Ejercicio_Outliers.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers en 1 Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:09.722788Z",
     "start_time": "2020-06-01T23:16:09.688933Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from math import pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:10.052976Z",
     "start_time": "2020-06-01T23:16:09.729252Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edades = np.array([22,22,23,23,23,23,26,27,27,28,30,30,30,30,31,32,33,34,80])\n",
    "edad_unique, counts = np.unique(edades, return_counts=True)\n",
    "sizes = counts*100\n",
    "colors = ['blue']*len(edad_unique)\n",
    "colors[-1] = 'red'\n",
    "\n",
    "plt.axhline(1, color='k', linestyle='--')\n",
    "plt.scatter(edad_unique, np.ones(len(edad_unique)), s=sizes, color=colors)\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers en 2 Dimensiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:10.615068Z",
     "start_time": "2020-06-01T23:16:10.055915Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "salario_anual_miles = np.array([16,20,15,21,19,17,33,22,31,32,56,30,22,31,30,16,2,22,23])\n",
    "media = (salario_anual_miles).mean()\n",
    "std_x = (salario_anual_miles).std()*2\n",
    "media_y = (edades).mean()\n",
    "std_y = (edades).std()*2\n",
    "\n",
    "colors = ['blue']*len(salario_anual_miles)\n",
    "for index, x in enumerate(salario_anual_miles):\n",
    "    if abs(x-media) > std_x:\n",
    "        colors[index] = 'red'\n",
    "        \n",
    "for index, x in enumerate(edades):\n",
    "    if abs(x-media_y) > std_y:\n",
    "        colors[index] = 'red'\n",
    "\n",
    "plt.scatter(edades, salario_anual_miles, s=100, color=colors)\n",
    "plt.axhline(media, color='k', linestyle='--')\n",
    "plt.axvline(media_y, color='k', linestyle='--')\n",
    "\n",
    "v=media     #y-position of the center\n",
    "u=media_y    #x-position of the center\n",
    "b=std_x     #radius on the y-axis\n",
    "a=std_y    #radius on the x-axis\n",
    "\n",
    "t = np.linspace(0, 2*pi, 100)\n",
    "plt.plot( u+a*np.cos(t) , v+b*np.sin(t) )\n",
    "\n",
    "plt.xlabel('Edad')\n",
    "plt.ylabel('Salario Anual (miles)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers en 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:10.839244Z",
     "start_time": "2020-06-01T23:16:10.618600Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure(figsize=(7,7))\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "compras_mes = np.array([1,2,1,20,1,0,3,2,3,0,5,3,2,1,0,1,2,2,2])\n",
    "media_z = (compras_mes).mean()\n",
    "std_z = (compras_mes).std()*2\n",
    "\n",
    "for index, x in enumerate(compras_mes):\n",
    "    if abs(x-media_z) > std_z:\n",
    "        colors[index] = 'red'\n",
    "\n",
    "ax.scatter(edades, salario_anual_miles, compras_mes, s=20, c=colors)\n",
    "plt.xlabel('Edad')\n",
    "plt.ylabel('Salario Anual (miles)')\n",
    "ax.set_zlabel('Compras mensuales')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers usando Boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:10.996385Z",
     "start_time": "2020-06-01T23:16:10.841529Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1a2529d908>,\n",
       "  <matplotlib.lines.Line2D at 0x1a2529ddd8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1a25286278>,\n",
       "  <matplotlib.lines.Line2D at 0x1a252866d8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1a2529d780>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1a25286b38>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1a25286f98>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Boxplot por Edades')\n",
    "ax.boxplot(edades, flierprops=green_diamond, labels=[\"Edad\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:11.147116Z",
     "start_time": "2020-06-01T23:16:10.999771Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1a252eac50>,\n",
       "  <matplotlib.lines.Line2D at 0x1a252f0278>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1a252f06d8>,\n",
       "  <matplotlib.lines.Line2D at 0x1a252f0b38>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1a252ea748>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1a252f0f98>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1a252f4438>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Boxplot por Salario')\n",
    "ax.boxplot(salario_anual_miles, flierprops=green_diamond, labels=[\"Salarios\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:11.310832Z",
     "start_time": "2020-06-01T23:16:11.153087Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x1a259e4668>,\n",
       "  <matplotlib.lines.Line2D at 0x1a259e4b38>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1a259e4f98>,\n",
       "  <matplotlib.lines.Line2D at 0x1a25bbd438>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x1a259e44e0>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1a25bbd898>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x1a25bbdcf8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title('Boxplot por Compras')\n",
    "ax.boxplot(compras_mes, flierprops=green_diamond, labels=[\"Compras\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Como afecta ouliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:11.636161Z",
     "start_time": "2020-06-01T23:16:11.314499Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/python36/lib/python3.6/site-packages/scipy/stats/stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25bd6d68>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E4eHh5P2RSASBQKCgwtL0xHUTEc1ATOfiy0SUUFCgP/vss8mfb731Vmzfvh27d+9GX18fVq1ahZ6eHlx11VVFKyRNSMx8qMMw2TdOROlmNcol1aZNm/Dkk0/illtuga7rWLNmTbF2TQBiuonzoypGI3GGORHlNOtx6Pv370/+fODAgdnujjLohoVQNI64wWGgRDQ5XlhUpezhh9GYUemiEJFDMNCrDBeWIKJCMdCrBIOciGaLgV5hQghEYwYiKoOciGaHgQ7g+Mkh9B4bwOCIitamOnSsuBDLl8wv+euqMQNhlbMfElFxzPlAP35yCAdf/SB5+1xQTd7OFerFCP+4biIU1aGbHLlCRMVTtHHoTtV7bGDa2+3wPxdUYYmJ8D9+cmhar2WYFoKhGIZDMYY5ERXdnG+hD46oObefOhfG3t8cT2uJTxb+ma10uyUfDMfR5K/BFZ9oxSUXcDoEIiqdOd9Cb22qy9qmxgyEovGslvipc7kn2Roc0dJuHz85hBf+628YGI5CN0z0n4/i/x4+hfdOj5SkDkREAFvo6FhxYVofOgCEVR2NdTVZj9UNC74cs9K2NvnG7zehxU0ceus0IqqOiGbAtAQUWYLf58F//fdp/PHdjxAMxdDcWIuVly/E0sVNJakXEc09FQ305EyBAoAEKLIEUeYl6O2uksQXnRpam3zQ4gZ83uxfTU3K3O1CiMR0tQK44rIFGBxRk6NVTg+GMRaJJx4oSTBNCyOhGEbCcVzQknjM0FgM//nmhwDAUCeioqhooAdDsaxtlqJgdESFR5GhyBI8igRFlqEoEhRZSs65XkzLl8xP6wPf+5vjOBfM7lu/aKEf//vyhThy/CyGRrVkK/viCwJpQw+NHPOuWAKQcsxY/sd3P2KgE1FRVGWXi2kJmFbueb7lPHmeq10vS1Lin5z4p8iJ24osAYn/kDg/SLDPE5IEfHr5P+HFnkQ3jBCAgIAQwL+0zcdFCxtxy/+ZfAUijyf7qwkxXp5MuU5qRESFqMpAn8xMrsExhYAJAcxwDYiFzfX4jysXF9zfvWi+H0IAUbsPXZEBKdGXnqm5sXZmhSMiysNxgV4uSxc3FdwVsvLyhRh680P4vB54FAmGKaDFDeT642Ll5QtnV1AionEM9BKwTwR/fPcjhFQd8/1erLz8ouQ2jnIholJgoJeI3cLPXAyWAU5EpTLnLywiInILttCJiGbIvg5FiPGfU0bD2bftx1nj25DyeCgKgiFt/Pb44wBApO7b3m/Kz0KgvtaDS5csyFkuBjrRuJwHUMZBmrUNiW3IPAhTDtLMAzw9BHK9Vq7XzX6t9KCYeG69fwzhcCzncyd7XaT8bOV5buZrpZU563cw/Toiq3wTj7OEgMcjQ9fNSd+D7Ppmv36u98DK2DZZWZK/qwq7ftXHcm53faBP72CZ/AOCnB/ayT7AE48fjugYHVOn+OBNfJAsMcmHK60+iXVHReIonLKOiX1PfhCkvlbe1oIQ8Ho90GL6NA+uKcqSs/zTOEjz7DffQTqd1yJyuooG+g9/+eesg1SSE5fKZ55dkXGQZoVsrqCuZOWIKI2UvJhv4kI+WZKybtsX/cn2dkmCokgQlkg8NnMfcvq25P8zHjexPfF/OascgH2RYXI/mPg59XH2Y+XU+8bLgfFt8vhrIqUswZCGs8NRxHULtTUyFrU2YME8X/Kiw/TXzX4tSIC/Nn9sVzTQc11eT86SeeBI45/erA9o2od84iBI+wDLUtrVu3LGc/O/VuLxmQepJKUfcPkOltraGuhxI+sAznw8JKQdpGn7Tiv/RFmAlJBKCZNcgZA7ZHK/1kR4ZP9e5gXqEAprWb+r1PpAyv5d2Y9L/b1jitdKrc9UrzUbmaPFnOi90yP4zzdHUeNRUFfrgWEKfBRU8W9LF8xo9FudV8l7X0UDfdECf9YHpKZGgWFY6We+jDNV/g+SfRDnOoBztwxSDxb7IyfLEoKhGAaGItDiJuq8SvJMOp0PsCQl5ln/x7kQ1LiJOq8HSy5sxAUt9dktkpSDZXxGguTPSPm9SJKE/sEw/t+HIwhHdTT6a3D5Jc24eGFj3hbJRJBI6S0eTH6Q5gzj8d+OLAPz5zcgOBxJvj9O54awSOW2+rjFH9/9KO/2Yg1nrmig3/vlf8naVsiH8b3TI2kX7PzbJ1pn9QtKnEk/RI1HSc6wOJMz6XunR/DHvw4CkNBQVwPDFPhgIISlFxV+9el7p0fwlw+GASROelrcwtvvDeGC5vqyj21PngyJaNryzdtUzPmcHD8O3Q7fobEYLDExLe1sFpOY7ExajueXa59EVD755m0q5nxOjhvlktkaD0f1nI+bzZ8xsz2TluJMXI6zOxGVzsrLFybXQMjcXiwFBbqu69i8eTP6+/sRj8exYcMGXHrppeju7oYkSVi6dCm2bdsGWS7uHwB2a9w2NBbD4EgUgXovajMWpJhN0DU31mJoLPv50z2Tzvb55donEZVP7jmeijufU0GB/tJLL6GpqQm7d+9GMBjEl770JVx++eXo6urCqlWrsHXrVhw6dAirV68uWkGB3N0LiixjLBqHohkwLQuKLKPe58H/WuAv+HVmeyYtxZm4HGd3IiqtfHM8FUtBgX7DDTdgzZo1yduKouDEiRNob28HAHR2duLw4cNTBnog4IPHkz0Ep6UldxiPRXV4lPQv42prZIxGTMAjAEgwLYFQVEfb+C9NkgCPLKcNDQMSF7Zg/Cq01BEuALCwtRHzAnU4/OczOD8axYJ59fj3f12Ef26bj0xpF7SMb1vV4kdDgw+vHzuD86MqFjXX4+oVuZ8/XZn7XDCvbtb7nI1875FTsT7Vz211KrQ+9cUeh+73JwoSDodx3333oaurC7t27UqGpt/vRygUmnI/Y2Na1rbJzlyB+pqsboeYbqHGk1iuzrQseBQZDXU16D8XgseyIMsSYE6scGGHrh3guUZ0SgBWfKwZKz7WnH6Hlb20XKrEvA2Ji5qWX9yEf75oHubPb8C5j8ZgWAKRkJa8IjPx+PTnTHUh1IVNPny5sy1tWyWGp7ltWBzrU/3cVqfZ1Ef1KmgO+HLeV/CXogMDA9i4cSPWr1+Pm266Cbt3707eF4lEEAgECt11Xrm6HUzLQl2tB7phjY+ZTvwbGoslwryMJEmCkjGcz1frQb2vZlrPt4SAZSX+mZaAEIn/W5aAYQqYljWjFZuIaG4pKNDPnz+PO++8E1u3bsXVV18NAFi2bBn6+vqwatUq9PT04KqrripqQYGJLxX++6+DCIZiWNhcB1lKjBG3GYaFkVAMzQ3eor9+qcmSBFmRcv/ZMM6yEi16O+iToS8A07RgmAx9ormqoEDfu3cvxsbG8NRTT+Gpp54CADz88MPYsWMH9uzZg7a2trQ+9mKQJMDn9aCxvga1XgWKIk3eAnfQhS/HTw6h99gABkdUtDbVoWPFhVi+JHffuCxLkCEhx1cPSYZpQTcsxHUTMd1kwBPNEQUF+iOPPIJHHnkka/uBAwdmXaBMHllCvc8DX60H//P3Yfym9+/J+84FVXw0oqLe50HcSLROPYqMxroaxPXJ+7urxfGTQzj46gfJ2+eCavJ2vlCfikeR4VFk1I1/eRLXTeimBdOcaNmb4907ROQeVXthkdcjw+9LtMZtvccGsh7nUWSoMRNKxuiX1qbcXxpUm1x1srcXGuiZvDUKvDXZTXr7C1ndsBDTrURrniFP5FhVF+g+r4L5gdrkHCqpBkeyZ2es8chQY3HUiMRFTIZhIRiK4d9XXFjyshZDrjoltmePACo2+0tcxSvDN/6Vg91dY1qJoDdNCwZDnsgRqibQa2sUNNR5MH9eHQbjRs7HtDbVZU25qxsWvB4ZiiInu1wa6mpw+qNwOYo9a7nqlNhemb8w7O6aVEIkRtkY41+6+rwKPLLEoCeqMhUP9ESQ16DGM/U0AR0rLkzrbwYSLcrmhlr4Mgbbl6OFWwy56mRvrxaSJKHGIyXfo/nz6mDFjaygN0zBFj1RBVU00OcHfNMKcpvdp5wYEaKhtckHX40CTTezHuuUPvRcdZpslEs1yQx6W2rQm1Yi5E1LwLD4RSxRKVU00GcS5rblS+anhV3mKBFbNbVwp5JZJ6fLF/QAkuPmzZQLqOzQT15QVYEyE7lBxbtcZsvJLdy5SJIkeJTJx9FPBH0i5O2WvWlyuCXRZBwf6ID7WrhznSwnLhqrybP+SuqUCGZGK5/dOjSXuSLQaW6xW/n5pkiwxHi4p/Tj2/8XzHpyMQY6uY4sSZA9CmpyfLpzdefU1XpQo8jsziHHY6DTnJKrO6cl4IMZSyxlmKs7J/UkwFY+VTMGOlGKqbpzgIlpju25cezWfuoJgJlPlcBAJ5ohe5rjyUbqZIZ8sqXPkTpUQgx0ohJQZBmKDORb2iTfeHzLvhBLsGuHZo6BTlQB0xqPLxIt+swvctm1Q/kw0ImqVGK0zuQLtSRH65gTK1n5vApH7cxRDHQiB0t27aQcyfbkaUD+i7AsjtpxJQY6kYvNaNROcuSOlfYzG/nOwUAnmuPSRu3k+BbXskRyemTDSixlaF95S9WFgU5Ek5JlCV5ZgTcj7O3unLS58MdDn904lcFAJ6KCTIzUyZ5EzbTskGervpwY6ERUdPaXtZldOJmLn9irXfHL2eJgoBNR2Uy2+MlEq35ipkxOhzwzDPQqdfzk0PiiHSpam+q4aAe53kSrPn1IjiUEWprrYMZ0tuqnwECvQpnL6p0LqsnbDHWaa2RJQo1HQV1tdlyljqdPXd3KmqNX0zLQq1DvsYG82xnoRBMS0yEreefMAdInSnN76Bc10C3Lwvbt2/HXv/4VXq8XO3bswCWXXFLMl5gTBkfUPNu1MpeEyPmmmigNcE/oFzXQf//73yMej+MXv/gF3n77bTz++OP48Y9/XMyXmBNam+pwLpgd6q1NvgqUhsj9Cgn9zNkyqyH0ixrob731Fq655hoAwKc+9SkcP368mLufMzpWXJjWh566nYgqYzqhn9mnX+7QL2qgh8NhNDQ0JG8rigLDMODx5H6Z5uZ6eHLMH9ra2ljMYlXcTOtzfWsj5s2rx6E3/4GzwxH8U4sf/9F+Ma74xMISlXDm5vp7VO3cVh/APXUyx+e8bwn4kj/bM2bafwVMFvr1Ob4cthU10BsaGhCJRJK3LcvKG+YAEAxGs7a1tjZicDBUzGJVVKH1uailDnfc8Im0bdXye+F7VN3cVh/AfXXKVx9l/F++OfBNU0CrkdEcyN39mj26fxauuOIK9PT0AADefvttXHbZZcXcPRHRnCDLiYuvfF4P6n01aKz3oqmhFvPn+RCo9+Z9XlFb6KtXr8bhw4exdu1aCCHw2GOPFXP3REQ0iaIGuizL+O53v1vMXRIR0TQVtcuFiIgqh4FOROQSDHQiIpdgoBMRuQQDnYjIJRjoREQuwUAnInIJSQiu+UFE5AZsoRMRuQQDnYjIJRjoREQuwUAnInIJBjoRkUsw0ImIXIKBTkTkEkWdD30mdF3H5s2b0d/fj3g8jg0bNuDSSy9Fd3c3JEnC0qVLsW3bNsiyc845pmnikUcewcmTJ6EoCnbu3AkhhKPrBABDQ0P48pe/jJ/97GfweDyOr88Xv/hFNDYm1qdcvHgxbrnlFnzve9+Doijo6OjAvffeW+ESzsy+ffvwyiuvQNd1rFu3Du3t7Y5+j1588UX86le/AgDEYjG888472L9/v2PfI13X0d3djf7+fsiyjEcffbR0x5GokBdeeEHs2LFDCCHE8PCwuPbaa8Xdd98t3njjDSGEEFu2bBG/+93vKlW8grz88suiu7tbCCHEG2+8Ie655x7H1ykej4tvfOMb4rOf/ax4//33HV8fTdPEF77whbRtn//858WpU6eEZVni61//ujh+/HiFSjdzb7zxhrj77ruFaZoiHA6LJ554wvHvUart27eL559/3tHv0csvvyzuu+8+IYQQvb294t577y3Ze1Sx0/YNN9yAb33rW8nbiqLgxIkTaG9vBwB0dnbiyJEjlSpeQT7zmc/g0UcfBQCcOXMGCxYscHyddu3ahbVr12LhwoUA4Pj6vPvuu1BVFXfeeSduu+02HD16FPF4HBdffDEkSUJHRwdef/31Shdz2np7e3HZZZeIw/axAAADNElEQVRh48aNuOeee3Ddddc5/j2y/eUvf8H777+Pz33uc45+j5YsWQLTNGFZFsLhMDweT8neo4p1ufj9fgBAOBzGfffdh66uLuzatQuSJCXvD4Wct8q3x+PBpk2b8PLLL+OJJ57AH/7wB8fW6cUXX0RLSwuuueYaPP300wAAIYRj6wMAPp8PX/va1/CVr3wFf//733HXXXchEAgk7/f7/fjwww8rWMKZCQaDOHPmDPbu3YvTp09jw4YNjn+PbPv27cPGjRsRDofR0NCQ3O6096i+vh79/f248cYbEQwGsXfvXhw9erQk71HFAh0ABgYGsHHjRqxfvx433XQTdu/enbwvEomkHWhOsmvXLjz44IP46le/ilgsltzutDodPHgQkiTh9ddfxzvvvINNmzZheHg4eb/T6gMkWkuXXHIJJEnCkiVL0NjYiJGRkeT9TqtTU1MT2tra4PV60dbWhtraWpw9ezZ5v9PqYxsbG8MHH3yAq666CuFwGJFIJHmf0+r0zDPPoKOjAw888AAGBgZw++23Q9f15P3FrE/FulzOnz+PO++8E9/+9rdx8803AwCWLVuGvr4+AEBPTw9WrlxZqeIV5Ne//jX27dsHAKirq4MkSVi+fLlj6/Tss8/iwIED2L9/Pz75yU9i165d6OzsdGx9AOCFF17A448/DgA4d+4cVFVFfX09/vGPf0AIgd7eXkfV6corr8Rrr70GIUSyPldffbWj3yMAOHr0KD796U8DABoaGlBTU+PY9ygQCCS/hJ83bx4MwyhZ1lVstsUdO3bgt7/9Ldra2pLbHn74YezYsQO6rqOtrQ07duyAoiiVKF5BotEoHnroIZw/fx6GYeCuu+7Cxz/+cWzZssWxdbLdeuut2L59O2RZdnR94vE4HnroIZw5cwaSJOHBBx+ELMt47LHHYJomOjo6cP/991e6mDPy/e9/H319fRBC4P7778fixYsd/R4BwE9/+lN4PB7ccccdAIC3337bse9RJBLB5s2bMTg4CF3Xcdttt2H58uUleY84fS4RkUs4Z3AqERFNioFOROQSDHQiIpdgoBMRuQQDnYjIJRjoREQuwUAnInKJ/w8FVqlfwh/cMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "#CON OUTLIERS\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "sns.regplot(x=edades, y=salario_anual_miles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.019733Z",
     "start_time": "2020-06-01T23:16:11.639270Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/envs/python36/lib/python3.6/site-packages/scipy/stats/stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a25cb3630>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oaJW7x5cTw5tKThcC8bgOTRcQQkDTBeKaQCzOjTRWocb1xPGn3skVIOOB/IqKXcmgvqitwfJFxXJieFNJRGJxhKMa4prOJX0WI4SAzx9NHH+aHFWfHQ1l/To7HXJyl+LkUr0Gp72CrbY+hjcVJRrTEAir3DRjIZFYHIPe4OSpet4AQpGZi4pSaqdiek11I9qbWVQsN4Y3FSyu6fCHYhg5F+YKEZPTdYEhX2jKCpBhX/aiYmODfUpQz/e4UMeiYsUxvCkviSV9iUKjqunQZZnBbUL+UCwd0qeGAjg9HMhZVJzX7sLCjsZ0YXGOy2Ga8z+sjOFNMxJCIJZcHcKNM+ajxnWcHQ2mg3pwOACfP5r1Y9qaMo8/deOi1gYoMouKRsTwrmG6EIBI/C2Sf+vJFSJRVefqEBMRQmB4PIz/+mik4KLigoyiootFRdNgeFdIIiAT65zVZCgKIB2eAJJBKSDS/068KDMfI+OxSF5Mh3DG25H6/ACk5H+kxGUGsgWkioqppXr5FBU7WxqSuxRZVLQCy4a3mBJiIh1YkgRIySQr1Teurgtoug5dT0UvoOuAqumIx3XENT0dpJpNxthE9l9dS+38MCdz0XUB73g4uVMxEdQ5i4r19ikj6vkeN4uKFlOR8I5reiJMBaYN1PNHl+K80aNSF8F4IDpltCmSnyP1PiJ5URQYUhISgQ5Jgi15wSZJ6YKMLfk2KTlyBQBdID29ENd03omcSsqf2qmYLCwODgcQU3MXFZcubIEnOWfd7GZR0eoqEt7+kIqoOvs7noSi8bLdMSX9Q0MI6FOuEpVfXEsUFTPv/pKrqNjaVJfcTp5YAZIqKnJLeW3JGt6qqqK3txenT59GLBbD5s2bcdFFF2HTpk1YtGgRAGD9+vVYt25dJdpKZGqpnYqZa6rPjASzFhXr7PKUGwp0dbjhrmdRkXKE9/79+9Hc3Iy9e/fC5/Ph1ltvxX333Yd77rkHGzdurFQbiUwpGtMwODy5pnrA60ewgKJiV4cbnuZ6FhVpWpIQM689CAaDEELA7XbD5/Phtttuw8qVK9Hf3w9N03DxxRejt7cXbrc765N4x4JQuaGDLEzXBc6OBvH/zkzgkzPn0H/mHM4OB7Mff+pyYPG8JiyeNweL583BxXMb4XRYdg0BzcJ8z8zZmjW8UwKBADZv3ow77rgDsVgMl156Kbq7u/HUU09hYmICPT09WT/+g09GiprzttJcnlX6YpV+ALPrSyCsTln9cXo4mPV7XLZJ591TsbHkRcVa/5oYUbH9uOrSzhnflvPH/NmzZ3HffffhzjvvxC233IKJiQk0NTUBAFavXo2+vr5ZN4zIDFJFxcnpj9xFxZbGuvR66gUdbsxt405FKq2s4T0yMoKNGzdix44duP766wEA9957L7Zv345ly5bh6NGjuPLKKyvSUKJKEEJgPBCdsvojn6JiV4cLCzzJeyqyqEgVkDW8n376aUxMTGDfvn3Yt28fAGDr1q145JFHYLfb0d7ezpE3mVo0puG//z2GEx+NpMM6EFZnfH8JU++p2NXhRkdzPWw2FhWpsvKa8y4W57wnWaUvZuyHLgSGfYmdiqe8AQx6AxjyhbIeF+Cqt2Nhap66042udjfqHMbcqWjGr8lMrNKXqs55E5lVIKxm7FT0Y9Cbu6iYOP40eauuTjea3XXcqUiGxPAmS0gUFUPp+ykODAUwlkdRcUGHG5ctbkOb2465bS4WFck0GN5kOomiYiwR1EMBDAwniorZbg5RZ5cx35MYVS/obESXx4XGBgcA6/yKTrWF4U2GF41pGBxJzFGfGkr87c9RVOxoqceCzsZ0YZFFRbIahjcZip68qcBAxlK9fIqKCzyTW8q7PC7uVCTL43c4VVUwoqbnqFNhnU9RsSs5ol7Y4UZLI4uKVHsY3lQxcU3Hp6Oh9OqPAW8g540pUkXF1J+5bS7YFRYViRjeVBaTRcXJoM5VVHTYbYl7KiZH1F0d7nRRkYimYnhTSURVDaeHJ6c+BoZyFxU9LfXpoF7Q2ciiIlEBGN5UMF0InB0J4r0Pvemw/nQsR1HRqaRP00tsK2dRkagYfPVQTumiYnJEPTgcyHpbOtkmYW5bQyKoO1lUJMpGkgBZkmCzSZBtyfvn5nGDdIY3TRHXdHw6Fpqy+mN0IpL1Y1oa6ybnqjtZVCQCElODimyDQ7FBtk2Gc2ZIp8N6FhjeNUwIgXPB2JSleqdHAtmLiooNXR3u5J3KEytBWFSkWiAlR8O25Cg5HcYSIKWuSRJstuRjSYKntQE2rTw3T2d415BEUTE4ef6HNwB/KI+ioidxot6CDjc6Wxpgs0ncUk4VJwHpwJwcwdogy1J6iiFzEJuowYh0LUZkfI5UACfe78LByvlXjHgfUYa3RelCYGQ8MiWocxUVG5zK5Il6HY0sKlLZSABkORm+NglScvQKJAJ4jrsO8UgMkiRBkSeDuixtmSaYjRfVF+Ir0yJCyaLiqVkWFRd0uNHKoiIVSc4YGcuybcooOVGHS3x/5VoS6q63I+zk3YiyYXibULqomJqrHg5g9Fz2omKz25Feqmf0ouKHg+N4+30vfP4oWhrrcM1lHVja1VztZhlW6v/XREhFU4O95P+/JCAdxpnTFqlQtklTR84EvNc/ijePn4UvEEOL24GVy+aie3FbSZ+D4W1wU4qKybDOp6g43zO5pXxBpxtNJikqfjg4jr/+x0D68ehENP2YAX6hzP9fiixhdCKK1//vABx2G7oXt6WD9Xy57p8lSUhPaXDjVGHe6x/FS3/7BABgV2wY8oXTj0sZ4Axvg4mpGk6PBDOW6vkxkaWoCACe5vrkLsXk8actDZBN+oJ7+33vjNcZ3gmSBCg2GxRZwrsfjaTnjO2KDZKUSOX//GAEKy6/qMotrU1vHj8743WGt0XoQmDkXCR5TnWisDg0FkKWG5WjoU5J3/h2YacbXR436uus82X0zXD3m5muW1HmkrREUS9RrFNkCUpy6iLF54+mHyemLRLfPMPj2afRqHyGx8MzXC/t18Q6r3oTCEXiOPPxCE5+PHmn8mxFRZskYW57Q2KpXocbCzsb0dpk7aJiS2MdRqc5abClsa4KrSk9CclwzljmpmT8u9BNG57megz5LgwLT7OzhK2mQlTqa8LwLhNN1/HpWBgDQ5NL9UbyKCqmlukt6HBjXrtxi4rlcs1lHVPmvDOvV5skAXbZlt6QkRohp1ZRJIp2iRFwIqQzrqE8Rb2Vy+am51PPv07VUamvCcO7RM4FoumQPuUN4PRw9qKiXbGhy+OaclhTk8scRcVySs1rV3O1SebqitRUhV2xGfLmxKk51DePn8V4MIbOlvqyrGyg/FXqayKJ6bYXldgHn4xkvTtKLkbbzZcuKmYs1ZsIxrJ+jKfZiQUdjbhscStaXQ50tpq3qAgY72tSiNQIOrUOubOjEb6xYHrLsxF30+XD42nE8LC/2s0oCav0pdh+eDyNM76NI29kX1esC4HRc5GMpXp+fJqjqFhfp6SX6Z1fVDRz6JmVIktwKDLsyvQj6AanHUG7XKXWEc1OzYf3+euKh8cj+POb/VjY4UYoGseAN4BwNEdRsa1hyprqtianpYuKRpY6yc1ut6FOkWG320w7kibKpqbDW9MF/v7uGQTDKmJxHbG4Bi05Tz3TvRXnuBzpkE4VFR0KR22VlDknnfhbgjLL1RpEZlVT4Z1ZVBzwBnB6OAhV02d8f7tsw3yPCws7WVSspAsOp0/+nQpoIxYOiSrNsuEdi2s4k9ypeCoZ1rmKiooswa7IcCg2eJqd+N//63JTFxWNKLU7UE6eFKfYpClHdJq5YEhUSZYIb5FRVEwF9aejwRxFRRkLOhrR4FRwasgPhyJP2bn2hf85j8FdhClTG7IEu4GX2xGZkSnDO5wsJGb+CUfjM76/TQIuanNl3Kl8alGRp9gVR7ZJkys5OPdMVBGGD29NFzj1qR8nPhpO31gg1xkBc1ypnYqJM0Dme7IXFZd2NTOs82STALsiw11vh+52wK7YynZIPhHNLGt4q6qK3t5enD59GrFYDJs3b8ZnPvMZbN26FZIkYenSpdi5cydsJXzxTgRjOOUNYNDrx38PjMPrC2c9vtIu2zDP40qOqBNFxTksKhbl/NUcipz4tz3jUKQ57jrEwtlrCERUPlnDe//+/WhubsbevXvh8/lw66234rLLLsOWLVuwYsUK7NixAwcPHsTq1atn9eRqXMeZkSBOef3pI1DP5SgqNrkcuGReU/qgps7Weo78ZiHzNlSKPPVgJM5LExlf1vBeu3Yt1qxZk34syzJOnDiB5cuXAwBWrVqFf/zjH3mFtxACoxORjHOqAzg7GoKeZVgtSYBDkVHnSBS6HIoMT7MTt9/4mXz7R8g4/1mxpQuHtXbgFZHVZA1vl8sFAAgEArj//vuxZcsW7NmzJ12Icrlc8Ptz79v/9f95H5+cPodgJFtRUcL8DjcWz2vCknlz8Pqxf0O2XXgKmz+sorXVlfM5jayc7U/9wLMrNjjsiWWPcplG0tnOXTAbq/TFKv0ArNOXcvUjZ8Hy7NmzuO+++3DnnXfilltuwd69e9NvCwaDaGpqyvkk//Xx6AXXmlI7FZN/zi8q/rMpda6zgCJL6RP62lwOU58NUqqzTVLTHoqc+K3ELtugKInpD6HGEVOB2PRnwpeEVQ4OAqzTF6v0A7BOX6p2MNXIyAg2btyIHTt24PrrrwcAXHHFFTh27BhWrFiBw4cP47rrrsvZgMZ6O1rnOBNFxeSfOe7sh+sb+VznSrPZEuukjX48KRFVTtbwfvrppzExMYF9+/Zh3759AIAf/ehH2LVrFx5//HEsWbJkypz4TB7euLzgI2Ezz3X2h1W0uRyWX3+deVSpPTWqVniwEhFdiOd5V9j5fbHZJDjtMursMhx2m2k2tljl11rAOn2xSj8A6/SF53mXWSV3WNoVG1xOJb3ig8sciWg2aj68zz/Pe3Qimn5cigBPL3e0J5Y8drQ0QIrP/rcQIiKA4Y233/fOeL3Q8E7dCCBxOqEN9uSSPSKiUqv58Pb5p7/pwkzXzycBcDpkNDgV2HlTBiKqkJoP75bG1HryC6/PJLUqxGGX0VCnTDlKloioEmo+vHOtJ09thsncVq7I5lkVQkTWVPPhnbmePLXa5NrLO3DVknbU2ROja66zJiKjqfnwBhIB/j+6mlHnkFFfp6DOzrlrIjK2mg9vu2yDs05GvYNz10RkHjUZ3opNgrNOgdMh84wQIjKlmglvm01CvUOG06Fw7TURmZ6lw1sCEvPYDgV1Ds5jE5F1WDK8ZZuE+jqFa7CJyLIsFd51yU0zHGUTkdWZPrwlAM46BS6nwuIjEdUM04a3Q7El1mQ7uImGiGqPqcI7NZdd6iV+7/WP4s3jZzE8HoanuR4rl81F9+K2kn1+IqJSM3x4SwDq6xS0NNaVZefje/2jeOlvn6QfD/nC6ccMcCIyKsNOEis2CU0Ndnia69Ha5CzblvU3j58t6DoRkREYbuTtUGxwOe0VWzEyPB6e4XqkIs9PRDQbhgnvOrsMl1OBo8KHQnma6zHkuzDAPc3OiraDiKgQVZ82cTpktDU50dJYV/HgBoCVy+YWdJ2IyAiqMvI20trsVFEysdokAk+zk6tNiMjwKhreEoB6ZyK0ZVvVB/1p3YvbGNZEZCoVCW+bBLicClxOe0FnjaTWX/sCMbS4HRwRE1kcX/P5q0h4z3HPfDPfmWSuv7YrNq6/JrI4vuYLY5y5i/Nw/TVRbeFrvjCGDW+uvyaqLXzNF8aw4e1prp/hOtdfE1kRX/OFMWx4c/01UW3ha74whtlheb7M9dfjwRg6W3jaH5GV8TVfGMOGNzC5/trjacTwsL/azSGiMuNrPn+GnTYhIqKZ5TXyfvfdd/HYY4/hueeew4kTJ7Bp0yYsWrQIALB+/XqsW7eunG0sO96MgYjMJmd4P/vss9i/fz/q6xOV4JMnT+Kee+7Bxo0by964SuDNGIjIjHJOmyxcuBBPPvlk+vF7772HN954Axs2bEBvby8CgUBZG1hu3BhARGaUc+S9Zs0aDA4Oph8vW7YMt99+O7q7u/HUU0/hl7/8JXp6erJ+jpaWBihKcce9ejyNRX38THyBGOzKhT/HX810AAAFoklEQVTDxoOxsj1nuT5vpVmlH4B1+mKVfgDW6Uu5+lHwapPVq1ejqakp/e++vr6cH+PzhQpvWYZyVp5b3I5pb8bQ2VJflue0ShXdKv0ArNMXq/QDsE5fiu1HtuAveLXJvffei+PHjwMAjh49iiuvvHLWDTMCbgwgIjMqeOT98MMPo6+vD3a7He3t7XmNvI2MN2MgIjOShBCi3E9S7K8/VvkVCrBOX6zSD8A6fbFKPwDr9MVQ0yZERFR9DG8iIhNieBMRmRDDm4jIhBjeREQmVJHVJkREVFoceRMRmRDDm4jIhBjeREQmxPAmIjIhhjcRkQkxvImITIjhTURkQgUfCVtuqqqit7cXp0+fRiwWw+bNmzFv3jz09fVBlmU4HA7s2bMH7e3t1W5qVtP148tf/jIA4NVXX8Xzzz+P3//+91VuZX6m68tnP/tZbNu2DRMTE9A0DT/96U+xcOHCajc1q5m+t3bu3AlZlrFo0SL85Cc/gc1m/DGNpmnYtm0b+vv7Icsydu/eDSEEtm7dCkmSsHTpUuzcudPwfZmuH8Fg0HSvd2D6vqReE2V5zQuDefHFF8WuXbuEEEKMjY2JG264QWzYsEGcPHlSCCHE7373O/HII49Us4l5ma4fQghx8uRJ8Y1vfEPcfvvtVWxdYabrS09Pj3jttdeEEEIcPXpUHDp0qIotzM90/fjOd74j3njjDSGEEA8++KA4ePBgNZuYtwMHDoitW7cKIYR46623xKZNm8S3v/1t8dZbbwkhhNi+fbt4/fXXq9nEvEzXDzO+3oWYvi9ClO81b7gfy2vXrsUDDzyQfizLMh5//HFcfvnlABI/3erq6qrVvLxN1w+fz4fHHnsMvb29VWxZ4abryzvvvIOhoSHcfffdePXVV7F8+fIqtjA/0/Xj8ssvx/j4OIQQCAaDUBTD/TI6rZtuuil9I5QzZ86gvb0dJ06cSH8dVq1ahSNHjlSziXmZrh9mfL0D0/elrK/5kv4oKCG/3y/uuususX///vS1f/7zn2Lt2rVidHS0ii0rTKoff/7zn8XmzZvFRx99JAYGBkw18k7J/JpcccUV4sUXXxRCCPHkk0+Kn//851VuXf4y+/Hqq6+Ka6+9VqxZs0bcdtttIhKJVLt5BfnhD38orr76avH3v/9dfP7zn09fP3LkiPj+979fxZYVJrMfKWZ8vQsx2ZfDhw+X9TVvyPA+c+aMuPXWW8Uf//jH9LXXXntNfOUrXxGnTp2qYssKk9mPd999V6xbt07cdddd4vbbbxdXX311+ld4Mzj/a/K5z31OjI2NCSGEOHHihPjmN79Zzebl7fx+XHfddeKDDz4QQgjx/PPPi4cffriazZsVr9crvvjFL4prrrkmfe3AgQPixz/+cRVbVbhUP4LBoClf75m8Xq+46qqrxI033li217zhwnt4eFisXbtWHDlyJH3tlVdeEevXrxc+n6+KLSvMdP1IMdvIe7q+fO973xMvv/yyEEKIX//61+LRRx+tVvPyNl0/1qxZI86cOSOEEOL1118XDz74YLWaV5CXX35ZPP3000KIxG8SN954o7j77runzHmnahJGNl0/zPh6F2L6vqR+kyvHa95wpwru2rULf/nLX7BkyRIAiTmvDz/8EPPmzUNTUxMA4Nprr8X9999fzWbmdH4/AODZZ5+F0+nE4OAgHnzwQfzhD3+oYgvzN11fHn30UWzbtg3hcBhutxs/+9nPMGfOnCq2Mrfp+vHAAw/gscceg6IosNvt6OvrQ1dXVxVbmZ9QKISHHnoIIyMjiMfj+Na3voVLLrkE27dvh6qqWLJkCXbt2gVZlqvd1Kym60dvby/mzp1rqtc7MH1fbrrpJgAoy2vecOFNRES5GW61CRER5cbwJiIyIYY3EZEJMbyJiEyI4U1EZEIMbyIiE2J4ExGZ0P8HvwmmiaDJPxAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SIN OUTLIERS\n",
    "\n",
    "edades_fix=[]\n",
    "salario_anual_miles_fix=[]\n",
    "\n",
    "for index, x in enumerate(salario_anual_miles):\n",
    "    y= edades[index]\n",
    "    if abs(x-media) > std_x or abs(y-media_y) > std_y:\n",
    "        pass\n",
    "    else:\n",
    "        edades_fix.append(y)\n",
    "        salario_anual_miles_fix.append(x)\n",
    "        \n",
    "sns.regplot(x=np.array(edades_fix), y=np.array(salario_anual_miles_fix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outliers usando Libreria Pyod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.037233Z",
     "start_time": "2020-06-01T23:16:12.028378Z"
    }
   },
   "outputs": [],
   "source": [
    "#!pip install pyod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.053030Z",
     "start_time": "2020-06-01T23:16:12.045440Z"
    }
   },
   "outputs": [],
   "source": [
    "from pyod.models.knn import KNN\n",
    "from pyod.models.pca import PCA\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.083017Z",
     "start_time": "2020-06-01T23:16:12.064669Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={'edad':edades,'salario':salario_anual_miles, 'compras':compras_mes})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.134795Z",
     "start_time": "2020-06-01T23:16:12.091513Z"
    }
   },
   "outputs": [],
   "source": [
    "clf = KNN(contamination=0.18)\n",
    "#clf = PCA(contamination=0.17)\n",
    "clf.fit(df)\n",
    "\n",
    "X=df\n",
    "clf.fit(X)\n",
    "scores_pred = clf.decision_function(X)\n",
    "y_pred = clf.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.168806Z",
     "start_time": "2020-06-01T23:16:12.148309Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.12310563,  5.19615242,  5.19615242, 19.33907961,  4.        ,\n",
       "        3.74165739,  5.        ,  6.70820393,  3.60555128,  3.74165739,\n",
       "       25.3179778 ,  4.12310563,  7.68114575,  3.60555128,  5.09901951,\n",
       "        9.05538514, 18.13835715,  8.77496439, 50.009999  ])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.189546Z",
     "start_time": "2020-06-01T23:16:12.178616Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-01T23:16:12.237165Z",
     "start_time": "2020-06-01T23:16:12.205552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>compras</th>\n",
       "      <th>edad</th>\n",
       "      <th>salario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>30</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    compras  edad  salario\n",
       "3        20    23       21\n",
       "10        5    30       56\n",
       "16        2    33        2\n",
       "18        2    80       23"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[y_pred == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El artículo completo y más en www.aprendemachinelearning.com"
   ]
  }
 ],
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