HW2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model \n",
    "%matplotlib inline\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data = pd.read_csv('./orders_20190822.csv', sep=\";\", decimal=',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2002804 entries, 0 to 2002803\n",
      "Data columns (total 4 columns):\n",
      "id_o       int64\n",
      "user_id    int64\n",
      "price      float64\n",
      "o_date     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2)\n",
      "memory usage: 61.1 MB\n"
     ]
    }
   ],
   "source": [
    "my_data[\"o_date\"]=pd.to_datetime(my_data[\"o_date\"], format='%d.%m.%Y')\n",
    "my_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(my_data.id_o.unique())== len(my_data.id_o)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1970296 entries, 0 to 2002803\n",
      "Data columns (total 4 columns):\n",
      "id_o       int64\n",
      "user_id    int64\n",
      "price      float64\n",
      "o_date     datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(1), int64(2)\n",
      "memory usage: 75.2 MB\n"
     ]
    }
   ],
   "source": [
    "my_data_filtered=my_data[(my_data.price>150) & (my_data.price<=100000)]\n",
    "my_data_filtered.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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>id_o</th>\n",
       "      <th>user_id</th>\n",
       "      <th>price</th>\n",
       "      <th>o_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1234491</td>\n",
       "      <td>337544</td>\n",
       "      <td>539.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1234494</td>\n",
       "      <td>171642</td>\n",
       "      <td>153.3</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1234498</td>\n",
       "      <td>1105609</td>\n",
       "      <td>752.5</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1234500</td>\n",
       "      <td>982696</td>\n",
       "      <td>4410.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1234503</td>\n",
       "      <td>1105614</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_o  user_id   price     o_date  year  month\n",
       "0  1234491   337544   539.0 2016-01-01  2016      1\n",
       "1  1234494   171642   153.3 2016-01-01  2016      1\n",
       "3  1234498  1105609   752.5 2016-01-01  2016      1\n",
       "4  1234500   982696  4410.0 2016-01-01  2016      1\n",
       "5  1234503  1105614   595.0 2016-01-01  2016      1"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data_filtered[\"year\"]=pd.DatetimeIndex(my_data_filtered[\"o_date\"]).year\n",
    "my_data_filtered[\"month\"]=pd.DatetimeIndex(my_data_filtered[\"o_date\"]).month\n",
    "my_data_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x141be6a90>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FpaYf8Pb21svzlVKWOXL6CF8kfMF1ra6jnX87q+NUik4/oJRSxd7f9D4AD3d92OIklafFXSmlgN3Hd7No7yLuaH8HwbWDrY5TaVrclVIKeG/De/h5+XFf+H1WR7ELLe5KqWpvw9EN/JbyG/d2vpd6NepZHccutLgrpaq9eTvnUa9GPf6nw/9YHcVutLgrpaq103mnWXFgBUNDh1LTq6bVcexGi7tSqlr7Zf8v5BTkMLzlcKuj2JUWd6VUtbZ472JC6oTQJaiL1VHsSou7UqraOnL6COuOrGN4y+FVdg6Zi9HirpSqtpbsW4LBuN0hGdDirpSqpowxRO+NpktQF5rVbWZ1HLvT4q6UqpZ2Ht/JnhN73HLUDlrclVLVVPTeaLw8vBgaWnLtIfegxV0pVe3kF+azZN8S+jfpT33f+lbHcQiXmvJXKUfJL8znRO4Jjucc53jOcTJyM+jo39Etj7Wqsq07vI5j2ce4rtV1VkdxGC3uqspLykwiISOBjJyMosJ94c/comKemZuJKbFme8eAjswfPt+i1MpK0YnR1PGpwxVNr7A6isNocVdVWlJmEjctuon8wnwABKF+jfr4+/rTwLcBreu3Pn+/QY0G5++vSlnF7O2zOXL6CI38Gln8LpQznck7w/IDyxnWchg+nj5Wx3GYMou7iIQAnwGNgEJgujHmvRJtooDvgX3Fm74xxky2b1Sl/u7dDe/i4+HDnGvmEFw7mHo+9fD08CzzdUE1g5i9fTa/Jf/GiPYjnJBUuYrlB5aTnZ/ttmfJnGPLF6r5wBPGmA5AH+AhEelYSrtVxpiuxTct7Mrh1h9dz/IDy7m38710CuyEv6+/TYUdoEW9FjSr04wVKSscnFK5mui90TSp3YRuDf+2NLRbKbO4G2MOG2M2FN/PAhKAJo4OptSlFJpC3op9i4a1GnJ3x7vL/XoRISokinWH13E677QDEipXlHomlbVH1jKs5TA8xL1PFizXuxORUKAbsLaUpy8Xkc0i8qOIdLrI6+8XkTgRiUtLSyt3WKXO+TnpZ+LT43mk2yMVnqY1KiSKvMI8/jj0h53TKVf1474fKTSFXNfSfc+SOcfm4i4itYGvgUeNMSdLPL0BaG6M6QJMBb4rbR/GmOnGmAhjTERQUFBFM6tqLrcgl3fXv0u7Bu0qddy0W8Nu1PWpS0xyjP3CKZcWvTeazoGdCa0XanUUh7OpuIuIN0WF/QtjzDclnzfGnDTGnCq+vwTwFpFAuyZVqtjchLkcOn2IJyKesPkYe2m8PLwY0HQAK1NWnj/bRrmvXcd3sfP4Toa1HGZ1FKcos7hL0TyYM4AEY8zbF2nTqLgdItKreL/p9gyqFMCJnBNM3zqdfk36cXnjyyu9v6iQKE7knmBz2mY7pFOubPHexXiJF9e0uMbqKE5hy3nukcDdwFYR2VS87TmgGYAx5kPgVmC8iOQD2cBIY4wpbWdKVcZHWz7idN5pHu/xuF32F9k4Ei8PL35L/o0el/Wwyz6V6ykoLOCHxB+IbBKJv6+/1XGcosziboxZDVxyFntjzH+B/9orlFKlOXDyAPN2zuOm1jfRpkEbu+yztk9tejXqxYrkFTweYZ8/GMr1xB6NJTU7ladaPWV1FKdx73OBlFt5d8O7eHt481DXh+y636iQKJJOJrEvc1/ZjVWVFL03mtretYlqGmV1FKfR4q6qhE2pm/hl/y+MDhtNUC37nml17n/435J/s+t+lWvIzs9m2f5lDAkdgq+Xr9VxnEaLu3J5xhjejHuToJpB3NPxHrvvP7h2MO3927MiWa9WdUe/HviVM/ln3H66gZK0uCuXt3T/UrakbWFCtwnU8q7lkD6iQqLYlLaJ4znHHbJ/ZZ3oxGiC/YKr3RfmWtyVSztbcJZ3179LmwZtuL7V9Q7rJyokikJTyKqDqxzWh3K+Y9nHWHNoTbWYbqCk6vVuVZUzb8c8Uk6l8ESPyl2wVJaO/h1pWLOhXq3qZqrTdAMlaXFXLiszN5OPtnxE38Z9iWwS6dC+zk0ktvrganILch3al3Ke6L3RdAzoSMv6La2O4nRa3JXLmr5lOllns+x2wVJZokKiyM7PJvZIrFP6U46198ReEjISqt0XqedocVcuKTkrmS93fMmNrW+knX87p/TZK7gXNb1q6qEZNxG9NxpP8aw20w2UpMVduaT3NrznkAuWLqWGZw0iG0eyInkFOntG1VZoCvlh3w9c3vhyAmtWzzkMtbgrl7M5bTM/J/3MPZ3u4TK/y5zad1RIFKlnUknISHBqv1bJK8gjMzfT6hh2t/7oeo6cPlItv0g9R4u7cinGGP4T9x8CfAMY3Wm00/vv37Q/HuJh+aGZgsICh/exL3Mft0bfyvBvh3Po1CGH9+dM0Xuj8fP2Y2CzgVZHsYwWd+VSlh9YzsbUjTzc7WGHXbB0Kf6+/nQN6mppcU87k0bUgigej3mcEzknHNLHsv3LuOOHOziec5z8wnyeiHmCswVnHdKXs+Xk5/DL/l+4qtlVFV6lyx1ocVcuI68gj3fWv0Pr+q25sfWNluWICokiISOBI6ePWNL/nIQ5nDx7khXJK7h50c38fvB3u+07vzCfd9a/w2Mxj9GyXksWXLeA/xf5/4hPj+ffsf+2Wz9W+mX/L5zKO8XwVtXzLJlztLgrl/FT0k8cyDrAo90fxcvDlqUGHCMqJArAktF71tksFuxcwODmg/ny2i+p61OXB5Y9wOtrXycnP6dS+87IyeCBZQ8wM34mt7W9jVlDZ9HIrxGDmg9iVKdRzN85n8WJi+30TpzjTN4Z4o7E8Wn8pzwe8zhXLbyK51Y/R2O/xvS8rKfV8Sxl3f9BSpUwf+d8QuuG0r9pf0tztKjXgtC6ocQkxzCy/Uin9r1w10JO5Z1iTNgYOgR0YN7weby34T3mJMzhz8N/8nr/1+kY0LHc+92atpXHf3ucjOwMJvedzE1tbvrL8xO7T2RL2hYmr5lM+wbtad2gtb3ekt0UFBaQmJnI1mNb2ZK2ha3HtrLnxB4KTSEATWo3oXvD7nQO6sygZoMcekVzVaDFXbmEHRk72Jy2macinnKJOUCiQqKYkzCHU2dPUduntlP6PFtwljnb59AnuM/5Au7r5cvTvZ6mf9P+vLj6Re784U4e6vYQozuNtrl4fbXrK15b+xpBNYP4/NrPS/3j4OXhxVtXvMVt0bfxWMxjzB0212nv+1I2HN3AypSVbD22lfhj8ZzJPwNAHe86hAWGEdU5ivDAcMICwwioGWBxWtdSZnEXkRDgM6ARUAhMN8a8V6KNAO8B1wJngFHGmA32j6vc1fyd8/H19OWG1jdYHQUoKu6zts3ij0N/MCR0iFP6jN4bTVp2Gq/2e/Vvz/Vt3JdvbviGyWsm896G91iVsopX+71K0zpNL7q/3IJcXlv7Gt/s/oa+jfvyRv83qO9b/6Ltg2oF8eYVb3Lf0vuY9Mck/nPFfyheGtnp8gvzmbpxKjPjZ+IlXrT1b8t1ra4jPCiczoGdaV63uUsMAlyaMeaSNyAY6F58vw6wC+hYos21wI8ULcfXB1hb1n579OhhlDLGmKzcLNNzTk/zwuoXrI5yXl5Bnuk3t595duWzTukvvyDfDPtmmLk9+nZTWFh40XaFhYVm0Z5Fps8XfUzvL3qb73Z/V2r7lKwUc3v07SZsVpiZsmGKyS/ItznLjK0zTNisMPPZts8q9F4qK+1Mmhn14ygTNivM/POPf5rTZ09bksNVAXGmjPpqjCn7C1VjzGFTPAo3xmQBCUCTEs1uAM79JvwJ1BeRYDv87VHVQHRiNNn52Yxs59zj25fi5eHFgKYDWHlwJfmF+Q7v79fkX9l/cj9jwsZccrQsIlzX6jq+vv5r2vu354XfX+CJ3574yymTfxz8gxGLR3Dg5AGmDJzChG4TynX8eXSn0VwZciVvx73NxtSNlXpf5RV7JJbbom8j/lg8r/V7jUmXT7LklFh3UK5/14hIKNANWFviqSZA8gWPU/j7HwBE5H4RiRORuLS0tPIlVW7JGMP8HfPpFNCJToGdrI7zF1EhUWTmZrIpdZND+zHGMHPrTJrVacZVza6y6TWNazdmxpAZPNbjsfOnTK4+uJrpW6bzwLIHCKoZxLzh8yp0EY+I8Eq/VwiuHcyTMU+Snp1e7n2UV6EpZMbWGYxdOpba3rX5ctiXXNeq+l5dag82F3cRqQ18DTxqjDlZ8ulSXvK3yTmMMdONMRHGmIigIPuug6mqpvVH17M3cy8j2o2wOsrf9G3cF28Pb4efEhl7JJb49Hju6XRPuUbYnh6ejAkbc/6UyfHLxjN141SGthjKF9d+QfO6zSucqa5PXd6JeofMs5k8vfJph14xm5mbycRfJ/LuhncZ3Hww84bPo02DNg7rr7qwqbiLiDdFhf0LY8w3pTRJAUIueNwUcK/rmZVDzN85nzo+dRjaYqjVUf7Gz9uPXsG9HD6R2Mz4mQT4BlT4y+Rzp0yOCx/Hi31e5I3+b9jlUEY7/3Y83/t51h5Zy/ub3q/0/kqzLX0bIxaPYPWh1TzT6xneHPAmft5+DumruimzuBefCTMDSDDGvH2RZouAf0iRPkCmMeawHXMqN3Qs+xjLDizjhlY3uOxl4gObDuRA1gH2ndznkP3vyNjB74d+566Od1HDs0aF9+Pr5cvD3R7m9na32/UMl5va3MQtbW7h460f81vyb3bbrzGGBTsXcPeSuykwBcwaOos7O9xp2dk57siWkXskcDdwpYhsKr5dKyIPiMgDxW2WAInAHuBj4EHHxFXu5Nvd35JfmM/t7W63OspFXRFyBeC4q1Vnxs/Ez9vPpT+DZ3s/Swf/Djy7+lmSs5LLfkEZzuSd4fnVz/PKn6/Qq1EvFgxfQJegLnZIqi5ky9kyq40xYowJN8Z0Lb4tMcZ8aIz5sLiNMcY8ZIxpZYzpbIyJc3x0VZUVFBawcNdCegf3pkW9FlbHuahGfo3o4N/BIcU9JSuFn5N+5ra2t1HXp67d928vNTxr8J+o/wDwRMwTlVqGcF/mPu5ccieLExfzUNeHmHbVNBr4NrBXVHUBvQpAWWLVwVUcPn3YJb9ILWlgyEA2pW4iIyfDrvudvW02HuLBXR3usut+HSGkTgiv9XuNhIwEXl/7erlem52fTUpWCt/v+Z6Ri0eSnp3Oh4M/5IEuD+iFSA6k0w8oS8zbOY+GNRuen6TLlUWFRDFt8zRWpqy022yVGTkZfLfnO65reZ3TFySpqKiQKMZ2HssnWz+hc2Bn+jbuy7HsY6TnpBf9zE4//zg9O/389tN5p8/vo0tQF9664i0a+TWy8J1UD1rcldMlZyXzx8E/eKDLA3h7eFsdp0zt/dtzWa3LiEmOsVtx/zLhS3ILchkVNsou+3OWh7o+xJa0Lby85uVSn6/jU4fAmoEE+AbQwb8DATUDzj9uWKshvYJ7VYn/5u5Ai7tyuoW7FuIhHtzS5haro9hERIgKiWLR3kXkFuRW6qwWKPpCce6OuQwMGUjLei3tlNI5vDy8eDvqbRYnLsbX0/cvxTugZgA+nj5WR1TFtLgrp8otyOXb3d8yMGRglTkcAUXH3efvnM/aw2sZ0HRApfb19e6vOXn2JGM6j7FTOueqV6Med3a40+oYqgz6bYZyqqVJSzmRe8KlT/0rTc9GPanlVavSZ83kFebx2fbP6HFZDz39TzmUFnflVOcW5Ogd3NvqKOXi4+lDZJNIlu5fytrDJadWst2P+37kyOkjjAmrmqN2VXVocVdOszNjJ5vTNnNb29uq5Clw48LHUdenLmOXjuX51c9zPOd4uV5faAr5NP5T2jRoQ/8m1q42pdxf1fs/TFVZrrYgR3m182/HN9d/w32d72NJ4hKu/+56vt/zvc3zzqxKWcWeE3vKnNZXKXvQ4q6c4tTZUyxOXMzQFkOpV6Oe1XEqzNfLl0e6P8KC6xYQWjeUF35/gbFLx5KUmVTma2fEz6CxX2OGhrreJGnK/WhxV05xbkGOqnBFqi3aNGjD7Gtm82KfF0lIT+DmRTfzweYPOFtwttT2G1M3sjF1I//o9A+8PPQkNeV4WtyVw52bAbBTQCfCAsOsjmM3HuLB7e1uZ9FNixjUbBDTNk3j1uhbiTvy96mVZm6dSf0a9bmp9U1PFCZRAAAP6klEQVQWJFXVkRZ35XDrj65nz4k9bjNqLymwZiBvXvEm0wZN42zBWUb/PJqX/niJzNxMAPYc30NMSgz/0/5/dMk45TT670PlcAt2LnDZBTnsqX/T/nxz2Td8uOVDPtv2GTHJMTzV8ynWHFpDTa+a3NH+DqsjqmpER+7KoY5lH+OXA7+49IIc9lTLuxaP93ic+cPn06R2E55d9SyL9i7i5jY3U9+3vtXxVDWiI3flUFVhQQ5HaOffjs+v+ZyFuxayZN8SRnUaZXUkVc1ocVcOU1UW5HAUTw9PRrYfycj2I62OoqohPSyjHKYqLcihlLuxZYHsmSKSKiLxF3k+SkQyL1hfdZL9Y6qqaP7O+VVmQQ6l3I0tI/dZQFmnOay6YH3VyZWPpaq65JPJ/H7wd25pe4suzqCUBco85m6MWSkioY6PotzBybMnmZswl88TPsfLw6vKLMihlLux1xeql4vIZuAQ8KQxZltpjUTkfuB+gGbNmtmpa+UKMnIymLN9DnN3zOVU3imuaHoF47uMr1ILcijlTuxR3DcAzY0xp0TkWuA7oE1pDY0x04HpABEREbZNpadcWuqZVGZtm8VXu74iJz+Hwc0Hc1/4fbT3b291NKWqtUoXd2PMyQvuLxGRaSISaIw5Vtl9K9d18NRBPo3/lG92f0OhKWRYy2HcG3YvLetXrTVBlXJXlS7uItIIOGqMMSLSi6IvadMrnUy5pKTMJD7Z+gk/JP4AAje2vpExYWMIqRNidTSl1AXKLO4iMheIAgJFJAV4CfAGMMZ8CNwKjBeRfCAbGGlsXb1AVRk7M3byydZP+DnpZ3w8fRjRfgSjOo2ikV8jq6MppUphy9kyl5ztyBjzX+C/dktUhRlj+GTrJ6ScSuHFPi+6xbzdxhheX/c6c3fMpZZXLUaHjebujncTWDPQ6mhKqUuo+tXHRRhj+Hfsv5mTMAeA2t61earnUxanqrx3N7zL3B1zGdluJA93e7hKr6KkVHWixd0OCk0hr/z5Cl/t+oq7OtxFgSngs+2f0c6/Hde3ut7qeBU2e9tsZsbPZES7ETzX+zld91OpKkSLeyXlF+Yz6fdJRCdGc1/n+5jQbQL5Jp+9J/byzz/+SYu6Legc1NnqmOX2/Z7veSvuLYY0H8KzvZ7Vwq5UFaMTh1VCXkEe/7vyf4lOjGZCtwk80v0RRARvD2/euuItgmoF8eiKR0k7k2Z11HL5Lfk3XvrjJfoE9+H1/q/j6eFpdSSlVDlpca+g3IJcHo95nF/2/8KTEU9yf/j9f3m+gW8Dplw5hay8LB6NeZTcglyLkpbPhqMbeOK3J2jv3553B76Lj6eP1ZGUUhWgxb0CsvOzmbB8AjEpMbzQ+wXu6XRPqe3aNmjLq/1eZUvaFl5Z8wqufobozoydPLz8YYL9gpl21TT8vP2sjqSUqiAt7uV0Ou80D/zyAGuPrOWVyFcY0f7Sc5UPbj6YB7o8wPd7v+fLHV86KWX5pWSlMH7ZeGp61+SjwR/h7+tvdSSlVCXoF6rlkJmbyYPLHmRb+jbe6P+GzQs+j+8ynl0Zu3gz9k1a1W9Fn+A+Dk5aPunZ6Yz7ZRy5BbnMHjqbxrUbWx1JKVVJOnK3UUZOBmOXjiUhI4G3o962ubADeIgHr/V/jRb1WvDkb0+SnJXswKTlc+rsKcYvG0/qmVTeH/Q+rRu0tjqSUsoOtLjbIO1MGmN+GsO+zH1MvXIqVza7stz78PP2Y8rAKRhjeOTXRzidd9oBScsntyCXiSsmsvv4bt6OepuuDbtaHUkpZSda3Mtw+NRhRv00ikOnD/HBVR8Q2SSywvsKqRvCW1e8RWJmIs+vfp5CU2jHpOVTUFjAMyufYd2RdbzS7xX6N+1vWRallP3pMfdLSD6ZzNilYzl59iTTB0+3y8j28saX82TEk/w79t98tPkjxncdX+59JGYmsnz/ctYdWUfDWg1pU78NbRu0pa1/WwJ8A8q84MgYwyt/vsKyA8t4uufTDG85vKJvRynlorS4X8Sm1E08uuJR8k0+n1z9CZ0COtlt33d1uIsdGTuYtnkabRq04armV12yvTGG7RnbWb5/OcsPLCcxMxEoOtUyMTORRXsXnW/r7+tPmwbFxb741qp+K2p41jjfZurGqXy9+2vu63wfd3W8y27vSynlOrS4l+KrXV/x6tpXaezXmClXTqFV/VZ23b+IMOnySSRlJvHc6udoVrcZbRu0/UubgsICNqZuZPmBooJ++PRhPMWTiMsiGNl+JFeGXHl+CbsTOSfYfWI3u47vKrpl7GLhzoXkFOQA4CmeNK/bnLYN2lLTqybf7vmWW9rcwoRuE+z6vpRSrkOsurAmIiLCxMXFWdL3xeQV5PFG7BvM3zmfvo378u8B/3boLIipZ1IZuXgkPp4+zBs2Dz9vP9YeWcuy/ctYkbyCjJwMfDx86Nu4L4OaDyKqaRT1fevbtO+CwgKSs5L/r+AX3w6eOsjQ0KH8q/+/dFoBpaogEVlvjIkos50W9yLHso/xRMwTbEjdwOhOo5nYfaJTit+WtC2M+mkUwX7BZORkcCrvFH7efgxoMoBBzQfRr0k/u14pmluQ+5dDNEqpqsXW4q6HZYBt6duY+OtETuSe4F/9/8WwlsOc1nd4UDiTIyczdcNUhoQOYVCzQfQJ7uOwOV20sCtVPVT74r44cTEv//Ey/r7+fHbNZ3QM6Oj0DMNbDtczVpRSdlXmee4iMlNEUkUk/iLPi4hMEZE9IrJFRLrbP6b95Rfm81bsWzy76lnCAsOYO2yuJYVdKaUcwZaLmGYBl7rW/hqgTfHtfuCDysdyrHNzxMzePpuR7Uby8ZCPCagZYHUspZSyG1sWyF4pIqGXaHID8Jkp+mb2TxGpLyLBxpjDdspoV7uP7+aRXx/hyJkj/LPvP7m5zc1WR1JKKbuzxzH3JsCFM2GlFG/7W3EXkfspGt3TrFmzCnWWnp1OYmYidXzqUNenLnV86uDn7YeHlP2PkGX7l/Hc6ufw8/bj06s/1blUlFJuyx7FvbRr3Us9v9IYMx2YDkWnQlaks9ijsTz121N/2eYhHtT2rv2Xgn/u57n7x7KPMW/nPDoHduadqHfOXwCklFLuyB7FPQUIueBxU+CQHfZbql6NejFjyAyyzmZx8uxJTp49ef5+1tms87ekk0nnt2XnZwNwY+sbeaHPC3o6oFLK7dmjuC8CHhaReUBvINORx9v9ff3pFdyrXK/JK8gjpyCHOj51HJRKKaVcS5nFXUTmAlFAoIikAC8B3gDGmA+BJcC1wB7gDDDaUWErytvTG29Pb6tjKKWU09hytswdZTxvgIfslkgppVSl6WIdSinlhrS4K6WUG9LirpRSbkiLu1JKuSEt7kop5Ya0uCullBvS4q6UUm5Ii7tSSrkhLe5KKeWGtLgrpZQb0uKulFJuSIu7Ukq5IS3uSinlhrS4K6WUG9LirpRSbkiLu1JKuSEt7kop5YZsKu4iMlREdorIHhF5ppTnR4lImohsKr6NtX9UpZRStrJlDVVP4H1gMJACxIrIImPM9hJN5xtjHnZARqWUUuVky8i9F7DHGJNojDkLzANucGwspZRSlWFLcW8CJF/wOKV4W0m3iMgWEflKREJK25GI3C8icSISl5aWVoG4SimlbGFLcZdStpkSj6OBUGNMOLAMmF3ajowx040xEcaYiKCgoPIlVUopZTNbinsKcOFIvClw6MIGxph0Y0xu8cOPgR72iaeUUqoibCnusUAbEWkhIj7ASGDRhQ1EJPiCh9cDCfaLqJRSqrzKPFvGGJMvIg8DPwOewExjzDYRmQzEGWMWAY+IyPVAPpABjHJgZqWUUmUQY0oePneOiIgIExcXZ0nfSilVVYnIemNMRFnt9ApVpZRyQ1rclVLKDWlxV0opN6TFXSml3JAWd6WUckNa3JVSyg1pcVdKKTekxV0ppdyQFnellHJDWtyVUsoNaXFXSik3pMVdKaXckBZ3pZRyQ1rclVLKDWlxV0opN6TFXSml3JAWd6WUckNa3JVSyg3ZVNxFZKiI7BSRPSLyTCnP1xCR+cXPrxWRUHsHVUopZbsyi7uIeALvA9cAHYE7RKRjiWb3AseNMa2Bd4A37B1UKaWU7bxsaNML2GOMSQQQkXnADcD2C9rcALxcfP8r4L8iIsYBq2//Oe0+6pxIsPdulVLKabLqd6DPgx87tA9bDss0AZIveJxSvK3UNsaYfCATCCi5IxG5X0TiRCQuLS2tYomVUkqVyZaRu5SyreSI3JY2GGOmA9MBIiIiKjSqd/RfO6WUcge2jNxTgJALHjcFDl2sjYh4AfWADHsEVEopVX62FPdYoI2ItBARH2AksKhEm0XAPcX3bwV+dcTxdqWUUrYp87CMMSZfRB4GfgY8gZnGmG0iMhmIM8YsAmYAn4vIHopG7CMdGVoppdSl2XLMHWPMEmBJiW2TLrifA9xm32hKKaUqSq9QVUopN6TFXSml3JAWd6WUckNa3JVSyg2JVWcsikgasL+CLw8EjtkxTlWmn0UR/RyK6OdQxJ0/h+bGmKCyGllW3CtDROKMMRFW53AF+lkU0c+hiH4ORfRz0MMySinllrS4K6WUG6qqxX261QFciH4WRfRzKKKfQ5Fq/zlUyWPuSimlLq2qjtyVUkpdghZ3pZRyQ1WuuJe1WHd1ISJJIrJVRDaJSJzVeZxJRGaKSKqIxF+wzV9EfhGR3cU/G1iZ0Rku8jm8LCIHi38vNonItVZmdAYRCRGRFSKSICLbRGRi8fZq9ztxoSpV3G1crLs6GWiM6VoNz+edBQwtse0ZYLkxpg2wvPixu5vF3z8HgHeKfy+6Fs/o6u7ygSeMMR2APsBDxXWhOv5OnFelijsXLNZtjDkLnFusW1UjxpiV/H2lrxuA2cX3ZwM3OjWUBS7yOVQ7xpjDxpgNxfezgASK1nWudr8TF6pqxd2WxbqrCwMsFZH1InK/1WFcwGXGmMNQ9D870NDiPFZ6WES2FB+2qVaHIkQkFOgGrKWa/05UteJu00Lc1USkMaY7RYeoHhKRAVYHUi7hA6AV0BU4DPzH2jjOIyK1ga+BR40xJ63OY7WqVtxtWay7WjDGHCr+mQp8S9Ehq+rsqIgEAxT/TLU4jyWMMUeNMQXGmELgY6rJ74WIeFNU2L8wxnxTvLla/05UteJuy2Ldbk9E/ESkzrn7wBAg/tKvcnsXLtJ+D/C9hVksc66YFbuJavB7ISJC0TrOCcaYty94qlr/TlS5K1SLT+16l/9brPtViyM5nYi0pGi0DkXr4H5ZnT4HEZkLRFE0retR4CXgO2AB0Aw4ANxmjHHrLxsv8jlEUXRIxgBJwLhzx53dlYj0A1YBW4HC4s3PUXTcvVr9TlyoyhV3pZRSZatqh2WUUkrZQIu7Ukq5IS3uSinlhrS4K6WUG9LirpRSbkiLu1JKuSEt7kop5Yb+P53eVlOEHq5WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "price_by_months=my_data_filtered.loc[:, [\"price\", \"year\", \"month\"]].groupby([\"year\",\"month\"]).sum()\n",
    "price_by_months[\"price\"]=price_by_months[\"price\"].round()\n",
    "price_by_months=pd.DataFrame(price_by_months.to_records())\n",
    "price_by_months.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_by_months[\"index\"]=range(1, len(price_by_months)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "x=np.array(price_by_months[\"index\"]).reshape(len(price_by_months), 1)\n",
    "y=np.array(price_by_months[\"price\"]).reshape(len(price_by_months), 1)\n",
    "regr.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y,  color='black')\n",
    "plt.plot(x, regr.predict(x), color='blue', linewidth=3)\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "deviation    1.00168\n",
       "dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_by_months[\"trend\"]=regr.predict(x)\n",
    "price_by_months[\"deviation\"]=price_by_months[\"price\"]/price_by_months[\"trend\"]\n",
    "\n",
    "month_deviation=price_by_months.loc[:, [\"month\", \"deviation\"]].groupby(\"month\").mean()\n",
    "common_deviation = month_deviation.mean()\n",
    "common_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>deviation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.981557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.871994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.038758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.043108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.908183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       deviation\n",
       "month           \n",
       "1       0.981557\n",
       "2       0.871994\n",
       "3       1.038758\n",
       "4       1.043108\n",
       "5       0.908183"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seazon_coeff = month_deviation/common_deviation\n",
    "seazon_coeff.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "trend_nexsts=regr.predict(np.array(range(25, 37)).reshape(12, 1))\n",
    "trend_nexsts=trend_nexsts.reshape(1, 12)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=pd.DataFrame({\"month\":range(1, 13), \"trend\":trend_nexsts})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "for ind in predict.index.values:\n",
    "    predict[\"price\"][ind]=(predict[\"trend\"][ind] * seazon_coeff[\"deviation\"][predict[\"month\"][ind]])\n",
    "predict[\"price\"]=predict[\"price\"].astype('int32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict[\"real_price\"]=[ 256798898,\n",
    " 232640416,\n",
    " 267994924,\n",
    " 262849522,\n",
    " 276933049,\n",
    " 251486085,\n",
    " 250559778,\n",
    " 261724749,\n",
    " 276675505,\n",
    " 287647539,\n",
    " 363102609,\n",
    " 422386052]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     288420639\n",
       "1     263562734\n",
       "2     322706935\n",
       "3     332834242\n",
       "4     297422997\n",
       "5     277143260\n",
       "6     266840682\n",
       "7     306149589\n",
       "8     317199478\n",
       "9     424605656\n",
       "10    496307011\n",
       "11    520870406\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict[\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "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": [
    "import calendar\n",
    "plt.plot(predict['month'], predict['price'],  color='black', label=\"predict\")\n",
    "plt.plot(predict['month'], predict['real_price'], color='blue', linewidth=3, label=\"real\")\n",
    "plt.plot(predict['month'], price_by_months.loc[price_by_months.year==2017, \"price\"], color='orange', label=\"2017\")\n",
    "plt.plot(predict['month'], price_by_months.loc[price_by_months.year==2016, \"price\"], color='red', label=\"2016\")\n",
    "\n",
    "plt.xticks(np.arange(12), calendar.month_name[1:13], rotation=20)\n",
    "plt.ticklabel_format(axis='y',style='plain',scilimits=(0,3))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}