn1kse6EsATm9J7D9K7TARgXP44o3yitqEiId4hm8PX6PLN7zq7x3P6e/niaPFmdvBqQ\nSVzZpdnaeQ0MnFFvl46iKImKokQpitJJUZROSLfNYEVRzgBfA/Ms0TojgDxFURpep87AALTJUTUs\n0SRMxPjHsC9rH7evvR2Q1ZeamnEdxmnL9j18IQSrLlvF0klL5Wtkb7tdYDvWzV6nadpE+0WTXZpN\nZkkm076YBkiNG3ciagSC4/nH2Z62neLKYkoqSwj3NQy+gWuaKg7/O+QI4AiwBLijic5jcA6iTsbq\nhcBUf70qrHY2Ki3dN/Q+bdmZUFiHoA5az39ItAyrHBU3yqbN+I7jqVaq+fnkz9qIQcE9VczXxr8G\nyMlaNXnL8Nsb1ESjiadZevnqsgLc2VjHNjDQkxCWwPKpy7XwRICLO15Mcl4yv536jT7hfc6K/zrI\nK4jHRz7O76d+r7VHPjBqIJuu2eRQgLp7SHfCfMJs9OrtI3RcMSJ2BN5mbzKKM/ji8BcARqFwgxox\n1DINWiX2hm1w9GDenPgmpZWl9dbNqQ+zeszSip7Xhr2xB+n6aRfQjk2nNmnrLmh/gVvHE0LgZfLS\nKllN6TzFSK4yqBFDWsGgTeHj4dPqim6cKjyl6eTc3O9mrk24tpY9rOh99oZYmUFtGAbfwKCZySrN\n0pYndJhQJ3fU/8b9T1tWE7IMDFxhGHwDg2bmjgEypmFq56n0iahbdFGX4C4sn7qcHXN2YDaZm+Ly\nDNoQor51MpuCoUOHKtu2bWvuyzAwMDBoVQghtiuKMrS2dkYP38DAwOAcwTD4BgYGBucIhsE3MDAw\nOEcwDL6BgYHBOYJh8A0MDAzOEQyDb2BgYHCOYBh8AwMDg3MEw+AbGBgYnCO0qMQrIUQB8KebzYOB\nvEZoU9e2zdWuOc/dFO8lAsistVXLfy9t6f41xTHdvc/uHrMtfTaNee6eiqIE1noURVFazB+wrQ5t\nFzdGm7q2ba52reEa6/he3LrXLf29tKX710TnbpbfdCv5bBrt3O5+zq3ZpfNNI7Wpa9vmatec526K\n9+IuLf29tKX711THbMxzt6XPpinOXSMtzaWzTXFDD8Kg9WPc63MD4z6fHdz9nFtaD39xc1+AwVnD\nuNfnBsZ9Pju49Tm3qB6+gYGBgUHT0dJ6+AYGBgYGTYRh8M8yQojCWrZvEEIYPs9WjnGfzw1a231u\nFoNf24dk0HYw7vW5gXGfWwdGD78ZEEJcKIT4Vvf6NSHE9c14SQZNgHGfzw1a031uNoMvhAgQQqwT\nQuwQQiQKIWZa1ncSQhwQQiwRQuwTQvwghPBtrus0aDjGvT43MO5zy6c5e/ilwGWKogwGLgL+K4QQ\nlm3dgYWKovQBcoFZzXSNBo2Dca/PDYz73MLxaMZzC+AZIcQFQDXQDoi2bDumKMouy/J2oNPZv7wm\npRLbh61Pc13IWeJcvdfGfTbuc4uiOXv41wGRwBBFUQYCaVg/qDJduyqa98HUFBwHegshvIUQIcD4\n5r6gJuZcvdfGfTbuc4uiOT/0YCBdUZQKIcRFQMdmvJazghDCAyhTFOWkEGIFsBc4Buxs3itrcs6p\ne23cZ+M+N++VueasG3z1QwI+AL4RQiQC24CDZ/tamoE+wFEARVEWAAvsGyiKcuFZvqYm4xy+18Z9\nNu4zlvUXnuVrqpGzLq0ghBgALFEUZdhZPXEzI4S4DbgbuEdRlB+a+3rOBufivTbu87lBa73PZ9Xg\nt9YPyaDuGPf63MC4z60LQzzNwMDA4BzByLQ1MDAwOEdoUoMvhIgXQvwkhNhvybD7P8v6MCHEj0KI\nw5b/oZb1QgjxihDiiBBijxBisO5Y8y3tDwsh5jfldRvUnUa+16uFELn6dHWDlkFj3WchxEAhxCbL\nMfYIIa5qzvd1zuBuTcX6/AGxwGDLciBwCOgNPAc8ZFn/EPCsZXkq8D0ygWMEsMWyPgxIsvwPtSyH\nNuW1G3/Nc68t28YDM4Bvm/t9GX9Nc5+BHkB3y3IccBoIae7319b/mrSHryjKaUVRdliWC4ADyOy7\nmcAyS7NlwKWW5ZnAe4pkMxAihIgFJgE/KoqSrShKDvAjMLkpr92gbjTivUZRlHVAwdm8fgP3aKz7\nrCjKIUVRDluOcwpIRyZtGTQhZ82HL4ToBAwCtgDRiqKctmw6gzX9uh1wUrdbimWdq/UGLZAG3muD\nVkJj3WchxDDAC0tMu0HTcVYMvhAiAPgMGbqVr9+myDGdESrURjDu9blBY91ny6jufeAGRVGqG/1C\nDWxocoMvhPBEfjE+UBTlc8vqNHX4bvmfblmfCsTrdm9vWedqvUELopHutUELp7HusxAiCFgF/N3i\n7jFoYpo6SkcAS4EDiqK8qNv0NaBG2swHvtKtn2eZ2R8B5FmGiWuAi4UQoZbZ/4st6wxaCI14rw1a\nMI11n4UQXsAXSP/+p2fp8g2ackYYGI0c2u0Bdln+pgLhwDrgMLAWCLO0F8BCpC8vERiqO9aNwBHL\n3w3NPdtt/DXpvd4IZAAlSJ/vpOZ+f8Zf495nYA5QoTvGLmBgc7+/tv5nZNoaGBgYnCMYmbYGBgYG\n5wiGwTcwMDA4RzAMvoGBgcE5gmHwDQwMDP6/nToQAAAAABDkbz3IBdGE8AEmhA8wIXyAiQAWAhvu\nWIH4IgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa030cd4a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "   .....:                   columns=['A', 'B', 'C', 'D'])\n",
    "df = df.cumsum()\n",
    "plt.figure(); df.plot(); plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8f173711-84cf-405d-a974-8f3d467a1699",
    "_uuid": "6707cccfbd1da29773303b644ad02a47e917e182"
   },
   "source": [
    "# Getting Data In/Out\n",
    "### CSV\n",
    "[Writing to a csv file](https://pandas.pydata.org/pandas-docs/stable/io.html#io-store-in-csv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_cell_guid": "c2854c44-5039-4c4f-9333-f4b0f33edc5b",
    "_uuid": "f321aa65d66246497c509d32969d3e1a3d717391",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_cell_guid": "746340a9-c552-4995-8cbd-067f7dd62e35",
    "_uuid": "c0daece574ad9366f247716b0bfe7f3fd0c9351e",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-0.165439</td>\n",
       "      <td>-1.241892</td>\n",
       "      <td>-0.342680</td>\n",
       "      <td>0.106041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-1.298616</td>\n",
       "      <td>-1.023435</td>\n",
       "      <td>-0.944371</td>\n",
       "      <td>0.586668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>-0.714936</td>\n",
       "      <td>-1.392907</td>\n",
       "      <td>-0.323638</td>\n",
       "      <td>1.589524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>0.202948</td>\n",
       "      <td>-0.704013</td>\n",
       "      <td>-0.162558</td>\n",
       "      <td>1.615731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-1.143432</td>\n",
       "      <td>-0.318759</td>\n",
       "      <td>-1.912439</td>\n",
       "      <td>2.029570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>-0.641053</td>\n",
       "      <td>-0.928672</td>\n",
       "      <td>-1.370465</td>\n",
       "      <td>1.346422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>0.133213</td>\n",
       "      <td>-1.686737</td>\n",
       "      <td>-2.174208</td>\n",
       "      <td>1.097845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-08</td>\n",
       "      <td>0.281839</td>\n",
       "      <td>-1.026250</td>\n",
       "      <td>-3.980469</td>\n",
       "      <td>1.487319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-09</td>\n",
       "      <td>0.221473</td>\n",
       "      <td>-2.580209</td>\n",
       "      <td>-4.595592</td>\n",
       "      <td>1.661385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>-0.146512</td>\n",
       "      <td>-3.149283</td>\n",
       "      <td>-3.549284</td>\n",
       "      <td>1.125317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>0.514869</td>\n",
       "      <td>-4.022301</td>\n",
       "      <td>-2.768194</td>\n",
       "      <td>1.446292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>-0.104987</td>\n",
       "      <td>-3.493369</td>\n",
       "      <td>-4.076654</td>\n",
       "      <td>2.125191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>0.978066</td>\n",
       "      <td>-3.475903</td>\n",
       "      <td>-3.835260</td>\n",
       "      <td>3.059128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>1.116126</td>\n",
       "      <td>-2.797326</td>\n",
       "      <td>-2.255736</td>\n",
       "      <td>2.763711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-15</td>\n",
       "      <td>1.039718</td>\n",
       "      <td>-1.324396</td>\n",
       "      <td>-3.059282</td>\n",
       "      <td>3.110216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-16</td>\n",
       "      <td>0.116109</td>\n",
       "      <td>-2.185032</td>\n",
       "      <td>-4.550271</td>\n",
       "      <td>1.112215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-17</td>\n",
       "      <td>0.920563</td>\n",
       "      <td>-3.287660</td>\n",
       "      <td>-4.124656</td>\n",
       "      <td>0.563663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>0.394087</td>\n",
       "      <td>-2.860464</td>\n",
       "      <td>-3.431849</td>\n",
       "      <td>1.494053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>2.133781</td>\n",
       "      <td>-1.701251</td>\n",
       "      <td>-3.878129</td>\n",
       "      <td>1.981117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>1.380864</td>\n",
       "      <td>-2.028198</td>\n",
       "      <td>-5.349861</td>\n",
       "      <td>1.759223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>1.787964</td>\n",
       "      <td>-1.835131</td>\n",
       "      <td>-5.951531</td>\n",
       "      <td>1.855332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2000-01-22</td>\n",
       "      <td>1.159440</td>\n",
       "      <td>-3.579943</td>\n",
       "      <td>-6.824187</td>\n",
       "      <td>2.528383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2000-01-23</td>\n",
       "      <td>1.659971</td>\n",
       "      <td>-3.561345</td>\n",
       "      <td>-5.747614</td>\n",
       "      <td>3.150220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>1.459270</td>\n",
       "      <td>-3.092126</td>\n",
       "      <td>-5.337020</td>\n",
       "      <td>4.048097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>2.199406</td>\n",
       "      <td>-4.002160</td>\n",
       "      <td>-4.287942</td>\n",
       "      <td>4.292009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>3.640403</td>\n",
       "      <td>-2.616529</td>\n",
       "      <td>-3.849604</td>\n",
       "      <td>4.566062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>5.672637</td>\n",
       "      <td>-1.289733</td>\n",
       "      <td>-4.526165</td>\n",
       "      <td>4.850854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>6.343960</td>\n",
       "      <td>-1.790023</td>\n",
       "      <td>-7.023424</td>\n",
       "      <td>5.369450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2000-01-29</td>\n",
       "      <td>4.637036</td>\n",
       "      <td>-2.715463</td>\n",
       "      <td>-6.508805</td>\n",
       "      <td>5.646366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2000-01-30</td>\n",
       "      <td>3.660189</td>\n",
       "      <td>-3.525145</td>\n",
       "      <td>-6.281121</td>\n",
       "      <td>6.730217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>2002-08-28</td>\n",
       "      <td>9.469093</td>\n",
       "      <td>-4.992221</td>\n",
       "      <td>-28.638227</td>\n",
       "      <td>-11.587743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>2002-08-29</td>\n",
       "      <td>9.821526</td>\n",
       "      <td>-4.469792</td>\n",
       "      <td>-28.074909</td>\n",
       "      <td>-11.036200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>2002-08-30</td>\n",
       "      <td>10.068815</td>\n",
       "      <td>-3.832841</td>\n",
       "      <td>-29.561092</td>\n",
       "      <td>-10.526491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>2002-08-31</td>\n",
       "      <td>9.323920</td>\n",
       "      <td>-4.548075</td>\n",
       "      <td>-29.192959</td>\n",
       "      <td>-12.557164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>2002-09-01</td>\n",
       "      <td>6.950864</td>\n",
       "      <td>-4.787274</td>\n",
       "      <td>-26.883097</td>\n",
       "      <td>-12.624429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>2002-09-02</td>\n",
       "      <td>6.846803</td>\n",
       "      <td>-5.235593</td>\n",
       "      <td>-26.833349</td>\n",
       "      <td>-13.542963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>2002-09-03</td>\n",
       "      <td>6.667062</td>\n",
       "      <td>-5.059516</td>\n",
       "      <td>-28.155307</td>\n",
       "      <td>-13.254016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>2002-09-04</td>\n",
       "      <td>6.298975</td>\n",
       "      <td>-5.485590</td>\n",
       "      <td>-28.053691</td>\n",
       "      <td>-12.700793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>2002-09-05</td>\n",
       "      <td>5.366252</td>\n",
       "      <td>-6.737480</td>\n",
       "      <td>-28.153503</td>\n",
       "      <td>-12.465665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>2002-09-06</td>\n",
       "      <td>5.509072</td>\n",
       "      <td>-5.853635</td>\n",
       "      <td>-30.543992</td>\n",
       "      <td>-13.024947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>2002-09-07</td>\n",
       "      <td>6.444464</td>\n",
       "      <td>-5.587841</td>\n",
       "      <td>-30.385368</td>\n",
       "      <td>-12.371840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>2002-09-08</td>\n",
       "      <td>5.360427</td>\n",
       "      <td>-6.239088</td>\n",
       "      <td>-30.947826</td>\n",
       "      <td>-12.350999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2002-09-09</td>\n",
       "      <td>6.102599</td>\n",
       "      <td>-6.966094</td>\n",
       "      <td>-32.770292</td>\n",
       "      <td>-13.189024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2002-09-10</td>\n",
       "      <td>5.133590</td>\n",
       "      <td>-7.309252</td>\n",
       "      <td>-31.912150</td>\n",
       "      <td>-14.124116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2002-09-11</td>\n",
       "      <td>3.610601</td>\n",
       "      <td>-6.932118</td>\n",
       "      <td>-30.153711</td>\n",
       "      <td>-14.557608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2002-09-12</td>\n",
       "      <td>3.463178</td>\n",
       "      <td>-6.437623</td>\n",
       "      <td>-28.860936</td>\n",
       "      <td>-16.446850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2002-09-13</td>\n",
       "      <td>3.738684</td>\n",
       "      <td>-5.711190</td>\n",
       "      <td>-30.617586</td>\n",
       "      <td>-18.253673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>2002-09-14</td>\n",
       "      <td>2.101444</td>\n",
       "      <td>-3.126954</td>\n",
       "      <td>-30.272345</td>\n",
       "      <td>-18.420285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>2002-09-15</td>\n",
       "      <td>2.186508</td>\n",
       "      <td>-1.863179</td>\n",
       "      <td>-29.196949</td>\n",
       "      <td>-17.554745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>2002-09-16</td>\n",
       "      <td>1.007592</td>\n",
       "      <td>-0.972601</td>\n",
       "      <td>-29.340069</td>\n",
       "      <td>-17.038153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>2002-09-17</td>\n",
       "      <td>0.898140</td>\n",
       "      <td>-1.518578</td>\n",
       "      <td>-28.219815</td>\n",
       "      <td>-17.687190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>2002-09-18</td>\n",
       "      <td>-0.426689</td>\n",
       "      <td>-1.445230</td>\n",
       "      <td>-27.705361</td>\n",
       "      <td>-17.168442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>2002-09-19</td>\n",
       "      <td>0.028842</td>\n",
       "      <td>-0.404111</td>\n",
       "      <td>-29.834897</td>\n",
       "      <td>-16.507815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>2002-09-20</td>\n",
       "      <td>1.980734</td>\n",
       "      <td>1.093095</td>\n",
       "      <td>-28.867127</td>\n",
       "      <td>-17.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>2002-09-21</td>\n",
       "      <td>1.459866</td>\n",
       "      <td>0.377640</td>\n",
       "      <td>-29.285711</td>\n",
       "      <td>-18.649799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>2.322038</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>-28.750717</td>\n",
       "      <td>-16.201329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>2.417480</td>\n",
       "      <td>-1.706039</td>\n",
       "      <td>-30.009441</td>\n",
       "      <td>-15.682765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>1.254112</td>\n",
       "      <td>-1.391825</td>\n",
       "      <td>-30.832163</td>\n",
       "      <td>-15.530378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>1.090618</td>\n",
       "      <td>-1.138936</td>\n",
       "      <td>-30.554229</td>\n",
       "      <td>-15.262121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>0.108832</td>\n",
       "      <td>-2.789369</td>\n",
       "      <td>-30.090637</td>\n",
       "      <td>-14.773398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          A         B          C          D\n",
       "0    2000-01-01  -0.165439 -1.241892  -0.342680   0.106041\n",
       "1    2000-01-02  -1.298616 -1.023435  -0.944371   0.586668\n",
       "2    2000-01-03  -0.714936 -1.392907  -0.323638   1.589524\n",
       "3    2000-01-04   0.202948 -0.704013  -0.162558   1.615731\n",
       "4    2000-01-05  -1.143432 -0.318759  -1.912439   2.029570\n",
       "5    2000-01-06  -0.641053 -0.928672  -1.370465   1.346422\n",
       "6    2000-01-07   0.133213 -1.686737  -2.174208   1.097845\n",
       "7    2000-01-08   0.281839 -1.026250  -3.980469   1.487319\n",
       "8    2000-01-09   0.221473 -2.580209  -4.595592   1.661385\n",
       "9    2000-01-10  -0.146512 -3.149283  -3.549284   1.125317\n",
       "10   2000-01-11   0.514869 -4.022301  -2.768194   1.446292\n",
       "11   2000-01-12  -0.104987 -3.493369  -4.076654   2.125191\n",
       "12   2000-01-13   0.978066 -3.475903  -3.835260   3.059128\n",
       "13   2000-01-14   1.116126 -2.797326  -2.255736   2.763711\n",
       "14   2000-01-15   1.039718 -1.324396  -3.059282   3.110216\n",
       "15   2000-01-16   0.116109 -2.185032  -4.550271   1.112215\n",
       "16   2000-01-17   0.920563 -3.287660  -4.124656   0.563663\n",
       "17   2000-01-18   0.394087 -2.860464  -3.431849   1.494053\n",
       "18   2000-01-19   2.133781 -1.701251  -3.878129   1.981117\n",
       "19   2000-01-20   1.380864 -2.028198  -5.349861   1.759223\n",
       "20   2000-01-21   1.787964 -1.835131  -5.951531   1.855332\n",
       "21   2000-01-22   1.159440 -3.579943  -6.824187   2.528383\n",
       "22   2000-01-23   1.659971 -3.561345  -5.747614   3.150220\n",
       "23   2000-01-24   1.459270 -3.092126  -5.337020   4.048097\n",
       "24   2000-01-25   2.199406 -4.002160  -4.287942   4.292009\n",
       "25   2000-01-26   3.640403 -2.616529  -3.849604   4.566062\n",
       "26   2000-01-27   5.672637 -1.289733  -4.526165   4.850854\n",
       "27   2000-01-28   6.343960 -1.790023  -7.023424   5.369450\n",
       "28   2000-01-29   4.637036 -2.715463  -6.508805   5.646366\n",
       "29   2000-01-30   3.660189 -3.525145  -6.281121   6.730217\n",
       "..          ...        ...       ...        ...        ...\n",
       "970  2002-08-28   9.469093 -4.992221 -28.638227 -11.587743\n",
       "971  2002-08-29   9.821526 -4.469792 -28.074909 -11.036200\n",
       "972  2002-08-30  10.068815 -3.832841 -29.561092 -10.526491\n",
       "973  2002-08-31   9.323920 -4.548075 -29.192959 -12.557164\n",
       "974  2002-09-01   6.950864 -4.787274 -26.883097 -12.624429\n",
       "975  2002-09-02   6.846803 -5.235593 -26.833349 -13.542963\n",
       "976  2002-09-03   6.667062 -5.059516 -28.155307 -13.254016\n",
       "977  2002-09-04   6.298975 -5.485590 -28.053691 -12.700793\n",
       "978  2002-09-05   5.366252 -6.737480 -28.153503 -12.465665\n",
       "979  2002-09-06   5.509072 -5.853635 -30.543992 -13.024947\n",
       "980  2002-09-07   6.444464 -5.587841 -30.385368 -12.371840\n",
       "981  2002-09-08   5.360427 -6.239088 -30.947826 -12.350999\n",
       "982  2002-09-09   6.102599 -6.966094 -32.770292 -13.189024\n",
       "983  2002-09-10   5.133590 -7.309252 -31.912150 -14.124116\n",
       "984  2002-09-11   3.610601 -6.932118 -30.153711 -14.557608\n",
       "985  2002-09-12   3.463178 -6.437623 -28.860936 -16.446850\n",
       "986  2002-09-13   3.738684 -5.711190 -30.617586 -18.253673\n",
       "987  2002-09-14   2.101444 -3.126954 -30.272345 -18.420285\n",
       "988  2002-09-15   2.186508 -1.863179 -29.196949 -17.554745\n",
       "989  2002-09-16   1.007592 -0.972601 -29.340069 -17.038153\n",
       "990  2002-09-17   0.898140 -1.518578 -28.219815 -17.687190\n",
       "991  2002-09-18  -0.426689 -1.445230 -27.705361 -17.168442\n",
       "992  2002-09-19   0.028842 -0.404111 -29.834897 -16.507815\n",
       "993  2002-09-20   1.980734  1.093095 -28.867127 -17.254193\n",
       "994  2002-09-21   1.459866  0.377640 -29.285711 -18.649799\n",
       "995  2002-09-22   2.322038  0.098393 -28.750717 -16.201329\n",
       "996  2002-09-23   2.417480 -1.706039 -30.009441 -15.682765\n",
       "997  2002-09-24   1.254112 -1.391825 -30.832163 -15.530378\n",
       "998  2002-09-25   1.090618 -1.138936 -30.554229 -15.262121\n",
       "999  2002-09-26   0.108832 -2.789369 -30.090637 -14.773398\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('foo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9150ae74-87f2-45cc-be98-402a4306eb0a",
    "_uuid": "fce91364024568435d238e35732efc3679beccd5"
   },
   "source": [
    "### HDF5\n",
    "Reading and writing to [HDFStores](https://pandas.pydata.org/pandas-docs/stable/io.html#io-hdf5).\n",
    "\n",
    "Writing to a HDF5 Store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_cell_guid": "b94c044e-244b-493f-9278-43690d36694f",
    "_uuid": "a3fb0c63d5aadac15d06e76c56ada820058ef667",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1ba92b6b-8e82-438d-91c8-cbe9990d0aef",
    "_uuid": "ae23a982a9599ad470198ba616318d781b71496a"
   },
   "source": [
    "Reading from a HDF5 Store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_cell_guid": "0d0cc9f7-50e5-4aef-870b-2bb728223f2d",
    "_uuid": "6920cb4f6cfcbb0cfcf806447ef236051f8c1b45",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.165439</td>\n",
       "      <td>-1.241892</td>\n",
       "      <td>-0.342680</td>\n",
       "      <td>0.106041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.298616</td>\n",
       "      <td>-1.023435</td>\n",
       "      <td>-0.944371</td>\n",
       "      <td>0.586668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.714936</td>\n",
       "      <td>-1.392907</td>\n",
       "      <td>-0.323638</td>\n",
       "      <td>1.589524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.202948</td>\n",
       "      <td>-0.704013</td>\n",
       "      <td>-0.162558</td>\n",
       "      <td>1.615731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.143432</td>\n",
       "      <td>-0.318759</td>\n",
       "      <td>-1.912439</td>\n",
       "      <td>2.029570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.641053</td>\n",
       "      <td>-0.928672</td>\n",
       "      <td>-1.370465</td>\n",
       "      <td>1.346422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.133213</td>\n",
       "      <td>-1.686737</td>\n",
       "      <td>-2.174208</td>\n",
       "      <td>1.097845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.281839</td>\n",
       "      <td>-1.026250</td>\n",
       "      <td>-3.980469</td>\n",
       "      <td>1.487319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>0.221473</td>\n",
       "      <td>-2.580209</td>\n",
       "      <td>-4.595592</td>\n",
       "      <td>1.661385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-0.146512</td>\n",
       "      <td>-3.149283</td>\n",
       "      <td>-3.549284</td>\n",
       "      <td>1.125317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.514869</td>\n",
       "      <td>-4.022301</td>\n",
       "      <td>-2.768194</td>\n",
       "      <td>1.446292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.104987</td>\n",
       "      <td>-3.493369</td>\n",
       "      <td>-4.076654</td>\n",
       "      <td>2.125191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>0.978066</td>\n",
       "      <td>-3.475903</td>\n",
       "      <td>-3.835260</td>\n",
       "      <td>3.059128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.116126</td>\n",
       "      <td>-2.797326</td>\n",
       "      <td>-2.255736</td>\n",
       "      <td>2.763711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.039718</td>\n",
       "      <td>-1.324396</td>\n",
       "      <td>-3.059282</td>\n",
       "      <td>3.110216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>0.116109</td>\n",
       "      <td>-2.185032</td>\n",
       "      <td>-4.550271</td>\n",
       "      <td>1.112215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>0.920563</td>\n",
       "      <td>-3.287660</td>\n",
       "      <td>-4.124656</td>\n",
       "      <td>0.563663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>0.394087</td>\n",
       "      <td>-2.860464</td>\n",
       "      <td>-3.431849</td>\n",
       "      <td>1.494053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>2.133781</td>\n",
       "      <td>-1.701251</td>\n",
       "      <td>-3.878129</td>\n",
       "      <td>1.981117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>1.380864</td>\n",
       "      <td>-2.028198</td>\n",
       "      <td>-5.349861</td>\n",
       "      <td>1.759223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>1.787964</td>\n",
       "      <td>-1.835131</td>\n",
       "      <td>-5.951531</td>\n",
       "      <td>1.855332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>1.159440</td>\n",
       "      <td>-3.579943</td>\n",
       "      <td>-6.824187</td>\n",
       "      <td>2.528383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>1.659971</td>\n",
       "      <td>-3.561345</td>\n",
       "      <td>-5.747614</td>\n",
       "      <td>3.150220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>1.459270</td>\n",
       "      <td>-3.092126</td>\n",
       "      <td>-5.337020</td>\n",
       "      <td>4.048097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>2.199406</td>\n",
       "      <td>-4.002160</td>\n",
       "      <td>-4.287942</td>\n",
       "      <td>4.292009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>3.640403</td>\n",
       "      <td>-2.616529</td>\n",
       "      <td>-3.849604</td>\n",
       "      <td>4.566062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>5.672637</td>\n",
       "      <td>-1.289733</td>\n",
       "      <td>-4.526165</td>\n",
       "      <td>4.850854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>6.343960</td>\n",
       "      <td>-1.790023</td>\n",
       "      <td>-7.023424</td>\n",
       "      <td>5.369450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>4.637036</td>\n",
       "      <td>-2.715463</td>\n",
       "      <td>-6.508805</td>\n",
       "      <td>5.646366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>3.660189</td>\n",
       "      <td>-3.525145</td>\n",
       "      <td>-6.281121</td>\n",
       "      <td>6.730217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>9.469093</td>\n",
       "      <td>-4.992221</td>\n",
       "      <td>-28.638227</td>\n",
       "      <td>-11.587743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>9.821526</td>\n",
       "      <td>-4.469792</td>\n",
       "      <td>-28.074909</td>\n",
       "      <td>-11.036200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>10.068815</td>\n",
       "      <td>-3.832841</td>\n",
       "      <td>-29.561092</td>\n",
       "      <td>-10.526491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>9.323920</td>\n",
       "      <td>-4.548075</td>\n",
       "      <td>-29.192959</td>\n",
       "      <td>-12.557164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>6.950864</td>\n",
       "      <td>-4.787274</td>\n",
       "      <td>-26.883097</td>\n",
       "      <td>-12.624429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>6.846803</td>\n",
       "      <td>-5.235593</td>\n",
       "      <td>-26.833349</td>\n",
       "      <td>-13.542963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>6.667062</td>\n",
       "      <td>-5.059516</td>\n",
       "      <td>-28.155307</td>\n",
       "      <td>-13.254016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>6.298975</td>\n",
       "      <td>-5.485590</td>\n",
       "      <td>-28.053691</td>\n",
       "      <td>-12.700793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>5.366252</td>\n",
       "      <td>-6.737480</td>\n",
       "      <td>-28.153503</td>\n",
       "      <td>-12.465665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>5.509072</td>\n",
       "      <td>-5.853635</td>\n",
       "      <td>-30.543992</td>\n",
       "      <td>-13.024947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>6.444464</td>\n",
       "      <td>-5.587841</td>\n",
       "      <td>-30.385368</td>\n",
       "      <td>-12.371840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>5.360427</td>\n",
       "      <td>-6.239088</td>\n",
       "      <td>-30.947826</td>\n",
       "      <td>-12.350999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>6.102599</td>\n",
       "      <td>-6.966094</td>\n",
       "      <td>-32.770292</td>\n",
       "      <td>-13.189024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>5.133590</td>\n",
       "      <td>-7.309252</td>\n",
       "      <td>-31.912150</td>\n",
       "      <td>-14.124116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>3.610601</td>\n",
       "      <td>-6.932118</td>\n",
       "      <td>-30.153711</td>\n",
       "      <td>-14.557608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>3.463178</td>\n",
       "      <td>-6.437623</td>\n",
       "      <td>-28.860936</td>\n",
       "      <td>-16.446850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>3.738684</td>\n",
       "      <td>-5.711190</td>\n",
       "      <td>-30.617586</td>\n",
       "      <td>-18.253673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>2.101444</td>\n",
       "      <td>-3.126954</td>\n",
       "      <td>-30.272345</td>\n",
       "      <td>-18.420285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>2.186508</td>\n",
       "      <td>-1.863179</td>\n",
       "      <td>-29.196949</td>\n",
       "      <td>-17.554745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>1.007592</td>\n",
       "      <td>-0.972601</td>\n",
       "      <td>-29.340069</td>\n",
       "      <td>-17.038153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>0.898140</td>\n",
       "      <td>-1.518578</td>\n",
       "      <td>-28.219815</td>\n",
       "      <td>-17.687190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>-0.426689</td>\n",
       "      <td>-1.445230</td>\n",
       "      <td>-27.705361</td>\n",
       "      <td>-17.168442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>0.028842</td>\n",
       "      <td>-0.404111</td>\n",
       "      <td>-29.834897</td>\n",
       "      <td>-16.507815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>1.980734</td>\n",
       "      <td>1.093095</td>\n",
       "      <td>-28.867127</td>\n",
       "      <td>-17.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>1.459866</td>\n",
       "      <td>0.377640</td>\n",
       "      <td>-29.285711</td>\n",
       "      <td>-18.649799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>2.322038</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>-28.750717</td>\n",
       "      <td>-16.201329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>2.417480</td>\n",
       "      <td>-1.706039</td>\n",
       "      <td>-30.009441</td>\n",
       "      <td>-15.682765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>1.254112</td>\n",
       "      <td>-1.391825</td>\n",
       "      <td>-30.832163</td>\n",
       "      <td>-15.530378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>1.090618</td>\n",
       "      <td>-1.138936</td>\n",
       "      <td>-30.554229</td>\n",
       "      <td>-15.262121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>0.108832</td>\n",
       "      <td>-2.789369</td>\n",
       "      <td>-30.090637</td>\n",
       "      <td>-14.773398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A         B          C          D\n",
       "2000-01-01  -0.165439 -1.241892  -0.342680   0.106041\n",
       "2000-01-02  -1.298616 -1.023435  -0.944371   0.586668\n",
       "2000-01-03  -0.714936 -1.392907  -0.323638   1.589524\n",
       "2000-01-04   0.202948 -0.704013  -0.162558   1.615731\n",
       "2000-01-05  -1.143432 -0.318759  -1.912439   2.029570\n",
       "2000-01-06  -0.641053 -0.928672  -1.370465   1.346422\n",
       "2000-01-07   0.133213 -1.686737  -2.174208   1.097845\n",
       "2000-01-08   0.281839 -1.026250  -3.980469   1.487319\n",
       "2000-01-09   0.221473 -2.580209  -4.595592   1.661385\n",
       "2000-01-10  -0.146512 -3.149283  -3.549284   1.125317\n",
       "2000-01-11   0.514869 -4.022301  -2.768194   1.446292\n",
       "2000-01-12  -0.104987 -3.493369  -4.076654   2.125191\n",
       "2000-01-13   0.978066 -3.475903  -3.835260   3.059128\n",
       "2000-01-14   1.116126 -2.797326  -2.255736   2.763711\n",
       "2000-01-15   1.039718 -1.324396  -3.059282   3.110216\n",
       "2000-01-16   0.116109 -2.185032  -4.550271   1.112215\n",
       "2000-01-17   0.920563 -3.287660  -4.124656   0.563663\n",
       "2000-01-18   0.394087 -2.860464  -3.431849   1.494053\n",
       "2000-01-19   2.133781 -1.701251  -3.878129   1.981117\n",
       "2000-01-20   1.380864 -2.028198  -5.349861   1.759223\n",
       "2000-01-21   1.787964 -1.835131  -5.951531   1.855332\n",
       "2000-01-22   1.159440 -3.579943  -6.824187   2.528383\n",
       "2000-01-23   1.659971 -3.561345  -5.747614   3.150220\n",
       "2000-01-24   1.459270 -3.092126  -5.337020   4.048097\n",
       "2000-01-25   2.199406 -4.002160  -4.287942   4.292009\n",
       "2000-01-26   3.640403 -2.616529  -3.849604   4.566062\n",
       "2000-01-27   5.672637 -1.289733  -4.526165   4.850854\n",
       "2000-01-28   6.343960 -1.790023  -7.023424   5.369450\n",
       "2000-01-29   4.637036 -2.715463  -6.508805   5.646366\n",
       "2000-01-30   3.660189 -3.525145  -6.281121   6.730217\n",
       "...               ...       ...        ...        ...\n",
       "2002-08-28   9.469093 -4.992221 -28.638227 -11.587743\n",
       "2002-08-29   9.821526 -4.469792 -28.074909 -11.036200\n",
       "2002-08-30  10.068815 -3.832841 -29.561092 -10.526491\n",
       "2002-08-31   9.323920 -4.548075 -29.192959 -12.557164\n",
       "2002-09-01   6.950864 -4.787274 -26.883097 -12.624429\n",
       "2002-09-02   6.846803 -5.235593 -26.833349 -13.542963\n",
       "2002-09-03   6.667062 -5.059516 -28.155307 -13.254016\n",
       "2002-09-04   6.298975 -5.485590 -28.053691 -12.700793\n",
       "2002-09-05   5.366252 -6.737480 -28.153503 -12.465665\n",
       "2002-09-06   5.509072 -5.853635 -30.543992 -13.024947\n",
       "2002-09-07   6.444464 -5.587841 -30.385368 -12.371840\n",
       "2002-09-08   5.360427 -6.239088 -30.947826 -12.350999\n",
       "2002-09-09   6.102599 -6.966094 -32.770292 -13.189024\n",
       "2002-09-10   5.133590 -7.309252 -31.912150 -14.124116\n",
       "2002-09-11   3.610601 -6.932118 -30.153711 -14.557608\n",
       "2002-09-12   3.463178 -6.437623 -28.860936 -16.446850\n",
       "2002-09-13   3.738684 -5.711190 -30.617586 -18.253673\n",
       "2002-09-14   2.101444 -3.126954 -30.272345 -18.420285\n",
       "2002-09-15   2.186508 -1.863179 -29.196949 -17.554745\n",
       "2002-09-16   1.007592 -0.972601 -29.340069 -17.038153\n",
       "2002-09-17   0.898140 -1.518578 -28.219815 -17.687190\n",
       "2002-09-18  -0.426689 -1.445230 -27.705361 -17.168442\n",
       "2002-09-19   0.028842 -0.404111 -29.834897 -16.507815\n",
       "2002-09-20   1.980734  1.093095 -28.867127 -17.254193\n",
       "2002-09-21   1.459866  0.377640 -29.285711 -18.649799\n",
       "2002-09-22   2.322038  0.098393 -28.750717 -16.201329\n",
       "2002-09-23   2.417480 -1.706039 -30.009441 -15.682765\n",
       "2002-09-24   1.254112 -1.391825 -30.832163 -15.530378\n",
       "2002-09-25   1.090618 -1.138936 -30.554229 -15.262121\n",
       "2002-09-26   0.108832 -2.789369 -30.090637 -14.773398\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_hdf('foo.h5','df')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "577ea484-5412-4e56-bce9-e7ac085f43e7",
    "_uuid": "1ed3197b98a526670ad2e5fc2fd3d783f4aa26df"
   },
   "source": [
    "### Excel\n",
    "Reading and writing to [MS Excel](https://pandas.pydata.org/pandas-docs/stable/io.html#io-excel).\n",
    "\n",
    "Writing to an excel file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_cell_guid": "56ebcc49-1c6e-4fba-b0be-885bd38c8edd",
    "_uuid": "b4aa25c0d51d6981aefeb56d369f73892d89bf75",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_excel('foo.xlsx', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "516aeff5-882d-464f-a4a8-23c39a9faa78",
    "_uuid": "53c3b30580b3f53be31cf66c379f0a930b5ebe6d"
   },
   "source": [
    "Reading from an excel file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_cell_guid": "7d1fb689-56d4-4ebf-bf7e-775f55268861",
    "_uuid": "80517bdf809673c3b298888c8b2e0835e1f862dc",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>-0.165439</td>\n",
       "      <td>-1.241892</td>\n",
       "      <td>-0.342680</td>\n",
       "      <td>0.106041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-1.298616</td>\n",
       "      <td>-1.023435</td>\n",
       "      <td>-0.944371</td>\n",
       "      <td>0.586668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>-0.714936</td>\n",
       "      <td>-1.392907</td>\n",
       "      <td>-0.323638</td>\n",
       "      <td>1.589524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>0.202948</td>\n",
       "      <td>-0.704013</td>\n",
       "      <td>-0.162558</td>\n",
       "      <td>1.615731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>-1.143432</td>\n",
       "      <td>-0.318759</td>\n",
       "      <td>-1.912439</td>\n",
       "      <td>2.029570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>-0.641053</td>\n",
       "      <td>-0.928672</td>\n",
       "      <td>-1.370465</td>\n",
       "      <td>1.346422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-07</th>\n",
       "      <td>0.133213</td>\n",
       "      <td>-1.686737</td>\n",
       "      <td>-2.174208</td>\n",
       "      <td>1.097845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-08</th>\n",
       "      <td>0.281839</td>\n",
       "      <td>-1.026250</td>\n",
       "      <td>-3.980469</td>\n",
       "      <td>1.487319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-09</th>\n",
       "      <td>0.221473</td>\n",
       "      <td>-2.580209</td>\n",
       "      <td>-4.595592</td>\n",
       "      <td>1.661385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-10</th>\n",
       "      <td>-0.146512</td>\n",
       "      <td>-3.149283</td>\n",
       "      <td>-3.549284</td>\n",
       "      <td>1.125317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-11</th>\n",
       "      <td>0.514869</td>\n",
       "      <td>-4.022301</td>\n",
       "      <td>-2.768194</td>\n",
       "      <td>1.446292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-12</th>\n",
       "      <td>-0.104987</td>\n",
       "      <td>-3.493369</td>\n",
       "      <td>-4.076654</td>\n",
       "      <td>2.125191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-13</th>\n",
       "      <td>0.978066</td>\n",
       "      <td>-3.475903</td>\n",
       "      <td>-3.835260</td>\n",
       "      <td>3.059128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-14</th>\n",
       "      <td>1.116126</td>\n",
       "      <td>-2.797326</td>\n",
       "      <td>-2.255736</td>\n",
       "      <td>2.763711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-15</th>\n",
       "      <td>1.039718</td>\n",
       "      <td>-1.324396</td>\n",
       "      <td>-3.059282</td>\n",
       "      <td>3.110216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-16</th>\n",
       "      <td>0.116109</td>\n",
       "      <td>-2.185032</td>\n",
       "      <td>-4.550271</td>\n",
       "      <td>1.112215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-17</th>\n",
       "      <td>0.920563</td>\n",
       "      <td>-3.287660</td>\n",
       "      <td>-4.124656</td>\n",
       "      <td>0.563663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-18</th>\n",
       "      <td>0.394087</td>\n",
       "      <td>-2.860464</td>\n",
       "      <td>-3.431849</td>\n",
       "      <td>1.494053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-19</th>\n",
       "      <td>2.133781</td>\n",
       "      <td>-1.701251</td>\n",
       "      <td>-3.878129</td>\n",
       "      <td>1.981117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-20</th>\n",
       "      <td>1.380864</td>\n",
       "      <td>-2.028198</td>\n",
       "      <td>-5.349861</td>\n",
       "      <td>1.759223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-21</th>\n",
       "      <td>1.787964</td>\n",
       "      <td>-1.835131</td>\n",
       "      <td>-5.951531</td>\n",
       "      <td>1.855332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-22</th>\n",
       "      <td>1.159440</td>\n",
       "      <td>-3.579943</td>\n",
       "      <td>-6.824187</td>\n",
       "      <td>2.528383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-23</th>\n",
       "      <td>1.659971</td>\n",
       "      <td>-3.561345</td>\n",
       "      <td>-5.747614</td>\n",
       "      <td>3.150220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-24</th>\n",
       "      <td>1.459270</td>\n",
       "      <td>-3.092126</td>\n",
       "      <td>-5.337020</td>\n",
       "      <td>4.048097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-25</th>\n",
       "      <td>2.199406</td>\n",
       "      <td>-4.002160</td>\n",
       "      <td>-4.287942</td>\n",
       "      <td>4.292009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-26</th>\n",
       "      <td>3.640403</td>\n",
       "      <td>-2.616529</td>\n",
       "      <td>-3.849604</td>\n",
       "      <td>4.566062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-27</th>\n",
       "      <td>5.672637</td>\n",
       "      <td>-1.289733</td>\n",
       "      <td>-4.526165</td>\n",
       "      <td>4.850854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-28</th>\n",
       "      <td>6.343960</td>\n",
       "      <td>-1.790023</td>\n",
       "      <td>-7.023424</td>\n",
       "      <td>5.369450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-29</th>\n",
       "      <td>4.637036</td>\n",
       "      <td>-2.715463</td>\n",
       "      <td>-6.508805</td>\n",
       "      <td>5.646366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-30</th>\n",
       "      <td>3.660189</td>\n",
       "      <td>-3.525145</td>\n",
       "      <td>-6.281121</td>\n",
       "      <td>6.730217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-28</th>\n",
       "      <td>9.469093</td>\n",
       "      <td>-4.992221</td>\n",
       "      <td>-28.638227</td>\n",
       "      <td>-11.587743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-29</th>\n",
       "      <td>9.821526</td>\n",
       "      <td>-4.469792</td>\n",
       "      <td>-28.074909</td>\n",
       "      <td>-11.036200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-30</th>\n",
       "      <td>10.068815</td>\n",
       "      <td>-3.832841</td>\n",
       "      <td>-29.561092</td>\n",
       "      <td>-10.526491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-31</th>\n",
       "      <td>9.323920</td>\n",
       "      <td>-4.548075</td>\n",
       "      <td>-29.192959</td>\n",
       "      <td>-12.557164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-01</th>\n",
       "      <td>6.950864</td>\n",
       "      <td>-4.787274</td>\n",
       "      <td>-26.883097</td>\n",
       "      <td>-12.624429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-02</th>\n",
       "      <td>6.846803</td>\n",
       "      <td>-5.235593</td>\n",
       "      <td>-26.833349</td>\n",
       "      <td>-13.542963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-03</th>\n",
       "      <td>6.667062</td>\n",
       "      <td>-5.059516</td>\n",
       "      <td>-28.155307</td>\n",
       "      <td>-13.254016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-04</th>\n",
       "      <td>6.298975</td>\n",
       "      <td>-5.485590</td>\n",
       "      <td>-28.053691</td>\n",
       "      <td>-12.700793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-05</th>\n",
       "      <td>5.366252</td>\n",
       "      <td>-6.737480</td>\n",
       "      <td>-28.153503</td>\n",
       "      <td>-12.465665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-06</th>\n",
       "      <td>5.509072</td>\n",
       "      <td>-5.853635</td>\n",
       "      <td>-30.543992</td>\n",
       "      <td>-13.024947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-07</th>\n",
       "      <td>6.444464</td>\n",
       "      <td>-5.587841</td>\n",
       "      <td>-30.385368</td>\n",
       "      <td>-12.371840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-08</th>\n",
       "      <td>5.360427</td>\n",
       "      <td>-6.239088</td>\n",
       "      <td>-30.947826</td>\n",
       "      <td>-12.350999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-09</th>\n",
       "      <td>6.102599</td>\n",
       "      <td>-6.966094</td>\n",
       "      <td>-32.770292</td>\n",
       "      <td>-13.189024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-10</th>\n",
       "      <td>5.133590</td>\n",
       "      <td>-7.309252</td>\n",
       "      <td>-31.912150</td>\n",
       "      <td>-14.124116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-11</th>\n",
       "      <td>3.610601</td>\n",
       "      <td>-6.932118</td>\n",
       "      <td>-30.153711</td>\n",
       "      <td>-14.557608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-12</th>\n",
       "      <td>3.463178</td>\n",
       "      <td>-6.437623</td>\n",
       "      <td>-28.860936</td>\n",
       "      <td>-16.446850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-13</th>\n",
       "      <td>3.738684</td>\n",
       "      <td>-5.711190</td>\n",
       "      <td>-30.617586</td>\n",
       "      <td>-18.253673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-14</th>\n",
       "      <td>2.101444</td>\n",
       "      <td>-3.126954</td>\n",
       "      <td>-30.272345</td>\n",
       "      <td>-18.420285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-15</th>\n",
       "      <td>2.186508</td>\n",
       "      <td>-1.863179</td>\n",
       "      <td>-29.196949</td>\n",
       "      <td>-17.554745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-16</th>\n",
       "      <td>1.007592</td>\n",
       "      <td>-0.972601</td>\n",
       "      <td>-29.340069</td>\n",
       "      <td>-17.038153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-17</th>\n",
       "      <td>0.898140</td>\n",
       "      <td>-1.518578</td>\n",
       "      <td>-28.219815</td>\n",
       "      <td>-17.687190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-18</th>\n",
       "      <td>-0.426689</td>\n",
       "      <td>-1.445230</td>\n",
       "      <td>-27.705361</td>\n",
       "      <td>-17.168442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-19</th>\n",
       "      <td>0.028842</td>\n",
       "      <td>-0.404111</td>\n",
       "      <td>-29.834897</td>\n",
       "      <td>-16.507815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-20</th>\n",
       "      <td>1.980734</td>\n",
       "      <td>1.093095</td>\n",
       "      <td>-28.867127</td>\n",
       "      <td>-17.254193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-21</th>\n",
       "      <td>1.459866</td>\n",
       "      <td>0.377640</td>\n",
       "      <td>-29.285711</td>\n",
       "      <td>-18.649799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-22</th>\n",
       "      <td>2.322038</td>\n",
       "      <td>0.098393</td>\n",
       "      <td>-28.750717</td>\n",
       "      <td>-16.201329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-23</th>\n",
       "      <td>2.417480</td>\n",
       "      <td>-1.706039</td>\n",
       "      <td>-30.009441</td>\n",
       "      <td>-15.682765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-24</th>\n",
       "      <td>1.254112</td>\n",
       "      <td>-1.391825</td>\n",
       "      <td>-30.832163</td>\n",
       "      <td>-15.530378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-25</th>\n",
       "      <td>1.090618</td>\n",
       "      <td>-1.138936</td>\n",
       "      <td>-30.554229</td>\n",
       "      <td>-15.262121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-09-26</th>\n",
       "      <td>0.108832</td>\n",
       "      <td>-2.789369</td>\n",
       "      <td>-30.090637</td>\n",
       "      <td>-14.773398</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    A         B          C          D\n",
       "2000-01-01  -0.165439 -1.241892  -0.342680   0.106041\n",
       "2000-01-02  -1.298616 -1.023435  -0.944371   0.586668\n",
       "2000-01-03  -0.714936 -1.392907  -0.323638   1.589524\n",
       "2000-01-04   0.202948 -0.704013  -0.162558   1.615731\n",
       "2000-01-05  -1.143432 -0.318759  -1.912439   2.029570\n",
       "2000-01-06  -0.641053 -0.928672  -1.370465   1.346422\n",
       "2000-01-07   0.133213 -1.686737  -2.174208   1.097845\n",
       "2000-01-08   0.281839 -1.026250  -3.980469   1.487319\n",
       "2000-01-09   0.221473 -2.580209  -4.595592   1.661385\n",
       "2000-01-10  -0.146512 -3.149283  -3.549284   1.125317\n",
       "2000-01-11   0.514869 -4.022301  -2.768194   1.446292\n",
       "2000-01-12  -0.104987 -3.493369  -4.076654   2.125191\n",
       "2000-01-13   0.978066 -3.475903  -3.835260   3.059128\n",
       "2000-01-14   1.116126 -2.797326  -2.255736   2.763711\n",
       "2000-01-15   1.039718 -1.324396  -3.059282   3.110216\n",
       "2000-01-16   0.116109 -2.185032  -4.550271   1.112215\n",
       "2000-01-17   0.920563 -3.287660  -4.124656   0.563663\n",
       "2000-01-18   0.394087 -2.860464  -3.431849   1.494053\n",
       "2000-01-19   2.133781 -1.701251  -3.878129   1.981117\n",
       "2000-01-20   1.380864 -2.028198  -5.349861   1.759223\n",
       "2000-01-21   1.787964 -1.835131  -5.951531   1.855332\n",
       "2000-01-22   1.159440 -3.579943  -6.824187   2.528383\n",
       "2000-01-23   1.659971 -3.561345  -5.747614   3.150220\n",
       "2000-01-24   1.459270 -3.092126  -5.337020   4.048097\n",
       "2000-01-25   2.199406 -4.002160  -4.287942   4.292009\n",
       "2000-01-26   3.640403 -2.616529  -3.849604   4.566062\n",
       "2000-01-27   5.672637 -1.289733  -4.526165   4.850854\n",
       "2000-01-28   6.343960 -1.790023  -7.023424   5.369450\n",
       "2000-01-29   4.637036 -2.715463  -6.508805   5.646366\n",
       "2000-01-30   3.660189 -3.525145  -6.281121   6.730217\n",
       "...               ...       ...        ...        ...\n",
       "2002-08-28   9.469093 -4.992221 -28.638227 -11.587743\n",
       "2002-08-29   9.821526 -4.469792 -28.074909 -11.036200\n",
       "2002-08-30  10.068815 -3.832841 -29.561092 -10.526491\n",
       "2002-08-31   9.323920 -4.548075 -29.192959 -12.557164\n",
       "2002-09-01   6.950864 -4.787274 -26.883097 -12.624429\n",
       "2002-09-02   6.846803 -5.235593 -26.833349 -13.542963\n",
       "2002-09-03   6.667062 -5.059516 -28.155307 -13.254016\n",
       "2002-09-04   6.298975 -5.485590 -28.053691 -12.700793\n",
       "2002-09-05   5.366252 -6.737480 -28.153503 -12.465665\n",
       "2002-09-06   5.509072 -5.853635 -30.543992 -13.024947\n",
       "2002-09-07   6.444464 -5.587841 -30.385368 -12.371840\n",
       "2002-09-08   5.360427 -6.239088 -30.947826 -12.350999\n",
       "2002-09-09   6.102599 -6.966094 -32.770292 -13.189024\n",
       "2002-09-10   5.133590 -7.309252 -31.912150 -14.124116\n",
       "2002-09-11   3.610601 -6.932118 -30.153711 -14.557608\n",
       "2002-09-12   3.463178 -6.437623 -28.860936 -16.446850\n",
       "2002-09-13   3.738684 -5.711190 -30.617586 -18.253673\n",
       "2002-09-14   2.101444 -3.126954 -30.272345 -18.420285\n",
       "2002-09-15   2.186508 -1.863179 -29.196949 -17.554745\n",
       "2002-09-16   1.007592 -0.972601 -29.340069 -17.038153\n",
       "2002-09-17   0.898140 -1.518578 -28.219815 -17.687190\n",
       "2002-09-18  -0.426689 -1.445230 -27.705361 -17.168442\n",
       "2002-09-19   0.028842 -0.404111 -29.834897 -16.507815\n",
       "2002-09-20   1.980734  1.093095 -28.867127 -17.254193\n",
       "2002-09-21   1.459866  0.377640 -29.285711 -18.649799\n",
       "2002-09-22   2.322038  0.098393 -28.750717 -16.201329\n",
       "2002-09-23   2.417480 -1.706039 -30.009441 -15.682765\n",
       "2002-09-24   1.254112 -1.391825 -30.832163 -15.530378\n",
       "2002-09-25   1.090618 -1.138936 -30.554229 -15.262121\n",
       "2002-09-26   0.108832 -2.789369 -30.090637 -14.773398\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "830c58b5-67de-4ba4-9085-1bd6f1c36103",
    "_uuid": "94a7efe1ddf6a862c752a87095e6b3d9fe895a93"
   },
   "source": [
    "# Gotchas\n",
    "If you are trying an operation and you see an exception like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_cell_guid": "68e21fe7-fdee-4f1f-a6be-421232f995be",
    "_uuid": "dff1fa5f0b4cedf3d85de3d630ee37071442a840",
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-100-9cae3ab0f79f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"I was true\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    953\u001b[0m         raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m    954\u001b[0m                          \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 955\u001b[0;31m                          .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m    956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m     \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "if pd.Series([False, True, False]):\n",
    "    print(\"I was true\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8ec225d7-810b-445a-a9e2-4b8ca63c9585",
    "_uuid": "ee08221d61b141f4442820ecc5b749b1f66127a0"
   },
   "source": [
    "See[ Comparisons](https://pandas.pydata.org/pandas-docs/stable/basics.html#basics-compare) for an explanation and what to do.\n",
    "\n",
    "See [Gotchas](https://pandas.pydata.org/pandas-docs/stable/gotchas.html#gotchas) as well."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}