Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_excel('data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Unnamed: 0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13872.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7340.801471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4310.403475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3618.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7398.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11070.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14742.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0\n",
       "count  13872.000000\n",
       "mean    7340.801471\n",
       "std     4310.403475\n",
       "min        1.000000\n",
       "25%     3618.750000\n",
       "50%     7398.500000\n",
       "75%    11070.250000\n",
       "max    14742.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            NaN\n",
       "1            NaN\n",
       "2            NaN\n",
       "3            NaN\n",
       "4            NaN\n",
       "5            NaN\n",
       "6            NaN\n",
       "7            NaN\n",
       "8            NaN\n",
       "9            1.0\n",
       "10           NaN\n",
       "11           2.0\n",
       "12           3.0\n",
       "13           4.0\n",
       "14           5.0\n",
       "15           6.0\n",
       "16           7.0\n",
       "17           8.0\n",
       "18           9.0\n",
       "19          10.0\n",
       "20          11.0\n",
       "21          12.0\n",
       "22          13.0\n",
       "23          14.0\n",
       "24           NaN\n",
       "25          16.0\n",
       "26          17.0\n",
       "27          18.0\n",
       "28          19.0\n",
       "29           NaN\n",
       "          ...   \n",
       "15845    14714.0\n",
       "15846    14715.0\n",
       "15847    14716.0\n",
       "15848    14717.0\n",
       "15849    14718.0\n",
       "15850    14719.0\n",
       "15851    14720.0\n",
       "15852        NaN\n",
       "15853    14722.0\n",
       "15854    14723.0\n",
       "15855    14724.0\n",
       "15856    14725.0\n",
       "15857        NaN\n",
       "15858    14726.0\n",
       "15859    14727.0\n",
       "15860    14728.0\n",
       "15861    14729.0\n",
       "15862    14730.0\n",
       "15863    14731.0\n",
       "15864    14732.0\n",
       "15865    14733.0\n",
       "15866    14734.0\n",
       "15867    14735.0\n",
       "15868    14736.0\n",
       "15869    14737.0\n",
       "15870    14738.0\n",
       "15871        NaN\n",
       "15872    14740.0\n",
       "15873    14741.0\n",
       "15874    14742.0\n",
       "Name: Unnamed: 0, Length: 15875, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Unnamed: 0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dt7j7OqCp8ubKJ05oJiUikoRagtRZwLaix9tDWdlz3D0LHAAmDnBtpfKJwP5QR6W2yhlpZmvM7BkzW1DuBDNbFM5Zs2fPnhqqrE1PLt+7qWxBdLsvsSZERN6yTpXEiXPcfS7wSeAbZnZe6Qnufo+7z3X3ue3t7Yk1HM2k+g9jWokTIiKJqCVI7QDOLno8NZSVPcfMWoBxQNcA11Yq7wLGhzoqtXUCd98R/t0EPAVcVP1lJSOT8xNS0FMp7YIuIpKEWoLUamBWyLprJUqEKM2gWw5cG46vBla4u4fyhSH7bwYwC1hVqc5wzZOhDkKdjw7UOTM7w8zawvEk4APAhhpeVyLKrUlpJiUikoyqQSqsD90APA68DDzk7uvN7DYzK2TS3QtMNLNO4Ebg5nDteuAhoqDxI+B6d89VqjPUdRNwY6hrYqgbM3uvmW0HPgHcbWaF8y8A1pjZC0QB7qvuPmRBqqdMdl9KKegiIoloqX4KuPtjwGMlZbcWHR8nCh7lrr0duL2WOkP5JqLsv9Ly1US3/0rLfwa8q+qLGCSZXJ7Rrf2HUe+TEhFJxqmSODFsyr6ZV7f7REQSoSAVU9n3SWkmJSKSCAWpmCq+mVcxSkQkNgWpmMolTqRTkNdMSkQkNgWpmDI5p6V0xwkzfZ6UiEgCFKRiqrQmpZmUiEh8ClIx5PNONl8+u0+JEyIi8SlIxZDJR7vIlgaplrThRLMsERFpnIJUDJmQwlcapE5ri97cu/dIz5D3SUTkVKIgFUMmW34mdVrYgeLNw91D3icRkVOJglQMhdt5pUFqTJhJdR3WTEpEJA4FqRh6QpAq/aiO3iB1RDMpEZE4FKRiqLYmpZmUiEg8ClIx9N3u6z+MI0ekSKeMNxWkRERiUZCKoaeQOFGy44SZMaathS4lToiIxKIgFUOlxAmA09rSdCkFXUQkFgWpGCqtSUGUPKEUdBGReBSkYhhwJtXaosQJEZGYFKRiqJSCDn0zKddu6CIiDVOQiqHSjhMQpaF3Z/Mc6ckNdbdERE4ZClIxVFuTApThJyISg4JUDANn9xX279O6lIhIoxSkYugZIEiNGamZlIhIXDUFKTObZ2YdZtZpZjeXeb7NzB4Mz680s+lFz90SyjvM7IpqdZrZjFBHZ6izNZR/yMyeNbOsmV1d0v61ZvZq+Lq2/mFozEAzqTGaSYmIxFY1SJlZGrgTuBKYDVxjZrNLTrsO2OfuM4ElwOJw7WxgITAHmAd8y8zSVepcDCwJde0LdQNsBT4DLC3p3wTgC8AlwMXAF8zsjFoHII5C4kSLlUtBTwOaSYmIxFHLTOpioNPdN7l7D7AMmF9yznzg/nD8MHCZmVkoX+bu3e6+GegM9ZWtM1xzaaiDUOcCAHff4u7rgNKPu70C+LG773X3fcCPiQLioBsocaIlnWLsyBbtOiEiEkMtQeosYFvR4+2hrOw57p4FDgATB7i2UvlEYH+oo1JbjfQPM1tkZmvMbM2ePXuqVFmbgdakACaNadOuEyIiMbxlEifc/R53n+vuc9vb2xOps7AmlaoQpCae1qpdJ0REYqglSO0Azi56PDWUlT3HzFqAcUDXANdWKu8Cxoc6KrXVSP8GRSaXpyVlpMqsSQFMHNOqDz4UEYmhliC1GpgVsu5aiRIhlpecsxwoZNVdDazwaD+g5cDCkP03A5gFrKpUZ7jmyVAHoc5Hq/TvceByMzsjJExcHsoGXSbnjEhXHsJJY9o0kxIRiaFqkArrQzcQ/eF/GXjI3deb2W1mdlU47V5gopl1AjcCN4dr1wMPARuAHwHXu3uuUp2hrpuAG0NdE0PdmNl7zWw78AngbjNbH9rYC3yJKPCtBm4LZYOuJ5tnRLr8LApg4pg29h7tIZfX/n0iIo1oqX4KuPtjwGMlZbcWHR8nCh7lrr0duL2WOkP5JqLsv9Ly1US38sq1cR9w34AvYhD05PK0tgw0k2rFHfYe6aF9bNsQ9kxE5NTwlkmcGAyZbH7A230TT4sCk9alREQaoyAVQyZXJUiNaQXQupSISIMUpGKIEicqr0lNCkFK75USEWmMglQMPVVmUhPC7b692nVCRKQhClIxZKokTowbNQKA/UczQ9UlEZFTioJUDNXWpNIp4/SRLew/qpmUiEgjFKRiyGQHXpMCGD+6lf3HNJMSEWmEglQM1dakAM4YPYJ9ut0nItIQBakYMrk8rVWC1LjRrRzQ7T4RkYYoSMVQbU0KopmUbveJiDRGQSqGTM4ZMUB2H8D4USPYpxR0EZGGKEjFUG2DWYgSJw4ez2qTWRGRBihIxVDLmtT40dF7pQ7olp+ISN0UpGKobU0q2hpJ75USEamfglQM1T70EGBcmEkpDV1EpH4KUjH05PKMaBl4TaowkzpwTDMpEZF6KUg1yN1rW5MK+/ftO6KZlIhIvWr6ZF45US7vuDPg7b6lK7dyrCcHwIpXdtOdzQPwyUumDUkfRUROdppJNSiTi1LKq61JtY1IYcDRnuwQ9EpE5NSiINWgnlw0K6r2PqmUGaNa0xwNMyoREamdglSDMiFIDfR5UgWjRihIiYg0QkGqQZnemVT1IRzdmuZYRkFKRKReClINymRrW5MCGN3a0ptAISIitaspSJnZPDPrMLNOM7u5zPNtZvZgeH6lmU0veu6WUN5hZldUq9PMZoQ6OkOdrQO1YWbTzeyYmT0fvu5qdDDqUeuaFEQzKSVOiIjUr2qQMrM0cCdwJTAbuMbMZpecdh2wz91nAkuAxeHa2cBCYA4wD/iWmaWr1LkYWBLq2hfqrthG8Jq7Xxi+PlvXCDSod02qhpmUEidERBpTy0zqYqDT3Te5ew+wDJhfcs584P5w/DBwmZlZKF/m7t3uvhnoDPWVrTNcc2mog1DngiptDIt616S6s3nthC4iUqdagtRZwLaix9tDWdlz3D0LHAAmDnBtpfKJwP5QR2lbldoAmGFmz5nZ02b2wRpeU2y9QaqW7L7W6D3TSp4QEanPqbDjxE5gmrt3mdmvAP9sZnPc/WDxSWa2CFgEMG1a/B0fenoTJ2pYkxqRBuBod5YxbafCkIuIDI1aZlI7gLOLHk8NZWXPMbMWYBzQNcC1lcq7gPGhjtK2yrYRbiV2Abj7WuA14O2lL8Ld73H3ue4+t729vYaXPbB61qRGt0ZBSjMpEZH61BKkVgOzQtZdK1EixPKSc5YD14bjq4EV7u6hfGHIzJsBzAJWVaozXPNkqINQ56MDtWFm7SERAzM7N7SxqfYhaEw9a1KjQpBS8oSISH2q3nty96yZ3QA8DqSB+9x9vZndBqxx9+XAvcADZtYJ7CUKOoTzHgI2AFngenfPAZSrMzR5E7DMzL4MPBfqplIbwIeA28wsA+SBz7r73saHpDb1JU5Ew6wgJSJSn5oWSNz9MeCxkrJbi46PA5+ocO3twO211BnKNxFl/5WWl23D3R8BHqn6IhLWEzaYba3yeVLQd7tP75USEamPdpxoUCZb+0yqrSVFytCuEyIidVKQalA9t/vMTJvMiog0QEGqQfUEKYjWpY4qu09EpC4KUg3qXZOqMUiNak1zTGtSIiJ1UZBqUN+OE7XtzDSmrYWDxxWkRETqoSDVoHoSJwDOHNtG1+FusiG4iYhIdQpSDSrMpFpStc2kJo8bSd5hz+HuweyWiMgpRUGqQT05pzWdotaN2CefPhKAXQcVpEREaqUg1aBMLl/T5rIFk8a0kjZj18Hjg9grEZFTi4JUgzK5fE0f01HQkkoxaWyrgpSISB0UpBoUzaTqG77Jp49UkBIRqYOCVIN6sl7ze6QK3nb6SPYdzXC4W6noIiK1UJBqUL1rUtCXPLFx16HB6JKIyClHQapBmVye1jrWpKAvSHX8QkFKRKQWClIN2n2om/GjWuu6ZvzoEbSmUwpSIiI1UpBqQD7vvLzzILN/6fS6rkuZMfn0toaD1N1Pv8Yzm7oaulZE5GSkINWALV1HONqTqztIQXTLr5E1qd2HjvOVH77CFx5dj7vXfb2IyMlIQaoBG3YeBGD2lMaCVNeRHvYcGnjniZ9s2MUP1u3sffzUK3sA6Nh1iJ9rNiUibxEKUg1Y/8ZBWlLGrMlj6r72l8aPAmDt6/sqnnOkO8ufffcFbnpkXe9Hzj/xyi7edvpIJpzWyt//x5aG+i0icrJRkGrAhjcOMmvyWNpa0nVfO23CaMa2tfD0xt29Zd3ZHFvePNL7+MHV2zhwLHo/1fdf2Mn9P9vCk6/s4ZyJo3n31HH8ZMMuvrmiM5HXIiLSzBSkGrD+jYMN3eoDSKeMD8ycxFMde3rXlv7HD17msr95mqc37iGTy3PvTzdz8fQJzDxzDEtXbWXzm0foyeU5/22nc8mMiZjRVAkUG3cd4gfrdmqtTEQSpyBVp92HjvPm4W7mNJA0UfCRd7Sz88BxNu46zLGeHN97dge5vHPDt5/lGz/ZyI79x/jcR87jmoun8fy2/Ty9cQ8j0sa57acxbtQI3nnWOFZv2UtXE3zsR8cvDvHbd/+c65c+y62PrtfnZYk0iee37efrj3eQz5/c/3lUkKrT+jdC0kSMIPXhd7QD8FTHbh57cSeHurMs+Z1fxoE7n3yNyae38cb+Y7g7LSlj85tHmNk+pnevwEvfcSaZXJ7/+eONNbX3wxd38s0VryY+09nadZRP37uS1nSKT71vGg888zp/+MBajvXkEm1HROqTzzs3PbyObz7ZydJVW4e7O7HUFKTMbJ6ZdZhZp5ndXOb5NjN7MDy/0symFz13SyjvMLMrqtVpZjNCHZ2hztZG2xgMGxIIUlPGjeIdk8fy9MY9PLh6GzMmncaCC8/i0+87h7FtLXzsgsmYGaNbW3jnWeMAOP9tfe2defpI3n/uRL6zaivr3zhQsZ2DxzPc+ODzfO7bz/L1f93Id9dub7jPpXYfPM6n7l1JTy7P//uDS/jygnfxpQXvZEXHbm56ZJ1u/YkMo8de2knHrkOcObaNxT96hd0n8cbWLdVOMLM0cCfwMWA7sNrMlrv7hqLTrgP2uftMM1sILAZ+x8xmAwuBOcAvAT8xs7eHayrVuRhY4u7LzOyuUPff1duGuw/Kf+c3vHGQsyeM4vSRIxquY+nKrZx5ehv/0fkmeYd5c97Gd1Zt4+wJo7n5yvP7fZDiB2dNoutwNxeUBMVLz5/My784xBf/ZQMPLnpf7zVLV27F3Vn/xkEee3EnB49nuOz8Mzl0PMuX/mUDH5w1iSnjRsXq+9GeLP/n3zex72iG6z4wgzVb9rFmyz7SZnzsgsksf+ENsnnnV2dO4pOXTGu4LRGp3dKV0Ywp787fPvEqZ45t43cvOYc7n+rki9/fwJ2ffM8w97AxtcykLgY63X2Tu/cAy4D5JefMB+4Pxw8Dl1n0V3M+sMzdu919M9AZ6itbZ7jm0lAHoc4FDbYxKDbsPMicKeNi1/P2yWPJO6QMLpo2vre89JN+p4wbxec+MpMxbf3/PzGqNc2fXf52Vm3ey3/5x7Ws3NTF7oPHeWH7fv7hZ1tYumorI0ekWfSh87jsgsnccfW7yeTz3PK9F9mx/xgbdx1i85tHOBJ2ZD/Wk2PngWN0He4mm8vj7hzP5Nh7pIejPVncnYPHM7zedYT7f7aFrsM9fPp953D2hNH9+vXht7cze8rp/Oilnax9fS/b9x3tnVXl8t5vhuXuZctK76EXykq/NFuTWrif+LNS7ueu8HNfkM87mZKyTC5PdzbXW1b4PSkuy+byHDqe4Xgm19t24XepUJbPOweOZth96DjHM9H/p7uzOX5x4Dg79h/rPe/A0Qwbdx1iy5tHONaTI5vLs23vUZ7Z1MXGXYc41pPj0PEMa7bs5flt+9m29yjPbd3P7kPdXHr+mbSPbeOPf20mP1i3k19dvIJf+/pTXL/02UEb68FQdSYFnAVsK3q8Hbik0jnunjWzA8DEUP5MybVnheNydU4E9rt7tsz5jbSRqMPdWbZ0HeG3Lopf/TkTRzNyRIpzJ41hbIOzsoXvncaug93848+38JOXd/WWjxqR5jfePYVLZkwknYqC3vRJp3HTvPP54r9s4ANfXdGvnhFpI5PzActaUkY2BI+UwScvPofz2k98n5iZcfWvTOWup1/jkWd38MizO2hNp8iFgATQmk5hBj0lhPcIAAAJ9UlEQVS5PO5gFpU5hD8KURbkiLSR976ycszAiLacSplhRu+/eXfcib5w8h7+YNF3jVnU5+LH7uFagKLjwh+h0usKbRbaif4t/HGMjgltRv0tXN93XNpmob8FqXAuxgl97W2zpP1+bRb1tdBmvrh/xe3X0dfi11jr+OS9f3v54uvCv1jfay70tff7532Pq/0MZHL53p/j1nSKVAoyuaKfxZYURpmfRY/KCvW3taTI5rzf70BrS4rubN/PZjplpFNGT7YveaglVehH3zdzRDr6XSr+mS73O1j8O1f8c1BLHsSZY9t6lwr+8MPn0Z3N88b+Y2TzzrSS/1g2u1qC1CnBzBYBi8LDw2bW0Whdf/JV+JP+RZOANxupqwP4YYP9+N0BnnuF6H7qAOc23GeA2xu9MJ5YfR4m6vPQOBn7DIPU79eBT/9VdFzu78RfNF71OWa2yN3vabyK+tQSpHYAZxc9nhrKyp2z3cxagHFAV5Vry5V3AePNrCXMporPb6SNXmFQB2VgzWyNu88djLoHi/o8NNTnoXEy9hlOzn6b2RoG6W9pObWsSa0GZoWsu1aiJIXlJecsB64Nx1cDKzya+y8HFobMvBnALGBVpTrDNU+GOgh1PtpgGyIicpKrOpMK6z83AI8DaeA+d19vZrcBa9x9OXAv8ICZdQJ7iYIO4byHgA1AFri+kHVXrs7Q5E3AMjP7MvBcqJtG2hARkZObKUMqvqG+R5sE9XloqM9D42TsM5yc/R7qPitIiYhI09K2SCIi0rQUpGKotl3UILV5tpk9aWYbzGy9mf1JKJ9gZj82s1fDv2eEcjOzvw19XGdm7ymq69pw/qtmdm1R+a+Y2Yvhmr8Nb5qu2EYdfU+b2XNm9v3wOLEtsCp9Lyq1UWN/x5vZw2b2ipm9bGbvb/ZxNrP/Fn4uXjKz75jZyGYcZzO7z8x2m9lLRWXDNrYDtVGlz18LPx/rzOyfzGx80XPDviVcuT4XPfdnZuZmNqmZxvkEhXdE66u+L6KEj9eAc4FW4AVg9hC0OwV4TzgeC2wEZgN3ADeH8puBxeH440RvxTLgfcDKUD4B2BT+PSMcnxGeWxXOtXDtlaG8bBt19P1GYCnw/fD4IWBhOL4L+Fw4/iPgrnC8EHgwHM8O49wGzAjjnx7oe1GpjRr7ez/wB+G4FRjfzONM9Cb2zcCootf+mWYcZ+BDwHuAl4rKhm1sK7VRQ58vB1rC8eKi+hIbw3q/T9X6HMrPJkpcex2Y1EzjfMK4D8Yf0rfCF/B+4PGix7cAtwxDPx4l2gOxA5gSyqYAHeH4buCaovM7wvPXAHcXld8dyqYArxSV955XqY0a+zkVeIJo26vvhx/SN4t+wXvHM/zyvD8ct4TzrHSMC+dV+l4M1EYN/R1H9AffSsqbdpzp25VlQhi37wNXNOs4A9Pp/wd/2Ma2UhvV+lzy3G8B3y739yDOGNb7faqlz0Rby/0ysIW+INU041z8pdt9jSu3XdSgbMdUSZj2XwSsBCa7+87w1C+AyeG4Uj8HKt9eppwB2qjFN4C/BAp7xtS8BRZQvAVWPa9loDaqmQHsAf7eoluU/9fMTqOJx9nddwBfB7YCO4nGbS3NPc7FhnNsk/h9/n36NpBJcgzr/T4NyMzmAzvc/YWSp5pynBWkTlJmNgZ4BPhTdz9Y/JxH/0XxwWy/njbM7DeA3e6+djD7lLAWotskf+fuFwFHiG5b9GrCcT6DaMPlGUSfCHAaMG/wejd4mm1sqzGzzxO9T/PbSdQ3WMxsNPDfgVuHqs2446wg1biatmMaDGY2gihAfdvdvxeKd5nZlPD8FGB3lX4OVD61TPlAbVTzAeAqM9tCtOP9pcD/ImyBVaad3r5ZbVtgVSrv3WarTBvVbAe2u/vK8PhhoqDVzOP8UWCzu+9x9wzwPaKxb+ZxLjacY9vw77OZfQb4DeB3wx/kRvo80BjW+30ayHlE/4l5Ifw+TgWeNbO3NdDnoRnnWu5166vs/ecWogXEGfQtgM4ZgnYN+EfgGyXlX6P/QuUd4fjX6b9QuSqUTyBaczkjfG0GJoTnShdDPz5QG3X2/yP0JU58l/4LxX8Ujq+n/0LxQ+F4Dv0XijcRLURX/F5UaqPGvv478I5w/Nfh9TftOBN9ksB6YHSo837gj5t1nDlxTWrYxrZSGzX0eR7RbjftJeclNob1fp+q9bnkuS30rUk1zTj362Mjfyj11fsN/jhRdt1rwOeHqM1fJZo6rwOeD18fJ7pH/QTwKvCToh8iI9oQ/TXgRWBuUV2/T/T5W53Afy4qnwu8FK75Jn1v+i7bRp39/wh9Qerc8EPeGX5B20L5yPC4Mzx/btH1nw/96iBkEg30vajURo19vRBYE8b6n8MvaFOPM/BFok3wXwIeIPoD1nTjDHyHaN0sQzRrvW44x3agNqr0uZNojaXwu3hX0mPYyPdpoD6XPL+FviDVFONc+qUdJ0REpGlpTUpERJqWgpSIiDQtBSkREWlaClIiItK0FKRERKRpKUiJlGFm00t3jjazvzazPx+uPtWilj6a2cfMbG3YvXqtmV06VP0TqVfVj48XkVPOm8BvuvsbZvZOoo1Jh3TfSZFaaSYl0gAze8rMFpvZKjPbaGYfDOWfMbPvmdmPwmfp3FF0zd+Z2RqLPu/pi0XlW8zsK2b2fHj+PWb2uJm9ZmafLTrvL8xsdfgcnuLrPx/68FPgHdX67u7Pufsb4eF6YJSZtSUwLCKJ00xKpHEt7n6xmX0c+ALR3nkQ7VRxEdANdJjZ/3b3bUS7C+w1szTwhJm9293XhWu2uvuFZrYE+AeiPfdGEr2b/y4zuxyYBVxM9K795Wb2IaKNbxeGNluAZ4l2PqcQ4Nz9rgFew38CnnX37gTGQyRxClIi5VXaiqW4vLC571qi/dEKnnD3AwBmtgE4h2jrnN82s0VEv3dTiD60rhCklod/XwTGuPsh4JCZdVv0aa+Xh6/nwnljiILWWOCf3P1oaK9QT7XghJnNIfqgvssHOk9kOClIiZTXRbRXX7HCRpsFhdlHjv6/S8WzkhzQYmYzgD8H3uvu+8zsH4hmSqXX5Euuz4e6DfiKu99d3CEz+9NaX1DJdVOBfwJ+z91fa6QOkaGgNSmRMtz9MLCzkPlmZhOIdrz+aYNVnk50a+6AmU0Grqzz+seB3w+fI4aZnWVmZwL/Biwws1FmNhb4zWoVhZnZD4h2qf6POvshMqQ0kxKp7PeAO83sb8LjLzY663D3F8zsOaIdyrcBdQUHd/9XM7sA+LmZARwGPuXuz5rZg0Qf2bAbWF24ZoA1qRuAmcCtZlb48LvL3b3Wz60SGTLaBV1ERJqWbveJiEjTUpASEZGmpSAlIiJNS0FKRESaloKUiIg0LQUpERFpWgpSIiLStBSkRESkaf1/Upu+NYwLAXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "sns.distplot(data['Unnamed: 2'].dropna())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "dts = data['Unnamed: 2'].dropna()\n",
    "dts = dts.drop(dts[dts>100000].index)\n",
    "sns.distplot(dts)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xlrd import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "wb = open_workbook('data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<xlrd.sheet.Sheet object at 0x7f5dd53b0dd8>]\n",
      "15876\n"
     ]
    }
   ],
   "source": [
    "print(wb.sheets())\n",
    "print(wb.sheets()[0].nrows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions = ['Российская Федерация', 'Центральный федеральный округ']\n",
    "education = ['послевузовское', 'высшее', 'неполное высшее', 'среднее', 'начальное', 'без образования', 'неграмотные']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_name_to_id = {\n",
    "    'Белгородская область': 36,\n",
    "    'Владимирская область': 37,\n",
    "    'Ивановская область': 41,\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Мужчины и женщины в возрасте 15 лет\n",
      "и более\n",
      "Мужчины и женщины в возрасте 15 лет\n",
      "и более\n",
      "Мужчины и женщины в возрасте 15 лет\n",
      "и более\n"
     ]
    }
   ],
   "source": [
    "sheet = wb.sheets()[0]\n",
    "for i in range(sheet.nrows):\n",
    "    if sheet.cell(i,1).value in region_name_to_id:\n",
    "        postgrad = sheet.cell(i+2,4).value\n",
    "        higher  =  sheet.cell(i+2,5).value\n",
    "        higherip = sheet.cell(i+2,9).value\n",
    "        middle = sheet.cell(i+2,10).value\n",
    "        basic = sheet.cell(i+2,11).value\n",
    "        noeducation = sheet.cell(i+2,15).value\n",
    "        nothing = sheet.cell(i+2, 16).value\n",
    "        \n",
    "        col = 10\n",
    "        print(sheet.cell(i+118, 1).value)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}