image_generator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import pandas as pd \n",
    "data = pd.read_csv('weight-height.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.02634590621737 63.708773603424916\n",
      "187.0206206581929 135.8600930074687\n"
     ]
    }
   ],
   "source": [
    "male_df =  data.loc[data['Gender'] == 'Male']\n",
    "female_df =  data.loc[data['Gender'] == 'Female']\n",
    "print(male_df.Height.mean(), female_df.Height.mean())\n",
    "print(male_df.Weight.mean(), female_df.Weight.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a213aa0f0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fddff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_df.Weight.plot.kde(label = 'male')\n",
    "female_df.Weight.plot.kde(label = 'female', color = 'b')\n",
    "plt.axvline(187.02)\n",
    "plt.axvline(135.86, color = 'b')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a1fd486d8>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8/RxfumNQr6Uo/ghJ4tA0jc2bN/Pwww9jtVp58MEHycvLIyvr8/H0NpuNe+65xzfjsUN4eDgrV64kIyMDu93Oj3/8Y2bOnElMzMCrmyojS3mjt1bPqPjAJg6LSZCdEM6pAXaQy+oKSBsVkFjEtJnI7a8h21t7XXZWUQIlJJ3jxcXFpKenk5aWhsViIT8/n717u852TU1NZcyYMd3aIDMzM8nIyAAgOTmZhIQEGhsbQxG2MkwEK3HA56VH+ktKCVUViNSMgMQhps4CjweOB768hKJcLCRXHHa7HavV6rtvtVo5ceJEv1+nuLgYt9tNWlr3QmWFhYUUFhYCsH79emy2/hWMMzKLxTKs3s9g9fd81DrsWKPDGJ0R+AJ3M7Ic/ONUI+boBJL6UardU3ueGqeD2JxJRA/y39ZisWC76mqqw8OJPHWMuCU3DOr1hgP1nekq0OcjJImjp/HC/R3dUFdXx8aNG7n33nt7HEZWUFBAQUGB7/5wKtGhSo501d/zcfJ8ExmxlqCcw5Rw7zDPj09WMCvD/+ZTecxb3K4lJp7WQcZls9mobWyCCdNo3b8Hx83qs6K+M135ez4MVXLEarVSW/t56efa2lqSkpL83r+1tZX169dzxx13MGnSpGCEqAxjFY0ORsUHdihuh3G+kVX9K7Euqy6UCAlQHwdcqJZbcRZZr8qsK8EVksSRk5NDZWUl1dXVuN1udu/eTV5enl/7ut1uHn/8cRYuXMi8efOCHKky3DS2u2lyakHp3wCIj7SQHGXpfwd5VSVYwiApcM0HYsYcAGTRhwF7TUXpSUiaqsxmMytWrGDdunVomsbixYvJzs5m27Zt5OTkkJeXR3FxMY8//rhvBasXX3yRJ598kt27d3P06FGamprYuXMnAPfeey9jx44NRejKEBfMjvEOYxMjON3PxCGryiE1AzGI2bvdZI6GjGzkvvdh0RcD97qKcpGQzePIzc0lNze3y2PLly/33Z4wYYJvOcXOFi5cyMKFC4MenzI8lTeFIHEkRfBpVQsujyTM7GffXXUlpAeumQq8/YYibz7y9W3IhjpEgv/NwYrSH6pWlTKslTc6sZgIyOJNvRmXFIlbg7JG/646pOaB6kpEqn8dkf0h5lwNUiL37w74aytKB5U4lGGtvNFJemw4ZlPwahSNTfJ2kPvdz1F7HjxuSAtC4hjV0Vz1XsBfW1E6qMShDGvljc6gNlMBjIoLJ8wkOF3n58iqKu/qlCIIiQNA5F0NJ46o0VVK0KjEoQxbHk1yrjn4icNsEoxOjOCUnzPI5YXEEcihuJ2JKy40V+17PyivrygqcSjDVlWzC7cW3I7xDuOTIiixt/u3OE51BUREQXxiUGIRGdmQNQ65992gvL6iqMShDFuhGIrbYZItimanRmWTq89tZVU5pGUGdW0IceVCKDmGPH8uaMdQRi6VOJRhq7zJ23QUrFnjnU2yetfjOF7b1vfG1ZVB69/oIK64GkBddShBoRKHMmyVNzqJizATH2EO+rGyEyKItAiO11w6cUi3C2qqgzKiqjNhS4OcKSpxKEGhEocybJU3OhkVF/xmKvB2kE9IjuR4bR8jq85XgdSCnjgAxBULoOw0suJs0I+ljCwqcSjDVkUIhuJ2NskWxak6By6P1vtGF4obBmPy38VE3tUgTOqqQwk4lTiUYanV5aGu3UNmCBPHRGskbk1ScomJgJ8PxQ1B4khIgskzkB/t8m+0l6L4SSUOZVgK5YiqDpNsUQCX7ueoroDYOERMXEhiElcu9NbFOnsyJMdTRgaVOJRhSY/EYYsOwxpt4dglEoesCtw64/4QufPAbEHuVSVIlMBRiUMZlsobnZgEZMQGr7hhT6bYoi6ZOAK5zrg/REwcTJiKPPRxyI6pDH8qcSjDUnmjk9SYMMLMof2IT7ZFUd3ixt7m7vacdLRDfW1IrzgAxGVzoPwMsk7VrlICQyUOZVgKRXHDnky+0M9x7HwPVx3VlUDwihv2Rkz3roMjD+8P6XGV4UslDmXY0aSkokmfxJGTHIHFJPisp+aqjnXGQzAUt4tRYyAxGQ5/EtrjKsOWShzKsFPT4sbpkbokjjCziZzkiB77OXxDcUPYxwEXVgacNANZfEQNy1UCImRLxxYVFbFlyxY0TWPp0qXccsstXZ4/cuQIzz33HGfOnGH16tXMnTvX99zOnTt55ZVXALjttttYtGhRqMJWhqBQLBd7KZNtUfzv8fruS8lWVUBiMiIyKvRBTZgGH+2C2mqwpYX++MqwEpIrDk3T2Lx5Mw899BAbNmzg/fffp6ysrMs2NpuNe+65h6uvvrrL483Nzbz00kv84he/4Be/+AUvvfQSzc3NoQhbGaLKG0NX3LAnU2xRuDTJ6fqu5UdkdWiH4nYmJk71xlB8RJfjK8NLSBJHcXEx6enppKWlYbFYyM/PZ+/evV22SU1NZcyYMd1KTRcVFXH55ZcTGxtLbGwsl19+OUVFRaEIWxmiyhudRFlMJEUGv7hhTyaneK8oPru4gzzEQ3G7yBwNUdFQfFSf4yvDSkgSh91ux2q1+u5brVbsdvuA9k1OTvZ7X2Vk6hhRFcz1Li6lp4mAsqUZmhv1u+IwmWH8ZOTJz3Q5vjK8hKSPo6cOucF8qXvat7CwkMLCQgDWr1+PzWYb8OsbjcViGVbvZ7D6Oh8VzSXMyUrQ9ZzNHFXDkXNNvhhc9mrsQPzEKUQGOC5/Px/NUy6j5bWtWBPiEWH69P+EivrOdBXo8xGSxGG1Wqmt/XzyUW1tLUlJSX7tm5yczJEjn7fL2u12pk2b1m27goICCgoKfPdramoGEbGx2Gy2YfV+ButS56PV5eF8s5OUCH0/A2PjTOw44aC47ByJkRa044cBaIqKpTnAcfn7+ZApGeDxUPPpfsSYCQGNwWjUd6Yrf89HZqZ/Q8VD0lSVk5NDZWUl1dXVuN1udu/eTV5enl/7zpo1iwMHDtDc3ExzczMHDhxg1qxZQY5YGap8NaoS9P1FPTbJ2zF/uqNSblUlCBPY0vULKns8APJsiX4xKMNCSK44zGYzK1asYN26dWiaxuLFi8nOzmbbtm3k5OSQl5dHcXExjz/+OC0tLXz88ce8+OKLPPnkk8TGxvLlL3+ZBx98EICvfOUrxMbGhiJsZQgqa/AmjmydhuJ2GJfoTRyn6tqZlRHjnfxnTUGEhbZ2Vhe2NG8HuUocyiCFbB5Hbm4uubm5XR5bvny57/aECRN45plnetx3yZIlLFmyJKjxKcNDWaMTs4D0EK3815v4SAvJURbfFYe3Km6IZ4xfRJhMkD0OWaoShzI4aua4MqyUNTrIiAvHYtJnRFVn45IiOFXv8A4Oqa4Iyap/fRHZ473LyWqXWKVQUfqgEocyrJQ16FOjqidjEyMoa3DgstdCextkZOkdEqRngaMdVKVcZRBU4lCGDbcmqWxykp2gz4zxi41NisQjofS0t0aV0GkOR2ciI9t7o7JU30CUIU0lDmXYONfsxCP1q1F1sXEXRladqar3PpBugCuOTG/ikOdU4lAGTiUOZdjwjajSeShuh4y4cMwCyurbISISkqx97xRkIi4BYuOgsqzvjRWlFypxKMNGmQ7rjF+KxSRIiw2nvF1AWqZuJVC6Sc9GVqgrDmXgVOJQho2yBgfWKAvRYfoUN+xJVkI45TIKYYRmqgtERhaopiplEFTiUIaNskan7jPGLzYqxkxleCKeVP07xn3Ss6C5CdncqHckyhClEocyLEgpKWtw6j5j/GJZtOA2Wai2jtY7FB9faffz5/QNRBmyVOJQhgV7m5s2t6bb4k29GdXmLSxXHmOgVfdSvIlDVlfqHIgyVKnEoQwLHcUNjTKiqkNmnXf0Urk5TudIOkm5kMTOq8ShDIxKHMqwUNpgrBFVHeKqz5DgaqGs1TglPkR4BCRaoVo1VSkDoxKHMiyUNzqIDjORHBWyup1+kefKGSWbfVdEhpGajlRXHMoA+Z049u3bh8fjCWYsijJgpTovF9sTqWlwroxR4ZpvjolRiJQM1TmuDJjfiWPbtm1897vfZfPmzZw4cSKYMSlKv3WsM24otdXgaCczIZImh4cmh4F+eKWkQ0Md0tGudyTKEOT3df1jjz3G6dOneffdd3niiSeIiIhg4cKFLFiwgNTU1GDGqCiX1O7WqG11Gy9xVJwFYFRaEjRARZOTyRFROgd1QechuVljdQ1FGXr61SA8duxYxo4dyze+8Q0OHjzI888/z4svvsiUKVMoKChg/vz5mEyq20QJrQqDlRrpIMtOA5A5JgOOV1LR6GSyzRiJQ6RmIAGqK1XiUPqt3z2J586d49133+Xdd99FCMHy5cux2Wy88cYbfPjhh/zgBz8IRpyK0itfjSqdV/3rpuIsWFNJt8ZjEpVUNBmonyPFu/a5PH8O4/QKKUOF34njjTfe4N133+XcuXPMmzePlStXMmnSJN/zV111Fd/+9rd73b+oqIgtW7agaRpLly7llltu6fK8y+Vi06ZNlJSUEBcXx+rVq0lNTcXtdvPMM89w6tQpNE1j4cKF3HrrrQN4q8pwVdHkROCtRmsksvwMZI4mzCxIjQkzVOIQ0bEQE6fmcigD4nfiKCoq4sYbb+SKK67AYum+W0RERK9XG5qmsXnzZh5++GGsVisPPvggeXl5ZGV9Xvhtx44dxMTEsHHjRt5//322bt3KmjVr2LNnD263myeeeAKHw8HatWuZP3++6ldRfMobnaTEWIiwGKeZVLrdcK4ccVkeAJlx4b4mNcNISUeqkVXKAPj9TZs2bRrz5s3rljRef/113+2ZM2f2uG9xcTHp6emkpaVhsVjIz89n7969XbbZt28fixYtAmDu3LkcOnTIu1Yz0N7ejsfjwel0YrFYiI6O9jdsZQQob3SSabBSI1RVgMcNo8YAkBkfTkWT0/eZNgKRkq6G5CoD4nfiePnll/v1eGd2ux2r9fNFbKxWK3a7vddtzGYz0dHRNDU1MXfuXCIjI/nud7/LPffcw5e+9CViY2P9DVsZ5qSUhhyKKyvOACA6EkdcOO1uSV27wYbk2s8j1fwspZ/6bKo6dOgQAB6Px3e7Q1VVFVFRfY8S6elX1sUTtXrbpri4GJPJxLPPPktLSwv/9//+Xy677DLS0roWjSssLKSwsBCA9evXY7PZ+oxrqLBYLMPq/QxW5/NR0+Kk3a0xOSPJUOeo2V5Ni8mMbfrliPAIpraYYV8VLSKKSbaEgB5roJ+PtnETaPR4SBYaZpuBijAGgPrOdBXo89Fn4nj66acBb+d1x23w/lFPTExkxYoVfR7EarVSW1vru19bW0tSUlKP21itVjweD62trcTGxvLee+8xa9YsLBYLCQkJTJ48mZMnT3ZLHAUFBRQUFPju19TU9BnXUGGz2YbV+xmszufjYFULAAkml6HOkefYYUgfRW1jE9BEjPT2bxwtO092pCugxxro50NGxgBgP3YEYQoLaEx6U9+Zrvw9H5mZmX69Xp+J4ze/+Q0AmzZtYuXKlX696MVycnKorKykurqa5ORkdu/ezX333ddlmzlz5rBz504mTZrEnj17mD59OkIIbDYbhw4dYsGCBTgcDk6cOMGyZcsGFIcy/JQbdA4HZ0sQ0z7v87NFhxFmEsaqWdUxJLemSg3JVfrF71FVA00a4O2zWLFiBevWrUPTNBYvXkx2djbbtm0jJyeHvLw8lixZwqZNm1i1ahWxsbGsXr0agOuvv57f/va33H///UgpWbx4MWPGjBlwLMrwUt7oJNwssEYbp7ihrLdDgx1G5/geM5sEGXFhVBpoSC5JVjCbVQe50m+X/LatWbOGDRs2AHD33Xf3ul3nJqze5Obmkpub2+Wx5cuX+26Hh4ezdu3abvtFRkb2+LiigHfW+Kj4cEwGKm7I2ZMAiE6JA7wjq8oajJM4hMkMySlQU6V3KMoQc8nEcdddd/lur1q1KujBKEp/lTc5GZ8UqXcYXcizJ0EIGD2uy+OZceHsK2/Go0nMJoMkupR0pEocSj9dMnFMmTLFd3vatGlBD0ZR+sPlkVQ1u1gwJl7vULqQZ0ogLRMR2XW+UWZcOG4Nzre4SDfILHdhS0fu3613GMoQ4/c8jtdff53Tp08DcPz4ce6++25WrlzJ8ePHgxUH3GzaAAAgAElEQVSbolzSuWYnmjRix3hxt2Yq8DZVAYYqPUJKGjQ3Itta9Y5EGUL8Thx/+9vffGU+XnjhBW688UZuu+02/vCHPwQrNkW5JCOOqJJNDWCv6dIx3qGjCKOREoe4MLJK9XMo/eF34mhtbSU6Opq2tjZOnz7NDTfcwJIlS6ioqAhmfIrSq47aT5kGafYB4GwJAGL0+G5PJUSaibKYjFWzynYhcaiRVUo/+D2G0Wq1cuzYMUpLS5k6dSomk4nW1la1/oaim/ImJ4mRZmLCzXqH4iPPFHtvjOl+xSGEuFCzKrATAAclxTuRVtao8uqK//xOHN/4xjd48sknsVgs3H///QDs37+fCRMmBC04RbkUQ9aoOnUcUjO9Zct7MCounGO1bSGOqnciOhaiY+G8aqpS/Od34sjNzeXZZ5/t8tjcuXOZO3duwINSFH+UNzqZm22cgpdSSjj5GWJGbq/bZMaH8e6ZRlwejTCzQa7WU9KRNaqpSvFfv6bbtra2UlFRQXt71wXuZ8yYEdCgFKUvTQ4PjQ6Psa44aqqgqQHGT+l1k4y4cCRQ2exidIIxSsELW5pvmVtF8YffiWPnzp1s3ryZyMhIwsM//7IKIdi0aVNQglOU3nSMTDJSx7g8+RkAYvzkXrfpSHSVjU7DJA5S0uHAh0jN451Nrih98DtxvPDCC6xdu5bZs2cHMx5F8cvnQ3EN8scXoOQziIj0Ld7Uk47lbcsNNCSXlDRwu6He7i1Boih98LuRVdO0Xlf4U5RQK290YhaQFmuccuDy5DEYOxFh7v1Xe2y4mYRIs6GG5IqOtThUB7niJ78Tx80338zLL7+MpmnBjEdR/FLe6CQ9LhyLQWo+SUc7lJ1C5PTev9EhMy7cUJMAO+ZyqA5yxV9+N1X97W9/o76+nr/85S/dlm71pzquogRSRaPTUP0bnC4GTUNcomO8Q2ZcOPsrW0IQlJ+SU0CY1CRAxW9+Jw5VHVcxCo8mqWhyMjszRu9QfGSJt2OcS3SMd8iMD+ftkgZaXR6iw/TvjBYWCyTbVNkRxW9+Jw5VHVcxiqomBy5NGmoorjz5mXfiX1zflXo7alZVNrnISdY/cQCqvLrSL373cbhcLl544QVWrlzJP/3TPwFw4MAB3njjjaAFpyg9Ka33zrw2SuKQUkLJMURO31cbABlx3g59Iy0jK1LSVVOV4je/E8dzzz1HaWkp9913H+LCamvZ2dls3749aMEpSk/O1hkrcfgz8a+zDN8Vh3ESB7Y0aKz3dvIrSh/8bqr66KOP+PWvf01kZKQvcSQnJ2O324MWnKL05GxdGzFhJhIijNHM45v458eIKoAIi4mUaIuhhuTSubz6JeahKAr0I3FYLJZuQ3EbGxuJi4vza/+ioiK2bNmCpmksXbqUW265pcvzLpeLTZs2UVJSQlxcHKtXr/at/3HmzBl+97vf0dbWhhCCX/7yl11mrysjy9m6VjLjw30/YHRX8hlERMGo0X7vkhkfbqhJgMKWjgRvc5VKHEof/G6qmjt3Lps2baK6uhqAuro6Nm/eTH5+fp/7aprG5s2beeihh9iwYQPvv/8+ZWVlXbbZsWMHMTExbNy4kWXLlrF161YAPB4PGzdu5Dvf+Q5PPvkkP/vZz7BY+lViSxlmSuvajdNMxYWJf+Mm9qtcR8dcDillECPrh07l1RWlL34njq9//eukpqZy//3309rayn333UdSUhK33357n/sWFxeTnp5OWloaFouF/Px89u7d22Wbffv2sWjRIsCbpA4dOoSUkgMHDjB69GjGjh0LQFxcnFoDZARrd2tUNTsMkzikw+Gd+OfHMNzOMuPDaXFqNDk8QYqsn2LiICpazR5X/OL3T/dz584xatQobr31VjRN48orr2T0aP8uze12O1ar1XffarVy4sSJXrcxm81ER0fT1NREZWUlQgjWrVtHY2Mj+fn53Hzzzf6GrQwzHf0Co4wy+e/MCb8n/nWW2almVXyk/lfQQgiwpiHVyCrFD31+YqWUPP3007zzzjtYrVaSkpKw2+289NJLLFy4kLvvvrvPtuaeLscv3qe3bTweD5999hm//OUviYiI4Oc//znjx4/nsssu67JtYWEhhYWFAKxfvx6bzdbXWxsyLBbLsHo/g1FkPw/A9NFp2Gz6TwBs2VVKM2C9Yh6m+ES/95thiQHKaNQiBv1vG6jPR/2o0bjLzwyLz5r6znQV6PPRZ+IoLCzkyJEjrFu3rstqf8XFxTz11FO89dZbXHfddZd8DavVSm1tre9+bW0tSUlJPW5jtVrxeDy0trYSGxuL1Wpl2rRpxMd7J1bNnj2bU6dOdUscBQUFFBQU+O7X1NT09daGDJvNNqzez2B8Vl6LAKI8LdTU6L+SnufgfkjNxO50Qz/+jcI0SbhZcKS8lqvSBjc6LFCfDy0hGfnxbs5XVw358urqO9OVv+cjMzPTr9frs7Ng165dfOtb3+q2ROyECRP453/+Z959990+D5KTk0NlZSXV1dW43W52795NXl5el23mzJnDzp07AdizZw/Tp09HCMHMmTM5e/YsDocDj8fD0aNHycrK8uvNKcNPRaOTtLgIIiz693P5Jv71s38DwGwSZCeEc7reEYTIBigjC9wuqKnWOxLF4Pq84igrK+u13Mi0adP8WsTJbDazYsUK1q1bh6ZpLF68mOzsbLZt20ZOTg55eXksWbKETZs2sWrVKmJjY1m9ejUAsbGxLFu2jAcffBAhBLNnzyY3t/elOZXhrbzRSXZSlN5heNVUQWM9+Dlj/GKjEyIoOtca4KAGTmRke4fkVpyF1Ay9w1EMrM/EoWkaUVE9f1GjoqL8LrOem5vb7Q/+8uXLfbfDw8NZu3Ztj/suXLiQhQsX+nUcZfiSUlLW6GRWdlLfG4eALDkG0O+O8Q5jEiP4x6lGGh0e4o0wmTEjGwBZWYqYdZXOwShG1mfi8Hg8HDp0qNfn1focSqjY29y0uzXGJEXrHYpXybE+V/y7lDGJ3tULz9Y7mJGm/3sS0TGQaIWKUr1DUQyuz8SRkJBwyfU2OjqtFSXYOooCjkmOAtz6BgPIM8WQPf6SK/5dSkfiOGOQxAFA5mhkpUocyqX1mTh+85vfhCIORelT2YXEMTopCtqbdI1Fah4oPYVYcOkRhZeSHGUhNtzEGQN1kIvMbOSuN5GahlATbZVeqE+GMmSUNTqJtJiwxRhg8l9VBTgdMHr8gF9CCMGYxAhDJQ4ysr3v68J8GUXpiUocypBR3uAgyyDFDeWZkwCI0TmDep3RCRGcbXAYpmaVyPR2kFNxVt9AFENTiUMZMsoanWQZpEYVpSUQFu4biTRQYxIjaHVp1LTq32cDQKa3o1+WntI5EMXIVOJQhoR2t/eP66gEYyQOeeYkZI0dcMd4h84d5EYgomMgNQN59qTeoSgGphKHMiT4ihsa4IpDSglnSxCD6N/oMPpC4jhdZ4zEARea386oxKH0TiUOZUjoGFGVFR+hcyR4Z4y3tcAg+zcAYsPNpMaEUVJnoCVbx+RAbTWyuVHvSBSDUolDGRLKGh2YBGTEhekdClxoxhFjBp84AMYnR3DKQIlDjLlQl+5sib6BKIalEocyJJQ1OEmNCSPcrP9HVp45CWazryN5sMYnRVLR5KLVZZBFnS40wUnVXKX0Qv9voaL44Uy9w9eRrDd59iRkjkaEBebqZ3xSJGCcfg4REwe2NDhTrHcoikGpxKEYntOjUdHkNETi+LxjPDDNVOBtqgIM1c8hxk5ElhwzzPwSxVhU4lAMr7TBiSZhrAESB3W10NTg7UAOkOQoCwmRZk7ajXHFAcDEaVBXA7VqbQ6lO5U4FMPrmONghCsOX8d4AK84hBCMT4o0Vgf5xOkAyBNHdI5EMSKVOBTDO1PvIMwkyIgzwByOsydBmCBrbEBfd3xSBGfrHbg8BlmmYNRoiI6BE4f1jkQxIJU4FMM7Xe9gdGI4ZpMBalSdLYH0UYiIyIC+7vjkSDwSzjY4A/q6AyVMZsiZilSJQ+mBShyK4RlpRFWgZoxfLCfZm4hK7AZrrjpXjmys1zsUxWBCljiKior4/ve/z6pVq3jttde6Pe9yudiwYQOrVq3ioYceorq6a6dcTU0N3/zmN/nLX/4SqpAVA2hsd1PX5jZE4pBNDd4O4yAkjrTYMKIsJmONrJpyGQDy6AGdI1GMJiSJQ9M0Nm/ezEMPPcSGDRt4//33KSsr67LNjh07iImJYePGjSxbtoytW7d2ef4Pf/gDs2fPDkW4ioGcutAxPjYxsE1DA3JhJnUgO8Y7mIRgXFIEJUYaWTUmB2Li4PAnekeiGExIEkdxcTHp6emkpaVhsVjIz89n7969XbbZt28fixYtAmDu3LkcOnTIN4b8o48+Ii0tjaysrFCEqxjIiVrvL/COphw9+SrGZgf+igO8/Ryn6trxaMaYOyFMZsS0Wcgjn6j5HEoXIUkcdrsdq9Xqu2+1WrHb7b1uYzabiY6Opqmpifb2dv7nf/6H22+/PRShKgZTXNtGemwYcRGDK18eEGdLwJaGiIkNysvnJEfi8EjKm4zRQQ7A9NnQUAdlp/WORDGQPtccD4Sefq1cvIpbb9u8+OKLLFu2jMjIS//iLCwspLCwEID169djs9kGEbGxWCyWYfV++qOk/hSXZSZ0ef96nY+a8tNYJkwlMUjHvtIUDR9UUumwkNuPYwTzfHiuXkrNH35N9OljxMy+IijHCIaR/J3pSaDPR0gSh9Vqpba21ne/traWpKSkHrexWq14PB5aW1uJjY2luLiYDz/8kK1bt9LS0oIQgvDwcK6//vou+xcUFFBQUOC7X1NTE9w3FUI2m21YvR9/1be7qWpy8MWJ8V3evx7nQ7a1olWWoV21KGjHjpaS6DATn5w+z1Wp/l9hBfd8CBg1huaP3qNtwfV9b24QI/U70xt/z0dmZqZfrxeSxJGTk0NlZSXV1dUkJyeze/du7rvvvi7bzJkzh507dzJp0iT27NnD9OnTEULw85//3LfNiy++SGRkZLekoQxPxRf6NyYmR+kcCd6lYiEoQ3E7mIRggjWS47XGGVkFIKbnIt/+K7K9DRFpgH8LRXch6eMwm82sWLGCdevWsWbNGubNm0d2djbbtm1j3759ACxZsoTm5mZWrVrF66+/zp133hmK0BQDO17bhsDbaaw3efpCpdggjKjqbJI1itN17TjcBplBDogZueBxw7FDeoeiGERIrjgAcnNzyc3N7fLY8uXLfbfDw8NZu3btJV/jq1/9alBiU4zpSHUb45IiiArTf56qLPkMrKmIhKS+Nx6ESVbvDPJTdQ6mpBjk1/2EaRAegTz8MWLm0OnnUIJH/2+kovTA5ZEcq2ljemq03qF4nTyGyJkS9MNMtHmTxYnatqAfy18iLAymXI48tF/vUBSDUIlDMaRiextOj2R6mv6JQ9rPQ30tjA9+4kiOsmCNtnC8xmD9HDNy4fw5ZHWF3qEoBqASh2JIh6u9v7inGaC5Rp48BoAYPzkkx5tkjeK4ga44wNtBDqirDgVQiUMxqCPVrWQnhJMQGbJuuN6VfAZh4ZA9NiSHm2SN5Fyzi8Z2d0iO5w+RmgGpmSpxKIBKHIoBOT0ah6paucwAzVQA8uRnMGYCwhKYNcb7MvnCVdbRGoNddczIhWOfIl0Gmtmu6EIlDsVwjlS34fBI5mQGp7RHf8jWFjhd7KsUGwoTrZGEmQSHq1pDdkx/iBm54HSqxZ0UlTgU49lf0UyYSRjjiuP4QZAaYurMkB0y3Gxisi2SQ9XGuuJg0gywhKnmKkUlDsV4Pq5oYXpaNBEW/T+e8sgBCI+AEHWMd5ieFs2punZanJ6QHvdSREQkTJquEoeiEodiLNXNLsoanczJjNE7FODCIkaTpoesf6PDjNRoNAlHzxvrqkNMz4XKUmTteb1DUXSkEodiKB9XNAOQa4DEIe01cK4spM1UHSbborCYBIeM1s9x2RwA5GF11TGSqcShGMrHFc2kxYYxKi5c71CQn34EgJg+J+THjrCYmGSN5NOqlpAf+5LSsyA5BXnoY70jUXSkEodiGK0uD59UtnJVVmy39Vr0ID/ZA2mjIDNbl+PnZsZw0u7A3mag+RxCeEdXffYp0m2cuJTQUolDMYx95S24Ncm87Di9Q0G2NMOxg4jZc3VLYnmjvMOR919ovjMKMX02tLVCyTG9Q1F0ohKHYhh7SptIjDQz2WaAMiOf7gWPBzF7rm4xjE2MwBptYV+5sRIHU2aCyYQ8/InekSg6UYlDMQSHW+PjimbmZsdhNhmhmeoDSEyGsRN1i0EIQV5mLJ9UtuLyGGh9jugYGD9FdZCPYCpxKIZQdK6FdrdBmqlaW+DQfm8zlUnfr8hVWbG0uzX2Vxqrk1zMyIUzxcjGer1DUXSgEodiCHtKm4gJNzHDALPF5f7d4HIirlqkdyjMzIghLsLMu6cb9Q6lCzF9NgDySJHOkSh6UIlD0Z1bk3xU1syVo2KxGKGZas9OSM0M+WzxnlhMgvmj4/iorJl2Ay0ny+gciI0H1Vw1IqnEoejuUFUrzU7NGM1Utee9o6nmLjLEkGCABWPicXi8ydUohMmEmDYbefgTpGaghKaERMgWOygqKmLLli1omsbSpUu55ZZbujzvcrnYtGkTJSUlxMXFsXr1alJTU/n000/ZunUrbrcbi8XCN7/5TWbMmBGqsJUQeP9sIxFmwawMA8wW/+gdAMTcRfoG0sm01Cis0RZ2nmpg4dh4vcP53Ixc+OgdKD0FY3L0jkYJoZBccWiaxubNm3nooYfYsGED77//PmVlZV222bFjBzExMWzcuJFly5axdetWAOLi4vjRj37EE088wb333svGjRtDEbISIi6PZPfZJq7KitO9qKGUEvnBPyBnCiIlXddYOjMJwZJxCXxS2cL5Fpfe4fiI6bMA1CzyESgk39Ti4mLS09NJS0vDYrGQn5/P3r17u2yzb98+Fi1aBMDcuXM5dOgQUkrGjRtHcnIyANnZ2bhcLlwu43x5lMEpqmyh2amxYKz+zVScLobKUsTcxXpH0s21ExLQJOwoadA7FB8RnwSjxyOPqPkcI01ImqrsdjtWq9V332q1cuLEiV63MZvNREdH09TURHz855fmH374IePGjSMsrHul0sLCQgoLCwFYv349NpstGG9FFxaLZVi9n84+3FdLXISFay8bQ5jZv98xwTofDdt+T3tEJLYbbsUUo/8iUp3ZbJCXbWfHqSbuXjQZU6f+Fz0/H01582n9n/8iOSrSUOdsOH9nBiLQ5yMkiUNK2e2xizse+9qmtLSUrVu38pOf/KTHYxQUFFBQUOC7X1NTM9BwDcdmsw2r99PB4dZ492QNC8fG01Bn93u/YJwP2dqCtms74sqF2Nvaoa09oK8fCItGR/N4aT1vHzrL7E79QXp+PmTONPB4qHlnO6YrF+oSQ0+G63dmoPw9H5mZmX69XkiaqqxWK7W1tb77tbW1JCUl9bqNx+OhtbWV2NhY3/aPP/449957L+npxml7VgbHO8RUsmCM/h2+8sN3wOlALLxe71B6NTc7lrgIM28VG2jS3YQpkJCE/Hi33pEoIRSSxJGTk0NlZSXV1dW43W52795NXl5el23mzJnDzp07AdizZw/Tp09HCEFLSwvr16/na1/7GlOmTAlFuEqI7DzVQHKUhemp+k76k1Ii3/lfGD0exk7QNZZLCTObWDwung/LmmhoN0ZlWmEyI2bPg0P7kA7jXaUpwRGSxGE2m1mxYgXr1q1jzZo1zJs3j+zsbLZt28a+ffsAWLJkCc3NzaxatYrXX3+dO++8E4A33niDc+fO8fLLL/PAAw/wwAMP0NBgnA5CZWDOt7jYX9nC0vEJ+temOrgPys8gln7JMHM3enNtTiJuzWCd5HPywekENbpqxAjZPI7c3Fxyc3O7PLZ8+XLf7fDwcNauXdttvy9/+ct8+ctfDnp8Smi9fbIBTXpHC+lNe+NlSLYhDNRG35vRiRFMS4nijRP13Dw1uUsnuW4mToe4BOTHuxFz5usdjRICaua4EnIeTfLWyXpmZcSQFqvvSn/y+CE4cQRx7c0hX1d8oJZNTuJcs4v9FcYofCjMZkTuPOSBD5GtxpndrgSPShxKyH1U1kxNq5sv6Hy1ITUN7c9bINGKWGDcTvGLzc2OIynKwt+O1ekdio+4+lpwOpEf7tI7FCUEVOJQQkpKyctHakmPDeOqLH0n/cm978LpE4hbv4GIiNA1lv6wmATXT0xkf2UL5Y1OvcPxGjMBssch332zx6H1yvCiEocSUgerWjlR286t05J17RSXTQ3Ibf8BYyYYcqZ4X74wIRGLCf5+3BhXHUIIxIIveOtWnTqudzhKkKnEoYSMJiV/LDpPcpSFJeP1a6aSUqL96bfQ1oLpn+/TfbGmgUiKsjB/dDyFJ+upazVGCR4xbxFEx6L9/c96h6IE2dD7xihD1ntnmjhR2843ZtoI97O8SDDI996C/R8gbroTkTVWtzgG6yszrDjckhf2l/W9cQiIyGhEwU1w4CNk6Sm9w1GCSCUOJSSaHR7+sL+acUkRLNbzaqPkGPK/noGpMxFfuKXvHQxsdEIEC8bG8/KBSuqNMiFw6Y0QFY32P1v1DkUJIpU4lJD43b4q6tvdrLwqQ7e5B7KhDu3pX0KiFdN3H0CYzLrEEUjLL7Pi8mj81wFj1GUS0bGIG273XnUc2Nv3DsqQpBKHEnTvnGrgndONfPUyGxOskbrEIB3taJv+H7Q2Y7rnIUSs/vWxAiErPoKvzMrkzeJ6jtW06R0OAOLamyAjG23r08hmY62VrgSGShxKUJ2obWPTh+eYnhrFV6Zb+94hCKTHg/bso3DmJKbvPIDIHqdLHMHyL3NHY42ysHFPJQ4DrEsuLGGYVqyGxnq0//wV0uPROyQlwFTiUIKmrs3NL98pJzHSzI8WjMKiw/BbKSXyT7+Fg/sQd34PMeuqkMcQbDHhFlbNy6C0wcnmj6v1DgcAMXYi4o5vw8F9yOd+rdYlH2ZU4lCCot2tse6dMpqdHh66JouEyJCVRfORmob809PI995C3Lgc0zVDZ3Z4f83OiOG2acm8WVzPe2eM0TxkWvRFxE1fR37wD7RnH0G2G6MpTRk8lTiUgHNrkkffLeekvZ3752cyLin0/RrS7Ub+4dfIXW8gbvgK4qavhzyGULtzZgqTbVFs3FPJ6TpjlDgXNy5H3L4CPvkQ7f+tRRYf0TskJQBU4lACSpOSTXsq+biihbuvTOeq7NCXFZGN9Wgb/hX5wQ7ETV9H3PpNw5dLDwSLSfCjBZlEhZn55a5ymhz69y0IITBddwumNf8Gbhfaow+i/ffv1dodQ5xKHErAeDTJUx9U8o9TjXz9chvXTUgMeQxy/wdo/3YfnDqB+Pb9mL50x4hIGh2s0WE8uHAUNa1uHn+vHI9mjLpRYupMTD/biFj0ReTbf0X7+feRxUf1DksZIJU4lIBoc2k8+l45O081cuflNr46I7QjqGRNFZ5n1l+Yp5GM6aHHMF11TUhjMIrJtijuvjKNonOt/LHovN7h+IjIKExfvwvTD9aBx4P26I/RXvkj0m2MyYuK/0LfY6kMO2cbHDyyq5yKJiffnpPKl6Ykh+zYsqUJ+fc/I3e8DsKEuOUbiC/chrCM7I92QU4iJXUOXjtqJynKzC1T9RkK3RMx+TJMP/s1cttm5P++hDx2ENN3foCwpekdmuKnkf3tUgbF6dF46XAtLx+2ExNm4t+WZHN5ekxIji2bG5Fvv47c8Vdoa0XkL/HWnkq2heT4Q8G/5KZS3+Zmy/7zCAQ3TUkyTLOdiIxG/NMqtGmzkM//Bu3nqzH9n3sReVfrHZrih5AljqKiIrZs2YKmaSxdupRbbulaJ8jlcrFp0yZKSkqIi4tj9erVpKamAvDqq6+yY8cOTCYT3/rWt5g1a1aowlZ64PRovH2ygZcP13K+1c2isfF8KzeVxKjgf5ykvYamv21D+9+XwdEOs+ZiuvnrQ7pYYbCYTYI1+Zlospz/3F/NSXs7K+akkqjD0OjemK5YgBw7Ee33j6M9+yjiSBHiy/+MiInVOzTlEkLyCdI0jc2bN/Pwww9jtVp58MEHycvLIysry7fNjh07iImJYePGjbz//vts3bqVNWvWUFZWxu7du3nyySepq6vj3//933nqqacwDcFS2ENZfZubozVt7K9o5v2zTbQ4NSbborhvXkbQrzKkywlHDqC9tx0O7KVVgLhiAeKG2xGjRgf12ENdmFnwwwWj2Hawhj8fquXDsmbmj45jdkYMk2yRpMSE6b5uuUhJx/TD9ci/bEW+8Qpy77uIuYsQM/Jg1GhIThmSpe+Hs5AkjuLiYtLT00lL87Zh5ufns3fv3i6JY9++fdx+++0AzJ07l//8z/9ESsnevXvJz88nLCyM1NRU0tPTKS4uZtKkSaEIfUSQUtLs1Ghod1PX7qaq2UVVs4tzzS6qmp2ca3bR0O4d2hlhFszLjmNpTgKXpUUPqulDejzgaIP2du/Vg6MN2tvA0Y5sa4WqCuTZk3DsoPf5uATE9bdhvfkO6sz6rlU+lJiE4GuXp7BgbDyvHLbz/tkm3i5pACDMJEiPCyMjLpyMWO//rdEWYsLNxISZiAozEW42EWYShJm9/wUj0QiLBXHbPyGvWOhNHrt3IHf+r/dJsxnikyAxGRKSEYnJkOC9Ly48RnwixMUPi8KVQ0FIEofdbsdq/bxzzmq1cuLEiV63MZvNREdH09TUhN1uZ+LEib7tkpOTsdvtQYmzyeHhx9vPdHmsYzDj56thyks8d+n9ZFMj0tN5BInouu1Fj1/qOSk+f0Je9EX+fJ/uX3B58f6AwxSGR3T9wpmkRoqzgTRHPVc66slsr2VySxk5recI+9D7HrROUXV/wz081/kxtxtcfSx7KkyQlomYtxhx+ZUw9XKEJQyzzQY1xqgGO5RkxUdw37wM7r5HHzIAAAd5SURBVLkqnTP1Dk7a2ylvdFLZ5ORck4uiyhacnr6H75oFhJlNhJkF4RcSSkDLyaTfCl+4xfv5cLvA4wHN4/2/xwNtHmjRoAKg+cJ/Z737dlyZiI7/X+pA3Z8U9Pz+r68/yA31Bwf2fkJMZI3F9N0HgnqMkCSOntYgvviXam/b+Lt+cWFhIYWFhQCsX78em63/naRRDjeT0rovxdkRakfEovMH7uLnuj/le9B9shrZ1ub9cF547OIPasejvseF8KUX4dtCXnQs0e0rIHq4ffEPxY67EWgkCpfvv3TRTgpOLBFAXMeOMSAmA5MveuOd3/BFJ6NTFL5/747/my2IqGhEZJT3v6goTJExiKgo32PmlHRERPdZ5xaLZUD/vsPVQM5HeipcXLVLk5LzzU7sLU6aHG6anR5anW5cHonTo+F0a5/f9mhdHneHeL6I1DzgdKI5HeByIp1OpMsBbg9IDaTmrY/Vj/XPL7WlNTmBiMQJgw88BMyZ2cRd9HkI9HcmJInDarVSW1vru19bW0tSUlKP21itVjweD62trcTGxnbb1263k5zcfbhnQUEBBQUFvvs1A/xF+v0rg/gHKTd/QLvZbLYBv5+B0oA+rgeCr6nZ+99F9DgfRhbI82EGUize/4iBoTrwMhifkaEy28QNOC567/6ej8zMTL+OEZIep5ycHCorK6mursbtdrN7927y8vK6bDNnzhx27twJwJ49e5g+fTpCCPLy8ti9ezcul4vq6moqKyuZMGFoZH5FUZThKCQ/J8xmMytWrGDdunVomsbixYvJzs5m27Zt5OTkkJeXx5IlS9i0aROrVq0iNjaW1atXA5Cdnc28efNYu3YtJpOJf/mXf1EjqhRFUXQkpL+dCENMRUWF3iEEjGqa6Uqdj67U+ehOnZOuhmRTlaIoijJ8qMShKIqi9ItKHIqiKEq/qMShKIqi9ItKHIqiKEq/DNtRVYqiKEpwqCuOIeDHP/6x3iEYijofXanz0Z06J10F+nyoxKEoiqL0i0ociqIoSr+oxDEEdC7eqKjzcTF1PrpT56SrQJ8P1TmuKIqi9Iu64lAURVH6ZWgW2x/G7r33XiIjIzGZTJjNZtavX09zczMbNmzg/PnzpKSksGbNGmJjY/UONWRaWlp45plnKC0tRQjB3XffTWZm5og8JxUVFWzYsMF3v7q6mq9+9atcc801I/J8ALz++uvs2LEDIQTZ2dncc8891NfX86tf/Yrm5mbGjRvHqlWrsFhGxp+7v//977z99ttIKVm6dCnLli0L/N8QqRjKPffcIxsaGro89vzzz8tXX31VSinlq6++Kp9//nk9QtPNxo0bZWFhoZRSSpfLJZubm0f8OZFSSo/HI7/97W/L6urqEXs+amtr5T333CMdDoeUUsonnnhC/uMf/5BPPPGEfO+996SUUj777LPyzTff1DPMkDlz5oxcu3atbG9vl263W/785z+XFRUVAf98qKaqIWDv3r1cc801AFxzzTXs3btX54hCp7W1laNHj7JkyRLAuwRmTEzMiD4nHQ4ePEh6ejopKSkj+nxomobT6cTj8eB0OklMTOTw4cPMnTsXgEWLFo2Y81FeXs7EiROJiIjAbDYzdepUPvroo4B/PkbGtdsQs27dOgCuvfZaCgoKaGho8C21m5SURGPj/2/v/lkaacMoDv8gMcggkzATAkL8F0klYpOgECt7ESwtRKyMpR9AENMIoo2Kna1Y+RFMoYgiEVOJQrRUwoTsiBpIyBbLG3jZZgfcjOycq0oxxeHmHg48THh++Bmvq15fXzFNk4ODA56fn0mlUiwtLQV6Jv85Pz8nl8sBBHYelmUxOztLPp8nEokwMTFBKpXCMAxCoVDnGcdxfE7aHQMDAxwfH+O6LpFIhFKpxOjo6Jfvh4rjm9nc3MSyLOr1OoVC4Y8vVvlXtVotKpUKy8vLpNNpjo6OOD099TuW75rNJjc3NywsLPgdxVdvb29cX1+zv7+PYRjs7Oxwe3vrdyzfJJNJ5ubmKBQK9Pb2MjQ09FduTNVR1TdjWRYA0WiUbDbL4+Mj0WiUWq0GQK1WwzRNPyN2lW3b2LZNOp0GYGpqikqlEuiZAJRKJUZGRojFYgCBnUe5XCaRSGCaJuFwmMnJSe7v73l/f6fVagHgOE7nvQqCmZkZtra22NjYoK+vj/7+/i/fDxXHN/L5+cnHx0fn993dHYODg2QyGYrFIgDFYpFsNutnzK6KxWLYtt25CrhcLpNMJgM9E/j/MRUQ2HnE43EeHh5oNBq02+3OfoyNjXF5eQnA2dkZmUzG56TdU6/XAahWq1xdXZHL5b58P/QHwG/k5eWF7e1t4NcRzfT0NPPz87iuy+7uLtVqlXg8ztraWmA+tQR4enri8PCQZrNJIpFgdXWVdrsd2Jk0Gg3y+Tx7e3sYhgEQ6B05OTnh4uKCUCjE8PAwKysrOI7z2+e4PT09fkftivX1dVzXJRwOs7i4yPj4+Jfvh4pDREQ80VGViIh4ouIQERFPVBwiIuKJikNERDxRcYiIiCcqDhER8UTFISIinqg4RETEk5/cZiizKqACegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fd614e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_df.Height.plot.kde(label = 'male')\n",
    "female_df.Height.plot.kde(label = 'female')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(data['Height'].groupby(data['Gender']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "female = x[0]\n",
    "male = x[1]\n",
    "new = pd.DataFrame([female, male])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>2      185\n",
       "3      195\n",
       "9      169\n",
       "11     159\n",
       "12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>0      174\n",
       "1      189\n",
       "4      149\n",
       "5      189\n",
       "6 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0                                                  1\n",
       "0  Female  2      185\n",
       "3      195\n",
       "9      169\n",
       "11     159\n",
       "12...\n",
       "1    Male  0      174\n",
       "1      189\n",
       "4      149\n",
       "5      189\n",
       "6 ..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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