ExJour2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"Visualisation d’un nuage de points et exploration des variables\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./data/house_pricing.csv\", sep=\",\")\n",
    "print(\"Dataset loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id                 int64\n",
      "MSSubClass         int64\n",
      "MSZoning          object\n",
      "LotFrontage      float64\n",
      "LotArea            int64\n",
      "                  ...   \n",
      "MoSold             int64\n",
      "YrSold             int64\n",
      "SaleType          object\n",
      "SaleCondition     object\n",
      "SalePrice          int64\n",
      "Length: 81, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(dataset.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126c07410>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dfprice = dataset[\"SalePrice\"]\n",
    "dfarea = dataset[\"LotArea\"]\n",
    "\n",
    "sns.scatterplot(dfprice, dfarea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a56f1d0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = dataset[(dataset['LotArea'] < 20000) & (df['SalePrice'] < 500000)]\n",
    "dfprice = df['SalePrice']\n",
    "dfarea = df['LotArea']\n",
    "\n",
    "sns.scatterplot(dfarea, dfprice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12a0d5910>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dfprice = dataset[\"LotFrontage\"]\n",
    "dfarea = dataset[\"LotArea\"]\n",
    "\n",
    "sns.scatterplot(dfprice, dfarea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x109aad9d0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = dataset[(dataset['LotFrontage'] < 200) & (df['LotArea'] < 100000)]\n",
    "dffrontage = df['LotFrontage']\n",
    "dfarea = df['LotArea']\n",
    "\n",
    "sns.scatterplot(dffrontage, dfarea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"Visualisation de relations continues\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./data/sales_predictions.csv\", sep=\",\")\n",
    "print(\"Dataset loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.935849e+06</td>\n",
       "      <td>2.935849e+06</td>\n",
       "      <td>2.935849e+06</td>\n",
       "      <td>2.935849e+06</td>\n",
       "      <td>2.935849e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.456991e+01</td>\n",
       "      <td>3.300173e+01</td>\n",
       "      <td>1.019723e+04</td>\n",
       "      <td>8.908532e+02</td>\n",
       "      <td>1.242641e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.422988e+00</td>\n",
       "      <td>1.622697e+01</td>\n",
       "      <td>6.324297e+03</td>\n",
       "      <td>1.729800e+03</td>\n",
       "      <td>2.618834e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-2.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.200000e+01</td>\n",
       "      <td>4.476000e+03</td>\n",
       "      <td>2.490000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>9.343000e+03</td>\n",
       "      <td>3.990000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>1.568400e+04</td>\n",
       "      <td>9.990000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.300000e+01</td>\n",
       "      <td>5.900000e+01</td>\n",
       "      <td>2.216900e+04</td>\n",
       "      <td>3.079800e+05</td>\n",
       "      <td>2.169000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       date_block_num       shop_id       item_id    item_price  item_cnt_day\n",
       "count    2.935849e+06  2.935849e+06  2.935849e+06  2.935849e+06  2.935849e+06\n",
       "mean     1.456991e+01  3.300173e+01  1.019723e+04  8.908532e+02  1.242641e+00\n",
       "std      9.422988e+00  1.622697e+01  6.324297e+03  1.729800e+03  2.618834e+00\n",
       "min      0.000000e+00  0.000000e+00  0.000000e+00 -1.000000e+00 -2.200000e+01\n",
       "25%      7.000000e+00  2.200000e+01  4.476000e+03  2.490000e+02  1.000000e+00\n",
       "50%      1.400000e+01  3.100000e+01  9.343000e+03  3.990000e+02  1.000000e+00\n",
       "75%      2.300000e+01  4.700000e+01  1.568400e+04  9.990000e+02  1.000000e+00\n",
       "max      3.300000e+01  5.900000e+01  2.216900e+04  3.079800e+05  2.169000e+03"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hlOqbei3zqict4l+JyBXAU0AyV2iMeW2P10oppfYiPQnEhwPzcHJ3c10Txv1bKaXUbupJID4JGKmLwSul1J7Vk/S1t4Dy3qqIUkrtrXrSIi4H3hGRVynsI/76Hq+VUkrtRXoSiK/otVoopdRerCdfHvqCiOwHjDLG/K+IFOPk9SqllPocejKz7lyclcyWukVDcSZ5KKWU+hx6Mlh3IXA00AxgjHkfqOqNSiml1N6kJ4E4mZ+65i6mo+s3KKXU59STQPyCiPwAiIrIv+GstvZI71RLKaX2Hj0JxJfjfGHomzgL9TxmjPlhr9RKKaX2Ij1JX7vYGPMrnDWEARCRS9wypZRSu6knLeIzOyk7aw/VQyml9lqf2SIWkdOAbwEj8heCB8rY9S8GVUop1YVd6Zp4Cee75QZR+P1yLcDa3qiUUkrtTT4zEBtjPgY+Bib1fnWUUmrvsytdE381xnxFRFoozBsWwBhj+vVa7ZRSai+wKy3ir7i/y3q/OkoptffpSdaEUkqpXqCBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfKaBWCmlfNargVhE7hCRbSLyVl7ZQBF5WkTed38PcMtFRG4QkQ0islZExuXtc6a7/fsicmZeeY2IvOnuc4OISHePoZRSfVFvt4iXAce3K7sceMYYMwp4xv0b4ARglPuzALgZnKAKXAEcBUwArsgLrDcD5+btd/xnPIZSSvU5vRqIjTF/BhraFc8C7nJv3wV8I6/8buNYCZSLyBDgOOBpY0yDMaYReBo43r2vnzFmpTHGAHe3O1Znj6GUUn2OH33Eg40xW9zbnwKD3dtDgU1529W6Zd2V13ZS3t1jdCAiC0RktYisrqur242no5RSn4+vg3VuS9b4+RjGmFuNMeONMeMrKyt7sypKKdUpPwLxVrdbAff3Nrd8MzA8b7thbll35cM6Ke/uMZRSqs/xIxA/DOQyH84EHsorP8PNnpgI7HC7F54EZojIAHeQbgbwpHtfs4hMdLMlzmh3rM4eQyml+pxgbx5cRH4HTAEGiUgtTvbD1cByETkb+Bg42d38MeCrwAYgBswHMMY0iMhVwKvudj82xuQGAC/AycyIAo+7P3TzGEop1eeI04WqAMaPH29Wr17tdzWUUn2T9NaBdWadUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5TAOxUkr5LOh3BVTfZtuG+rYUqUyWcDBARUkYyxK/q6XUPxUNxKpLtm14d2sL5969mtrGOMMGRLntjPEcPLhMg7FSe5B2Tagu1belvCAMUNsY59y7V1PflvK5Zkr9c9FArLqUymS9IJxT2xgnlcn6VCOl/jlpIFZdCgcDDBsQLSgbNiBKOBjwqUZK/XPSQKy6VFES5rYzxnvBONdHXFES9rlmSv1z0cE61SXLEg4eXMYfLzhasyaU6kUaiFW3LEuoLIv4XQ2l/qn51jUhIh+JyJsi8oaIrHbLBorI0yLyvvt7gFsuInKDiGwQkbUiMi7vOGe6278vImfmlde4x9/g7qvNuL2EbRvqWpJsboxR15LEto3fVVKqW373EU81xowxxox3/74ceMYYMwp4xv0b4ARglPuzALgZnMANXAEcBUwArsgFb3ebc/P2O773n47yWy73+cSbXuToa57jxJte5N2tLRqMXXqS6pv8DsTtzQLucm/fBXwjr/xu41gJlIvIEOA44GljTIMxphF4Gjjeva+fMWalMcYAd+cdS/0T09znrulJqu/yMxAb4CkRWSMiC9yywcaYLe7tT4HB7u2hwKa8fWvdsu7Kazsp70BEFojIahFZXVdX93mej+oDNPe5a3qS6rv8DMRfMcaMw+l2uFBEjsm/023J9vqp2hhzqzFmvDFmfGVlZW8/nOplmvvcNT1J9V2+BWJjzGb39zbgjzh9vFvdbgXc39vczTcDw/N2H+aWdVc+rJNy9U9Oc5+7piepvsuXQCwiJSJSlrsNzADeAh4GcpkPZwIPubcfBs5wsycmAjvcLowngRkiMsAdpJsBPOne1ywiE91siTPyjqX+ieXnPr+4eCp/vOBoXaTIpSepvsuvPOLBwB/djLIg8FtjzBMi8iqwXETOBj4GTna3fwz4KrABiAHzAYwxDSJyFfCqu92PjTEN7u0LgGVAFHjc/VF7Ac197tzeNEHHtg3NiRRtySwZ2xAKWFSWhGlKZPrkcxenK1YBjB8/3qxevdrvavzD0rWLVV9g24bNTTGaYmnOv+81bwnXm+fWsGbjdoYMKKGiJExVWYR9+0cJBne5Y6DX3sx9LX1N/YPS1KgvluYDd62+LUXWxgvC4AxKnn/vGv710CGEAxZZ2/BBXRsfNbT1iddOpzirPaKr1Kg/XnC0dhPsYbpgf/dSmSwidJohks7aLHnoLe91u27OaAaWhBlY4u97VFvEao/Q1KgvjuYDdy8cDBCwpNMMke0tKZbMrOaBBRNZMrOaO1/cSDzl/3tUW8SqSz3p882lRuUH495OjdqdPuncPrZtkzVgjPmH68/Wk173KkrC1Lcluen0cVyQ10e8bP6R1LemuOrR9V7ZNbNH94n/uwZi1ameXv7mUqPab59LjdrTA3m7c3me2+cXT7/LmZNHsPjBtf+Ql/Z76qS3K/+Tf8QBWMsSyotCxNJZls2fgCVgGygOWZz1+78VXEksfnAty8+b5HONNWuigGZN7FTXkuTEm17s8GHvrs+3qw9tb/Rp7k79cvssmVnttYp2dd++pP3rOaO6iv/z79UELOnRlcFH29v4uCFGcThALJVl+MAo/YpCGGOwLIsB0RB/b4yxrSXJoNIwAUsoClpUlhZ5mQa2bWiKp4insmSNoSgUYFBJ5HOfZHcl+H/WdpmMTUsyRSxlk3EH5I697vkOx/nrZVMZNrB4V6rWa2cgbRGrTu3O5W9X+bu9MZC3O/XL7VMeDf1DX9rn5wPbts32thTf+s0rPTrJNcaSbG1JFAxc3fitsRSHAk7ebdBQ32ZT35rke24rMje41ZbKMnJQKQAf1bextTnBohWdX130tEW9qyftXdnOtm1qm5Kcf+8aahvj3HnWkZ1eSdhAKpUhHPYvHGog3gN25832RV/u9fQxo+EAd551JMXhAE3xNLc8/wF1rcnd6vPtjT7N3bk8z+3TFE9/4f3Ze1rupFfXkuS8e9b0+CQXT9te8ASoLI0QS2W58pF1XH7Cl8mmDUHL4ra/fFhw7EUr1nLVrMPoH3W6nD6uj3nBvP3jV5SEdzmo5t6bIrJLJ+3cyb2yNMKN3xrL4H5FhIPClh1xMrahKGiRtg3n37uGytIIS2ZWU14c4v4FR1HbmECAWCpLVVmYrTsSBASGaiD+x9XTy24/Uo92p45bm5Md0nwG9yvaremwvTGQ91l90t3t84un3+Wa2aM79BH/I0713dWTXC7YJTNZBMjapmC/hVMO4M4XN3LB1AM5445VBYNZdS0pXt/U5B27OBwglckSCVqMGFTS5eN3dyXkDKilvBZ97mSyYuGkXXo+qUyWytIIt59VQyxlk8zYJDPw0z+tp64lxU2njyOdtaksjfC94w5m8YNrqSyNcNnxBxe03m8+fRzlxUFsn7toNX3tc+ppKpEfqUd7oo6LVqyltCi4WyeL3ljjYHfWlMjt89MTR3NgZQnLz5vU4/UoPs9Eit6YhPFZC/nYtqGhLcnbW5o58aYX+co1z3HKrSsJtUvvKo+GmF0znIt++3qHwayFUw4oOHYslaUkEqAxniZjmy4fv7OTxOSRFRjbZnNTnB3xNLF01gvCY4eXM6g0sksLE4WDAW6aO5ZPmpKceutKjr3ueU7/zSt8Z/pB/OasGrLGJhSwWHzCId4Jd+GUAwquAmob45x/32sUhYIEfP4CH20Rf049vezuaQtmT3Rf7Kk6pjP2bj1+b61xsDtrSnyedSg+z9VMb10JdXdlkMnYvLuthXTG5qLfFQZY2xiWzT+STQ1OC7eiNEJZUbDT/3tFSZiTa4ax4NgDiIYtMJBI22xvSXLnixsLri5mVFfxw3+vJpnJYolw3r/sz7j9K9i3fxFlRSGCAfi0Jemlla1YOIk7zqyhvDiCMYasMfz2nKOwLCEStCgOQSrrdKV8XN9GJGhhGxAMICzM73qIhqhrSVJeHCJrQzQsDO4X8Z5TV2MDtjvI6CcNxJ9TTy+7d2X7Pf2h7Y069pRlCWWhAG0YEpksmxpjhCyhpMgikTKURoQdcZu0bQi4H8J0xhntDlhCRTRMUZG/b9fPM+jYWzMPbdswoDjE7xdOxLYhYxuCltCSSJI1sP/ACE1xm2XzjyQgwqfNCVZvbCAQEJKJnbPMZlRXceXXDi34v48dXs53po+iqizChdMO5OPtLRxQ1Q9LnK6NoeVRrvjaoYQs4bfnHOWNJ3xY18ag0jDRcJDTjtqf4rBFXWuKube/0iFjZWRlMQ1taepaEiQyNpfc/4ZXn+tOOpzmpE19a9oLuJcdfzB3vriRn554GLFUYddD7rOy/LyJhIIWmYzho+0x7zl1NTZgiZDczUbGnqLpa3l2J32tN/qIdyc1K3fs/EGPgOClIb1f11rwmEvn1TC0vIi2ZMcWamd1XDqvhoOrygoWSOlJqz2RyLA1lmRHLF2QZH/L3BpGVETYWJ9koTu6PWxAlJtOH8f/PPs+T63f5i3YMrIiQlCCRCLBTusQCloELSGe6p1B0M2NMY6+5rkO5S8unsrQAd2nP9U2xPjKtR33ffWH0wgFhFQGggFIpJyTUdASgpYgAsY4ebCprE3AEorDFomUTf+oRUMsiwGa45mC1++RiyYTDsD2tixNsTTlxSEsEWxjKA0HaE1lOd3NtMhZ+f1pbG6Kc8n9b3ToT71y5iHUjBjEo2/UcsqE/WhoS1HfluLBNZuYf/QIVm9sYN7kL9GatMm6g2WprCFj24QDFj96ZB3zjx7B8IHFJNJZ76TwpYHFvLe1FaBg0G/pvBoO27cfBjj11pXUNsZZOq+GB9dsYtFxhzj91Fmbj7YXDhYCrP7hdBrjacIBi0vvf6PbPuJb5tbw3NtbmTV2KF+qKPmst4Cmr/VVPb3s3pXtdyfLoLPgec3s0dz10kb+498O5sBBJfz2nKPY1pKkvi3Fr/73PeYfPYJrn3iXutZkwcnAsoRRlaUdtv+Pfzu4IC2pJyeg+niKdMZ4QTj3nBbeu4YHFkz0gkiu/IL7XmPJzGqeWr/N6cu7dw33L5hINJwBIBIJdlqH6+aM7vQ57YrPOrHs7pVCKpXBsuiQhTJh/3IsS9jemiZgQVvSLgimt59ZgyVCLGVz4W9f81qKF08/iH3LwySzkDWGgFgdXr9wAD6qT3Ke25L8wVcP4T+WO2loLy6e6m2XzxLoVxTiqlmHMWJQCXNv3xmop1UP4SePruPCqaM6DOY9/85WvjP9AD6sT3pZCvkB78lL/4Xvf/UQ2pK2F1RzJ9eMbSgOByiOFHaLlEdDiEAqY3vl+/Yv4szJI5i/7FXuO+comuNp9h9U3OF5ZG3D9pYkibRNXWuSnz35rtd1YYlw97cnUNeSJJbKUhwO8MCaWmbXDMNPGoj3gM76Hbub3NDVhz13H9DpJWLWOIM9ne2TymQ7XPoufnAtS2ZWc+7dq1l+3iQv1zRn/ZYWlsys5rx71nS4TG6MpzvdPrdNTy+1M7bB6mIhlky7EfxceXk0VPB3KmMjQCKVYmgk2OWgYlfPqTu7cmLZnUwNgKZEhi07Eh2yUA6sKiWRstnUEOfAqlIW3vtqwXMJWAE2NRS2+GbXDOf8e9fw/y6YjMEJVOGAFPSTNsXTNMVtfvXMe/z6tDHsV1FMW9Lm3nOOwhKhf9RiR9zucFJJZw3zlzl1eOa7xxbcZxvD7Jrh3gkhV0SsNh8AACAASURBVMfFD67lzrOOpClue/m6S2ZWFwyKDSgJEwpAKGBz3zlHEbCEl96v43z3JGyJUB4NFdSnKZ5mmIkWdC0UhQLeimpFQYtUxmZrc7Lj87ANi1Y4LeBc//V596zxThzfXf43Lwvkue8dy82nj+vFtu6u0UDcC7r6UI+qLO3QRZD7sAPePpWlEa6bM9p7M7W/nOpsn+tPOqLLYFbb6Kw61V2wa9/i/qxWeU9b7UFLSGdNpy3KoDuC3768KZ4u+DsUcIZojHtC6qoO+/Yv4t6zJ7BP/yKSmSxNsSSprCGdsRERggFIZwxZ2xAMWFSVRmiMpz/zxLK7g46p7M6+z9yxF61YywMLJnotwqzpeDKyBIrDgQ4txdrGOMmMTSRoEQ0FCAasgvfIjOoqDh96KJf860EUhy02501qGDYgyvIFEzEY7z2WK8+dEMcOL+/wP7FEqCgJd/p6F4WsgpNp+0GxoiB83FBYh5vn1vCzOYcTCgr79C+iOZHm+pOO4Lvu5JEH12yiekg1NzzzPr88ZQyXPvAGWdv2TjYG+I/lf6OyNFKw37ABUS81r7Yx7rWGD6gsYVOD83cuCA8b4AT6aDjAQJ97aDUQ94KuWovLz5vU5Ycd8O6rbYxz7RPvctWswxg1uNS7nOtun64GInLloYDVbbBrf4n9WZfhPb1MLyuyyBjDLXNrCi6/r5k9mvKo1aE810ecO+6y+UeSzNjeKH8slWXC/v1Y83+mk0jb3iBVcdgZVa8oCdM/ahFP431LQ5E74h5P2QwqCZDIQCZr+LQlQTggXmAcVBKgLeVc4sZSGWobswQtwRKhJOycaA0QS2VIZ7NEAs7kgbKI0JwwpLK2V5dYysa2DZNHVnDuMSMJWELWNjzxpvNl5UFLiIYDFAUtfn/eJKr6OUE/YAkC7FteVPA6F4Usnv3usYjA1man6+Hqbx7O5X94s6DVjIFtzUkOrCrl/HYt7bRtSGdtrn3i3YJWdC74LpxyAL9d+VHBojnPrt/C9Oohnf7PRYSg7LyKa/9ebEnsbC3n6nD+vWu475yjSGUMxj0JPbhmE3d/ewIAoYCFJVBZFqZ/NMj9C45iRyzjDfS9sGgKtY1xKksjREIWPzvpCAb3i/DR9ljBSeT1TU2cd88aZlRX8Z3pB1HXmvTqfdPp47j35Y85f+qBRHZ9cfheoXnEvaDL9K8uWqWpTLbDPq9vamL+slc7JN53tc8tz3/ANbNHF+TqXjN7NA+u2cRtZ4ynqjRSkMs7o7qKu789gaqyCHeedSR3f3tCwSX2Z+X+9jQ3uDGWZUtjjIqSUMEyhD978l3aUlASCXDP2RN4YdEU7l8wkSH9i7jya4fy/KIpXDXrMFoSaepanEkmp9y6knH79aM+lmVzU4JT3DzSU25dyeamJI2xNP2jFttjWbbscO6/9P43+HtDjJOWvsygkgD1MWfCwYfb27jnpY182pzklFtXMqgkwJbmNJub4py09GWOve55Tr11JRu3t1EShk+a03y4vc3LXT156Uq2NCcpjggfNSS9fe5+aSO1Tc4xS8IB5k7aj/nLXmXa9S8wf9mrTPnyYH70yDqKIxbF4QD1rUkyts3pv3nFe8zyqEUgINw8t4ZhA6KcXDMMS4Qz7liFMXj9v/v0L+rQak5309IOWML21hR1rc6svFNuXcl596zBsmDp3BoqSsKM27+C/3n2fe9/NWRACZGgxXVzCt9jN50+jkzWxjaGm04f5wyAPf9BwXZddT3VtSRJpLNYlvDgmk2c/ZWRXP3422zZkeC021YCcOXXD2V7a4r3t7ZxXl4wD+WdNC767evsiKeZd/sq5i9zvjUtV5dcPb8z/SAeeaO24L33P8++zwmHDyESFIzPfRPaIu4FXbUWu2qV5lqRn2ef1zc18bMnnVb0gVWlBCwna+KnJ472Lp/br0+QP+hy2xnjC57DZ12G9/QyPWgJd7z4Ef/3a4dSURImnbUpCQe4/IRDSGVt5t2+quA5zqiu4v9+7VC2tyRJZW0GFEcKBo9aEzapdoN/laXOlN/hA4tpitukM8brU1wys9obrGqKO/tubnT6be8860ivb7Qp7rS620/bzXUl1HZyX27A8Vf/+57XwhxSHuVbtzlXMomMzQX3vVbQj7u9Jcn8o0cQS9rEUlnKioJEQkHuO+coLIFU1tAUt4mnbdLpNP/vgskkMzanuFdHWWO81K1NDc7/sLI0wsIpB1DhLtATSznZCe3fPyFLqCgJ8YuTj/Bek2EDorQlswwbGKEl4aRyPbV+G0+t38bY4eVc+fVqEulsh1b0FQ+t4+enHEHWwL0vf8ydZx3pvPcs4ZenjKGqX6TTOgwbEKW+LUUinWX/QcXMP3oEz779KYtP+DJBy7k6ASdbZNGKtR263oKWsHRuDfF05+uHBCwpqKcxhqV/+Qj+8lHB+/L7X60m4Ha7+EkDcS/oalAn1yrtarDn8+5T15pkn/5FDC2PdhoQe7o+wWdNfujJ5IiisMXF0w/i1FtXev3eucvp3GUmOAOTlx1/MKWRYMEI+73nHFXwQWs/+Dd2eHlBPukLi6YU3J//Qc3tm+t/DVhScF/7ftnca9TdfZZFwdKaz+YNdmVs02m+682njyMUFAIi3okoP/Pjl6eOoaI0SCgY5ZMdCUrzMguClvCd6aO8tKybTx9HazLj9fm++sPpVJWFydhZbp5bU9A/a4CSSJCmWIZ7vj2BrDF8uiPBtU+8w7VzDicatqgsi3jB/XvHHUxZUYgP6tq8VnROro9fRHjpw3qWr6n17sudTEMB6bA28C1za7jhmfc4+ysj2dQQZ2BJiH8/Yihn5jUO7j1nAoJ02t0Ry9gMLA2RyYYKuuBqG+OIQDQUKMhXXjqvpvMGTUDIGuP70p4aiHtBd63F7lqRe3Kf7nzeRXi6yleuKAmTSmWpj6fI2IZoKEAmuzMvtqtR9dxlZu5Dn0jbLFqxs6X75CWT2RG3WfWDaWSyxhnhNhQM/i2ccoAX5KDj4GD+BzV3XyyV9QZ38u/Llbf/0HZ3n23jPf7Y4eVEwwH+vGgKhsKgWdBPet9rPLBgYsEld671vWRmtfN4SdtL+2u/elgudau2MU5JJFjwHW1Z2yYSCtCSyDJsQJHX/x10+54tcRZ2mpcX+G781ljakk5r+Lm3P+WWuTXUtSS566WN/ODfq3n8zS0d+vJvnltDWyrDgGi4YPBvRnUVF00bxam3rmT5gon86W+bvdZy1ja8s6WJi6cfxPaWJL9b9TE/+Go1Z9/1SsHrMPc3q1ixcJLX3ZE/g88CtjWnuG/lx9z4rXHc+Nz73v3GwJ/f3cq9Zx9FWypDOGBhdzI+cf1JR/CjR9ZxyfSDqCzJEvJxdp0G4l5iWVKwsMmnzYluvw0iF9ycvABnICiVyRIKCJGQOKlbAulMltrGGJYlhNxLwHTGJmsMho5Dv52ly32emXPd5Sv/4uTDvYkZ7bM9/nLZzlZv+8vI7a0J70O/+MHCy9B1V07nw/okj75Ry8wxw/j1M+9x5uQRjKoqJZDNeh+u9scsjzqDgzefPo7z73uNW57/wLsUL49aZI1h6IAirpszmhWr/+612MqjFsMHRvn1aWO52J0WnGulRsMWw9x9ChaOmVvj9eWPHV7Oj2cdykfb27xtVv1gWqf5rrlWdmfl5dEQRSGL1mTWa9nf9ucPvdatbaCo3f+x/XHCQcvNQ85S35r2Us/+ctkxJDJQURryAnTIbdUGA06K5fgRg3jkjVpOO2p/ZtcMxxI4cdxQHnmjlvsXTKSuJUlpJEhpUZB3trQwqCTCoLIIV806jOJwgMqyiNf1FQlafG3MMK/7JxcEB/dzBuIumX4QiS4aB3UtSZbOq+G8e9Z4XW8jKp3+6vLiEC99WA/A5Sd8maKQxQMLJlIctqgZMYj/emx9wVXKjOoq7jvnKLK2wRLhpuc28NT6bazf0sIDCyZ+5qSc3qSBeDckEhmak2nStvGm4IYDljdXPhSwSGdt0tn8GVIGYyBomYKl+mwDBkNpkUU6a8i4s6dCAUEQSsLCh/VJfu1exuWn6dz4rbEELKvgLN9+Ldiu0uh2Jx8WOs8IyeUr74jvnJDQvtVr5fUTtr/M3NKc4tAhZd5aB+msnbetM+Ke68ddMrOaxQ+u5bfnHMXmxgSRYIBl8ycQCRb2pb/1SRsDioMM6b+zNZjLTEhkoKI4QCIToH9RiOGTR3hZE9vbsgzpF6JfUYjfnzeJdNbGcv+HLYkssZTTp3n/golkbYNtDI++8QnfrBnmDR5tb00V9CNbIgStzvv6u0vdS6Ttgpb98jW1XPpvo1g2fwLFYYtIAH53rpOK1r4f1jZCUyzlLPg+IOoF4bHDy1m/pY1JI8tpSTgn96AlpG1DNCRsbd55Iv3O9FHO9PKSMNuanXUlzv7KSLY0JcjYNhf/7nV+fdpYisMBEuks/aNBooNLMcYUnGBaklmee9tpoW5vdSYI3f7XD/nujINIZgzJtM3QdtkhudehNZmhsizCvWcfhQG2tyRpiacp7h8hGrZYNv9I2pKZgjWR/7p4Kuffu8Z7r+SOmQu6uWnWS2ZWs3xNrXdC9JNmTfRQIpGhtjnOxw0xb7T+1FtX8kFdKy3JNG3JDLWNcU5eunMk/+8NMbY2J/lgW7M3kn7p/W/w4fY2Tlr6MkUhi7/XJzl56csc4+6zcXuMJ9/6xAtEs2uGe0EYnADY0Jbmhmfe4+pvHs7//ucx/OH8SfSPhtjUGKO2McanO+LEkykeXDiJvy6eym/POYp+RUEaYkmGDQizYuEk/nzZFB5YMJH+0SDbWxJ80hTn4/o2NjfGqG9N8EljjHT6s/OLcyP1XbV6ReAWd/S//ah6RUmYRMb2gnXAEu/+3Ac614+bO27GdhZq+a/H3sYYm1gqXTBSHktl+fZda2iMpb3/01H//SyTrn6WWTe+RO77Im3jpKmlsk6rMBrKpbXZBCxn0Ce3tkLQEoIBiw/rYl7WxLzbV3HMwVVY7vOrKAl36EdOZmyuenRdh6yWm04fRzRseVkRufLr5ozmluc/oF/UoixqEQ6K99x++fT7xFIZAgIfNSR54+/1FAUDhIOFxwlaUF4cYtiAItLZna317x13MFc9up6s7XQJNbTtfH3SWVNwNbN/RTHprM0+/YsIBSzmHz2CSNCiql+EoGVx9TcPpzwaIpbK8v62NjIZ4/XrZ23nuYwdXk7AEt78ZAdbdjgLCB1YVcpVsw4jFAgwoDjEfhVRYulsh4yMa2aPJhSwaGxLYzDMu/0VGmIptremSKYN37zpZaKhAA1t6YKTfu7qpKtFfg6oLKGyNOLl0OdOiH7StSby7MpaE5sbY3zSlKA1mWFQaZhid90DSyDiLv039/ZVTB5ZwYXTDvTm9xcFLUJBIelOJMjahg/r2nirtolv1gzzRsNzhg2I8ttzJ2KM4djrnueBBRM55daVBXV55KKjiaWy3P7XD7lw6oEdWsdPXjKZxrjtXYrZxpnQUBIJEA7gLLLjzteva45TPbSchfeu4clLJpPMOvm2uZakiGCMIRJy+y1tQyRoEQqIl8cbsMT7ID504WQGlISxRLwgFrKEdVtaKA4HsI3zGhSFAgzuV8TAYou2FCQyzkh/rj8T4O6XNvLtr4xkY32MffoVURQSApZzBdKcyBIOOF004n43WdgSECgJ71xIyFlD16lnSThA2rZpaEt7g5a5AHhAVQlZ2xCyLLY0O10ipx61H/WtKb68TzGNcbsgrzv3v1q+YCKW23rdsK21oEX8wqIpTqpbzTAvl9gAZZEA8bTNjc9u4OLpo7w1ccNBi093JIiGLAb3jxBP2dS1JBncr4iM2woPiPD0ui185aAq6lqSLFqxlskjK1hw7AGEAkJJOMCWHQkSaZtBZWHm3b7Kawn+bM7hlBaFvXTAXJD+5aljuPT+N7ji69XEU9mC7pffL5xEfWuqQ7dTZWmEK79ezZcGRmlL2TS2pYiGA2xvTTG0vIjGWJqmtgQDSqP8+pn33PUmojTFMt6xbjhtLKfd5gziXjtnNJYIf2+I8fibW7j030bRlsxgWRbTr3/By6aoKosw9foXePo/jyEoQlM8TWkkSDjoXH2ccuvKLr8S66pZh1EUctqgi1as5Za5NYwaVFKwhkkXei1aayDOsyuBeMuOGG3JLNtbU1SWRfh7fYwbnnmfutYkf/rOZBraMtz83Aece8wImmJpLz1o3ZXTSWQhmdq5wlhx2KI1aXvBtr0XFk0haDkfuH89dAg/fmQds2uGeyk5Bw0upao0SFvKmW0WS2e9wZCK0iB/r09ywzPvMbtmOBUlYSrLnMu5SEDYkchi2xCwnEtnEQiIUBoRGmJZEuksTTHnslAESsIBikLO+gW5D9DPTxldsD7Ca0ums7kxwaN/28yc8cMLnv/aK6ezrTntBY3cB/wXJx9B9ZASPm1OY3CWV2x/MmlO2mSykM46a8wGLGeCyEf1hbO1cn3V848eweihpV5/9eSRFcybtJ83mLXqB9NIZU2nAfXRiyfTnHCWcDTG0BzPeINpLy6eim0M/3Jtx//Vqz+cTsZ2uqPAmSiSWxjnsH378aNH1nX4wtLr5ozmSwOLufuljcw8YqjXpZELIL9fONEdAzD86BFnnYdcF8MLi6YgInzQLujnnscDCybyo0fWsWTmoVz1qPPYkaDFnFte5sXFUznl1pXcfPo4PtmRoDwaYmBJGBFhU0OMAcWhgmUzAf68aIo35X3pvJqCAHdyzTDOnLw/5927huvmjAbwBhyvenQ99y+YyI8fWccFUw8knsqSSO9c9e2PF0xiQHGYKT97AXCyX3IngkTa5tB9+5Fwl9Q81Q2u4YDF8IHF/P7Vj/namGHe//j8qQcQtCwaYyksER5xFyiKp7PegF1pJOidsG33ZB8JWSQzhn3LC9dB7kSvBWLtmuihgFjUtTjf4zX9+hdY8tBbfO+4g6ksjdCasNkRS3P+1AOob9sZhADSNmxpSnJyXnfG9tYUnzTFvf7NfLkZS+VRZ+Dhnpc2cvG0UVz16HpOuXUlVz26nqrSIFua02zZEefD7W3892Nvs6khzrzbV9GaMNzgDmxd9eh65tzyMqf/5hW2NqfIGNgRSzP39lf4l2udrpBNDXE+bU7QFLfdWWNZMrbNfz223km8z9gFfcCXHX8wVrvFZlIZw6+ffZ8zJo+gtjFR8PzbEoaz7nzVy0P93/88hqtmHcZ/PfYOsTQk0lkiwUCHxWtak4b61jSn3baSade/wGm3reTj+hjN8Y6ztRY/uJbZNcNZtGItTXl1PfeYkQUZBRnbYHcxSJbJQlMszam3rmT9lpaCjIaMvbMvtv3/KpbK0hxP0z8aoCWR4Yw7VjHnlpe56tH1RIIWP/z36g5ZE85i+xbzJo/g/Pte44Zn3vcm4VwzezRZ25mKvXVHjB/+e3XBOg9By7nS6iqdTsRJp9vanKCuJYUxhkp38k4unc42eO+nHfE0j6/9hC8PKaV/ceEl/djh5Rj3uGOHl3NQVSnXn3QES+fVMHZ4OV8fs6/3Ou3Tr8g70ea6BrK2s05Fo9uFkF/nffo77/Pca/r6piZ+9PB6gpbFuC/1ozmRZuuOBD9+ZJ332gwoCVEasfjWxP29IDx30n7Mu30VnzTFqSwLM6Q8wtfGDOPqx9+mKZbmuiffIZWx2dQYJ562eX9rK5fc/wYn37qSrc1JSsL+dk1oIO6hVNb2LsmWzqvh+pOOIJWxuez4g8nYxrscH1RaOC8/v+UIzuSD+tYU3/v93xA6zgS6ZvZonl2/xesj/peDqgqCSW4ga1ND3Osjm10z3Puwp7N2wd+5fc6/dw2JlN3hWN/9/d9oaHO+cUFw+g5zgzOXPvAGm9uN8O/Tr4i6lqT3OjywYCLJjE1dS6rTfNuUO6swN+U0nsqSytrOhI6MTSzlDBzdNX8Cf7lsKn+9bAq/O/co0rbpEJwXrVjbbbZB+2yE/DxhcFqrwUDnJ79kxvZmlB08uIzJIyt4+j+O4dnvHut0rwQtr687/38VsITSSIimWLZDjnZbKktzItNpfZtiWa9PMzcpZ3bNcPoVBcm6g8HfvOWVDgsmRcMWITedbkZ1lfc/WDqvhhnVVRjjpNOls7aXs33sdc+z5KG3vHS6/MBe35bisGHlZG28hXZyFk45gKAlzKiu4nvHHcy8O1Z5jYHvHXcwQ/rnDRKys55N8TQzqqu8AcmRlSUsm38k+5ZHveNnbcNP/7Sem/Pe/3WtSYrCAVqTzvv7P5b/jafWb+Oh1zez6LhDqCgJY9vOcrG5E20uR7kpnsa2nW61he7YyuIH1zL/6BG0JjOdNqAW3LOG1qS/6xFr1kQPZbtJzg9a4qaQCbYpHMVuHzjyv7Yl4a4qds+3J3h9XYl0liEDSrzWy77l0Q4f5FzAg52L3eRmE+VGuztt9XURxIrDAUJu/+WIQSUsPuHLGAM3nz6OolDA+zDOrhmOiDCwJMyVX6/mQvfrdVZ+fxpXzz6MgCUM7lfkrolgKAoFvVzdytIIPznxMHBbY7WNcV66fCrRcKBgAsd1c0Y7U3S7qGt32QbtsxHy84TBmU7dEEt7qW35j5lrSf753a2MHd6fc44Z4b02hwwpobbR6e5ZMrOaffoVUV4c4qd/Ws9/n3g4YkFzvONgZtAS2pIZ77XLdS09uGYTljj92/kzJHMrhTmL8zgTI3Ktxlyr1BIojlgcUFXCRdNGFUyWuHlujTe1OWubgnUoahvjhALCqMElLJlZTVVZhNJIEHBOTiLOehb5r01FSRjbGC4/4ctc/fjbXDdnNPv0KyJrDNtbU4TdK7rJIysKZoLe8vwHXH/yEUTDFk0xvPWPZ1RXeemCWdvw1PptzD96BFfNOozy4hD9oyFsN/Mid0IfO7ycWWOHMn/Zq1SWRvjlqWNIZ23+dPFXCtLwgpYzvhAKODPrRlWVsmRmNcMHRDn1tsI85Vy2z3n3rPE9a0IDcQ+FuknOf/KSyXzUkGRgSRhjpCDftH3gyB/RDblz/9unPYGz6Ph3po/i4/pYh8CTW4hmcP8If108haa8RVFW/XA6A0vCPUqZiqWyFEcsNjcmvUvNXIvv18++z3/PPpyLpx9U0C973ZzRVJZGqCyNELKcwciz7ypcczaVSVEeDXPHWeNpSWTYlvfFpA7ptNV71azDqCrrPK0p4mYJdNZHfN2cwoWEbvvzhwWBJZG2mX/nq0weWcG9Z+fySmF7a8prSd717QkkMzb1ef+TFxdP9Vq7T63fBjhBctFxhyCW041gm44rzJUVWYysLO7w2t10+jjKiy0+bU53yE2+ZvZoohGnP/zi6Qd5l+Z3vbSRBcccQDxlaEtl2Z73dffee9Fdu3nYAGeKfPsTQygg1LXsfK/kHm9UVQl1LSmef2crZx49wkvRC7rvz0hQvH7e/IkgT1wymTvnH0n/oiDJjO291q9vaqIlkSEaChR08eReu7u/PYGwG7ivfeJdr2+4KZZmyUNveUtk3nnWkexfUcy8O1ZRWRrhiq9XUxS0qCxzMlTyV5ebUV3FT2YdRl1rquD53TK3hsrSSMFrkbuC0qyJPmZXBuu2tyRoSWaY6g4u5Fv5/WnMueVlHlgwkZZkGkuEzY0JpzW1TwmbGnd+C8WdZx3pfcDXLJlOQ1uaWDJDzB2tzo2Al0QssrZhU0Oc0rzZUzOqq7h69uE0tKVpbEvRmswWjIDfMnccWWNobEsXBFWnC6SIT5qSHWYZFYUsBpVGOs3gWDKzmsP37cfJXdwHeINSs2uGs2//IopCzippA0vCRIKCbWBTQ9xbnvLTHQmuf+o9fnnqmE4HKx9YMJH9KorZ1BArWBPhujmj2a+imGDAIp7KIgKRgLMUo9MasggIGOCj+hhDy4sIWE5ud24wM/d4Y4eXs3DKAZRHQ1SVRQgEhGOufZ7nvzeFgCWcdtvO55vLfmhvxcJJDOlfhAF+3Mmg3B/On0w83fEbMXKDaqfcujNjYEc8TX1bilue/4D/+dbYgm+oGDu8nGvnjPYWRjcGtjYnOmTTALzy/WlsbUlS35rid6s+LmiJH7Zvv07/xysWTuLOv37IzDHDCk4YN35rLOXFYYKWsKkxXhD4AXeheUNTzBnYPKVmGN8YNwzbOBkqbalsp6/bXy6bQiRosanR+VaQX582hmTG8KWBUQIiiOXMnrvhmff4/glfZltLksqyCPFUhgHFYWJpZynQ/P/RvWdP4MCqUubc8nKH5/c/p42lJBL03gMrVv+dk47cDzAM6ReipKioQx3b0W/o6Cvi6Sz1ralOW2m5flAD/PwpJ1Vn/4piNxvAMHRAhOULJpK2DWG3r/GGZ94jkbK57ol3+MmJh5PO2Pzh/ElsbUlx1p3OFM8dsbTXL33VrMPYf1AxIcsinrLZ7vYz589GWzjlADcvF8rd1c5yH8J7X/6YC6cdiAgsmz+BVCZLWVGI5kSaZvdbebvqe013cx/gXdbf9dJGznQHoHIf5hULJ9GazJCxbe+DM2xAlF+eMqbbFnoqY/P71bVOQr8xRN3uCkuEdz5pYmBp1BuUzA9+DyyYyJYdCa55/B2+d9zB3PXSRi97ZN/+O1vZ+V0BS2ZWc+i+/Zx+zYDzYV0ys5pbnv+A1zc1eVOx29dzn/5F3qXtU+u3UdeS8l5zZ2KP7fVntn/tcq93ZWmEoDtTcuSgEq6dc7h3zPzFgkRyi8Y7U5W7mnKdNYaqsjCDSiN8/6tf5qPtMa5+/B3qWpPc127djlxdkhmbOeO/xHVPvlPwnrnxuQ384mTn++MqyyIdFqEXgXTWWQ1u8sgKjj2kitNuW8nkkRWcc8wIisNBr1sqd9IzgGU53WsHVhWz/LxJ3mSmoCXUtaYYUBzmkTdquXjaqIIW+E2nj3Mzj5wF63OBP2A5OdtdrXLYvzjMPHfhqNzVWjgoxFO2swLgZ8bh3qODdT1kufmbICxSQwAAGctJREFUN36r4+BawP3W2kjQ4qJpo1i0Yi1Tr3+BM+5YRdYYNjcmufKRdbzzaQubG+NUlYW5eNoosrahriVFczzNpsY4qazxWiS2u9xhLmjMX/Yq8253jpe1jTcomOsbBafbI2QJWXfQIjcyft49a1i+ppaA5Xyv26Lf/43GWJqrHl3n9h1aXlA8uWaYN0j13PeOZd/yIq8vM18uYDorXOFlLrTvujFua7j915lf+sAbBC3hV6eO6TCpYWCJ09d9wuFD+K/H1rMjkWZbizMhxgJGVPYrGJDJP27Gndr9nemjvBNDLnskmDdhJP/xbnn+A4qCFhe7ayQce93z3oDU2OHlREKWNxi2dF4ND114NA8smOjNnoyGAtx51pFcfsIhADzovta5unT22rUfBDvxppeYv+xV4u6sumgowJVfr2ZgcZgh5c7Eir8unko0aBGwhC8PKWVpu8HDW+bWEApY1LelOXnpy0z9WeHgVMh9zD9eMIm/XDaVFxZN4c+XOamSRSGLC6YeSDjghIZwwPm7KW5j29DQmuKy4w8uyN4xpvOBs3OPGcnmxgQhS1g2/8iC/e7464c0tGUoK7Koa0mz2e3PjqdtUlnDr/73PcAwd9KIDgPLF9z3GtFQgNJIkIqSAF8fO5StzQnaklm2Nie9iUGdvdaTR1Z4xzn/3jVY4ixcYvvcM6BdE3l2pWtiRyzB3xuTrNm4nRmH7etd7ubSiWyDd6a2bacsNyBz5cNveS3G2TXDOWhwKfNuX8UDCyaSztq0JTNsa0mxX0Ux0653uj66uhx+cfFULBESmSzzbl9VMIC4ZGY1Y4eXk7ZtWuJpMjYF3RB/+s5kmmLOpXL+guW2gcH9gs4XUhpDJutMVHGek6EkHOLT5kTBwNCvTh1DVVmE5kSGfkVB/uVaZ/LJ1Y+/47V+muJpqoeU0RRLe3mrTfE0z6zfyvTqwYwe1o/tLSkiQYvSoiDG4E0QCYhzO/H/2zvzILuqOo9/zl3efVt3Ot2dAEnICooBJ0DHhLTIZJnCBRVmENnCIowhBJGpQoZRhhnQsXQcqHJknBhwhEjAJTIORkEsKFeigAkEycjeYCckkKW7X/fb7nbmj3Pv7ff6dSdRhrzYOZ+qVLr7Lu+ce8/7ne37+/08pSGOQ2H+/LrFhKFkSSTyHzk9/9X1S/jshq1c//538OIbQ3W611/+/WJue+RFVi6ew94oFOOcyTk8X33mWEsz86ZNIJMS9JfUOmbsmJJNWbRnDXYUPHr3lunMp5iYSxFGdQC4ecPWBhf11ReezJQJ6VETecYa6moAQaiCvNcuMX33ikVkUoJtfcr9PR7tT25N4/oBecfi+Z0DzJncmmxi5R2lW8+kjChzcr2m+/5V3QSh5KWaOBmqnF105G0sQ5CyBFVPLQHFiU0DKfn9jkGmT3SYkHX43fYCbRmbqZEK5cgWi5IHz2wv0Nni0JlXz2bHQIWjo5lkKMGx1OAhLq9tCNxQORzF3n3pKHawYwlsEwajQPymIUhbanlKSokXqpACtilUGUNJa8akWB1OIpC2DapeiGUJwlBJ6faDdug4GByoZ93rAyWclN3glTWjI4sXqOlzyfXrDFnKFJQ9n/acgwwlbqRljR07DAMKkadbylLi8zcKKqzlw1t3cPrxR9Vl960GIY5pIJGU3JC9RRcvUJ5zU9vSWKZqM1IKLENFKou95PxQUihXacs5Sary2im9YcCuQbfO4K5Z3qViy0ZzqNjrzoriYvihcioxDNUBxWmITAEVP1TeeUYU18BXX5QglEmMjlzaoFRVX6qUqb5sXqDWAOPkpTecMRc/UMs6QjnPjelB9cQNy5BIgkB1jHH94yl9a8bA9dT0uOzJpEMNpeT+zdtZvmh6UkcnemZ5R6Ud+vLDz7PitDlManGSTMuWEGQcwWB52OvQACxLfV7KhKGqqncYSixTREZDtZFPfuupuo7r+R0FLjhlOkHUIXpBSMY2IHou+bTBUCWkUK7SkUtjWfXPtS1r0ru30aGnJW1S9STP7hxs2BjeetOyMT0H/2dVNyGSgbJPf9Hljl++zKolx1DxQmZ2ZMlFjkBEo+M4w/ONH5xLe9bklb1VfvDkNi5/z2wKFT/JtHLM5BxFN2Bi1mSwElL1lVE2hTKUuyNvvlqvwTjv3huDHkMVn7ZsirxjUo08J9syJr2RumXVkmPoK3rMaM9S9oK6zuxry7v4bc9u5s/qZEqbQ/v+1ya0IT4YHIgh7itWKLnhqKOmB6/pplSVpCxB0Q0hkbJJ8o6pHCWqIW4QJh55k1pS3HLOOxkoB3iBTEbXtZ5ur+6tsuLu4UAsMzuzOKaBYxv0lzx2Dbl05lPYpqEMgEEUZ1W1nFqjmUkZyCg1u+tL7v3NK3xk/nTM6Jjrq82uj65p3OzYcHU3Q5WAvpJXL5e68GQMIZjZ4SSZfGs7qC/9+DnuuqyLohsqr8QRI7GHrumm6Ek8X9aVs+Sq6fmdv3qZD86bWrfm/NA13bghbO+r0rOrwEkzOggiQ2sImJA2ki//GfOm8qMt2zl3wQxMQ6hUSNHngeoo07bA9SU5R6WBf6MwbADikfO0iRn+6f5nWHHaHP7uO0/VKQ5OnJZnTynA9SWmAMcySKcMdgxUmdnuJGU5f+FM9W6jEVzGhoFKyKt7SnUhJK9733F05Gxe61cG5eJFM5nZmWUg2hB76JpuAArVEIHADcI6b753Tmll7cYezl0wIxn1O5ZJZ4vSuRfKw7OTUErStsG0iSq2xO+2F5gyIU3OsZKOIm2r0X85uk8oVWfjBiEdWZOyB14YAiJx4VedtIp011+sMCGbxhDQUzPi3vSPy0hbsGOE1+Xpcydz04ePZ+2jPSxfNCuZWYKk6ktaHRNPhtiGiRso71RTiCTY1dqNPVx+2hwEyoPOi2ZVOwfK3PqT53myt1+5bl+xiHPW/PpAo69pQ3wwOBBDvGeoQiBVxCg7Gh76UcObmDXxQ9hT9NgzwgX6G5d28drAcED20+dO5jMfmEvKUj3/zgFlbOMv9+0XdTGpxSEIJU/07GbhrE7cSGYVT7UyKZP+ksu2SJkhgWOPyDFUUSMMCZRdSdWPIroZgkxKUHYldrRJ4UcbX7YJu4c8prY5DJQDFt/yMwCu/atjOevkaVEITxWW8dI7G7Np3PjB4zEEo3ZQd1+2IBrl0xCHAWDLPy+jd2+VDU9t4yPzp5O2jcS1eP3KU/B82ZBR+rFPL8WyBGkT+svKGQRJMmsQQtCSFhSiYynTSEZMecekr+QlbuoVNyBlG2rD9MwTVHbkyJ06frdxRL1ndw7Wjb7jDNvvnNrKjgHl3n3BKTOxo7VhM5IYtqaFcnH3JCCTKb0lBH+IlAiT8g7Xnv42pk3MJh3KNzf2sGrpHKRUTkGxwuKOi7tIWTBUDTGF4PXBal3n+N0rTmHPkNugk/7Sj59j9fKTKVSUqqfFsRis+hSrPke2ptlddJnZ7iQxOuLIghUvoCVtUqgESYqjiVmbK9Zt5kdXdxNI1e5HdrKrl3cxvd2JOlWVefncGhXIusu7GKyqdh1ISRiSdKi2KXh90K1bepk6MU3Vk8qDsapc8WNlUskNmNLmMDGbYqDskU9HcWAQyUwh3hCVSIYqKjPK+id6+fBJU5nRkdufidCqiT8FIcT7gH8HTODrUsovvtl7pi3YXVLTZ0NA2QuTqdipc9oo+7KhMf7r2X9B2ZOJET7p6DYuP3V2Ei91zqRcYoRBbSSsuHsTnzvzBE6c3sr8WR1UA4kfqsa87tc9PP5KP7df3FWnPY4TJK5ct4kHrulGoBo1qBaUtgWDlZCWtMHrgx7bahJxHntEnpXrNvH9Vd04luBHV59KPq2kPk6UutyNlBjxrvnkFocJGZsgUkLc+/HRd+P7y57SVtOYlRigWAnZ8JQaucahLmNjJxC8MVhpuCbjCEwYM0ToQ9d0E/tKWYZgZ2FYrvf4Z5bRV3QTGVYs0br81Nk4KcHuQY+hqg/A3qKXdHIzOrIc2ZpO6j9lQppQwld/+gJv/9Dx5FKCi7tn4YeSkhdy+89fYuPLe5LRa19RGf/JLQ6vRh30l887kbaMxX9d0oVpGPTuLfN6oULJDXj7kXkuP202loAhV7kKffeKhUgpsC3oL6sQcqGgLmWU+l9wW+QhGC933PloDysXz8EQIspnp1IyqbVS5cgTj95rBww3nDGXbMrktf76deq7Pjaf9SsXUfaUkRstjdSV6zax4RPduIHE9X1Slsndly3AMtVoOQRaHEHRBaQKaB93Qn4gk/jTazf2cNWSY9g95HHluk3895Wqfe8Z0f4/sfRYvECSdUy1TGgbhCGJTjhWohhRhhbHFpy3cDqu31zPunGrmhBCmMBXgfcDc4HzhRBz3+x9h6qSqhcwWPHZWahy4dcfS2IKVAM1IhupDLj+vqfxayQ1KxfP4dr1W5Ld/qo/utymM58iDNRI9fw7fsOSW1SshTPmTeWmD8+l6od1n3V219GJsTGBV/dU6xJr/qGvys0btlJ2VWcRJ+K88f5nEslPGEr2lpT2+C//7Wd8dsPWJAGnGW3WxLvff/2fG7n4G4+rkWXeGTMOQ96x1EhEDGe4qMULJR+ZPz0xJrXOLl4Qjqo4CALo20eIUFABis69/TcMVYM6hxEvCOviYGzrU27i167fQrEq+cPeMq6vMnjEz+hT67eQsgSTW1NJ/V8bqHDVvZs5u+to8mmDokfyvC+983GWL5qR7NIXqjKJUbI0crH9h/cfRz5lknUsWtJ2XXLUG+9/BoEgYwt2RAlLH966gz1Fn5s3bKXiSgZKHhfc8RgVr9GbTyATpUisbrike1bUeUgcy2SgpNqVKQSFik82pbJQ1w4YLumexYVffwzXD+ucMrpnd6i9iSEXiVpSass2hp2clFejYQG4fsgru4t84cHfs6fo0ru3RBhSl6w1DgP78u4SXhSjIla95Bw7WfZyAzW7Gdn+V92zmbRtIKIlwYGyz85ChZ6ahK/x/W/esJVtfVUcG1oyOovzW8UC4EUp5ctSShf4NnDmm72pF6qkk9v7Kg2jkLIbjumSWyv9ig1N/P9YsrC0bVLxG4PbrLpnM+05p+Gzag1Y/yhBcWKjNVpnEccX8EJZFyshbtzb+spRmh3RcO1133ualYvnIASjZpKueAE5x8A0YVp7pkE6FiebHC77sBTPECIJglN7TclVI/SRhjumtv4NsSZGeUfxSN0LQrIpk858qqGeZTeM0jjVB7Vpy9ijpoxfdc9mPn7abPrLav125P2uXb+Fiq/W6kd2qrGxKUTxRK773tMsnXtU3TuMlx12DlQa2k8oaZD0XX/f06RtEz+UuFFC0219ZYIoQedgJUy08EBd+qmRzyyWqbVm7CiKn6AlbTeU45PLjk3il2zrqyQxUfqioFhxPImra6K9xWW1Ijf9eMBSG28jXocerf1XPLXp5wfUxWIZef+zu47mynWbGCyHDJb1iPitYirQW/P7tuhvdQghVgghfiuE+O2uXbv2e9Mg8n8fbYod74KPZlSFINEex4Ym/t8eQ9eqHCBGN+yBlInmN6bWgO3LMWO0zuIrj7zA6gtP3qdxj9dYx7qvlLB2Y09dyvK1G3t4baBCoRzwq+d20ZGzmd2Z49srTuEX1y1m/RWLSJlGEqMASPKTxc/t6qXHJve9/6p3c8/fLkzWbmuf48h3EZdzpK50tI4vHqnH2Y8D2Zh6KIje78gOo38fjjCxjjgcJa19bOAMwajvJL42bmvxPUa+w1t/8jy3njOvrv2MNSAYqvpJMKFaoxaXZbQBA9DQ1uLOLb6Xaailr5Ed5vSOLFbkyRbXoy1jJz/vMyGrgEktThIzJX7fMBwWYLT2H7eNePlhrPvXBohqdqyJ8WyIDwgp5e1SyvlSyvmTJk3a7/kp06DkBqNOsS1D8K3HXmmIzrVmeRcCwQNPb+ebly1IpDPxSC/viCTf13dWnMIt58yjs8Whv+Q1fAHie8YbD7WOJfdt6k2iuI11XX/ZS/z7a9k1VKUjn0q8x2JqjVy80TGWU0c2ytRcOx3+2Ltn8bWfvUTKMvjKT1/i5M89wsp1m9n6WgE/VLvwaRsskyTDxJO9/azd2JO48Va8kFWLj+HtR7aQcyxcP8AyRJJr7r5NvQ2GqLb+u4cqdR1dyjQaOr6j2zOsuagL2xBMaUuP+vzsSGs9ssO4b1PvmM87lMMhK8dyMgglDUYl/rzahKVxhzLyHT7Z288XH3yW/zj/JH76qcV87swTxixPW9bGsYzkdxjumGLtbtx+a9992jbqHEfi5xAPCF7dU2ag5Dd0xLsGqziWgWUO16O/7CU/19ZvZFlBJPnvpk3McMcvXk7adzpl0JlP1TkC3bepl9XLu5LYGKFkzO9qbf1iSWgzGbeqCSHEIuAmKeV7o98/DSCl/MJY1xyIaiJOlVSsiQtRu0HUs0ft/p+3cEaUq0xtdmVs6KkJ1P62I/JYhgFI2iOFw0B5WGbWkjGoepC1aZCErV7exREtKXIptX4oIAqYLkhZBmUvpCNrjHrdbY88X5fkMz625qIuJrWkyNnDcrnaDZBVUVCjojcs7YqvvfWceRhCMPeobBLEPZTDUjwvCGnPmnX3VZs97wLgqFabSgAyHB51q5CTgly0RtpXdHn4f3fy0QUzsCIJWvysP7pgBhlbyfKqfhhNac2k/pPyDp//mxMolFWg+yNbrMT5It6snNae4dnX+ll63CR2FDwytkF/2a/Tij90TTd7SgEDZb8uyMynP/AOJuetUZ/3lFaHtI26ruTVqRhWL+9idofD7lKA6wV1gfTjz4NhaddzOwbomtWZvMPaTbV4FnV0ewYpBe1Zo+H4muVd2JZg6oRUXXnO7ZrGknccwawOh2LkrOH5Us3IAslV925OyrK7GESJQ028EH741DY+uWwOOwoeVU8Zvdo63PWxd3FUq40bwt4h5Rl556M9rFpyDJ4fMveoHDsKHoMVvy5ZaywJLHrK224gkkzGeuKjWq1EuhfnDhRCqKiFrbb6XgglXYuN8cgN9LUbe7h62duY1eFgYZFO6wwd/+8IISzgeWAZsB14ArhASrl1rGsOxBBDlDzU9ZNQfWEosS0jynk2bFCd2PFAqCzMbRmD/ih1kWmIRJTvh5LOnEnFQ6kjooSVtqGy6qYtteZZq7H1fYltC1KG2ghSHmjUXZt3RN11sWdV/Hm1x1KWQcpUMTHyjmCwqtYRjagj8fwQX0o6sia+VEqHxEEiko7l0gaWqC9rNqWSoiIEuZpnY5mCjK10y14omZgxGIikZoZQciMzcoZpyxgUq8POCkYkqcqlSGRW6ehZu8Gwo0z8vGNHFolyOonvOVAZTtbqWAYVPyQVGf+Bcqi+dpHMTEn8VDLXsqc2nhJ5lyGwLIFj1tc91tnK6PMqnnJuSfTOhko0kEtB2VNLBLHDR6wcaE2r0WuxqpyAMrZBxVPPaULaoFCRdQlOBWBHzh15RyR1NCLdchhpyCdmDCo+SZqrlrRJ2VVt1A9V7IhqlDk8lkt25FTI1bgNx55q8XMpRXWIvxOWaURyS0lr2sALwfXV55vRe1YOGCpNVhAOO9bEMs0JUXkqXv2zsaL2PRDVP5syMY1hzXxnzqRYlYmuOGQ4IUB8/7gdmAdmhEEb4j8NIcQHgC+j5GvfkFJ+fl/nH6gh1mg0hyVaR/ynIKV8AHig2eXQaDSafXHYb9ZpNBpNs9GGWKPRaJqMNsQajUbTZLQh1mg0miajDbFGo9E0GW2INRqNpsloQ6zRaDRNRhtijUajaTLj2rPuj0UIsQt49Y+45DjAAWpj6Bk1v4/186F+3qFQhmaddyiUoVnnHQplOJjnecCzHDi7pZTv+yPOP2C0IX4TCCEC9KxCo/lzJZRSms0uBGgjotFoNE1HG2KNRqNpMuM66M9B4AlgDjBY87eWmt/H+vlQP+9QKEOzzjsUytCs8w6FMhzM897gEEGvEWs0Gk2T0UsTGo1G02S0IdZoNJomM+7WiIUQLwDHNLscGo1GEzEkpWzZ1wnjao1YCOEAFZRgW4/2NRrNwURSk05JSnnAqZXGm7G6CQgYNsYajUZzyDPeDPHxgA+4KEM8fob7Go3mUKduBCyEkNG/Nfu7cLwZYo1GoznUWCGE6N7XCePNEG9FbUCmUHV7y9JfazQazX6QNf/O29eJ422zLgsU0Zt1Go3m0OK9UsqfjHVwXBkrKWUJeIVxVi+NRvNnzQ/3ZYRhnI2INRqN5s8RPXLUaDSaJqMNsUaj0TQZbYg1Go2myWhDrNFoNE1GG2KNRqNpMtoQaw5rhBA3CSE+tY/jZwkh5h7MMmkOP7Qh1mj2zVmANsSatxStI9YcdgghbgAuQeUs6wU2AQPACpR7/IvARcCJwA+jYwPA2dEtvgpMAkrAx6WUzx7M8mvGH9oQaw4rhBBdwF3AQlRcks3A14A7pZR7onP+BXhdSnmbEOIulGfU96JjjwArpZQvCCEWAl+QUi49+DXRjCfGXYYOjWY/vAf4fuQOjxDiB9HfT4gMcBuQBx4aeaEQIg90A+uFSOJJOW95iTXjHm2INRrFXcBZUsotQohLgcWjnGMA/VLKEw9iuTSHAXqzTnO48QvgLCFERgjRAnwo+nsLsEMIYQMX1pw/GB1DSlkAeoQQ5wAIxbyDV3TNeEUbYs1hhZRyM/AdYAvwIPBEdOhG4DHgUaB28+3bwHVCiCeFEHNQRvpyIcQWVPzrMw9W2TXjF71Zp9FoNE1Gj4g1Go2myWhDrNFoNE1GG2KNRqNpMtoQazQaTZPRhlij0WiajDbEGo1G02S0IdZoNJom83/WS1nJTUW76gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.relplot(x=\"date\",y=\"item_price\",data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405917</th>\n",
       "      <td>16.04.2013</td>\n",
       "      <td>3</td>\n",
       "      <td>58</td>\n",
       "      <td>18225</td>\n",
       "      <td>199.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2530105</th>\n",
       "      <td>11.03.2015</td>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>4872</td>\n",
       "      <td>1199.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327808</th>\n",
       "      <td>16.01.2014</td>\n",
       "      <td>12</td>\n",
       "      <td>45</td>\n",
       "      <td>16455</td>\n",
       "      <td>699.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308190</th>\n",
       "      <td>14.03.2013</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>9910</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688776</th>\n",
       "      <td>22.07.2013</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>13496</td>\n",
       "      <td>12998.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2055376</th>\n",
       "      <td>04.10.2014</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>2814</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>656582</th>\n",
       "      <td>19.07.2013</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>21438</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962396</th>\n",
       "      <td>02.09.2014</td>\n",
       "      <td>20</td>\n",
       "      <td>46</td>\n",
       "      <td>10039</td>\n",
       "      <td>132.67</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1362109</th>\n",
       "      <td>23.01.2014</td>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "      <td>1131</td>\n",
       "      <td>399.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443069</th>\n",
       "      <td>15.02.2015</td>\n",
       "      <td>25</td>\n",
       "      <td>54</td>\n",
       "      <td>16549</td>\n",
       "      <td>199.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515638</th>\n",
       "      <td>09.05.2013</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>12128</td>\n",
       "      <td>429.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009966</th>\n",
       "      <td>25.10.2013</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1035</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2728353</th>\n",
       "      <td>25.07.2015</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>16147</td>\n",
       "      <td>599.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127043</th>\n",
       "      <td>07.02.2013</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>8942</td>\n",
       "      <td>249.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82482</th>\n",
       "      <td>19.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>3328</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337461</th>\n",
       "      <td>02.03.2013</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>20249</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051170</th>\n",
       "      <td>09.11.2013</td>\n",
       "      <td>10</td>\n",
       "      <td>31</td>\n",
       "      <td>2897</td>\n",
       "      <td>599.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776145</th>\n",
       "      <td>28.08.2013</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>8314</td>\n",
       "      <td>399.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258141</th>\n",
       "      <td>02.03.2013</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>16599</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1572807</th>\n",
       "      <td>02.04.2014</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>15970</td>\n",
       "      <td>199.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412536</th>\n",
       "      <td>16.04.2013</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>1832</td>\n",
       "      <td>1199.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1781950</th>\n",
       "      <td>14.06.2014</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "      <td>6480</td>\n",
       "      <td>349.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2479494</th>\n",
       "      <td>28.02.2015</td>\n",
       "      <td>25</td>\n",
       "      <td>27</td>\n",
       "      <td>9355</td>\n",
       "      <td>349.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422873</th>\n",
       "      <td>21.04.2013</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>13344</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724651</th>\n",
       "      <td>11.07.2015</td>\n",
       "      <td>30</td>\n",
       "      <td>59</td>\n",
       "      <td>22076</td>\n",
       "      <td>28.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1341457</th>\n",
       "      <td>02.01.2014</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>4373</td>\n",
       "      <td>1699.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1861114</th>\n",
       "      <td>28.07.2014</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "      <td>6497</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131306</th>\n",
       "      <td>28.02.2013</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>14050</td>\n",
       "      <td>349.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>903251</th>\n",
       "      <td>02.09.2013</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>3705</td>\n",
       "      <td>1499.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332575</th>\n",
       "      <td>14.01.2014</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>8460</td>\n",
       "      <td>230.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1018123</th>\n",
       "      <td>13.10.2013</td>\n",
       "      <td>9</td>\n",
       "      <td>27</td>\n",
       "      <td>14722</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1936000</th>\n",
       "      <td>25.08.2014</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>3840</td>\n",
       "      <td>499.50</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19949</th>\n",
       "      <td>12.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>6490</td>\n",
       "      <td>699.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1140911</th>\n",
       "      <td>05.12.2013</td>\n",
       "      <td>11</td>\n",
       "      <td>25</td>\n",
       "      <td>1398</td>\n",
       "      <td>599.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2319156</th>\n",
       "      <td>30.12.2014</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>16084</td>\n",
       "      <td>449.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2628123</th>\n",
       "      <td>22.05.2015</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>11927</td>\n",
       "      <td>599.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910902</th>\n",
       "      <td>05.08.2014</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>11181</td>\n",
       "      <td>149.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065131</th>\n",
       "      <td>24.11.2013</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>7736</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2842667</th>\n",
       "      <td>22.09.2015</td>\n",
       "      <td>32</td>\n",
       "      <td>57</td>\n",
       "      <td>2187</td>\n",
       "      <td>449.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15987</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>11870</td>\n",
       "      <td>160.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002887</th>\n",
       "      <td>27.09.2014</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>6504</td>\n",
       "      <td>2499.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512850</th>\n",
       "      <td>30.03.2014</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "      <td>5063</td>\n",
       "      <td>1149.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85184</th>\n",
       "      <td>23.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>11656</td>\n",
       "      <td>850.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447719</th>\n",
       "      <td>03.05.2013</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>20949</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981526</th>\n",
       "      <td>30.10.2013</td>\n",
       "      <td>9</td>\n",
       "      <td>21</td>\n",
       "      <td>6883</td>\n",
       "      <td>299.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1898591</th>\n",
       "      <td>02.08.2014</td>\n",
       "      <td>19</td>\n",
       "      <td>58</td>\n",
       "      <td>5439</td>\n",
       "      <td>399.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743735</th>\n",
       "      <td>05.06.2014</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>7790</td>\n",
       "      <td>799.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2232340</th>\n",
       "      <td>27.12.2014</td>\n",
       "      <td>23</td>\n",
       "      <td>31</td>\n",
       "      <td>6064</td>\n",
       "      <td>1999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2734565</th>\n",
       "      <td>16.07.2015</td>\n",
       "      <td>30</td>\n",
       "      <td>47</td>\n",
       "      <td>14176</td>\n",
       "      <td>28.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2445735</th>\n",
       "      <td>23.02.2015</td>\n",
       "      <td>25</td>\n",
       "      <td>53</td>\n",
       "      <td>17108</td>\n",
       "      <td>499.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               date  date_block_num  shop_id  item_id  item_price  \\\n",
       "405917   16.04.2013               3       58    18225      199.00   \n",
       "2530105  11.03.2015              26        3     4872     1199.00   \n",
       "1327808  16.01.2014              12       45    16455      699.00   \n",
       "308190   14.03.2013               2       28     9910      149.00   \n",
       "688776   22.07.2013               6       19    13496    12998.00   \n",
       "2055376  04.10.2014              21        5     2814      899.00   \n",
       "656582   19.07.2013               6       28    21438      149.00   \n",
       "1962396  02.09.2014              20       46    10039      132.67   \n",
       "1362109  23.01.2014              12       25     1131      399.00   \n",
       "2443069  15.02.2015              25       54    16549      199.00   \n",
       "515638   09.05.2013               4       18    12128      429.00   \n",
       "1009966  25.10.2013               9        7     1035      299.00   \n",
       "2728353  25.07.2015              30       16    16147      599.00   \n",
       "127043   07.02.2013               1       43     8942      249.00   \n",
       "82482    19.01.2013               0       54     3328     1999.00   \n",
       "337461   02.03.2013               2       57    20249      299.00   \n",
       "1051170  09.11.2013              10       31     2897      599.00   \n",
       "776145   28.08.2013               7       35     8314      399.00   \n",
       "258141   02.03.2013               2       56    16599      149.00   \n",
       "1572807  02.04.2014              15       27    15970      199.00   \n",
       "412536   16.04.2013               3       57     1832     1199.00   \n",
       "1781950  14.06.2014              17       47     6480      349.50   \n",
       "2479494  28.02.2015              25       27     9355      349.00   \n",
       "422873   21.04.2013               3       29    13344       25.00   \n",
       "2724651  11.07.2015              30       59    22076       28.00   \n",
       "1341457  02.01.2014              12        2     4373     1699.00   \n",
       "1861114  28.07.2014              18       19     6497      999.00   \n",
       "131306   28.02.2013               1       54    14050      349.00   \n",
       "903251   02.09.2013               8       28     3705     1499.00   \n",
       "1332575  14.01.2014              12       13     8460      230.00   \n",
       "1018123  13.10.2013               9       27    14722      299.00   \n",
       "1936000  25.08.2014              19       28     3840      499.50   \n",
       "19949    12.01.2013               0       29     6490      699.00   \n",
       "1140911  05.12.2013              11       25     1398      599.00   \n",
       "2319156  30.12.2014              23       25    16084      449.00   \n",
       "2628123  22.05.2015              28       31    11927      599.00   \n",
       "1910902  05.08.2014              19       17    11181      149.00   \n",
       "1065131  24.11.2013              10       14     7736      299.00   \n",
       "2842667  22.09.2015              32       57     2187      449.00   \n",
       "15987    05.01.2013               0       28    11870      160.00   \n",
       "2002887  27.09.2014              20       16     6504     2499.00   \n",
       "1512850  30.03.2014              14       28     5063     1149.00   \n",
       "85184    23.01.2013               0       54    11656      850.00   \n",
       "447719   03.05.2013               4       29    20949        5.00   \n",
       "981526   30.10.2013               9       21     6883      299.00   \n",
       "1898591  02.08.2014              19       58     5439      399.00   \n",
       "1743735  05.06.2014              17       14     7790      799.00   \n",
       "2232340  27.12.2014              23       31     6064     1999.00   \n",
       "2734565  16.07.2015              30       47    14176       28.00   \n",
       "2445735  23.02.2015              25       53    17108      499.00   \n",
       "\n",
       "         item_cnt_day  \n",
       "405917            1.0  \n",
       "2530105           1.0  \n",
       "1327808           1.0  \n",
       "308190            1.0  \n",
       "688776            1.0  \n",
       "2055376           1.0  \n",
       "656582            1.0  \n",
       "1962396           1.0  \n",
       "1362109           1.0  \n",
       "2443069           1.0  \n",
       "515638            1.0  \n",
       "1009966           1.0  \n",
       "2728353           1.0  \n",
       "127043            1.0  \n",
       "82482             1.0  \n",
       "337461            1.0  \n",
       "1051170           1.0  \n",
       "776145            1.0  \n",
       "258141            1.0  \n",
       "1572807           1.0  \n",
       "412536            1.0  \n",
       "1781950           1.0  \n",
       "2479494           2.0  \n",
       "422873            2.0  \n",
       "2724651           1.0  \n",
       "1341457           1.0  \n",
       "1861114           1.0  \n",
       "131306            1.0  \n",
       "903251            1.0  \n",
       "1332575           1.0  \n",
       "1018123           1.0  \n",
       "1936000           1.0  \n",
       "19949             1.0  \n",
       "1140911           1.0  \n",
       "2319156           1.0  \n",
       "2628123           1.0  \n",
       "1910902           1.0  \n",
       "1065131           1.0  \n",
       "2842667           1.0  \n",
       "15987             1.0  \n",
       "2002887           2.0  \n",
       "1512850           1.0  \n",
       "85184             1.0  \n",
       "447719            5.0  \n",
       "981526            1.0  \n",
       "1898591           1.0  \n",
       "1743735           1.0  \n",
       "2232340           1.0  \n",
       "2734565           2.0  \n",
       "2445735           1.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.sample(n=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-2a78dd0947f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem_price\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mrelplot\u001b[0;34m(x, y, hue, size, style, data, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   1611\u001b[0m     g.map_dataframe(func, x, y,\n\u001b[1;32m   1612\u001b[0m                     \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1613\u001b[0;31m                     **plot_kws)\n\u001b[0m\u001b[1;32m   1614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1615\u001b[0m     \u001b[0;31m# Show the legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_finalize_grid\u001b[0;34m(self, axlabels)\u001b[0m\n\u001b[1;32m    844\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;34m\"\"\"Finalize the annotations and layout.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_axis_labels\u001b[0;34m(self, x_var, y_var)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_xlabels\u001b[0;34m(self, label, **kwargs)\u001b[0m\n\u001b[1;32m    881\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bottom_axes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mset_xlabel\u001b[0;34m(self, xlabel, fontdict, labelpad, **kwargs)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlabelpad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabelpad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabelpad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_label_text\u001b[0;34m(self, label, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \"\"\"\n\u001b[1;32m   1595\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misDefault_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1596\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mset_text\u001b[0;34m(self, s)\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1553\u001b[0m             \u001b[0;34m\"The truth value of a {0} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \"Use a.empty, a.bool(), a.item(), a.any() or a.all().\".format(\n\u001b[0;32m-> 1555\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1556\u001b[0m             )\n\u001b[1;32m   1557\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "e = dataset.sample(n=50)\n",
    "\n",
    "g = sns.relplot(x=e.date,y=e.item_price,data=e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2078890   2014-10-09\n",
       "1942719   2014-08-29\n",
       "10156     2013-01-19\n",
       "712765    2013-07-02\n",
       "1242492   2013-12-31\n",
       "2093608   2014-10-01\n",
       "1924177   2014-08-11\n",
       "390386    2013-04-09\n",
       "414441    2013-04-05\n",
       "1161884   2013-12-19\n",
       "2120576   2014-11-15\n",
       "2580249   2015-04-05\n",
       "168581    2013-02-10\n",
       "2833589   2015-09-25\n",
       "1316666   2014-01-25\n",
       "2658618   2015-05-07\n",
       "1641369   2014-05-29\n",
       "1745339   2014-06-07\n",
       "2152480   2014-11-18\n",
       "255736    2013-03-20\n",
       "992154    2013-10-12\n",
       "1152815   2013-12-03\n",
       "2116239   2014-11-16\n",
       "668858    2013-07-22\n",
       "1297513   2014-01-05\n",
       "2058742   2014-10-20\n",
       "1043751   2013-11-21\n",
       "902244    2013-09-06\n",
       "128120    2013-02-10\n",
       "407437    2013-04-06\n",
       "80594     2013-01-12\n",
       "2471751   2015-02-27\n",
       "5930      2013-01-02\n",
       "2491197   2015-03-03\n",
       "477645    2013-05-04\n",
       "2080704   2014-10-24\n",
       "1611765   2014-04-14\n",
       "336480    2013-03-19\n",
       "674603    2013-07-03\n",
       "2246597   2014-12-28\n",
       "870055    2013-09-22\n",
       "2925430   2015-10-15\n",
       "1990947   2014-09-28\n",
       "530088    2013-05-03\n",
       "1853000   2014-07-11\n",
       "581595    2013-06-11\n",
       "1886850   2014-08-02\n",
       "2357324   2015-01-06\n",
       "1148867   2013-12-30\n",
       "2051553   2014-10-19\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = dataset.sample(n=50)\n",
    "pd.to_datetime(e.date, dayfirst=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-570bc35770a0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'item_price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/relational.py\u001b[0m in \u001b[0;36mrelplot\u001b[0;34m(x, y, hue, size, style, data, row, col, col_wrap, row_order, col_order, palette, hue_order, hue_norm, sizes, size_order, size_norm, markers, dashes, style_order, legend, kind, height, aspect, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   1611\u001b[0m     g.map_dataframe(func, x, y,\n\u001b[1;32m   1612\u001b[0m                     \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1613\u001b[0;31m                     **plot_kws)\n\u001b[0m\u001b[1;32m   1614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1615\u001b[0m     \u001b[0;31m# Show the legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_finalize_grid\u001b[0;34m(self, axlabels)\u001b[0m\n\u001b[1;32m    844\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;34m\"\"\"Finalize the annotations and layout.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_axis_labels\u001b[0;34m(self, x_var, y_var)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_xlabels\u001b[0;34m(self, label, **kwargs)\u001b[0m\n\u001b[1;32m    881\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bottom_axes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mset_xlabel\u001b[0;34m(self, xlabel, fontdict, labelpad, **kwargs)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlabelpad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabelpad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabelpad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_label_text\u001b[0;34m(self, label, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \"\"\"\n\u001b[1;32m   1595\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misDefault_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1596\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mset_text\u001b[0;34m(self, s)\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1553\u001b[0m             \u001b[0;34m\"The truth value of a {0} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \"Use a.empty, a.bool(), a.item(), a.any() or a.all().\".format(\n\u001b[0;32m-> 1555\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1556\u001b[0m             )\n\u001b[1;32m   1557\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "e = dataset.sample(n=50)\n",
    "x = pd.to_datetime(e.date, dayfirst=True)\n",
    "y = e['item_price']\n",
    "\n",
    "g = sns.relplot(x=x,y=y,data=e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"Visualisation de variables catégoriques\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./data/IBM_HR_ATTRITION.csv\", sep=\",\")\n",
    "print(\"Dataset loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>Age</th>\n",
       "      <th>DailyRate</th>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <th>Education</th>\n",
       "      <th>EmployeeCount</th>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <th>HourlyRate</th>\n",
       "      <th>JobInvolvement</th>\n",
       "      <th>JobLevel</th>\n",
       "      <th>...</th>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <th>StandardHours</th>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.0</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "      <td>1470.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36.923810</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1024.865306</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>...</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>4.123129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.135373</td>\n",
       "      <td>403.509100</td>\n",
       "      <td>8.106864</td>\n",
       "      <td>1.024165</td>\n",
       "      <td>0.0</td>\n",
       "      <td>602.024335</td>\n",
       "      <td>1.093082</td>\n",
       "      <td>20.329428</td>\n",
       "      <td>0.711561</td>\n",
       "      <td>1.106940</td>\n",
       "      <td>...</td>\n",
       "      <td>1.081209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.852077</td>\n",
       "      <td>7.780782</td>\n",
       "      <td>1.289271</td>\n",
       "      <td>0.706476</td>\n",
       "      <td>6.126525</td>\n",
       "      <td>3.623137</td>\n",
       "      <td>3.222430</td>\n",
       "      <td>3.568136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>465.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>491.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>802.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1020.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1157.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1555.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>83.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>1499.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2068.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Age    DailyRate  DistanceFromHome    Education  EmployeeCount  \\\n",
       "count  1470.000000  1470.000000       1470.000000  1470.000000         1470.0   \n",
       "mean     36.923810   802.485714          9.192517     2.912925            1.0   \n",
       "std       9.135373   403.509100          8.106864     1.024165            0.0   \n",
       "min      18.000000   102.000000          1.000000     1.000000            1.0   \n",
       "25%      30.000000   465.000000          2.000000     2.000000            1.0   \n",
       "50%      36.000000   802.000000          7.000000     3.000000            1.0   \n",
       "75%      43.000000  1157.000000         14.000000     4.000000            1.0   \n",
       "max      60.000000  1499.000000         29.000000     5.000000            1.0   \n",
       "\n",
       "       EmployeeNumber  EnvironmentSatisfaction   HourlyRate  JobInvolvement  \\\n",
       "count     1470.000000              1470.000000  1470.000000     1470.000000   \n",
       "mean      1024.865306                 2.721769    65.891156        2.729932   \n",
       "std        602.024335                 1.093082    20.329428        0.711561   \n",
       "min          1.000000                 1.000000    30.000000        1.000000   \n",
       "25%        491.250000                 2.000000    48.000000        2.000000   \n",
       "50%       1020.500000                 3.000000    66.000000        3.000000   \n",
       "75%       1555.750000                 4.000000    83.750000        3.000000   \n",
       "max       2068.000000                 4.000000   100.000000        4.000000   \n",
       "\n",
       "          JobLevel  ...  RelationshipSatisfaction  StandardHours  \\\n",
       "count  1470.000000  ...               1470.000000         1470.0   \n",
       "mean      2.063946  ...                  2.712245           80.0   \n",
       "std       1.106940  ...                  1.081209            0.0   \n",
       "min       1.000000  ...                  1.000000           80.0   \n",
       "25%       1.000000  ...                  2.000000           80.0   \n",
       "50%       2.000000  ...                  3.000000           80.0   \n",
       "75%       3.000000  ...                  4.000000           80.0   \n",
       "max       5.000000  ...                  4.000000           80.0   \n",
       "\n",
       "       StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "count       1470.000000        1470.000000            1470.000000   \n",
       "mean           0.793878          11.279592               2.799320   \n",
       "std            0.852077           7.780782               1.289271   \n",
       "min            0.000000           0.000000               0.000000   \n",
       "25%            0.000000           6.000000               2.000000   \n",
       "50%            1.000000          10.000000               3.000000   \n",
       "75%            1.000000          15.000000               3.000000   \n",
       "max            3.000000          40.000000               6.000000   \n",
       "\n",
       "       WorkLifeBalance  YearsAtCompany  YearsInCurrentRole  \\\n",
       "count      1470.000000     1470.000000         1470.000000   \n",
       "mean          2.761224        7.008163            4.229252   \n",
       "std           0.706476        6.126525            3.623137   \n",
       "min           1.000000        0.000000            0.000000   \n",
       "25%           2.000000        3.000000            2.000000   \n",
       "50%           3.000000        5.000000            3.000000   \n",
       "75%           3.000000        9.000000            7.000000   \n",
       "max           4.000000       40.000000           18.000000   \n",
       "\n",
       "       YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "count              1470.000000           1470.000000  \n",
       "mean                  2.187755              4.123129  \n",
       "std                   3.222430              3.568136  \n",
       "min                   0.000000              0.000000  \n",
       "25%                   0.000000              2.000000  \n",
       "50%                   1.000000              3.000000  \n",
       "75%                   3.000000              7.000000  \n",
       "max                  15.000000             17.000000  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-3f060fdec52d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Attrition'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcatplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mcatplot\u001b[0;34m(x, y, hue, data, row, col, col_wrap, estimator, ci, n_boot, units, order, hue_order, row_order, col_order, kind, height, aspect, orient, color, palette, legend, legend_out, sharex, sharey, margin_titles, facet_kws, **kwargs)\u001b[0m\n\u001b[1;32m   3753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3754\u001b[0m     \u001b[0;31m# Draw the plot onto the facets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3755\u001b[0;31m     \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplot_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3756\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3757\u001b[0m     \u001b[0;31m# Special case axis labels for a count type plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_dataframe\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    821\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 823\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    824\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    825\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_finalize_grid\u001b[0;34m(self, axlabels)\u001b[0m\n\u001b[1;32m    844\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_finalize_grid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;34m\"\"\"Finalize the annotations and layout.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0maxlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_titles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtight_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_axis_labels\u001b[0;34m(self, x_var, y_var)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my_var\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mset_xlabels\u001b[0;34m(self, label, **kwargs)\u001b[0m\n\u001b[1;32m    881\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    882\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bottom_axes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 883\u001b[0;31m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    884\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mset_xlabel\u001b[0;34m(self, xlabel, fontdict, labelpad, **kwargs)\u001b[0m\n\u001b[1;32m    245\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlabelpad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabelpad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabelpad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mset_label_text\u001b[0;34m(self, label, fontdict, **kwargs)\u001b[0m\n\u001b[1;32m   1594\u001b[0m         \"\"\"\n\u001b[1;32m   1595\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misDefault_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1596\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfontdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mset_text\u001b[0;34m(self, s)\u001b[0m\n\u001b[1;32m   1148\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1150\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1151\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1152\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1553\u001b[0m             \u001b[0;34m\"The truth value of a {0} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \"Use a.empty, a.bool(), a.item(), a.any() or a.all().\".format(\n\u001b[0;32m-> 1555\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1556\u001b[0m             )\n\u001b[1;32m   1557\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    },
    {
     "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": [
    "x = dataset['JobSatisfaction']\n",
    "y = dataset['Attrition']\n",
    "\n",
    "g = sns.catplot(x = x,y = y, data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "x = dataset['JobSatisfaction']\n",
    "y = dataset['Attrition']\n",
    "\n",
    "g = sns.boxplot(x = x,y = y, data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of       Age Attrition     BusinessTravel  DailyRate              Department  \\\n",
       "0      41       Yes      Travel_Rarely       1102                   Sales   \n",
       "1      49        No  Travel_Frequently        279  Research & Development   \n",
       "2      37       Yes      Travel_Rarely       1373  Research & Development   \n",
       "3      33        No  Travel_Frequently       1392  Research & Development   \n",
       "4      27        No      Travel_Rarely        591  Research & Development   \n",
       "...   ...       ...                ...        ...                     ...   \n",
       "1465   36        No  Travel_Frequently        884  Research & Development   \n",
       "1466   39        No      Travel_Rarely        613  Research & Development   \n",
       "1467   27        No      Travel_Rarely        155  Research & Development   \n",
       "1468   49        No  Travel_Frequently       1023                   Sales   \n",
       "1469   34        No      Travel_Rarely        628  Research & Development   \n",
       "\n",
       "      DistanceFromHome  Education EducationField  EmployeeCount  \\\n",
       "0                    1          2  Life Sciences              1   \n",
       "1                    8          1  Life Sciences              1   \n",
       "2                    2          2          Other              1   \n",
       "3                    3          4  Life Sciences              1   \n",
       "4                    2          1        Medical              1   \n",
       "...                ...        ...            ...            ...   \n",
       "1465                23          2        Medical              1   \n",
       "1466                 6          1        Medical              1   \n",
       "1467                 4          3  Life Sciences              1   \n",
       "1468                 2          3        Medical              1   \n",
       "1469                 8          3        Medical              1   \n",
       "\n",
       "      EmployeeNumber  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0                  1  ...                         1            80   \n",
       "1                  2  ...                         4            80   \n",
       "2                  4  ...                         2            80   \n",
       "3                  5  ...                         3            80   \n",
       "4                  7  ...                         4            80   \n",
       "...              ...  ...                       ...           ...   \n",
       "1465            2061  ...                         3            80   \n",
       "1466            2062  ...                         1            80   \n",
       "1467            2064  ...                         2            80   \n",
       "1468            2065  ...                         4            80   \n",
       "1469            2068  ...                         1            80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "0                    0                  8                      0   \n",
       "1                    1                 10                      3   \n",
       "2                    0                  7                      3   \n",
       "3                    0                  8                      3   \n",
       "4                    1                  6                      3   \n",
       "...                ...                ...                    ...   \n",
       "1465                 1                 17                      3   \n",
       "1466                 1                  9                      5   \n",
       "1467                 1                  6                      0   \n",
       "1468                 0                 17                      3   \n",
       "1469                 0                  6                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
       "0                  1               6                  4   \n",
       "1                  3              10                  7   \n",
       "2                  3               0                  0   \n",
       "3                  3               8                  7   \n",
       "4                  3               2                  2   \n",
       "...              ...             ...                ...   \n",
       "1465               3               5                  2   \n",
       "1466               3               7                  7   \n",
       "1467               3               6                  2   \n",
       "1468               2               9                  6   \n",
       "1469               4               4                  3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "0                           0                     5  \n",
       "1                           1                     7  \n",
       "2                           0                     0  \n",
       "3                           3                     0  \n",
       "4                           2                     2  \n",
       "...                       ...                   ...  \n",
       "1465                        0                     3  \n",
       "1466                        1                     7  \n",
       "1467                        0                     3  \n",
       "1468                        0                     8  \n",
       "1469                        1                     2  \n",
       "\n",
       "[1470 rows x 35 columns]>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123ecd610>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x = dataset[\"JobSatisfaction\"]\n",
    "y = dataset[\"TotalWorkingYears\"]\n",
    "\n",
    "sns.boxplot(x=x, y=y, data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1247dbbd0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df = dataset[dataset['Attrition'] == 'Yes']\n",
    "x = df[\"EducationField\"]\n",
    "y = df[\"JobSatisfaction\"]\n",
    "\n",
    "sns.barplot(x=x, y=y, data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x126235390>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = dataset[\"MonthlyIncome\"]\n",
    "\n",
    "sns.distplot(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for +: 'int' and 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-48b7fc247aab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"MonthlyIncome\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m5000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[0;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[1;32m    196\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m             \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m         ret = um.true_divide(\n",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'"
     ]
    },
    {
     "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": [
    "x = dataset[dataset[\"MonthlyIncome\"] < 5000]\n",
    "\n",
    "sns.distplot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1\n",
      "1       0\n",
      "2       1\n",
      "3       0\n",
      "4       0\n",
      "       ..\n",
      "1465    0\n",
      "1466    0\n",
      "1467    0\n",
      "1468    0\n",
      "1469    0\n",
      "Name: Attrition, Length: 1470, dtype: int64\n",
      "0        1\n",
      "1        8\n",
      "2        2\n",
      "3        3\n",
      "4        2\n",
      "        ..\n",
      "1465    23\n",
      "1466     6\n",
      "1467     4\n",
      "1468     2\n",
      "1469     8\n",
      "Name: DistanceFromHome, Length: 1470, dtype: int64\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "\"None of [Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\\n            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\\n           dtype='int64')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-104-b3a9d5d92456>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlmplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mlmplot\u001b[0;34m(x, y, data, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, size)\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0mneed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_partial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m     \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneed_cols\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0;31m# Initialize the grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2999\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3000\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3001\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3003\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m   1283\u001b[0m                 \u001b[0;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m                 \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"raise_missing\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1286\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1092\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m         )\n\u001b[1;32m   1094\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1175\u001b[0m                 raise KeyError(\n\u001b[1;32m   1176\u001b[0m                     \"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1177\u001b[0;31m                         \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1178\u001b[0m                     )\n\u001b[1;32m   1179\u001b[0m                 )\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\\n            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\\n           dtype='int64')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "y = dataset[\"Attrition\"].replace('Yes',1).replace('No',0)\n",
    "x = dataset[\"DistanceFromHome\"]\n",
    "\n",
    "sns.lmplot(x=x, y=y, data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], dtype='int64')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-105-96e7f637f6a7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"YearsWithCurrManager\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlmplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mlmplot\u001b[0;34m(x, y, data, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, size)\u001b[0m\n\u001b[1;32m    549\u001b[0m     \u001b[0mneed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_partial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_partial\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m     \u001b[0mcols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ma\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneed_cols\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    552\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m     \u001b[0;31m# Initialize the grid\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2999\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3000\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3001\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3003\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m   1283\u001b[0m                 \u001b[0;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m                 \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"raise_missing\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1286\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1092\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m         )\n\u001b[1;32m   1094\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1175\u001b[0m                 raise KeyError(\n\u001b[1;32m   1176\u001b[0m                     \"None of [{key}] are in the [{axis}]\".format(\n\u001b[0;32m-> 1177\u001b[0;31m                         \u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1178\u001b[0m                     )\n\u001b[1;32m   1179\u001b[0m                 )\n",
      "\u001b[0;31mKeyError\u001b[0m: \"None of [Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17], dtype='int64')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "y = dataset[\"Attrition\"].replace('Yes',1).replace('No',0)\n",
    "x = dataset[\"YearsWithCurrManager\"]\n",
    "\n",
    "sns.lmplot(x=x, y=y, data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset loaded\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./data/house_pricing.csv\", sep=\",\")\n",
    "print(\"Dataset loaded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.058661</td>\n",
       "      <td>-0.125989</td>\n",
       "      <td>-0.032771</td>\n",
       "      <td>-0.074181</td>\n",
       "      <td>0.073378</td>\n",
       "      <td>-0.009551</td>\n",
       "      <td>0.021011</td>\n",
       "      <td>0.022270</td>\n",
       "      <td>0.324413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>0.058661</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.093079</td>\n",
       "      <td>-0.005842</td>\n",
       "      <td>0.074304</td>\n",
       "      <td>0.060762</td>\n",
       "      <td>-0.018584</td>\n",
       "      <td>0.071255</td>\n",
       "      <td>-0.057619</td>\n",
       "      <td>0.315856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>-0.125989</td>\n",
       "      <td>-0.093079</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.037305</td>\n",
       "      <td>-0.082864</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>0.018361</td>\n",
       "      <td>-0.028887</td>\n",
       "      <td>-0.009916</td>\n",
       "      <td>-0.128578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>-0.032771</td>\n",
       "      <td>-0.005842</td>\n",
       "      <td>-0.037305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031436</td>\n",
       "      <td>-0.007992</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>0.018645</td>\n",
       "      <td>0.044584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>-0.074181</td>\n",
       "      <td>0.074304</td>\n",
       "      <td>-0.082864</td>\n",
       "      <td>-0.031436</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051307</td>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.023217</td>\n",
       "      <td>0.010694</td>\n",
       "      <td>0.111447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>0.073378</td>\n",
       "      <td>0.060762</td>\n",
       "      <td>0.054203</td>\n",
       "      <td>-0.007992</td>\n",
       "      <td>0.051307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.029669</td>\n",
       "      <td>-0.033737</td>\n",
       "      <td>-0.059689</td>\n",
       "      <td>0.092404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>-0.009551</td>\n",
       "      <td>-0.018584</td>\n",
       "      <td>0.018361</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>0.031946</td>\n",
       "      <td>0.029669</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006495</td>\n",
       "      <td>0.004906</td>\n",
       "      <td>-0.021190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>0.021011</td>\n",
       "      <td>0.071255</td>\n",
       "      <td>-0.028887</td>\n",
       "      <td>0.029474</td>\n",
       "      <td>0.023217</td>\n",
       "      <td>-0.033737</td>\n",
       "      <td>-0.006495</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.145721</td>\n",
       "      <td>0.046432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>0.022270</td>\n",
       "      <td>-0.057619</td>\n",
       "      <td>-0.009916</td>\n",
       "      <td>0.018645</td>\n",
       "      <td>0.010694</td>\n",
       "      <td>-0.059689</td>\n",
       "      <td>0.004906</td>\n",
       "      <td>-0.145721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>0.324413</td>\n",
       "      <td>0.315856</td>\n",
       "      <td>-0.128578</td>\n",
       "      <td>0.044584</td>\n",
       "      <td>0.111447</td>\n",
       "      <td>0.092404</td>\n",
       "      <td>-0.021190</td>\n",
       "      <td>0.046432</td>\n",
       "      <td>-0.028923</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "WoodDeckSF       1.000000     0.058661      -0.125989  -0.032771    -0.074181   \n",
       "OpenPorchSF      0.058661     1.000000      -0.093079  -0.005842     0.074304   \n",
       "EnclosedPorch   -0.125989    -0.093079       1.000000  -0.037305    -0.082864   \n",
       "3SsnPorch       -0.032771    -0.005842      -0.037305   1.000000    -0.031436   \n",
       "ScreenPorch     -0.074181     0.074304      -0.082864  -0.031436     1.000000   \n",
       "PoolArea         0.073378     0.060762       0.054203  -0.007992     0.051307   \n",
       "MiscVal         -0.009551    -0.018584       0.018361   0.000354     0.031946   \n",
       "MoSold           0.021011     0.071255      -0.028887   0.029474     0.023217   \n",
       "YrSold           0.022270    -0.057619      -0.009916   0.018645     0.010694   \n",
       "SalePrice        0.324413     0.315856      -0.128578   0.044584     0.111447   \n",
       "\n",
       "               PoolArea   MiscVal    MoSold    YrSold  SalePrice  \n",
       "WoodDeckSF     0.073378 -0.009551  0.021011  0.022270   0.324413  \n",
       "OpenPorchSF    0.060762 -0.018584  0.071255 -0.057619   0.315856  \n",
       "EnclosedPorch  0.054203  0.018361 -0.028887 -0.009916  -0.128578  \n",
       "3SsnPorch     -0.007992  0.000354  0.029474  0.018645   0.044584  \n",
       "ScreenPorch    0.051307  0.031946  0.023217  0.010694   0.111447  \n",
       "PoolArea       1.000000  0.029669 -0.033737 -0.059689   0.092404  \n",
       "MiscVal        0.029669  1.000000 -0.006495  0.004906  -0.021190  \n",
       "MoSold        -0.033737 -0.006495  1.000000 -0.145721   0.046432  \n",
       "YrSold        -0.059689  0.004906 -0.145721  1.000000  -0.028923  \n",
       "SalePrice      0.092404 -0.021190  0.046432 -0.028923   1.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dataset.iloc[:,-15:].corr()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.7/site-packages/ipykernel_launcher.py:6: DeprecationWarning: bias and ddof have no effect and are deprecated\n",
      "  \n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-123-bc143750c5fb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"YrSold\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"LotArea\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[0;34m(x, y, rowvar, bias, ddof)\u001b[0m\n\u001b[1;32m   2524\u001b[0m         warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[1;32m   2525\u001b[0m                       DeprecationWarning, stacklevel=3)\n\u001b[0;32m-> 2526\u001b[0;31m     \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2527\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2528\u001b[0m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mcov\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[1;32m   2380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2381\u001b[0m     \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2382\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mrowvar\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2383\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2384\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1553\u001b[0m             \u001b[0;34m\"The truth value of a {0} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1554\u001b[0m             \"Use a.empty, a.bool(), a.item(), a.any() or a.all().\".format(\n\u001b[0;32m-> 1555\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1556\u001b[0m             )\n\u001b[1;32m   1557\u001b[0m         )\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "v = dataset[\"PoolArea\"]\n",
    "w = dataset[\"SalePrice\"]\n",
    "x = dataset[\"MSSubClass\"]\n",
    "y = dataset[\"YrSold\"]\n",
    "z = dataset[\"LotArea\"]\n",
    "np.corrcoef(v,w,x,y,z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(20,5))\n",
    "sns.heatmap(df, annot=True, ax =ax)\n",
    "plt.savefig('heatmapEX1.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
      "Wall time: 6.91 µs\n"
     ]
    }
   ],
   "source": [
    "%time"
   ]
  },
  {
   "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}