5. Simpson_s Paradox.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n",
    "    <div style=\"float: left; margin-left: 10px;\"> \n",
    "        <h1>Applied Probability Theory From Scratch</h1>\n",
    "        <h1>Simpson's Paradox</h1>\n",
    "        <p>Bruno Gonçalves<br/>\n",
    "        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n",
    "        @bgoncalves, @data4sci</p></div>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import watermark\n",
    "\n",
    "%load_ext watermark\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python implementation: CPython\n",
      "Python version       : 3.8.5\n",
      "IPython version      : 7.19.0\n",
      "\n",
      "Compiler    : Clang 10.0.0 \n",
      "OS          : Darwin\n",
      "Release     : 21.4.0\n",
      "Machine     : x86_64\n",
      "Processor   : i386\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "Git hash: 967e6d5ea0aa8fdb11b3a19bce642a60fcedbb9a\n",
      "\n",
      "numpy     : 1.19.2\n",
      "pandas    : 1.1.3\n",
      "matplotlib: 3.3.2\n",
      "json      : 2.0.9\n",
      "watermark : 2.1.0\n",
      "sklearn   : 0.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%watermark -i -n -v -m -g -iv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('./d4sci.mplstyle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the iris dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('data/iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</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>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the dataset across species for convenience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "setosa = iris[['sepal_width', 'petal_width']][iris['species'] == 'setosa']\n",
    "versicolor = iris[['sepal_width', 'petal_width']][iris['species'] == 'versicolor']\n",
    "virginica = iris[['sepal_width', 'petal_width']][iris['species'] == 'virginica']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perform the fits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_setosa = LinearRegression()\n",
    "lm_setosa.fit(setosa['sepal_width'].values.reshape(-1,1), setosa['petal_width'])\n",
    "y_setosa = lm_setosa.predict(setosa['sepal_width'].values.reshape(-1,1))\n",
    "\n",
    "lm_versicolor = LinearRegression()\n",
    "lm_versicolor.fit(versicolor['sepal_width'].values.reshape(-1,1), versicolor['petal_width'])\n",
    "y_versicolor = lm_versicolor.predict(versicolor['sepal_width'].values.reshape(-1,1))\n",
    "\n",
    "lm_virginica = LinearRegression()\n",
    "lm_virginica.fit(virginica['sepal_width'].values.reshape(-1,1), virginica['petal_width'])\n",
    "y_virginica = lm_virginica.predict(virginica['sepal_width'].values.reshape(-1,1))\n",
    "\n",
    "lm_full = LinearRegression()\n",
    "lm_full.fit(iris['sepal_width'].values.reshape(-1,1), iris['petal_width'])\n",
    "y_full = lm_full.predict(iris['sepal_width'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa800b226d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 3840x2640 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, sharey=True)\n",
    "\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "setosa.plot.scatter(x='sepal_width', y='petal_width', label='setosa', ax=axs[0], c=colors[0])\n",
    "versicolor.plot.scatter(x='sepal_width', y='petal_width', label='versicolor', ax=axs[0], c=colors[1])\n",
    "virginica.plot.scatter(x='sepal_width', y='petal_width', label='virginica', ax=axs[0], c=colors[2])\n",
    "\n",
    "l4, = axs[0].plot(iris['sepal_width'].values.reshape(-1,1), y_full, '-', c=colors[3])\n",
    "\n",
    "setosa.plot.scatter(x='sepal_width', y='petal_width', ax=axs[1], c=colors[0])\n",
    "versicolor.plot.scatter(x='sepal_width', y='petal_width', ax=axs[1], c=colors[1])\n",
    "virginica.plot.scatter(x='sepal_width', y='petal_width', ax=axs[1], c=colors[2])\n",
    "\n",
    "l1, = axs[1].plot(setosa['sepal_width'].values.reshape(-1,1), y_setosa, '-', c=colors[0])\n",
    "l2, = axs[1].plot(versicolor['sepal_width'].values.reshape(-1,1), y_versicolor, '-', c=colors[1])\n",
    "l3, = axs[1].plot(virginica['sepal_width'].values.reshape(-1,1), y_virginica, '-', c=colors[2])\n",
    "\n",
    "axs[0].set_xlabel('Sepal Width')\n",
    "axs[1].set_xlabel('Sepal Width')\n",
    "axs[0].set_ylabel('Petal Width')\n",
    "\n",
    "fig.subplots_adjust(bottom=0.3, wspace=0.33)\n",
    "\n",
    "axs[0].legend(handles = [l1, l2, l3, l4] , labels=['Setosa', 'Versicolor', 'Virginica', 'Total'],\n",
    "             loc='lower left', bbox_to_anchor=(0, -0.4), ncol=2, fancybox=True, shadow=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing setosa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced = iris[iris['species'] != 'setosa'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm_reduced = LinearRegression()\n",
    "lm_reduced.fit(reduced['sepal_width'].values.reshape(-1,1), reduced['petal_width'])\n",
    "y_reduced = lm_reduced.predict(reduced['sepal_width'].values.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa7b866aa00>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U2Jtiv2gc9EUwg9SisbFfpC9iisx1nQAAM+3ZGZwAGeaUWuuX2k5mDA5pGHdOq1nsxNIftOc3CD10Fcs0ef3ndvTHddOfedPfITA5Zq0W9UIp5ZZJHtYw/O1t5jLEfhls0DQ5EXZDDkvy+0k+keTHpZQXlFL2XG1ywNRQi4a7b5pt/NyQPZL8ZpLXJzmrlPKmpSt8jpu+COgjtahFpZS5JE9uGP5/W0xlpfRFQCtKKWtKKf8jyWeT7NPgkP9M8pJWkxof+0XD6YuA1s14LeoF+0X6Iph1atGy2S8aD30RzBC1aHzsF/2cvoipYSATgE6UUg5K8ryG4Se2mcsY3bhh3Ng32Jex7m6llA3LfeJSyu5JmhzX59eeNP8dAhNgRmtRX/xdmvWjP0ry+ZZzGbfDMjgxeUop5Y9LKWu6TgjojlrUmQ1JnpTkm6WUfy6lHD6ORfVFQB+pRWPxwCQHN4i7Jt1+wXic9EUww0opNy6l/EGSbyd5WZp9mfbbSR5Wa93WanLjY79oNDm0QV8EM0At6hX7RfoimElqUW/YL1rduvoimGBqUSfsF/0ifRETbaHrBACYWX+bwS3Jh7ksyWtazmVc9m0Yd0GrWexakyseJ4PXceYyn3uaXjswPWaxFnWulPJrSX6jYfhLO7ri4zjsm+R/J/mdUsqTa61f7zohoBNqUbdKkt9K8rBSyguTvKrluqMvAvpILWrfcQ3j/rXWelGrmfSPvgimVCllIcleSfZMsneS2ya5Y5K7ZHBHk+V8T+GbSR5Ya+3q7+Q2TFNvYL8I6CW1qP/sF/2cvgimlFo0UewXXZ++CKaEWtQ79ot2TV/ERDKQCcDYlVIelcFJjCb+odZ6cZv5jNE+DeMuazWL1a+7T5a/wT5Nrx2YAjNcizpVStmY5P82DD8lyVtaTKcv7pDky6WUZ9daX9t1MsD4qEW9sluSVyZ5UCnlt2qtl7a0jr4I6BW1qH2llH2T/GbD8BPazKXn9EUwQUop/5HkqDEsVZO8Ksmf1FqvHsN64zRNvYH9ImDs1KLJZ79op/RFMEHUoqllv2h56+qLoENq0WSxX9SYvoiJMtd1AgDMllLK/kle1zD8zAxuRT4tmpyE2FZrvbL1THau6UmklZxMaXpMWyeyblCt9fIkiw1CnUiCKTDjtahrr05yUMPY59Vat7SZTI8sJPnHUsqrSiml62SA9qlFvfXgJP+vlHJIS8+vLwJ6Qy0am99Osq5B3GlJPtVyLn2nLwK29/+SPKDW+pwp/aKX/aLR5TBS+iJgO9Nei7pmv2jn9EXA9tSi7tgv0hcBA2rR6Ngvak5fxMQwkAnA2JRS5pO8M8mNGh7y/FrrFS2mNG67NYi5vPUsdq3p1a2avI6VHtPl62+y9kpeO9AjalF3SilPzeDkUhMfrbX+W5v59NQzk7i6F0w5taj3bpPk06WUA1t4bn0R0Atq0Vgd1zDujbXW2momk0NfBLPr2iQfSnJMrfWetdbPdJ1Qi+wXDacvArowS7WoM/aLGtEXwexSi/rDftFw+iKYTmpRO+wXLZ++iN5b6DoBAGbKS5Pct2Hs+2qt724zmQ6saRDT5ZUdm669dgXP3eS1LyeHNjRZeyWvHeiXWa9FnSil3DnJqxqGb07y1PayaeySJN9K8t2lx/eSnJ/BVSh/9lhIsiHJ/hlcyfn2Se6S5AFJbrrCdZ9WSrmo1vqnq8oe6DO1qLnFXPc5/LPP4lMz+HLwJRl8Fm/N4LN4UwafxbdIcqcMfsZ3SrKSKyYenuSTpZS711pHefVhfRHQF2rRGJRS7pbklxqELiZ5Y8vprJS+CBiHq5O8LMnLR/z3d5/ZLxpdDm3QF8HsmcVa1An7RcuiL4LZohbdMPtF46cvgtmjFrXEfpG+iOllIBOAsSilPCHJHzUMPyfJ01pMpytNTkJ0eSJla8O4pieFttf0BEzfX/9KXjvQE2pRN0op+yf51yTrGh7yzFrrmS2mtCs1yTeTfCTJR5P8Z611WG3YluSaDL4U8IMkn/zZvyil3D3J8Ul+J8n6ZebyglLKl2f0qs8w1dSiRs5J8rEMPos/Xmu9qMExly09zkjyn0nemvy8Bv1OBj/Hmy8zj1sneVOSRy7zuBuiLwI6pxaN1fEN4z5Zaz291Uya0xcBXVif5C+S/E4p5SNJ3lJr/a+Oc2qb/aLh+v769UUwXWaxFo2d/SJ9EXCD1KJfZL9IXwSMl1rUHvtF+iKm1FzXCQAw/UopRyU5oWH4YpIn1VovaDGlrjQ5CdF0k7sNfbjicd9fvyt7wYRSi7pRStmQ5ANJDm54yNtqrW9vMaWdOTvJi5McXmu9c631z2qtX2hwEukG1Vq/XGv9vQyumvn6DE5UNVWSvGlpYwiYEmrRDdqS5D1JfjXJAbXWJ9da39lwc32Xaq3n1Fr/NoPN8qdkcIXG5XhEKeXpq8lhB/oioFNq0fgs9UK/1TC86e+kTfoioA8OT/IHSb5cSvlsKeXXu06oRfaLhuv769cXwXSapVo0VvaL9EVAY7Nei+wXXZ++COjCrNeikbJfNKAvYloZyASgVaWUWyR5f5pf5fBFtdaPt5dRp5rU3eX8kTlqiw3jVvL3Q9Nj+v76/e0EE0gt6kYpZS7J25LcreEh3854775zRpJHJDl46eTRqW0sUms9u9b6tCT3y+CkVVObkvxdGzkB46cW3aC/SnJQrfUxtdZP1FpH3hPUWrfWWk9IctsMvvi1HC8updx4RKnoi4DOqEVj9+gkGxvEXZjB76Ur+iKgr+6b5EOllH+f0i/a2C8aru+vX18E02/aa9HY2C8a0BcBKzBrtch+0S/SFwFdm7Va1Ab7RdvRFzFtFrpOAIDpVUrZL4Nbld+o4SEfTvI37WXUuS0ZfmWoLmtz06tvNb0y8kqO6fvrX8lrBzqkFnXqlUke2TD20iSPqrVe2WI+11Nr/XGSH49xvc+VUu6awXvsjg0Pe0Ip5R9qrf/VYmpAy9SiG1ZrPXGMa11QSnl4kpcm+R8ND9s7yYsyuAroaumLgE6oRZ04vmHc22qt17aayQ3QFwEr9OEkPxoSs2cGXzTalEH9OWSFaz0kybdLKcfVWj+4wufoI/tFw/X99euLoFtq0WSxX3T99fRFMB3UohGzX7RT+iLghqhFk8F+0c7X0xcxFQxkAtCKUsrGDL7odfOGh3wzyePbuLpVj/R9g73p2iv5o38STiQ1WbuzhgdYPrWoO6WUv0jyzIbh1yZ5ZK315BZT6oVa61mllAck+VyS2zU87E+SPKq9rIA2qUX9s/Sz/ZNSSs3gM7aJ40opf1lrPW+Vy+uLgLFTi8Zv6W6k92kYfkKbufSRvggmX631xcs9ppSyd5K7ZnBnrEcnudMyDr9Rkn8tpTyh1vqu5a7dU/aLRpdDG/RF0HNq0eSwX7Rz+iKYfGrR5LNfNJK19UXQIbWo/+wX3TB9EdPA7cIBGLlSym5JPpTkyIaHnJzk12qtl7aWVD80OQnR9xMpycpOpjQ9pu+v34kkmBBqUXdKKc/J4MqQTWxL8oRa66fay6hfaq0XJvnNJJsbHvLwUsqhrSUEtEYt6r0XJvlAw9j1SZ4+gjX1RcBYqUWdOS5JaRD3lVrrt9tOpo/0RTB7aq0X11o/UWt9ca31zhl88esNSbY2fIr5JG8vpTy2tSTHy37R6HJog74IppBaNH72i26Yvghmj1rUW/aLVr62vggmjFo0dvaLhtAXMekMZAIwUqWUdUn+LclRDQ85I8kxI7hy1CS4pkHMbq1nsWu7N4xbycmUJq896f/rdyIJJoBa1J1SyvFJ/k/D8Jrk92qt72kxpV6qtf44yR81DJ9L8rgW0wFaoBb139KVj5+S5KKGhzx+BMvqi4CxUYu6UUqZT/KkhuEzd7Xj7emLYLbVWr9Wa/29JHdP8t8ND5tP8uZSyq3by2xs7BcN1/fXry+CCacWtct+UTP6IphtalE/2C/aJX0RzAC1qD32i5rTFzHJDGQCMDKllDVJ3p3kmIaHnJvBF71Oby+rXrmkQcz6UkpXV7fas2Hc5hU8d5PXvpwcRqqUsjbJ2gahm1tOBVgltag7pZTHJ3l9ml3ZK0meV2s9scWU+u7EJF9rGPvoNhMBRkstmhy11vOT/GXD8FuXUm63yiX1RcBYqEWdenCSAxrEXZnkn1vOZRLoi2DG1Vq/nuRuSV7Z8JD1Sd7S4T7KqNgvGl0OI6Uvgtkzw7WoNfaLlk1fBDNOLeqe/aLr0xfB7FGLWmG/aHn0RUwkA5kAjMTS1TzenuRhDQ+5KMkDa60/bC+r3rmwYdxerWax+nWbvo6VHDONrx0YE7WoO6WURyR5S5r3mH9Wa216Em8qLV1p86UNw+9cStnYZj7AaKhFE+kNSc5vGHu/Va6lLwJapxZ17riGce+ptV7aaiYTQF8EJEmtdUut9blJXtLwkLsleXZ7GY3FLPcGs/zagZ6a0VrUCvtFy6cvAhK1qCfsFy1/XX0RTBG1aOTsFy2DvohJZSATgFUrpcwleVOSxzQ85NIkv1Zr/XZrSfXTRQ3jNrWZxAjWbfo6VnJM0xxGrem6K3ntwBioRd0ppTwkyTuTNL3q2YtrrS9uMaVJ8m9JLm4QN5fkHi3nAqySWjSZaq1XZXAXuSaOWuVy+iKgVWpRt0opN0ny0IbhJ7SZy4TRFwFJklrrnyZ5f8Pw5034FfhnuTeY5dcO9NyM1aKRs1+0KvoiIIla1CX7RStaV18EU0gtWj37RSumL2LiGMgEYFVKKSXJ65P8dsNDrkjy67XWr7aXVW81vSrU/q1msbp1tyXZvILnvjjJ4ohyaEPTdV3ZC3pILepOKeVXk7w3ydqGh7yy1vpnLaY0UWqtW5J8qGH4ndrMBVgdtWji/WvDuNV+FuuLgNaoRb3wxCRrGsSdXGv9fNvJTAp9EbCD30uzPYiDkjy63VRaZb9oNDm0QV8EzEotGin7RaujLwJ2oBZ1x37R8tbVF8H0UotWx37RCuiLmEQGMgFYrdckOb5h7NVJHlZr/WKL+fTZmQ3j9ms1i11rcjLlrKVbwy9LrXUxydkNQvv82pPmv0NgvNSiDpRS7pfBFdHWNTzk9bXW57aW0OT6QsO4w1rNAlgttWiy/b8kWxvEHbo08LQi+iKgZWpR945rGHdiq1lMJn0RkCSptZ6f5OUNw5/UZi4ts180XJ9fe6Ivgqk1Q7VoZOwXjYy+CEiiFnXMftGAvghmnFq0avaLVk5fxEQxkAnAipVSXpnkGQ3Dr03yqFrrp1tMqe9OaRh3QKtZ7EQpZc8kuzcIbfoaVnrsXqWUJnmM2k0bxq3m9QMtUIu6UUq5d5IPJtnQ8JC3pPnvadZ8t2HcoW0mAaycWjT5aq1XJDm1Qei6rP5qxPoiYOTUou6VUu6V5DYNQrcleXPL6UwifRGwvTdk8Hk5zFGllPm2k2mJ/aLh9EVAl2ahFo2E/aKR0hcB21OLOmC/6Of0RUCiFq2I/aJV0xcxUQxkArAipZS/S/LshuFbkzyu1vrhFlOaBKc2jLt5m0nswi0axp26ijWaHjutrx8YMbWoG6WUeyb5cJp9MStJ3p3kuKWrPPKLmm5S7N1qFsCKqEVTZVyfx6c2jNMXAY2oRb3R9O6kH661Nrn6/azRFwE/V2s9J4O7kgyzZ5Ij282mNac2jJvWvqDpsdP6+oGem5FatGr2i0ZOXwT8nFrUKftF+iIgatEq2C9aHX0RE8VAJgDLVkp5SZLnNwzfluS3a63vby+jifGThnFHtJrFzjU9kbKaK1vN+usHRkgt6kYp5ZeTfDSDk2lNvD/JE2qtTa6YNqsuaRjXxZUvgRugFk2dcX0e64uAkVGL+qGUskeSxzYMP6HNXCaYvgjY0Rcbxt2p1SzaM+t9way/fmAyTHstWhX7Ra3QFwE7Uou6Yb9IXwRcRy1aBvtFI6EvYqIYyARgWUopf5XkBQ3DawZXOHxXiylNklOTXNYg7lYt57GaNb+9ijW+0zCur6//siSntZ0IMJxa1I1Syl2SfCzJXg0P+UgGd9/Z2l5WU+HqhnHrW80CWBa1aCqN6/NYXwSMhFrUK49NskeDuHOTfKjlXCaVvgjY0UkN427UahbtOTX2i5ro6+vXF8FsmPZatGL2i1qjLwJ2pBZ1w36Rvgi4jlq0PPaLVk9fxEQxkAlAY6WUv0jy/zUMr0meVmt9S4spTZRaa03y3w1Cb1FKaXolyVG5c8O4b6xija83jGuay0iUUjYmObxB6DeXfodAh9SibpRSjkzyiSSbGh7yySSPrLVe21ZOU6TpCaKmJ5yAlqlFU2tcn8f6ImDV1KLeOb5h3Jt9AXmX9EXAjs5vGLdvq1m0xH6RvgiYCFNdi1bKflGr9EXAjtSibtgv0hcB11GLlsd+0erpi5goBjIBaKSU8sIkL1rGIc+qtb6hpXQmWZOTKSXJndpOZAd3bRCzudb6k5UuUGv9cZLNDULHeiIpyV0y+JkP0/REGNAStagbpZQ7ZLBhvnfDQz6X5DdrrU58NLOxYdwVrWYBNKIWTbWxfB7ri4DVUov6pZRy6yT3ahh+Ypu5TDh9EbCjKxvGbWoziZbZLxpOXwR0aRZq0bLYL2qdvgjYkVrUDftF+iLgOmpRQ/aLRkZfxEQxkAnAUKWUP07y4mUc8vxa62vaymfCfaVhXNM/zFetlHJgkps1CP3qCJb7WoOYw0op+49grabu2TCu6e8OaIFa1I1Sym2TfCrNr2T2pSQPrbU2PSFHcljDuM1tJgEMpxZNvXF+HuuLgBVRi3qp6dWO/6PWelKrmUw2fRGwow0N4yb5blv2i4bTFwFdmoVa1Jj9orHQFwE7Uou6Yb+oGX0RzAa1qDn7RaOhL2KiGMgE4AaVUp6T5H8v45A/rbW+vK18psCnG8Yd02oW1/fAhnGfGsFaTZ+jj6+/6e8OGDG1qBullFtl8Ll944aHfCXJg2utl7eX1VS6XcO4U9tMArhhatF0K6XsluTQBqHXJDl7BEvqi4BlU4v6p5SyJsnvNAw/oc1cpoC+CNjRPg3jJvk8lP2iZvr4+vVFMBtmoRY1Yr9obPRFwI7UojGzX5REXwRcn1rUgP2ikdIXMVEMZAKwS6WU30/yimUc8qJa60taSmcq1FrPSvK9BqFHLZ3kGYemJ1I+MYK1Ptkw7ldHsNZQpZQ90uzKXt+ptY7iRBqwTGpRN0opt8jgBHrTKy1+I8mv1VovbS+rqXXvhnGntJoFsEtq0Uy4R5KFBnGn1VrrCNbTFwHLohb11kOT7Ncg7rIk/9JyLpNOXwTs6BYN4y5oNYsW2S/SFwG9N/W1qAn7RWOlLwJ2pBaNn/0ifRFwfWpRM/aLRkdfxEQxkAnATpVSnprk1cs45CW11r9sK58p8/EGMeuT/EbbiZRSNjRc58IMNk9W62tJLmoQ97BSyroRrDfMw5OsbRA3ii8XAMukFnWjlHJYBpvrBzQ85NtJHlhrvbi9rKbT0hXSHtIw/Ftt5gLsnFo0Mx7RMG5Un8X6IqAxtajXjm8Y985a6xWtZjLB9EXALty9YdyPWs2iffaLhtMXAV2ZlVq0S/aLxkdfBOzCzNeiDtgv0hcB16cWNWO/aAT0RUwiA5kA/IJSypOT/FOS0vCQl9da/7S9jKbOexvGHdtqFgMPTbJng7j31VoXV7vY0nP8a4PQjUl+fbXrNfD4hnHvaTUL4BeoRd0opRyc5DNJbtbwkO8nOabWemF7WU2130iyT4O4muQ/W84F2IFaNBtKKeuTPK5h+BdHsaa+CGhKLeqvUsoBSR7UMPyENnOZAvoi4HpKKbun+ZXQf9hmLmNgv2g4fREwdjNWi3bKftHY6YuA61GLxs9+URJ9EbAdtagZ+0UjpS9i4hjIBOB6SilPyOCPvqZf9HpVrfX5LaY0jb6YZrdLf0gp5ZCWc/mDhnFvG+GaTZ/r90e45i8opRyeZo3QT2qtX2ozF+D61KJulFIOzOBKx01rz8lJHlBrPa+9rKbeCxrGfbvW2uTKmMCIqEUz5fgkN24Y+7kRrqsvAm6QWtR7T0oy3yDuu7XWL7edzITTFwE7ekyS3RrEXZTB8Mcks1/UjL4IGLdZqkW/wH5RJ/RFwI5muhZ1xH6Rvgi4PrWoGftFo6MvYuIYyATg50opj03y5jSvD69P8pz2MppOtdaa5O0NQufT4s+3lHKXJPdtEHpaki+McOnPJTm9QdwxpZQ7jnDdHT03zd7rTX5XwIioRd0opeyfweb6zRse8pMk96+1nt1eVtNt6W5Hd2sY7gqTMEZq0ewopeyb5EUNw39Sa/3GCJfXFwG7pBZNhOMaxrna8Q3QFwE7KqXMp/kXbz43ijs1dsl+kb4I6J9Zq0U7sl80fvoiYEezXou6YL8oib4I2I5atCz2i0ZAX8SkMpAJQJKklPLwDBrmJlfqSJI3Jnn60mYxy/f6JFsaxD2jxasev7Rh3D+O8ve89FyvbRj+klGtu72lq3r9XoPQLRn8roAxUIu6UUq5cQab67dseMhpGWyun9leVtOtlHJYkpcv45B3tZULcH1q0ewopZQkb0hyo4aHjPSzWF8E7Ipa1H+llPsmuUWD0GuTvLXldCaWvgjYhT9OcquGse9tM5Exsl/UjL4IGJdZrEVJ7Bd1QV8E7MLM1qIu2C/SFwE7pRY1YL9oNPRFTDIDmQCklPKQDP5AWWh4yNuTPMUXvVau1npGknc2CF2f5f2h2Ugp5ZFJjmkQemmS1416/QxOJF3WIO7BpZTfaGH9/5NkXYO4d9hAgvFQi7pRStknySeT3KbhIWdmsLl+WntZTbdSyt5J/i3J3g0P+Vit9YctpgQsUYtmzl8neUTD2C1pvhm+HPoi4HrUoolxfMO4D9RaL2g1kwmlLwJ2ppRyvyR/1TD80iTvazGdsbFfpC8C+mNWa1Fiv6gL+iJgZ2a5FnXIfpG+CNiOWrQs9otWSV/EpDOQCTDjSikPzOAKJWsbHvKuJE+ahlvMl1LeVEqpDR5vaimF/52kyRfmHllKafqH+1CllAPT/GpVr621XjqqtX+m1npJmm/cn1BKuemo1i6lPD3JwxqELib521GtC+yaWtRNLSqlbEryiSS/1PCQszPYXP/JqHMZl1LKXUspj1y6ymUX6++X5FNJ7rCMw17cUjrAdtSi8daiUsqfLNWhsSsDf5Pkz5Zx2NuWviQ9UvoiYHtqUafn6JaT615JHtUw/IQ2c1kpfRGwvVLKQ0opzyilNL0YQFt53DfJ+5OsaXjIP9Varxrh+l3XIvtFzeiLYAqpRT9f337RGOmLgO2pRT9f337RcPaLgFaoRT9fv+tzdMvJ1X7R6tfXFzHxDGQCzLClP57/Lc2ucJQMrmTy27XWbe1lNTtqrd9JcmLD8FeVUo5e7ZqllI0Z/M73bRB+fpKXrnbNG/CSpTWGuXGS95dS9lztgqWUByR5RcPwE2qt313tmsANU4u6sfSZ+tEkd254yHkZbK6f3F5WY3FQBl9y/3op5dhSStMTiKtWSjkqyVeT3GkZh32w1vr5llIClqhFnXhhklNLKX+z9AXgsSiDqyu+J8vbXL8iyV+0k1ESfREQtWjCHJtktwZxZyb5eMu5rJS+CNjejZP8Y5Lvl1J+r5SyfpyLL30B9jkZfGbu1fCwS9Pu3sXY2S/SF8GMU4s6ZL9IXwQkUYu6ZL/oOvoimG1q0eSxX7QK+iKmhYFMgBlVSrlXkn9PsqHhIR9I8lu11q3tZTWTXpDk4gZxG5J8sJTyoJUuVErZP8nHktyl4SEvrLVuXul6w9RaL07ypw3DfznJx0opN1npeqWUh2TwPm7y5cbl5AaskFrUjVLKbkk+lOTuDQ+5IIPN9R+0l9XYHZnk7UlOL6W8uJRy27YWKqXsV0p5TZLPZ3Aiq6krkjyznayAn1GLOrUxg43uU0sp7y6lPLStE/yllPlSyhOTfD/JI5d5+P9s42rHP6MvAtSiiXNcw7g3TsDdS4+Mvgi4zi2S/FMGnwl/V0q5Y9sLLl2Q4EsZfPmz6R2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      "text/plain": [
       "<Figure size 3840x2640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=1, sharey=True)\n",
    "\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "versicolor.plot.scatter(x='sepal_width', y='petal_width', ax=axs, c=colors[1])\n",
    "virginica.plot.scatter(x='sepal_width', y='petal_width', ax=axs, c=colors[2])\n",
    "\n",
    "axs.plot(versicolor['sepal_width'].values.reshape(-1,1), y_versicolor, '-', c=colors[1], label='versicolor')\n",
    "axs.plot(virginica['sepal_width'].values.reshape(-1,1), y_virginica, '-', c=colors[2], label='virginica')\n",
    "\n",
    "axs.plot(reduced['sepal_width'].values.reshape(-1,1), y_reduced, '-', c=colors[3], label='reduced')\n",
    "\n",
    "axs.set_xlabel('Sepal Width')\n",
    "axs.set_ylabel('Petal Width')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"width: 100%; overflow: hidden;\">\n",
    "     <img src=\"data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n",
    "</div>"
   ]
  }
 ],
 "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": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}