{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simpson's paradox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**[Sex Bias in Graduate Admissions: Data from Berkeley](https://pdfs.semanticscholar.org/b704/3d57d399bd28b2d3e84fb9d342a307472458.pdf)**\n",
    "\n",
    "*[https://pdfs.semanticscholar.org/b704/3d57d399bd28b2d3e84fb9d342a307472458.pdf](https://pdfs.semanticscholar.org/b704/3d57d399bd28b2d3e84fb9d342a307472458.pdf)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example from article"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Actual data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4099349682676487"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admission_rate = (3738 + 1494) / 12763\n",
    "admission_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8442"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_men = 3738 + 4704\n",
    "total_men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3460.6710021154904"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expect_men_admitted = total_men * admission_rate\n",
    "expect_men_admitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4321"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_women = 1494 + 2827\n",
    "total_women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1771.32899788451"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expect_women_admitted = total_women * admission_rate\n",
    "expect_women_admitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data in the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4166666666666667"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "admission_rate = (250 + 250) / (250 + 250 + 300 + 400)\n",
    "admission_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "550"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_men = 250 + 300\n",
    "total_men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "650"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_women = 250 + 400\n",
    "total_women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229.16666666666669"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expect_men_admitted = total_men * admission_rate\n",
    "expect_men_admitted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "270.83333333333337"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expect_women_admitted = total_women * admission_rate\n",
    "expect_women_admitted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "men = pd.DataFrame({\"height\": np.random.normal(1.8, 0.05, 100), \"weight\": np.random.normal(85, 1, 100)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "men['bmi'] = men['weight'] / men['height']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.846326</td>\n",
       "      <td>84.350208</td>\n",
       "      <td>24.743971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.705580</td>\n",
       "      <td>83.648994</td>\n",
       "      <td>28.755193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.756489</td>\n",
       "      <td>84.277095</td>\n",
       "      <td>27.316110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.848740</td>\n",
       "      <td>84.762614</td>\n",
       "      <td>24.800061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.828322</td>\n",
       "      <td>85.054365</td>\n",
       "      <td>25.444335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.792602</td>\n",
       "      <td>85.289775</td>\n",
       "      <td>26.541740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.790928</td>\n",
       "      <td>86.048167</td>\n",
       "      <td>26.827812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.809917</td>\n",
       "      <td>83.658219</td>\n",
       "      <td>25.538256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.804939</td>\n",
       "      <td>84.539923</td>\n",
       "      <td>25.949953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.755725</td>\n",
       "      <td>86.369401</td>\n",
       "      <td>28.018621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.817654</td>\n",
       "      <td>85.530689</td>\n",
       "      <td>25.888063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.798073</td>\n",
       "      <td>84.488833</td>\n",
       "      <td>26.132729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.789017</td>\n",
       "      <td>85.597579</td>\n",
       "      <td>26.744371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.736679</td>\n",
       "      <td>85.642032</td>\n",
       "      <td>28.395390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.717038</td>\n",
       "      <td>84.934525</td>\n",
       "      <td>28.808750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.754468</td>\n",
       "      <td>85.220435</td>\n",
       "      <td>27.685526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.745762</td>\n",
       "      <td>86.236397</td>\n",
       "      <td>28.295705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.738966</td>\n",
       "      <td>83.296604</td>\n",
       "      <td>27.545156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.744043</td>\n",
       "      <td>84.484906</td>\n",
       "      <td>27.775682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.814423</td>\n",
       "      <td>87.287624</td>\n",
       "      <td>26.514010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.696548</td>\n",
       "      <td>85.193880</td>\n",
       "      <td>29.598924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.834508</td>\n",
       "      <td>86.174038</td>\n",
       "      <td>25.605727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.847978</td>\n",
       "      <td>85.316498</td>\n",
       "      <td>24.982692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.838084</td>\n",
       "      <td>84.265763</td>\n",
       "      <td>24.941385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.791247</td>\n",
       "      <td>86.197007</td>\n",
       "      <td>26.864651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.738719</td>\n",
       "      <td>85.308274</td>\n",
       "      <td>28.218388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.810138</td>\n",
       "      <td>84.111496</td>\n",
       "      <td>25.670353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.828684</td>\n",
       "      <td>84.670985</td>\n",
       "      <td>25.319619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.822241</td>\n",
       "      <td>85.705984</td>\n",
       "      <td>25.810675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.727342</td>\n",
       "      <td>87.253849</td>\n",
       "      <td>29.243388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1.812735</td>\n",
       "      <td>85.353152</td>\n",
       "      <td>25.974731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1.820731</td>\n",
       "      <td>85.452422</td>\n",
       "      <td>25.777023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1.850460</td>\n",
       "      <td>84.517401</td>\n",
       "      <td>24.682373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1.758177</td>\n",
       "      <td>84.955599</td>\n",
       "      <td>27.483171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.720298</td>\n",
       "      <td>83.389695</td>\n",
       "      <td>28.177659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.818238</td>\n",
       "      <td>84.249254</td>\n",
       "      <td>25.483829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1.801602</td>\n",
       "      <td>85.523859</td>\n",
       "      <td>26.349323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1.738942</td>\n",
       "      <td>85.662463</td>\n",
       "      <td>28.328297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1.828085</td>\n",
       "      <td>84.353179</td>\n",
       "      <td>25.241129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1.853273</td>\n",
       "      <td>84.966655</td>\n",
       "      <td>24.738281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.779977</td>\n",
       "      <td>84.804354</td>\n",
       "      <td>26.766354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1.851053</td>\n",
       "      <td>84.969054</td>\n",
       "      <td>24.798354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1.807441</td>\n",
       "      <td>84.043159</td>\n",
       "      <td>25.726102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1.856138</td>\n",
       "      <td>85.852565</td>\n",
       "      <td>24.919121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1.837212</td>\n",
       "      <td>85.128751</td>\n",
       "      <td>25.220738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1.794949</td>\n",
       "      <td>86.020067</td>\n",
       "      <td>26.699028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1.771492</td>\n",
       "      <td>84.545193</td>\n",
       "      <td>26.940797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1.882290</td>\n",
       "      <td>86.189506</td>\n",
       "      <td>24.326592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1.817829</td>\n",
       "      <td>85.770484</td>\n",
       "      <td>25.955643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1.799267</td>\n",
       "      <td>85.202430</td>\n",
       "      <td>26.318481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.846834</td>\n",
       "      <td>84.033850</td>\n",
       "      <td>24.637605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1.773097</td>\n",
       "      <td>85.051595</td>\n",
       "      <td>27.053130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.732057</td>\n",
       "      <td>84.835168</td>\n",
       "      <td>28.278197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1.800811</td>\n",
       "      <td>84.196582</td>\n",
       "      <td>25.963185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1.850443</td>\n",
       "      <td>85.347839</td>\n",
       "      <td>24.925330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.721525</td>\n",
       "      <td>83.980465</td>\n",
       "      <td>28.336855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.798503</td>\n",
       "      <td>83.329209</td>\n",
       "      <td>25.761735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.835831</td>\n",
       "      <td>86.217741</td>\n",
       "      <td>25.581795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.790648</td>\n",
       "      <td>85.394169</td>\n",
       "      <td>26.632243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.855777</td>\n",
       "      <td>84.535031</td>\n",
       "      <td>24.546248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      height     weight        bmi\n",
       "0   1.846326  84.350208  24.743971\n",
       "1   1.705580  83.648994  28.755193\n",
       "2   1.756489  84.277095  27.316110\n",
       "3   1.848740  84.762614  24.800061\n",
       "4   1.828322  85.054365  25.444335\n",
       "5   1.792602  85.289775  26.541740\n",
       "6   1.790928  86.048167  26.827812\n",
       "7   1.809917  83.658219  25.538256\n",
       "8   1.804939  84.539923  25.949953\n",
       "9   1.755725  86.369401  28.018621\n",
       "10  1.817654  85.530689  25.888063\n",
       "11  1.798073  84.488833  26.132729\n",
       "12  1.789017  85.597579  26.744371\n",
       "13  1.736679  85.642032  28.395390\n",
       "14  1.717038  84.934525  28.808750\n",
       "15  1.754468  85.220435  27.685526\n",
       "16  1.745762  86.236397  28.295705\n",
       "17  1.738966  83.296604  27.545156\n",
       "18  1.744043  84.484906  27.775682\n",
       "19  1.814423  87.287624  26.514010\n",
       "20  1.696548  85.193880  29.598924\n",
       "21  1.834508  86.174038  25.605727\n",
       "22  1.847978  85.316498  24.982692\n",
       "23  1.838084  84.265763  24.941385\n",
       "24  1.791247  86.197007  26.864651\n",
       "25  1.738719  85.308274  28.218388\n",
       "26  1.810138  84.111496  25.670353\n",
       "27  1.828684  84.670985  25.319619\n",
       "28  1.822241  85.705984  25.810675\n",
       "29  1.727342  87.253849  29.243388\n",
       "..       ...        ...        ...\n",
       "70  1.812735  85.353152  25.974731\n",
       "71  1.820731  85.452422  25.777023\n",
       "72  1.850460  84.517401  24.682373\n",
       "73  1.758177  84.955599  27.483171\n",
       "74  1.720298  83.389695  28.177659\n",
       "75  1.818238  84.249254  25.483829\n",
       "76  1.801602  85.523859  26.349323\n",
       "77  1.738942  85.662463  28.328297\n",
       "78  1.828085  84.353179  25.241129\n",
       "79  1.853273  84.966655  24.738281\n",
       "80  1.779977  84.804354  26.766354\n",
       "81  1.851053  84.969054  24.798354\n",
       "82  1.807441  84.043159  25.726102\n",
       "83  1.856138  85.852565  24.919121\n",
       "84  1.837212  85.128751  25.220738\n",
       "85  1.794949  86.020067  26.699028\n",
       "86  1.771492  84.545193  26.940797\n",
       "87  1.882290  86.189506  24.326592\n",
       "88  1.817829  85.770484  25.955643\n",
       "89  1.799267  85.202430  26.318481\n",
       "90  1.846834  84.033850  24.637605\n",
       "91  1.773097  85.051595  27.053130\n",
       "92  1.732057  84.835168  28.278197\n",
       "93  1.800811  84.196582  25.963185\n",
       "94  1.850443  85.347839  24.925330\n",
       "95  1.721525  83.980465  28.336855\n",
       "96  1.798503  83.329209  25.761735\n",
       "97  1.835831  86.217741  25.581795\n",
       "98  1.790648  85.394169  26.632243\n",
       "99  1.855777  84.535031  24.546248\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mclou\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: 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": [
       "<seaborn.axisgrid.FacetGrid at 0x1e360ad6c18>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='height', y='bmi', data=men)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "women = pd.DataFrame({\"height\": np.random.normal(1.6, 0.05, 100), \"weight\": np.random.normal(60, 1, 100)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "women['bmi'] = women['weight'] / women['height']**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.635764</td>\n",
       "      <td>60.580269</td>\n",
       "      <td>22.640706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.561134</td>\n",
       "      <td>59.842627</td>\n",
       "      <td>24.554452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.600291</td>\n",
       "      <td>58.834190</td>\n",
       "      <td>22.973758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.541475</td>\n",
       "      <td>59.004968</td>\n",
       "      <td>24.832234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.617897</td>\n",
       "      <td>60.302164</td>\n",
       "      <td>23.037285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.614042</td>\n",
       "      <td>61.837639</td>\n",
       "      <td>23.736871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.593266</td>\n",
       "      <td>59.645419</td>\n",
       "      <td>23.496357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.590521</td>\n",
       "      <td>60.987380</td>\n",
       "      <td>24.107989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.619571</td>\n",
       "      <td>58.195426</td>\n",
       "      <td>22.186507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.634805</td>\n",
       "      <td>59.926340</td>\n",
       "      <td>22.422582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.526086</td>\n",
       "      <td>60.773087</td>\n",
       "      <td>26.094743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.636303</td>\n",
       "      <td>60.233596</td>\n",
       "      <td>22.496313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.585557</td>\n",
       "      <td>59.798904</td>\n",
       "      <td>23.786453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.604561</td>\n",
       "      <td>60.725505</td>\n",
       "      <td>23.586247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.618125</td>\n",
       "      <td>60.008555</td>\n",
       "      <td>22.918658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.609263</td>\n",
       "      <td>61.205441</td>\n",
       "      <td>23.633928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.614334</td>\n",
       "      <td>60.998740</td>\n",
       "      <td>23.406365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.692908</td>\n",
       "      <td>59.084046</td>\n",
       "      <td>20.615958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.545380</td>\n",
       "      <td>58.278846</td>\n",
       "      <td>24.402824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.572084</td>\n",
       "      <td>59.759869</td>\n",
       "      <td>24.180088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.543847</td>\n",
       "      <td>59.237603</td>\n",
       "      <td>24.853593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.574731</td>\n",
       "      <td>60.450504</td>\n",
       "      <td>24.377392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.581154</td>\n",
       "      <td>58.665411</td>\n",
       "      <td>23.465723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.627137</td>\n",
       "      <td>60.157900</td>\n",
       "      <td>22.721890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.573138</td>\n",
       "      <td>60.682653</td>\n",
       "      <td>24.520576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.567199</td>\n",
       "      <td>60.141314</td>\n",
       "      <td>24.486370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.584278</td>\n",
       "      <td>60.423229</td>\n",
       "      <td>24.073614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.603280</td>\n",
       "      <td>60.133212</td>\n",
       "      <td>23.393512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.648226</td>\n",
       "      <td>59.678417</td>\n",
       "      <td>21.967655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.600238</td>\n",
       "      <td>58.983817</td>\n",
       "      <td>23.033699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>1.652888</td>\n",
       "      <td>57.802395</td>\n",
       "      <td>21.157243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1.596816</td>\n",
       "      <td>62.396018</td>\n",
       "      <td>24.470728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1.657528</td>\n",
       "      <td>61.250404</td>\n",
       "      <td>22.293958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1.673148</td>\n",
       "      <td>58.042489</td>\n",
       "      <td>20.733715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.507153</td>\n",
       "      <td>60.806251</td>\n",
       "      <td>26.769101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.619881</td>\n",
       "      <td>59.094099</td>\n",
       "      <td>22.520506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1.622517</td>\n",
       "      <td>59.199302</td>\n",
       "      <td>22.487330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1.595906</td>\n",
       "      <td>60.075127</td>\n",
       "      <td>23.587412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1.659761</td>\n",
       "      <td>58.601985</td>\n",
       "      <td>21.272637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1.711083</td>\n",
       "      <td>59.453638</td>\n",
       "      <td>20.306556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.632763</td>\n",
       "      <td>58.053691</td>\n",
       "      <td>21.776266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1.612295</td>\n",
       "      <td>59.658539</td>\n",
       "      <td>22.950060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1.624213</td>\n",
       "      <td>60.653229</td>\n",
       "      <td>22.991522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1.542336</td>\n",
       "      <td>59.840166</td>\n",
       "      <td>25.155607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1.591212</td>\n",
       "      <td>59.762734</td>\n",
       "      <td>23.603385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1.575955</td>\n",
       "      <td>59.963829</td>\n",
       "      <td>24.143583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1.623107</td>\n",
       "      <td>60.997220</td>\n",
       "      <td>23.153438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1.658766</td>\n",
       "      <td>60.111609</td>\n",
       "      <td>21.846824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1.561854</td>\n",
       "      <td>60.627608</td>\n",
       "      <td>24.853624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1.577502</td>\n",
       "      <td>60.144334</td>\n",
       "      <td>24.168787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.623968</td>\n",
       "      <td>59.850311</td>\n",
       "      <td>22.694030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1.597063</td>\n",
       "      <td>61.065658</td>\n",
       "      <td>23.941591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.682761</td>\n",
       "      <td>59.906006</td>\n",
       "      <td>21.155602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1.641190</td>\n",
       "      <td>60.122483</td>\n",
       "      <td>22.321282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1.581411</td>\n",
       "      <td>60.415878</td>\n",
       "      <td>24.158047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.546597</td>\n",
       "      <td>60.029364</td>\n",
       "      <td>25.096279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.591066</td>\n",
       "      <td>60.472437</td>\n",
       "      <td>23.888081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.490416</td>\n",
       "      <td>59.096371</td>\n",
       "      <td>26.603924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.555316</td>\n",
       "      <td>59.681624</td>\n",
       "      <td>24.671940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.515912</td>\n",
       "      <td>61.311259</td>\n",
       "      <td>26.680384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      height     weight        bmi\n",
       "0   1.635764  60.580269  22.640706\n",
       "1   1.561134  59.842627  24.554452\n",
       "2   1.600291  58.834190  22.973758\n",
       "3   1.541475  59.004968  24.832234\n",
       "4   1.617897  60.302164  23.037285\n",
       "5   1.614042  61.837639  23.736871\n",
       "6   1.593266  59.645419  23.496357\n",
       "7   1.590521  60.987380  24.107989\n",
       "8   1.619571  58.195426  22.186507\n",
       "9   1.634805  59.926340  22.422582\n",
       "10  1.526086  60.773087  26.094743\n",
       "11  1.636303  60.233596  22.496313\n",
       "12  1.585557  59.798904  23.786453\n",
       "13  1.604561  60.725505  23.586247\n",
       "14  1.618125  60.008555  22.918658\n",
       "15  1.609263  61.205441  23.633928\n",
       "16  1.614334  60.998740  23.406365\n",
       "17  1.692908  59.084046  20.615958\n",
       "18  1.545380  58.278846  24.402824\n",
       "19  1.572084  59.759869  24.180088\n",
       "20  1.543847  59.237603  24.853593\n",
       "21  1.574731  60.450504  24.377392\n",
       "22  1.581154  58.665411  23.465723\n",
       "23  1.627137  60.157900  22.721890\n",
       "24  1.573138  60.682653  24.520576\n",
       "25  1.567199  60.141314  24.486370\n",
       "26  1.584278  60.423229  24.073614\n",
       "27  1.603280  60.133212  23.393512\n",
       "28  1.648226  59.678417  21.967655\n",
       "29  1.600238  58.983817  23.033699\n",
       "..       ...        ...        ...\n",
       "70  1.652888  57.802395  21.157243\n",
       "71  1.596816  62.396018  24.470728\n",
       "72  1.657528  61.250404  22.293958\n",
       "73  1.673148  58.042489  20.733715\n",
       "74  1.507153  60.806251  26.769101\n",
       "75  1.619881  59.094099  22.520506\n",
       "76  1.622517  59.199302  22.487330\n",
       "77  1.595906  60.075127  23.587412\n",
       "78  1.659761  58.601985  21.272637\n",
       "79  1.711083  59.453638  20.306556\n",
       "80  1.632763  58.053691  21.776266\n",
       "81  1.612295  59.658539  22.950060\n",
       "82  1.624213  60.653229  22.991522\n",
       "83  1.542336  59.840166  25.155607\n",
       "84  1.591212  59.762734  23.603385\n",
       "85  1.575955  59.963829  24.143583\n",
       "86  1.623107  60.997220  23.153438\n",
       "87  1.658766  60.111609  21.846824\n",
       "88  1.561854  60.627608  24.853624\n",
       "89  1.577502  60.144334  24.168787\n",
       "90  1.623968  59.850311  22.694030\n",
       "91  1.597063  61.065658  23.941591\n",
       "92  1.682761  59.906006  21.155602\n",
       "93  1.641190  60.122483  22.321282\n",
       "94  1.581411  60.415878  24.158047\n",
       "95  1.546597  60.029364  25.096279\n",
       "96  1.591066  60.472437  23.888081\n",
       "97  1.490416  59.096371  26.603924\n",
       "98  1.555316  59.681624  24.671940\n",
       "99  1.515912  61.311259  26.680384\n",
       "\n",
       "[100 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mclou\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: 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": [
       "<seaborn.axisgrid.FacetGrid at 0x1e361e48c50>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='height', y='bmi', data=women)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "men['gender'] = 'male'\n",
    "women['gender'] = 'female'\n",
    "people = pd.concat([men, women])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>bmi</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.846326</td>\n",
       "      <td>84.350208</td>\n",
       "      <td>24.743971</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.705580</td>\n",
       "      <td>83.648994</td>\n",
       "      <td>28.755193</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.756489</td>\n",
       "      <td>84.277095</td>\n",
       "      <td>27.316110</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.848740</td>\n",
       "      <td>84.762614</td>\n",
       "      <td>24.800061</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.828322</td>\n",
       "      <td>85.054365</td>\n",
       "      <td>25.444335</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.792602</td>\n",
       "      <td>85.289775</td>\n",
       "      <td>26.541740</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.790928</td>\n",
       "      <td>86.048167</td>\n",
       "      <td>26.827812</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.809917</td>\n",
       "      <td>83.658219</td>\n",
       "      <td>25.538256</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.804939</td>\n",
       "      <td>84.539923</td>\n",
       "      <td>25.949953</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.755725</td>\n",
       "      <td>86.369401</td>\n",
       "      <td>28.018621</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.817654</td>\n",
       "      <td>85.530689</td>\n",
       "      <td>25.888063</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.798073</td>\n",
       "      <td>84.488833</td>\n",
       "      <td>26.132729</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.789017</td>\n",
       "      <td>85.597579</td>\n",
       "      <td>26.744371</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.736679</td>\n",
       "      <td>85.642032</td>\n",
       "      <td>28.395390</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.717038</td>\n",
       "      <td>84.934525</td>\n",
       "      <td>28.808750</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.754468</td>\n",
       "      <td>85.220435</td>\n",
       "      <td>27.685526</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1.745762</td>\n",
       "      <td>86.236397</td>\n",
       "      <td>28.295705</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.738966</td>\n",
       "      <td>83.296604</td>\n",
       "      <td>27.545156</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.744043</td>\n",
       "      <td>84.484906</td>\n",
       "      <td>27.775682</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1.814423</td>\n",
       "      <td>87.287624</td>\n",
       "      <td>26.514010</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1.696548</td>\n",
       "      <td>85.193880</td>\n",
       "      <td>29.598924</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.834508</td>\n",
       "      <td>86.174038</td>\n",
       "      <td>25.605727</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1.847978</td>\n",
       "      <td>85.316498</td>\n",
       "      <td>24.982692</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.838084</td>\n",
       "      <td>84.265763</td>\n",
       "      <td>24.941385</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1.791247</td>\n",
       "      <td>86.197007</td>\n",
       "      <td>26.864651</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1.738719</td>\n",
       "      <td>85.308274</td>\n",
       "      <td>28.218388</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.810138</td>\n",
       "      <td>84.111496</td>\n",
       "      <td>25.670353</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1.828684</td>\n",
       "      <td>84.670985</td>\n",
       "      <td>25.319619</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1.822241</td>\n",
       "      <td>85.705984</td>\n",
       "      <td>25.810675</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.727342</td>\n",
       "      <td>87.253849</td>\n",
       "      <td>29.243388</td>\n",
       "      <td>male</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>70</th>\n",
       "      <td>1.652888</td>\n",
       "      <td>57.802395</td>\n",
       "      <td>21.157243</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>1.596816</td>\n",
       "      <td>62.396018</td>\n",
       "      <td>24.470728</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>1.657528</td>\n",
       "      <td>61.250404</td>\n",
       "      <td>22.293958</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>1.673148</td>\n",
       "      <td>58.042489</td>\n",
       "      <td>20.733715</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>1.507153</td>\n",
       "      <td>60.806251</td>\n",
       "      <td>26.769101</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>1.619881</td>\n",
       "      <td>59.094099</td>\n",
       "      <td>22.520506</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>1.622517</td>\n",
       "      <td>59.199302</td>\n",
       "      <td>22.487330</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>1.595906</td>\n",
       "      <td>60.075127</td>\n",
       "      <td>23.587412</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1.659761</td>\n",
       "      <td>58.601985</td>\n",
       "      <td>21.272637</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1.711083</td>\n",
       "      <td>59.453638</td>\n",
       "      <td>20.306556</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1.632763</td>\n",
       "      <td>58.053691</td>\n",
       "      <td>21.776266</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1.612295</td>\n",
       "      <td>59.658539</td>\n",
       "      <td>22.950060</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1.624213</td>\n",
       "      <td>60.653229</td>\n",
       "      <td>22.991522</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>1.542336</td>\n",
       "      <td>59.840166</td>\n",
       "      <td>25.155607</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1.591212</td>\n",
       "      <td>59.762734</td>\n",
       "      <td>23.603385</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1.575955</td>\n",
       "      <td>59.963829</td>\n",
       "      <td>24.143583</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>1.623107</td>\n",
       "      <td>60.997220</td>\n",
       "      <td>23.153438</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>1.658766</td>\n",
       "      <td>60.111609</td>\n",
       "      <td>21.846824</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1.561854</td>\n",
       "      <td>60.627608</td>\n",
       "      <td>24.853624</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>1.577502</td>\n",
       "      <td>60.144334</td>\n",
       "      <td>24.168787</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1.623968</td>\n",
       "      <td>59.850311</td>\n",
       "      <td>22.694030</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1.597063</td>\n",
       "      <td>61.065658</td>\n",
       "      <td>23.941591</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.682761</td>\n",
       "      <td>59.906006</td>\n",
       "      <td>21.155602</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1.641190</td>\n",
       "      <td>60.122483</td>\n",
       "      <td>22.321282</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1.581411</td>\n",
       "      <td>60.415878</td>\n",
       "      <td>24.158047</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>1.546597</td>\n",
       "      <td>60.029364</td>\n",
       "      <td>25.096279</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>1.591066</td>\n",
       "      <td>60.472437</td>\n",
       "      <td>23.888081</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.490416</td>\n",
       "      <td>59.096371</td>\n",
       "      <td>26.603924</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.555316</td>\n",
       "      <td>59.681624</td>\n",
       "      <td>24.671940</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1.515912</td>\n",
       "      <td>61.311259</td>\n",
       "      <td>26.680384</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      height     weight        bmi  gender\n",
       "0   1.846326  84.350208  24.743971    male\n",
       "1   1.705580  83.648994  28.755193    male\n",
       "2   1.756489  84.277095  27.316110    male\n",
       "3   1.848740  84.762614  24.800061    male\n",
       "4   1.828322  85.054365  25.444335    male\n",
       "5   1.792602  85.289775  26.541740    male\n",
       "6   1.790928  86.048167  26.827812    male\n",
       "7   1.809917  83.658219  25.538256    male\n",
       "8   1.804939  84.539923  25.949953    male\n",
       "9   1.755725  86.369401  28.018621    male\n",
       "10  1.817654  85.530689  25.888063    male\n",
       "11  1.798073  84.488833  26.132729    male\n",
       "12  1.789017  85.597579  26.744371    male\n",
       "13  1.736679  85.642032  28.395390    male\n",
       "14  1.717038  84.934525  28.808750    male\n",
       "15  1.754468  85.220435  27.685526    male\n",
       "16  1.745762  86.236397  28.295705    male\n",
       "17  1.738966  83.296604  27.545156    male\n",
       "18  1.744043  84.484906  27.775682    male\n",
       "19  1.814423  87.287624  26.514010    male\n",
       "20  1.696548  85.193880  29.598924    male\n",
       "21  1.834508  86.174038  25.605727    male\n",
       "22  1.847978  85.316498  24.982692    male\n",
       "23  1.838084  84.265763  24.941385    male\n",
       "24  1.791247  86.197007  26.864651    male\n",
       "25  1.738719  85.308274  28.218388    male\n",
       "26  1.810138  84.111496  25.670353    male\n",
       "27  1.828684  84.670985  25.319619    male\n",
       "28  1.822241  85.705984  25.810675    male\n",
       "29  1.727342  87.253849  29.243388    male\n",
       "..       ...        ...        ...     ...\n",
       "70  1.652888  57.802395  21.157243  female\n",
       "71  1.596816  62.396018  24.470728  female\n",
       "72  1.657528  61.250404  22.293958  female\n",
       "73  1.673148  58.042489  20.733715  female\n",
       "74  1.507153  60.806251  26.769101  female\n",
       "75  1.619881  59.094099  22.520506  female\n",
       "76  1.622517  59.199302  22.487330  female\n",
       "77  1.595906  60.075127  23.587412  female\n",
       "78  1.659761  58.601985  21.272637  female\n",
       "79  1.711083  59.453638  20.306556  female\n",
       "80  1.632763  58.053691  21.776266  female\n",
       "81  1.612295  59.658539  22.950060  female\n",
       "82  1.624213  60.653229  22.991522  female\n",
       "83  1.542336  59.840166  25.155607  female\n",
       "84  1.591212  59.762734  23.603385  female\n",
       "85  1.575955  59.963829  24.143583  female\n",
       "86  1.623107  60.997220  23.153438  female\n",
       "87  1.658766  60.111609  21.846824  female\n",
       "88  1.561854  60.627608  24.853624  female\n",
       "89  1.577502  60.144334  24.168787  female\n",
       "90  1.623968  59.850311  22.694030  female\n",
       "91  1.597063  61.065658  23.941591  female\n",
       "92  1.682761  59.906006  21.155602  female\n",
       "93  1.641190  60.122483  22.321282  female\n",
       "94  1.581411  60.415878  24.158047  female\n",
       "95  1.546597  60.029364  25.096279  female\n",
       "96  1.591066  60.472437  23.888081  female\n",
       "97  1.490416  59.096371  26.603924  female\n",
       "98  1.555316  59.681624  24.671940  female\n",
       "99  1.515912  61.311259  26.680384  female\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mclou\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: 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": [
       "<seaborn.axisgrid.FacetGrid at 0x1e361e48860>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFgCAYAAACFYaNMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XucnOdV4Pnfed+6d/VV6rbukhUryJfEN9ljE48RhtlxCJMAa8AGQmDD2MOwxAGSSciEMJidz2KST5jkQwasJewQyMaACTjsEC8BR3ECdmxJseLYFnYsW3ep1epbddftvTz7x1tvdXV1dXd1q67d5/uJI6m6uvotqfr0U+c55zxijEEppVTrWe2+AKWUWq80ACulVJtoAFZKqTbRAKyUUm2iAVgppdpEA7BSSrWJBmCllGoTDcBKKdUmGoCVUqpNIu2+gHrcfffd5oknnmj3ZSilVL2knjt1xQp4bGys3ZeglFIN1xUBWCml1iINwEop1SYagJVSqk00ACulVJtoAFZKqTbRAKyUUm2iAVgppdpEA7BSSrWJBmCllGqTrmhFVip08Ngojzx1nFMTWbYPpnjgzt3s3zvS7stSalV0Bay6xsFjo3zsSy8ymskzkIwymsnzsS+9yMFjo+2+NKVWRQOw6hqPPHWcqC2kYhFEgl+jtvDIU8fbfWlKrYqmIFRXOHhslCMnJ/CNIWZbbEzH6UtGSUZtTk9k2315Sq2KBmDV8cLUgxDM+HM9w9mpHAARW9g2mGrr9Sm1WpqCUB0vTD1s6k8AEv6PC5k8jmd44M7d7b5EpVZFA7DqeKcmsiSjNr2JKFsGEkQswTcGY+Chd16rVRCqa2kKQnW87YMpRjN5UrEIvYkovYko2aLLSG9Cg6/qaroCVh3vgTt343iGbNHFmOBXTT2otUADsOp4+/eO8NA7r2WkN8FUzmGkN6GpB7UmaApCdYX9e0c04Ko1p2krYBFJiMizInJURF4Ukd8q3X6liHxTRF4VkT8XkVizrkEppTpZM1MQBeAuY8z1wA3A3SJyG/Aw8HvGmD3ABPDeJl6DUkp1rKYFYBOYKf0xWvrPAHcBj5Vu/xPgR5p1DUop1cmaugknIraIPA+MAl8BXgMmjTFu6S6nga3NvAallOpUTQ3AxhjPGHMDsA24Fbi61t1qfa6I3C8ih0Tk0MWLF5t5mUop1RYtKUMzxkwCB4HbgAERCasvtgFnF/mcA8aYfcaYfcPDw624TKWUaqlmVkEMi8hA6fdJ4AeBl4GvAveU7vYe4PFmXYNSSnWyZtYBbwb+RERsgkD/F8aY/1dEXgIeFZH/A/gW8NkmXoNSSnWspgVgY8y3gRtr3H6cIB+slFLrmrYiK6VUm2gAVkqpNtEArJRSbaLDeNS6pMfbq06gK2C17ujx9qpTaABW644eb686hQZgte6EZ8xV0uPtVTtoAFbrzvbBFDnHm3dbzvH0eHvVchqA1bqjZ8ypTqEBWK07esac6hRahqbWJT1jTnUCXQErpVSb6ApYKS6/MUMbO9Rq6ApYrXuX25ihjR1qtTQAq3XvchsztLFDrZYGYLXuXW5jhjZ2qNXSAKzWvcttzNDGDrVaGoDVune5jRna2KFWSwOwWvcutzFDGzvUaokxpt3XsKx9+/aZQ4cOtfsylFKqXlLPnXQFrJRSbaKNGEpV0aYK1Sq6AlaqgjZVqFbSAKxUBW2qUK2kKQilKpyayDKQjM67LWyq0NSEajRdAStVYbGminQ8oqkJ1XAagJWqcPvuIU5P5Hjp3BSvjWYYm8njeAZjjKYmVMNpAFaq5OCxUR47cobBVJRExKboGcZnHe65aSszRU/nPaiG0xywUiXhBlx/MsFwb3Bbtujy9PFxtg+mGM3kScXmvmV03oO6XLoCVqpkqalmOu9BNYMGYKVKlppqpvMeVDNoCkKpkgfu3M3HvvQi2aJLMmqTc7x5q1w9yFM1mq6AlSrRVa5qNV0BK1VBV7mqlXQFrJRSbaIrYKWaRFuX1XI0ACvVBJ/+h1f4zMHX8HxDPGLhej4f+9KLPAQahFWZBmClVmGp1e3BY6N85uBr+MYQsYSC45EteojA+x79Fp++90YNwgrQAKzUioUzg6O2zBvME65uH3nqOK7vE7UtPM/ghqd+GZgtunzwsaNs6IkxU/Q0NbHO6SacUiu03MzgVy5MYwzkHR/HD6KvAAaIWhYTWYc3xrM6VU1pAFZqpZZqWT54bJSZQpBuqBQugouejzEGzzc6VU1pCkKpleqNR/ju6AyeMcRsi+HeOLYlbBtM8chTxxnqiXJpxsGyDY43d+p4xBLc0oo4UhGhdara+qUrYKVW4OCxUS7OFHB9gwCO53N6Isd0zuGBO3dzaiLLhp44WwYSxOz5314RS+bOKjdzgVmnqq1fGoCVWoFHnjpOfzLK1oEkUdvCABFb2NATY//ekfJAn95ElN3DaaK2ELHAEvBMUJJmAZ5Bp6opDcBKrUSY/+1LBgF276Y+rhpOM1sMpqhVj620RTAGolaw9rUtoT8VIRWzdd6E0hywUiux3GD2/XtHeIhgpXx6Istwb5zTEzl8glVw0fPJu/DgXVfxvh98c3uehOoYTVsBi8h2EfmqiLwsIi+KyIOl228QkWdE5HkROSQitzbrGpRqtHoGs+/fO8IX7r+Nr3/oLrYNptjUHydmW/gGYrbFSG+Mp4+P13z8g8dGue/AM9zx8JPcd+AZLU9b45q5AnaBXzPGHBGRXuCwiHwF+F3gt4wxXxaRHyr9eX8Tr0Ophqle4W5bppEi3JTbmE6UbzPG1Kx6WK7BQ609TQvAxphzwLnS7zMi8jKwlaAksq90t37gbLOuQalmWMnIyuVSFpUtzdM5h1TMpj8ZBOtULEK26PLIU8c1AK9RLdmEE5FdwI3AN4H3Ax8XkVPAJ4BfX+Rz7i+lKA5dvHixFZepVMMtlbIIV7yjmTwDySizRZdLs0Wmc07587VGeG1regAWkTTwV8D7jTHTwC8Cv2KM2Q78CvDZWp9njDlgjNlnjNk3PDzc7MtUqiGqc7jAoqdsVLc0JyJBd93YTKH8eFojvLY1tQpCRKIEwffzxpgvlm5+D/Bg6fd/CfxRM69BqVZZNIf7zmv5wv23Lbj/qYlgHkRouDfOmYkcBTdoV64+k06tPU0LwCIiBKvbl40xn6z40Fng+4CDwF3Aq826BqVaqXJFC7VzuNU5X8/3yxt0vYkoG3s9ZgseUzln2Q0+1f2auQJ+G/Bu4AUReb5020eAfw98SkQiQB64v4nXoFTLVK9oYX4Ot3qF7Ho+o5kiABt64uQcj6ht8+l736pBd51oZhXENwBZ5MM3N+vrKtUuy1U8VK+Qh3uDle9swSNi6Yp3PdJOOKUa5IE7d/OxL71ItuiSjNoLcri1Vsgb03Gmcg5f/9Bd5fTERx//zrKD2vW8ubVBZ0Eo1SD7944sWvEAlAf1VApXyNUlaUsNal/JfVVn0xWwUg20VJPGUivkejbwQiu5r+psugJWqkWWWiEvdcpGtZXcV3U2XQEr1WT15GuX28Bb7X1VZ9MVsFJNVG++trJleTpX5NXRDG9cyjKZLS55Xx3q3t00ACvVRMudoBwK0xNRSzg9mQMD2wYSFD1/QcBebrNPdQ9NQSjVRMs1Z1QK50Ps8s289EKtDbaVTGRTnUsDsFJNtNJ8bWXAzuQdLmYKFFyP0xM5Dh4bZf/eEa0BXkM0ACvVRMs1Z1QLA7bnG85O5hEBSwQR+NiXXuSe05M8duTMokPbNTh3F80BK9VEK83X3r57iNMTOd64lKXo+Xi+wQBX9CaI2sIffeP1RXPK2qDRfXQFrFST1ZuvPXhslMeOnGEwFeXCdAEDeL5hOB2jLxnFGMNs0WPHIjXA2qDRfXQFrFSHCAPocG+CVMwmblvEIlb5yPuc49ETsxdtZ9YGje6jAVipDlEZQFMxm6LnU3B9ZoseYzN5HM/wC3dcuWgN8FKzJlRn0gCsVIcIA+h0zmEy52DL3DzX8VmHe27ayvt+8M2L5pS1QaP7iDGm3dewrH379plDhw61+zKUaqrKTTTfN4gIxsCWgQS2JYz0JmoebVT9GI88dZzTE1mdL9xei81Cn0c34ZTqEPv3jvAQ8MCfHcY3hrhtMdwbpzcRbMDVk8vVBo3uogFYqQ6yf+8IN+0YrKt5Q2t+u58GYKU6TD3NG4udwHzP6UmePj6uQblL6CacUh2mnuaNWkN+HM/jMwdf00aMLqIrYKU60HK53FdHM2QLLo5viJVyxVNZB69ikI82YnQ+DcBKdZmDx0bJ5F18Y7AtwS3NjXB9XxsxuowGYKW6zCNPHWcwFeXSbBHjgwj4GIyB3sT8b+nKQz91w665wlbx2YLLFX2Juj5HA7BSXebURJaN6TjxiM3YTIGi5xOzLRIRiEXsBZt3t+8e4oOPHSWTd3F9n7FMgQ8+dpSP33O9BuHLZIwh53jMFFyyBQ9/hX0VugmnVJcJO+b6klF2D6fZu6mPTf0Jrts6UHPz7svfOc9E1sEAEdvCABNZh4efONbup9K18o7HxUyBk+NZzk/lmSmlhFZKA3ANB4+Nct+BZ7jj4Se578AzuousOspiLce37x4qpxkqu+C+OzqD6xuKrk/R9TEGLIHjY7PtfipdwxhDrugxNlPg5KUsZydzZPLBpufl0BRElcXqK8OB10q1W9gxV9lyfPvuoZqD2u85PYlTESSMAcfzsS2wpa5u2XXL9w1ZxyNbdMkVvcsOtrXoLIgq9x14ZkEXUrboMtKb4IE7d/PwE8fKK4crN6T48Nuv1sCs2q7W63ZsJs9oprho4Ihawr5dQ7ohV8HzDbPFIJ+bczxWGx93D6fr+ummKYgqi81UffXCNB987Civjs5gjMEYw3cvzvKBx45qikK1XfXrNpN3GFsi+EIw5EebNYJ3BFNZh7OTOU5cmmUsUyind5ptXaUg6inFWewQxaJnKLoetiVYpbdu4htmCi4PP3FMS3xUW1W/bi9mCuV5XALUCiV9yRhQ+9Tlta7o+swWXGaLLkXXb9t1rJsVcL3nZS22wRGLWLi+T2XaTAQcz/DK6Iy2f6q2qn7d5t1gMLtF7eALMJ1zgPXTrJF3PMZni5waz3J6IstEttjW4AvrKADX6p0PDzOstFgf/p6RXiKWReW7EmOC3dF6HlepZqp+3fbEImzoiZGM2YsOpr0wnQfW7qkZ4QLqYmaucmEyW8Tx2ht0K62bFMSpiSwDyei82xb7yb9YH/4HHzsa1FNKEIV9EwThK3rjdT2uUs1SnV778ZuDqoi+JOUz5arlXZ+xmTxR217y1Ixu66LLFT0yBWdVjRGttm4C8GK53Xp/8u/fO8LH77l+XhXEVRuDz3WqNjrW6opCdZYwML46miGTdxlMRdmYjjOayfPYkTPcc9NWnj4+zthMEccLXqOWgCVS3pybLXh8+t63LhpQu6Uss+j6zBRcZkrdft1i3QTgemasLqfWyjh8gV7O4yq1UpWBMVsIurAuzRaJR2z6klGyRZenj4/zhftv4+CxUd77ueewJdhANgbEhi39CXyzdCDt5KPuvdIm+EzBpeDUXuW3kjGGM5M5Dp+Y4Ff+zffU9TnrJgDXKl5vxFupZj2uUkupDIyOH0xFMz6MzRToS0bnpcH27x1hz3CaN8azeKXxlRvTCSJ2cM7cUlaSumuFyoE32eLq63QbZTrncOTkJIdOjHP4xAQXpgsAGoBradZ5WXoOl2q1ysAYsy1c3yACRc9nOudwIZPHmKBB44E7d/Pht1/NBx47ykzBpej5XMjkSccj/MY7rpmX4+2NRzDGMFP02D6YIh0L3tWtNnXXCJc78KaRHM/npbPTHDoxwaETE7xyPrOgyqS/6gfWUtZVAFZqrdg+mOKNSzNM51zyro/vGywLIiKcmcwBsLWi0eKem7YG1RClyh1MkLp44E8PUfAMlkBvzOb8VH7e507nnHKAaWWKrbINuJ1B1xjDifEsh09McPjEBM+fmiTvzM8xR23hrVv7uXnnIDfvHORNI+m6H18DsFJd6PbdQzz7xjiWQMQC14DnA2KI2hab+hP0JoKVWLbo8kffeJ3h3jib+pMAXJjKMTpTLD+eb2Cq4GEJRC2LsZkiu4eDQBKzLQZSsaan2FzPZ7YYBN2847ctvTCZLXL4xFxaYazi7ym0e7iHfaWA+9at/cSrumfrpQFYqS709PFxhtMxMvkgpZCI2vQmIkxkHa4aSSMVHUPJqM1s0WNHRZAYm50LKlL6P2OCQBymMsLPnco5fPn9dzbleRRdn2zRZbbotW0jrej6vHBmisOltMJ3R2cW3GdDT6y8wr155yBDPbGGfG0NwEp1oXAo+3DFJpoxhomsUzNn21OVy11qsJcxwao3/NxG53vzjke2tJHWjqYIYwzHx2aDgPvGBN8+M7WgIy4esbh+Wz837xpi385Bdm1Izfuh1igagJXqQovVtV+5IUXW8ReURf7CHVfy2JEz5dstCYJwxCrVBFcEZM8YNqXj5Tb8y833GmPIO355ylg76nQvzRTKK9wjJycZn12YVtgzkubmnYPs2zXIdVv6iUWa3yisAVipLrRYXftvvOMaoHZZ5Fu3DZRvH0pFGZt1cKuWwht7gmaO2aJXHsG6mnxveROtVC7W6k20vOPx7dNzaYXXawyfH07H2bcrSCnctGOAgVRj0gorofOAG6Tb2jVV9wtfcyvdHAubOKZzRabzbjnv+yPXb+b37r1p1dfj+cHshdnLnKW7Gr4xfHd0hkNvTHD45ATfOTNV7v4LJaM212/vL2+e7RhqTloB6p8HrCvgBuiWdk21tqy2/jxs4tg+1FO+LVt0OT+98G35csIB5rOF1lcujE7n56UVpkrT3UKWwJuv6C2vcq/Z3EfU7qz5YxqAG6CT2zWVqna53W2VQTe3yKCfZsgVPZ4/NVmuyT0xvvB6N/Ul2LdrkH07B7lxx0C5FK9TNS0Ai8h24HPAJsAHDhhjPlX62C8D/zvgAv/TGPOfmnUdrdBp7ZpKLWUlg6nCNMfJ8Vm2DCT5qVt2cMPOwZasdD3f8MqFDIdKAfels9MLctY9MZsbdgywb+cQ+3YNsnUg2fTraqSm5YBFZDOw2RhzRER6gcPAjwBXAP8ZeIcxpiAiI8aYJaeXd3oOuNZ5XBczebLF4OhwzQmrTlKZMnM9nwuZAo5n2NKfoCdml9uQb7tykL88cgZbIBaxmJgtMpV3Sccj7Bzq4d5btnPr7qGGXtu5qVw5rfCtk5Nk8u68j1sC12zuK1cr7N3Uh2111uGiIsKVG3vquqiWbcKJyOPA7wP/nmA1/A/1fm6nB+DKF3QyajM2U+DiTJGR3hgbeuLlHeqH3nmtBmHVEQ4eG+V3vvwyr16cIWpZ9CUijGcdjDFsGUhgiXB2Ks9AMsJgKs5MwWU0E7QpRyxhuDeB6xsevGvPZQXhmYLL8ycny6vcsI260rbBZBBwdw5y/fYB0vHOypzalhCP2CSiFomoTTxiIXXu7rUkAIvILuAp4LrSr48DdwN54APGmOdqfM79wP0AO3bsuPnEiRNNv87LUbkjPZVz6InbbEzPFcmHJyt/4f7b2niVSs0J37klozbHL84Eb+8N2LawfTDFaxdniNrCjqEeTk1kKbo+YclwMmqRjttsHejhkz95fd1f0/MNL5+bLgfcl89NL2gK6UtEuHHHYDnobupfemJbq0XtINAmohbxiL1YvXBnVEGISBr4K+D9xphpEYkAg8BtwC3AX4jIblP1k8AYcwA4AMEKuNnXebkqd6TvePhJzQmrjndifJa+eISi51P0fCxL8H1D0TEcH5vBN1B0g2+9Qin4QhBZXN8wMevg+gvraytVzsg99EYwzKb6hI6IJVy7pa9crbBnpLdj0gqWCLHI/IDbyGtragAWkShB8P28MeaLpZtPA18sBdxnRcQHNgIXm3ktrXS5p28o1Sy5osds0eXJly4wlXU4P5UnHrGCUzI8Q1g6a4ngYfCBiWxh3lmIEUvABAOAJrIOv/rnR+flg8MZuWG1wvnS2XOVdg6lynnc67cNkIytbphNI0Usi1gk+C9qB4E3HmnudTWzCkKAzwIvG2M+WfGhvwHuAg6KyJuBGDDWrOtoh0acvqFUo+RLs3RnCy6eb3j2+DifevLVcjtyzlnYGlwsReKoBdmiX+5UtiVII1R+xqnxGX737/+F67f1c3YqX3NG7kAyyk0759IKw1XnKLaaJUI8apGI2MSbsLKtVzNXwG8D3g28ICLPl277CPDHwB+LyHeAIvCe6vRDt9NTMlS7LTXw5tHnTuG4QbeaLeAt8d3n+BAzPjdsG+C1ixkyhYV1v5eyQaXCV/9l7k1srRm5VpO6zpYjEq5mg/9asbKtV9MCsDHmGyyeiP6ZZn3dTqGnZKhWCk+NyBa9ZQfenJvOMVv0EATbFiKAwZTzvdVmHcPVm9IcPT255DVELOFHb9zKvl2DvGVrP4lVzsi9XBHLKudrg9Wt1bSW48vVWfUcSqm6uZ5P1vHIFYP/6h14s7kvyVimMO8t93Kf+oVDpxf9mF2aJXzdln5+cf+b6rqGRqqsSkhGbSId1m68FA3ASnWRMLWQLboLZtjW695btvPiuSl8ExxFZAyYpQYElwjB0B4zf3olImBZwr23bC/f9uzxcR597hTnpnNs7ks2rGmjMp2QiNoko+3J3TaKBmClOpjr+eRKq9xGjXW8dfcQP3PrDv70mydw3OAwz6UIYFvBr8O9wVlxxszljkWEn7l1RznAhpt8jusxW/QYyxR48dwUP3PrDt79vbtWdK1R2ypvkoU53E5NJ6yGBmClOky+FHBnL2OVu9jjhjNyD5+YIHzoypgusKCCIWJBOhFhKudiW8JIb5yx2SLGM+za0MP9/3r3vNVtuMk3mXOCPLMl+MbwZ8+e5Hs29S26Eo5YVjlnGwZcq4tXt/XQANwGOjtYVfL9uQ20XLFxJ0asbEZucPTOuckcv/U/Xyqf/Bsp5R2mcy7D6aC1/vx0jms29S+aVqjc5AsDqCXgeoZHnzvFrbuHyu278YjV1jKwdlsyAIvIXxhjfkJEXqAq7QMYY8xbm3p1a5DODlYQrEbDfG7Bbdwc3YuZAodOTHDojfFlZ+Tu2znI1VUzcndsSLGlL8GJ8SyWSDnnC5R/U+tKw5zvG5dmykPeAaIE+WEMxG3h4kyebYOplhz30w2WWwE/WPr1h5t9Id1qpavZlc4O1tXy2uB4PtmiV04vNOqInpXMyL155yA3bh+gL7n0jNxZx+OKvjgTWQfH88tVBhdnHWLRAn2JCJdmC3zqyVd5kD0APPz/HSOTc6iuZHN8g20MtmUx0BNlx1CPBt8KSwZgY8y50q8nAESkb7nPWS+qp0ld0RevazW7ktnBulruXpVphbzjNez033BGbjiycekZuYPs2znEloHEijauNvcluTRbYHtF6/wb47NErGDaH1Du8Hz0uVNM5x2mc86iDR2+gZF0lKhtazdolbqCqYg8ADwE5Jh7B2KAdfm3GQbG0UweWwQDnJsqsGUgQdSWJU/CWMmcCD1po7uEq9us41FwGndSxPmpPIdOjC85I/fqzX3ls86u3nx5M3LvvWU7n3ryVXKORyJqkXd8XM+wqa+ifVggFbO5mMlzcaZQbms2BGVplH7fE7PJuz67NqT13VsN9a5mPwBca4xZUzMbVisMjJ5vsEUQEXwMFzMFrtzYs+TUs9t3D/GZg6/h+j5x26I/tfjKQE/a6Gxh99lsIajL9eqopa1HOCM3XOXWmpG7dSBZDrg37GjsjNxbdw/xIHt49LlTnJ/OsakvScy2cH0f2wo21oRg4bBjQw9js0V8Y6hKFSPApv6EjmFdQr3/aq8B+l1fEgbGmG3hekEdpQgUSzWbi009O3hslMeOnGEwFSWTdym4PuOzDr+0f0fNlYFOVes8RXeuLrdRJ/+6ns+x85klZ+T2JiLcGB6904IZuW/bs5G7rh4hXhow/vR3x/jNv32JoueTtOYPmProX3+b01OFBY9hWegQqmXUG4B/HfhnEfkmUP6bNsa8rylX1eHCwLgxHefsVA78oJfeFlnyBReunPuTCYZ7g9uyRZenj49T6y9Sp6q1X+WMhVyxMbncemfkXrMlSCvs29X8GblzM29tEhFrQTvv9199BSJSc8BUbzIGNQJwPGLrKTDLqDcAPwI8CbwANK4yvEuFgTFqC1v6E1zIFHA9eNNwDx+6e29DNuBg8alqEJxmoJURzVFwPfKO39CKhbpm5G5IzR2908QZuSJSbuVNlEYy1tPwsNiAqdFMHtsC3y/lgAlWv7YYHnnqOB99/Dv6Ol1EvQHYNcb8alOvpItUB8Ybtw/W9eJaTUqh+kWvlRGNt9qhNktxPJ+Xzk1z6I0gj9vOGbmWyFywnTuzrGGP73gmODkiOrdqLrgeMwWf0UxeX6dLqDcAf7V0RtvfMj8FMd6Uq+oCS42bXKx294E7d/OBx45yZjIXbOBZQjoe4TfecU3dX1crIxqjEUNtKhljODmeLW+cHT01Ra6qEiJqC2/Z2l/ePGvWjNyoXWrnrThGp5liEaucnvGNmXd0kb5Ol1ZvAP4pgncXH666XZORVZZaoUJpQLIJvmE9zzCVc/jAY0fZM9Jb1ypaKyNWzhhDwS2lFByPguM3ZJU7mS1y+MRcWuHizMI86O7hHm7eEeRx37q1n3gTZuQul79ttj0jvfzL+Wkmsg4G5pWkZfIOvYng9aqv04XqDcDXAP8RuIPg7/XrwB8266K62VIrVIC+ZJRN/Ummc06wgQdkS0d+1/MWTSsjltesgFt0fb5zZipo9T0xwXdHZxbcZ6gnVj4FYt/OQYZ6Ypf9davFS2MYV5K/baYH7tzNA392mIgtQV28CSqCLAlao8MArK/TheoNwH8CTAOfLv35vtJtP9GMi+pmS61QDZQ/NjZTwEIQK2jXrPctmlZG1BbOVsg5wQZaI8rDjDEcH5stVyt8+8zUgnRFPGJx/ba5o3eu3NjT8HGJUdsiGbM7dv7t/r0jpOM2eSc4XTlmW/QlY0zmHPKuV64k0dfpQvUG4O8xxlxf8eevisjRZlxQt1tuhRp+rOj55dVCrPSWsdZbtFqO+rjOAAAgAElEQVT55Ifeee26P2+ucrZC3vEa1gQxPlss1+MePjHB+Gxx3scFuGokXZ6tcN2W/obPNuj0gFvLm6/oW/C6ty0hW/SYyjkLXqc64yRQbwD+lojcZox5BkBE/hXwT827rO613Ao1/FjMtih6PoKUd7+r36Itmk9+57XrrrPIK81WyDV4tkLe8XjhzFQwsvHEBMfHZhfcZzgdL08Pu2nHIP2ppYfZrFQ47CYZa08OtxFqve5jEZvf+bG3LgisWskzZ7lxlOEYyijwsyJysvTnncBLzb+87rPcicjhx6ayRVzfMNQTJR0P0g/Vb9HWc8WD4wU53ILrN6wBAoIZua+NzpRXuS8sOSM3GGazfSjZ0LRC5Qq3WwNutZXUrFe+rqdzDmMzBQquz3/8/BF2DCWZKXrrZlUsS+XKRGTnUp8cTklrtn379plDhw614ku1VPg2bLFUwh0PP8lAMjrvm9+YoHLi6x+6qx2X3DReeXqYS77oN2woOcDodL5cHvatk5NMLjEj9+adg1xTNSP3coRnmCUqKhW6IaXQCJUr3cp3g7MFh839STJ5l7NTOSwE3/g4ftABuHUgQcS2cDzTzZ10df0jLzeOsiUBdr2pzn/99ruuW3QWxOtjM2TybnlzozcR4cqN6TZcdeM1a3pYM2bkrkQsEpzOG6QU2l+l0C6LvYNzvOCHbbgRbVlC0S2dPSfC2EyR3cPpdfFuT2f7ttBKZwjfvnuIZ98Yx5JglVb0fC7OFPmpWy//dNl2aEaLL8zNyA3TCi+enV6wKTc3IzcYZrPSGbmLqVzhrteAu9iG2mIVQbFIsLrNO15QL1z62RuxpDzUKrzvWq8b1gDcIquZIfz08XFGemNM59yK8p7IosN7Oonvz9Xi5t3G1eKGWj0jNxQG3E6qw22npTbUFqsI2jPSy6a+GCer3pm4vsEYQyJqk8k7nJ/KYwhyyGs1H6wBuEVWM0P41ESWDT1xNqbnRg8aYzpyVeB6fkXA9Sk28JwzqG9G7rbBJDfvaPyM3LDxIQy6a+lY9Mu11EbxYhVBm/pi/M3z52o+nmeCFNKp8SwiQT54LVdJaABukdXMEO7UrrdwdVtwgyqFgtPYTTMI0govn5uuc0ZuUK3QqBm5tiUkYzapWKRr6nDbZanGo1qVEeGBBEv9aPYBMbCxJ0pfMugkXKv5YA3ALVLvDOHKfFpvPFI+1badXW9hSVjeCYJuI4bXVDPGcHZyLq3w/MmlZ+TevHOQN1/RuBm5UdsiFbPpiUdINGFew1q13CKhemjVfQeewfMNpVOLFhW1hUzeJVucoej5RC1ZcMLzWqABuEXqmSFcnU/LOR4C5RdfK7rewjkKhVKwzTdhdRuazjl8q1StcOiNRWbkDpVm5O5q7IxcS2Ret5me1Ls6K22NPzWRxRZYquYlGOZjKHqGeMTCtgTHN2TyLgePja6pVbAG4BZZaobwwWOj3HfgGY6cnCifoyUyl1cb7InzxK80p/PN9w15d25126g5CrU4ns9LZ6fLw2xqzcjtT0bnDbNp1IzccAh5KmyA0FVuQyzXeFQtHbM555slV7+2gOMHZWkCmNLP/8FUdM2lITQAt1CtGcKVq17fBG/Nzk7m2TIAvYkorudz5OQEdzz8ZEO6g1zPJx9uljnNSSeEqmfkPn9qkrwz/+uFM3LDgNvIGbmVudxUdH1XKzTTUrOxq0lpAzoiQdXDgo8TBF8IArFnDDHbYmM6QW8i0pEb0JdDA3CbVe4ihxt0lMb4AZyZzBNZpGe+noEmRdcvrXC9hneY1bLSGblv2drf0NWo5nI7W6bgsnUgwdhMEa+U449YUg7G4ZFGwYIEtvQny00y2aJbzi2vlWE+S7Yid4q12ooM89uNwxnBQpADi1gWrm/YOjD3IryYyZMtesQiFpm8y2AqysZ0nFxpNfvRH7qaW3dvqKv29tnj4zz63CnOTefY3Jfk3lu2c+vulTV5dMKM3FjEoicWIRW3m376g7o89x14prxpl8k7nJ3MYzDlE2IEYctAUM1yeiJHxBauGk6Xc8sPvfNagJotzh3Wtnz5rciq+Sp3kcMgeyGTR0zQrLF1IFG+fTrncGm2iG8Mrmfh+T5jMwUilkVPPEIRnz/42nH2bOpd9us+e3ycTz35KhFL6EtEuDRb4FNPvsqD7FkyCBtjeL00I/fwiQmOnp6iUGNG7lu3zR290+gZuSJSbvXtidlrYpjNelG5aZeOR9iQjjI+6+BhiFrCSF+iPMB964Dh/HRhwQb0fQeewfE8Ls3Mb1DqxvywBuA2Cd9CvXJhmpmCx1BPlA09cSK2MNKbKM/8Hc0ElQHGGC5m8phSTqzo+ViWYHy4NFsobS5ZnJ9e2KBQy6PPnSJiBYEM5srcHn3u1IIAfGmmwOGKE32rZ+QC7BlJl6sVmjEjV/O5a0P1pt2uDWn+zx/dXX6tV5azRWyLm3YMlkevhpvV33z9EsaAbQX3cX3DWKaI42Xa9KxWTwNwG1RuvG3uTzI2U2B81sHxTPlsuNuv2kC26PJf/+4YjlckHrEouD5C8JZ+fLZYbt3MOYbjYzNYInU3aZybztGXmP/PHwbwemfkhmmFm3cOMJBqbFpBqxbWrsU27ZYqZ6v8njGl8+ZcHwRDxLbwxTR1Q7lZNAC3QXX75oZ0nETUZmM6zid+4noc1+fMRI69m/v45e+/ikefO8X56RyJqE0qFuQ7IZiHEI6yFYLusYlskWePjy+by93cl+TSbIFk1C7X/k7lHTwf3vWZf1owIzcRtbhh+0DTZuRCUJubiusqdz1arpytspW/8pXp+gZLgltitnTd5pwG4BYLSrNm6U1Eysd4Y4Jd39MT2QVjGW/dPVQOpmHeNud4pGI2lgS980LwVmwwFcO2pGYaodrbr9vEf//ad7k0U6TgelTF26bOyK0UDrZJxSI6Z2GdW6qcLWzgODdVmNdFZwAENqRiDCSjXXfShgbgJvN8M28MY8H1GU4nyqvPUN7x2dSXXPKxbt09xIPsKa+IEWFLf4x0fK4X32Bq5oHrmZE7mIryvW/ayL5djZ+RGwo30FJxm1RUN9BUfbYPpvjWyQlEglJDx/PLJWsiEIvYiEjXnSCjAbjBwgE1+dJx6LWO0rn3lu3llWwiapXafQ333rJ92cevXBH/6p8f5dLs/DrbMJB7vuHV0Uw5j/vi2ekFhe9zM3KDtEKjZuRWExF6YjapuKYW1Oo8cOdu3vu557BFsESwLZmbKWHgoXdey0cf/86ig4E6lQbgyxCubguOv6KZt9Ur2U2rrMG9cXs/f/bsSTzfELOFVMzGMzCQivFjf/DPLZuRW0vECgaU98SDWQuaWlCXY//eEfYMp3ljPIvnBzMiNqbnqob27x1h+1OdOT1wKRqA61Q9pKbg1l7d1qtyJbsazx4f5+9ePE8iYjFb8Mi7hrwbBNwXzkyV7yfA91yR5qdv28n12xs3I7daZW2uDrdRzfDht19dswEjrJRY6WCgTqABuAZjDMXSgPEw4DqeadqQmnqFM3IPn5jgscOnF4xrrGYDYsErozMcHw2OQbrczrdKlgSrbk0tqFZYrlJipYOBOoG2IhO004Zzbgul/zrh72VuRu4Eh06M15yRu5xwx1iAgVSUdDwyL+/84F1Ld75Vi9pB110qZhOPaNWCUovQVuRawqNzyic6NPissssVzsgNN88Wm5E7WwzSDROzxQUlZJUqy3Wmc06Qj0WW7HyrFDZE9MQiJGOaWlCqkZoWgEVkO/A5YBPBKSMHjDGfqvj4B4CPA8PGmLFmXEO4SVZ0ffJOcE5Zs6eBrZTj+bxUSiscemOCVy5kFhy9M5CMclPVjNywJti2Bc+t7weIJTCRLZbzwIu1Lkdtq9T0EeRzNbWgVHM0cwXsAr9mjDkiIr3AYRH5ijHmpVJw/jfAyUZ9sfImWcXK9nI2yZplJTNyw2qFWjNyw0qKA0+9xvFLQZmNLQTHfNf4umHFceXfSViyJiIkolbQgRazm9JwoZRaqGkB2BhzDjhX+n1GRF4GtgIvAb8H/Cfg8dU+frHyUMgmnMLbSJPZIkdOzqUVGjUjN6ykePb4OAeeeo1TkzksglMHJnPuvNxvKmZzfiqPbQsGw0TWYWK2yLmpHP/2977Gm4bT5aORlFKt0ZJNOBHZBTwFXAfsB37AGPOgiLwB7KuVghCR+4H7AXbs2HHzi6+8RsHxguqEDsvbVlvJjNxwlbvUjNzVzO2tHDcZbrpNzBbwfMi7/rzB1xCsnAdTUT5+z/UahJW6fHXl7ZoegEUkDXwN+K/AE8BXgf/FGDO1VACu9JYbbjKPf+Wppl7n5TDGcLxiRu63Gzgjt1YgXax6oTpQ37i9n+dPT3FhOk86bjORdehLRjk3mSNbSntELQmmSZWOQ7qxYvyfUmrV2l8FISJR4K+AzxtjvigibwGuBI6Wgs824IiI3GqMOd/Ma2m0embkXjWSLrX5DnLd1tXNyH30uVM4rsdk0cPx/KAMLGYvqF6oDNT9iSiTuSL/cGyU337XdeUh1p4J+uOdimPBPWOIEPTTu57f0W2bSjVau6enNbMKQoDPAi8bYz4JYIx5ARipuM8b1LEC7gQrmZG7b9cgN+1ozIzcNy7NMFNwEYIeeNczTOYcPH8urWGJ8JeHT5OIBDW6IkI8ajM2k+d9j36LvmSUi5kCm/qCE4ZjtoXre2CCPnoIfo1YVke3bSrVSJUzhts1Pa2ZK+C3Ae8GXhCR50u3fcQY83dN/JoN4xvDa6MzHCqtcF84M9WWGbnh1wxLwUSC8jrHM6QTEdLxCMmozYVMvny2HEAm7zCWKWKAHUMpxmYKnJnMIyIM98Y5PZHDLUVfz/fLOeBabZvtXiUo1QzVc7nbMT2tmVUQ32CZPIgxZlezvv5qXMwUygH3yIkJJnPOvI+HM3LDVW6zZuRWikUsCo4X5GjDvIGBeNRipDdRvl/l2XLhc0EgbgeHdxrf4PqGE5ey7BhKMtwbY2ymiDEGEWHPcE/NKohOWCUo1QynJrJtn5627jrhKuWKHkdPz5WH1ZqRu6kvUT5656YdzZmRu5SdG3o4O5llJh8cQBiP2PSlI+zakJ53v+pBJHnXK89qODuVw0KIWAbXhxPjOaK28KaNPfzQWzbz9PFxTk1keeSp4+XHC1e80zmHVMymPxkE+26YsapUPaoXLdD66WnrKgB7vuGVC5nyxtmiM3K3D5RPgtg60Pi0wnIilkUqbtMTi/DgXVfxm3/7Er2J6JITnqoHkfSUmioyeRcLwbIEzwtPzxCiljCZc/jUk99lpDfGhp44o5k8H3zsKIagPG0q65B1fDJ5F883bOoPBsZ3+oxVperRCdPT1nwAPj+Vn0srnJxYdEZuWJO7d1NvW05piNrBAZQ98ci8Jozvv/oKRKSuCU+VR7qEqYOCW8S2gjrfsDLO8YIcctEtYFnC+EyR6Vywwg6HXNuWhUjw9+MbGJspkopF6EtGO37GqlL16ITpaV0xDW0ldcCzBZfnw2E2Jyc4PbFw1sG2wWS566yZM3KXs1jQbZSDx0Z536PfYrboIgaWGhkRkWDwjlPxjiBW+kFULLUv98RsNvUncDzDQ++8VlMQSi2u/XXAreD5hmPnp8t53JfOTS8YZtObiHBjxdE7m/oTtR+sBeJROzieJxZp+mSx/XtH+PS9N/KBx44yNrOwTrmSa5irSSsJ644jluAbQ971GelNaBWEUg3SdQF4wYzcU5PMFubPyLUt4dotc0fvvPmK3qYdvVOPWMQiHY/QE4+0ZdDNap+5IQjCEVsYSce5cmNau+SUaqCuCMCeb/jaKxfLm2fnpmrPyA3Lw67fNkAy1vi39CsRteeCbqtn6FbW7U7nHHriNiILFrhllcd8x2wLz/fnzRje0BMjFrE7+mgXpbpRV+SA45v3mM3v+W/zbutPRrlpxwD7dg2VZ+S2W8Sy6InbpBMR4pH2/ACorNtNRm2Onc8gBD/EFhvOWXlqRsy28DEMJKNkCi7GwE07Brl991C5XE2bMZRa1trKAYczcsNqhVozctshLBlLN2kjbaWqu3viEYui59ecERyyJOi0czyDUxpYnym4pOMRPnHP9QDajKFUE3RFAN42kOTxX3pbRwQ4aH71wuWo7u7ZmI5zdipXXuGGwvIyA/TEI/zCHVfyp8+cIJN3g1NDzNz9O6FlU6m1qCsCcKoDAl2YXujEoFupurunLxml4HpM5RwKbjDzwZZgo1JMMKz9E/dczyNPHacvGS03WwDlINsJLZtKrUV69swSorbFQCrGloEkOzak2JCOd3TwhaC7x/EM2aKLMcGvsYjNH/z0zfzxe27h6lKjiYhw1XAPnygNYD81kSVZ9dzCILt9MEXOmV9pos0YSl2+rlgBt1J47HpP3G7bRtrlWK67Z7GUwVJ98Z3QsqnUWtQVVRDNPhHDEqEnHqE30dnphWaqrp4Ig2zY8RaWtgVzJmxEhEzB1YoIpWrrjCOJGqFZATgRtelNROiJRfTodZgXZBfri68M1K7ncyFTwPEMbx7RQz2VqrC2ytAaJWyQSCfa05XWySqH+SwmrIhwPcPZqTwWgi3w+tislqYptULrIgJFLIu+ZJTN/Um2D6UY7Ilp8F2lcLNubKZQHnNpWYJnDFFb5s0UVkotbc2ugLulbKzbhJt1Rc/HLjXCGBN00GlpmlIrs6YCcKd1pXWLlZz5dvvuIT5z8DUcz+BiiNiCEJwzp6VpSq1M1wfgTu5K6wYHj43ywceOljvgxjIFPvjYUT5eqg+uvu9jR84wmIoyMVukUBrsHrEMpyayRCyLd12/pU3PRKnu05UBOGJZpBPdW6vbSR5+4hgTWQfbEiK2hTEwkXV4+Ilj88rPKs+HG+5NMNyb4PxUjrGZIq4fDGvvTUR47MgZ3rptQDfilKpD1wRgEaEnFkwaq2wWUJfn+NhsMIynlM/1/OBYopfPZ7j7977GpdkifckoA8ko56ZyzBZdJmaL+AQT1mwr+LfZPRwcEqozIpSqX1dEsogl7BhKtXWo+nrgev68I4neGM/ieoaeeAQRIWpZ5F0fH0M8YuEYg28gEZn7d9GNOKXq1xW1WLYlGnyb5MoNKXwDvm/wKppyEhELzzdYAhczhXmfY5g7Jw6Y93m6EadU/boiAKvm+fDbr2YgFUVKJydD8I5jU39iwaGcnjHYpZ+DlefuOZ5hOlckW3R1RoRSK6ABeJ3bv3eET9xzPTduHyQesUhELLYNJulNRNmYjpfGVwrGGGwRPBOcoJyM2iSjNhErmC18frrASG9CT0tWagW6YhbEvn37zKFDh9p9GWterYE8UzmH4XScmdIJGcfOZ4jaQfebMUETRn/SZjLnljfyrtyQ4sNvv1oDsVrP1s4wHg3ArbPcQJ63/7eneH1sFs8YYrZFRGCqdCq1EKyGRaQ86F2DsFqnNACrxquehnZiPLfgPhGBSMTiyg09DKRiepCnWo/qCsCaA1Yrsn/vCA+981pGehOcn56rjhAJ/gPwCTbmXhmdYTSTn3eQ58Fjo+25cKU6kAZgtWL7947whftvY7g3TipqBT/qK95I+QZMaTpaKhbUEKdiEZ2WplQVDcBq1bYPpuhPRbEtCeJvKQgLwebcFb3xeffXJg2l5uuKTjjVWcKNulcuTDNT8OhLRJgtuBS8IAJv7Y+TTkRxfMN0zmFsphCMr7SEXUPapKFUSFfAakXCTbjRTJ50PILv+4xnHXwDV2/q5X/83C1849d/kA+//Wqmcg5nJnM4no8Arme4NFvUPLBSJRqAVd0OHhvlfY9+izOTWU6PZzkzmccSi5gdtIrPFueOrt+/d4Rk1MbzDUXP4PqGDT0x+pJRzQMrVaIBWNUlXPlmix4RSyj6QVA1UPNIooPHRjk7lSdqC4moRdS2mMg6uJ6veWClSjQAq7qEh3HGIxYYKW+4ub5f80ii8P6+byi6PkXXx/F9zk7mdFiPUiUagNWiDh4b5b4Dz3DHw09y5OQErucH8yEqas78Ujty9ZFEpyay9MZtXFO6D8H9HB9ePDvFfQee0VywWvc0AKuaKjfbBpJRRODMZB4R2NKfJFYaiybA5v44tiXzJqFtH0yRKQTpiupJoq7nL9mYURn4NVCrtUwDsKopTCGEjRRX9CYAOD+VpzcRYdtQio3pGHs39eIbFkxCe+DO3TheME84FrHKXXIxW3B8s2hjRnXg1w46tZZpHbCq6dREloFktPznvmQUMJyfLjCVc9g2mOI33nHNorMd9u8d4c0j6WBwTykHEbWkdLJGEI1rNWZUBn6AVCyixxypNUtXwKqm7YMpco4377aIbXHTjkF++13XAfDRx7+zZIrgQ3fvZaQvwY6hFKmYDTKXL4bap2ecmsiSrDrdWjvo1FqlAVjVFKYQskUXY0z5tIvbdw/VnSKoHNyTjFpYImxIR0nHI4uenlEr8OsxR2qt0gCsaqoMnlM5p5zjffr4+IqG7ISDez7x4zdw5YYU47MOr47OELOtmqdnLBb49ZgjtRZpDlgtav/ekQUB8qOPf2debhiWTxFUzhDeM5Im53jzuuaqv+ZDsORQeKXWCg3AakW2D6YYzeTLm2SwfIpgpRtrtQK/UmtR01IQIrJdRL4qIi+LyIsi8mDp9o+LyDER+baI/LWIDDTrGlTjrSZFoBtrStXWtCOJRGQzsNkYc0REeoHDwI8A24AnjTGuiDwMYIz50FKPpUcSdZblzo2rdt+BZxasmrNFl5htrejIovDr6hFHqgt01plwIvI48PvGmK9U3PajwD3GmJ9e6nM1AHe3WqctT+ccDNCfjJZvczyz6LH2tR5jqfsr1WadcyaciOwCbgS+WfWh/w348iKfc7+IHBKRQxcvXmzuBaqmqlVRsaEnRn8yWnc1RXVnnh5xpNaCpm/CiUga+Cvg/caY6Yrb/zPgAp+v9XnGmAPAAQhWwM2+TtVc1Rtrdzz85JLVFNXphlcuTLO5P7no/ZXqRk0NwCISJQi+nzfGfLHi9vcAPwz8gGlVDkS1Ta3c7VLVFJXphrDZY6bgMTZTYLg0k6Ly/kp1q6YFYBER4LPAy8aYT1bcfjfwIeD7jDG6fFmDKgNubzzCxZkC/ckoA8kob1ya4YE/O0zMFoqeYTAVZWM6Xs7pPnDn7ppla0M9UcZnHXrikXk5YG3QUN2smTngtwHvBu4SkedL//0Q8PtAL/CV0m1/2MRrUC1WPc3s9bFZJrMOrmeYKbhcmnHwjcE3MNQTZSLrcH4qN2+aWq2ytQ09cXoTkQWdeboBp7pZ01bAxphvUHsn8O+a9TVV+1WvXj0TjKQcmykAIAIWQtHz2ZhOkIoFQfUL999WfozF0hN7Rnrn3U+pbqezIFRDVa9eY3bwEit6PkXPR0oT0cLba22k6TwItV5oK7JqqOrV63BvnNMTOSK2YAGOH+y5bkwHm2m1NtJWMw9CmzRUN9IArBrqgTt3l05PdklGbWxLGExF2dATY2y2SCbvMpiK0ptYfCQlrGweRK2qiY996UUeKj2OUp1KA7BqqFqr18qTM1baxlwPPUVDdSsNwKrhllq9NmPSWfXxSaBNGqo76Cac6np6iobqVhqAVdfTqgnVrTQFobreclUTWiGhOlXLxlFeDh1HqVZLx1iqNumccZRKtYuOsVSdTAOwWtP0OCTVyTQHrDpWI3K3qzlEVKlW0RWw6kjVU9XC7raDx0ZX9DhaIaE6mQZg1ZEalbutdRySbsCpTqEpCNURqtMNr45m2NSXmHefenO3tVIXOsZSdSJdAau2q5VuyORdLs0W5t2vntxto1IXSrWCBmDVdrXSDYOp4AiileZutexMdRMNwKrtapWKbUzH6Y3bK87datmZ6iaaA1Ztt+gRRFf0rTh3q2VnqpvoCli1XSNLxbTsTHUTDcCq7RpZKqZlZ6qb6DAepZRqPB3Go5RSnUwDsFJKtYkGYKWUahMtQ1NdS0+6UN1OA7DqSpUnXVS2HD8EiwZhDdiq02gKQnWllbYc64wI1Yk0AKuutNKWY50RoTqRpiBUV1ppy/GpiSwDySiZvMPFTIGi5xO1hKmc06pLVmoBXQGrrrTSluPtgykuzRY4O5nH9Q22JTi+IZN3NQ2h2kYDsOpKK205fuDO3YzPOhgMAhg/uH0wFdU0hGobTUGorrV/70jdVQz7946QjtvkHZ+i52MBlgiXZotMZCc4eGxUKyJUy+kKWK0bb76ij039Cbb0J/EBTNCwL6AVEaotNACrdSPMG1/I5INJKaX/29Sf0IoI1RYagNW6EeaNjQHfGCKWsGUgQW8iqqdmqLbQHLBaV/bvHeGmHYN6aobqCLoCVutOvSVsB4+Nct+BZ7jj4Se578AzmiNWDacBWK079ZSwaeuyagVNQah1abkStsrWZYBULEK26PLIU8e1XE01jK6AlapBj7dXraABWKkatg+myDnevNt0o041mgZgpWrQ4+1VK2gAVqoGPd5etYJuwim1iJXMmlBqNZq2AhaR7SLyVRF5WUReFJEHS7cPichXROTV0q+DzboGpZTqZM1MQbjArxljrgZuA35JRK4BPgz8ozFmD/CPpT8rpdS607QAbIw5Z4w5Uvp9BngZ2Aq8C/iT0t3+BPiRZl2DUkp1spZswonILuBG4JvAFcaYcxAEaaBmkk1E7heRQyJy6OLFi624TKWUaqmmB2ARSQN/BbzfGDNd7+cZYw4YY/YZY/YNDw837wKVUqpNmhqARSRKEHw/b4z5YunmCyKyufTxzYA21yul1qVmVkEI8FngZWPMJys+9CXgPaXfvwd4vFnXoJRSnayZdcBvA94NvCAiz5du+wjwO8BfiMh7gZPAjzfxGpRSqmM1LQAbY75B6dCXGn6gWV9XKaW6hRhj2n0NyxKRi8CJFnypjcBYC75Op1rPz389P3dY38+/Gc99zBhz93J36ooA3CoicsgYs6/d19Eu6/n5r+fnDuv7+aejhh4AAATISURBVLfzueswHqWUahMNwEop1SYagOc70O4LaLP1/PzX83OH9f382/bcNQeslFJtoitgpZRqEw3ASinVJusuAIvIH4vIqIh8Z5GP7xeRKRF5vvTfx1p9jc203PMv3Wd/6bm/KCJfa+X1NVMd//YfrPh3/46IeCIy1OrrbJY6nn+/iPytiBwt/dv/fKuvsVnqeO6DIvLXIvJtEXlWRK5ryXWttxywiNwJzACfM8Ys+EsWkf3AB4wxP9zqa2uFOp7/APDPwN3GmJMiMmKMWRMDk5Z77lX3/XfArxhj7mrJxbVAHf/2HwH6jTEfEpFh4F+ATcaYYosvteHqeO4fB2aMMb8lInuBzxhjmt6xu+5WwMaYp4Dxdl9Hu9Tx/H8K+KIx5mTp/msi+MKK/+3vA77QxMtpuTqevwF6S4O00qX7uq24tmar47lfQ3BCD8aYY8AuEbmi2de17gJwnW4vvQ37sohc2+6LabE3A4MiclBEDovIz7b7glpNRFLA3QSjVNeT3weuBs4CLwAPGmP89l5SyxwFfgxARG4FdgLbmv1F9VTkhY4AO40xMyLyQ8DfAHvafE2tFAFuJhiYlASeFpFnjDGvtPeyWurfAf9kjFlv75T+LfA8cBfwJuArIvL1lRyk0MV+B/hUaXLjC8C3aMHqX1fAVYwx08aYmdLv/w6IisjGNl9WK50GnjDGzBpjxoCngOvbfE2tdi9rLP1Qp58nSD8ZY8x3gdeBvW2+ppYofd//vDHmBuBngWGC599UGoCriMimUg4sfCtiAZfae1Ut9Tjwr0UkUnor/q8IDlRdF0SkH/g+1udBAScpjYot5T+/Bzje1itqEREZEJFY6Y+/ADzVipX/uktBiMgXgP3ARhE5DfwmEAUwxvwhcA/wiyLiAjngXrOGSkWWe/7GmJdF5Ang24AP/JExZtGStW5Sx789wI8Cf2+MmW3LRTZRHc//t4H/ISIvEMzy/lDpXVDXq+O5Xw18TkQ84CXgvS25rjUUW5RSqqtoCkIppdpEA7BSSrWJBmCllGoTDcBKKdUmGoCVUqpNNACrriYiu5aa7Fbj/v9hufZqEfk5Efn9RT72kZVeo1KL0QCs1pVSrfPnLuMhNACrhtEArNYCW0T+r9IM278XkaSIvElEnigNFPp6acQgIvJfROQDpd/fUpr/+rSIfLxqJb2l9Pmvisjvlu7/O0CyNC/4861/mmqt0QCs1oI9BPNbrwUmgf+V4KDFXzbG3Ax8APjvNT7v/wb+gzHmdsCr+tgNwE8CbwF+UkS2G2M+DOSMMTcYY366Sc9FrSPrrhVZrUmvG2OeL/3+MLAL+F7gL0tjPQDilZ9QGjzfa4z559JN/w9QOYT/H40xU6X7vkQwnvBUU65erVsagNVaUKj4vQdcAUyWJlstRpb4WK3H1O8V1XCaglBr0TTwuoj8OIAE5o3UNMZMABkRua100711PrYjItHGXapazzQAq7Xqp4H3ishR4EXgXTXu817ggIg8TbAinqrjcQ8A39ZNONUIOg1NrVsikg6H74vIh4HNxpgH23xZah3RvJZaz94hIr9O8H1wAvi59l6OWm90BayUUm2iOWCllGoTDcBKKdUmGoCVUqpNNAArpVSbaABWSqk2+f8BEN5QlOMGQvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x='height', y='bmi', data=people)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## End"
   ]
  }
 ],
 "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}