SberMarket.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\notebookfa5a3adad6.ipynb\n",
      "D:\\SberMarket\\sample_submission.csv\n",
      "D:\\SberMarket\\sample_submission.csv.zip\n",
      "D:\\SberMarket\\tab_4_user_profiles.csv\n",
      "D:\\SberMarket\\tab_4_user_profiles.csv.zip\n",
      "D:\\SberMarket\\tab_6_city.csv\n",
      "D:\\SberMarket\\test-recsys.zip\n",
      "D:\\SberMarket\\.ipynb_checkpoints\\notebookfa5a3adad6-checkpoint.ipynb\n",
      "D:\\SberMarket\\kaggle_tab_1345\\tab_1_orders.csv\n",
      "D:\\SberMarket\\kaggle_tab_1345\\tab_3_categories.csv\n",
      "D:\\SberMarket\\kaggle_tab_1345\\tab_4_user_profiles.csv\n",
      "D:\\SberMarket\\kaggle_tab_1345\\tab_5_product_properties.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-01-01.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-01-11.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-01-21.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-01-31.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-02-10.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-02-20.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-03-01.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-03-11.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-03-21.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_1\\tab_2_products_2020-03-31.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_2\\tab_2_products_2020-04-10.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_2\\tab_2_products_2020-04-20.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_2\\tab_2_products_2020-04-30.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_3\\tab_2_products_2020-05-10.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_3\\tab_2_products_2020-05-20.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_3\\tab_2_products_2020-05-30.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_4\\tab_2_products_2020-06-09.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_4\\tab_2_products_2020-06-19.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_4\\tab_2_products_2020-06-29.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_5\\tab_2_products_2020-07-09.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_5\\tab_2_products_2020-07-19.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_5\\tab_2_products_2020-07-29.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_6\\tab_2_products_2020-08-08.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_6\\tab_2_products_2020-08-18.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_6\\tab_2_products_2020-08-28.csv\n",
      "D:\\SberMarket\\sbermarket_tab_2_6\\tab_2_products_2020-09-07.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('D:\\SberMarket'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\kaggle_tab_1345\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\kaggle_tab_1345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('tab_1_orders.csv')\n",
    "df3 = pd.read_csv('tab_3_categories.csv')\n",
    "df4 = pd.read_csv('tab_4_user_profiles.csv')\n",
    "df5 = pd.read_csv('tab_5_product_properties.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\n"
     ]
    }
   ],
   "source": [
    "cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df6 = pd.read_csv('tab_6_city.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_1\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_1_1 = pd.read_csv('tab_2_products_2020-01-01.csv')\n",
    "df2_1_2 = pd.read_csv('tab_2_products_2020-01-11.csv')\n",
    "df2_1_3 = pd.read_csv('tab_2_products_2020-01-21.csv')\n",
    "df2_1_4 = pd.read_csv('tab_2_products_2020-01-31.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_1_1['month']=1\n",
    "df2_1_1['moment']=1\n",
    "df2_1_2['month']=1\n",
    "df2_1_2['moment']=2\n",
    "df2_1_3['month']=1\n",
    "df2_1_3['moment']=3\n",
    "df2_1_4['month']=1\n",
    "df2_1_4['moment']=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_2_1 = pd.read_csv('tab_2_products_2020-02-10.csv')\n",
    "df2_2_2 = pd.read_csv('tab_2_products_2020-02-20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_2_1['month']=2\n",
    "df2_2_1['moment']=1\n",
    "df2_2_2['month']=2\n",
    "df2_2_2['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_3_1 = pd.read_csv('tab_2_products_2020-03-01.csv')\n",
    "df2_3_2 = pd.read_csv('tab_2_products_2020-03-11.csv')\n",
    "df2_3_3 = pd.read_csv('tab_2_products_2020-03-21.csv')\n",
    "df2_3_4 = pd.read_csv('tab_2_products_2020-03-31.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_3_1['month']=3\n",
    "df2_3_1['moment']=1\n",
    "df2_3_2['month']=3\n",
    "df2_3_2['moment']=2\n",
    "df2_3_3['month']=3\n",
    "df2_3_3['moment']=3\n",
    "df2_3_4['month']=3\n",
    "df2_3_4['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_2\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_4_1 = pd.read_csv('tab_2_products_2020-04-10.csv')\n",
    "df2_4_2 = pd.read_csv('tab_2_products_2020-04-20.csv')\n",
    "df2_4_3 = pd.read_csv('tab_2_products_2020-04-30.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_4_1['month']=4\n",
    "df2_4_1['moment']=1\n",
    "df2_4_2['month']=4\n",
    "df2_4_2['moment']=2\n",
    "df2_4_3['month']=4\n",
    "df2_4_3['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_3\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_5_1 = pd.read_csv('tab_2_products_2020-05-10.csv')\n",
    "df2_5_2 = pd.read_csv('tab_2_products_2020-05-20.csv')\n",
    "df2_5_3 = pd.read_csv('tab_2_products_2020-05-30.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_5_1['month']=5\n",
    "df2_5_1['moment']=1\n",
    "df2_5_2['month']=5\n",
    "df2_5_2['moment']=2\n",
    "df2_5_3['month']=5\n",
    "df2_5_3['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_4\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_6_1 = pd.read_csv('tab_2_products_2020-06-09.csv')\n",
    "df2_6_2 = pd.read_csv('tab_2_products_2020-06-19.csv')\n",
    "df2_6_3 = pd.read_csv('tab_2_products_2020-06-29.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_6_1['month']=6\n",
    "df2_6_1['moment']=1\n",
    "df2_6_2['month']=6\n",
    "df2_6_2['moment']=2\n",
    "df2_6_3['month']=6\n",
    "df2_6_3['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_5\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_7_1 = pd.read_csv('tab_2_products_2020-07-09.csv')\n",
    "df2_7_2 = pd.read_csv('tab_2_products_2020-07-19.csv')\n",
    "df2_7_3 = pd.read_csv('tab_2_products_2020-07-29.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_7_1['month']=7\n",
    "df2_7_1['moment']=1\n",
    "df2_7_2['month']=7\n",
    "df2_7_2['moment']=2\n",
    "df2_7_3['month']=7\n",
    "df2_7_3['moment']=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\SberMarket\\sbermarket_tab_2_6\n"
     ]
    }
   ],
   "source": [
    "cd D:\\SberMarket\\sbermarket_tab_2_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_8_1 = pd.read_csv('tab_2_products_2020-08-08.csv')\n",
    "df2_8_2 = pd.read_csv('tab_2_products_2020-08-18.csv')\n",
    "df2_8_3 = pd.read_csv('tab_2_products_2020-08-28.csv')\n",
    "df2_9_1 = pd.read_csv('tab_2_products_2020-09-07.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_8_1['month']=8\n",
    "df2_8_1['moment']=1\n",
    "df2_8_2['month']=8\n",
    "df2_8_2['moment']=2\n",
    "df2_8_3['month']=8\n",
    "df2_8_3['moment']=3\n",
    "df2_9_1['month']=9\n",
    "df2_9_1['moment']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.concat([df2_1_1,df2_1_2,df2_1_3,df2_1_4,df2_2_1,df2_2_2,df2_3_1,df2_3_2,df2_3_3,df2_3_4,df2_4_1,df2_4_2,df2_4_3,df2_5_1,df2_5_2,df2_5_3,df2_6_1,df2_6_2,df2_6_3,df2_7_1,df2_7_2,df2_7_3,df2_8_1,df2_8_2,df2_8_3,df2_9_1], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>user_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>line_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>product_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>master_category_id</th>\n",
       "      <th>parent_category_id</th>\n",
       "      <th>month</th>\n",
       "      <th>moment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>28381452</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Лазанья Золотой Петушок мясной слоеный пирог з...</td>\n",
       "      <td>37548</td>\n",
       "      <td>Золотой Петушок</td>\n",
       "      <td>101.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29242911</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>2</td>\n",
       "      <td>13.080000</td>\n",
       "      <td>Хлеб Хлебный Дом Геркулес зерновой с отрубями ...</td>\n",
       "      <td>5636</td>\n",
       "      <td>Хлебный Дом</td>\n",
       "      <td>596.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29242919</td>\n",
       "      <td>64.989998</td>\n",
       "      <td>2</td>\n",
       "      <td>40.020000</td>\n",
       "      <td>Тортилья Delicados оригинальная пшеничная</td>\n",
       "      <td>22107</td>\n",
       "      <td>Delicados</td>\n",
       "      <td>596.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29243785</td>\n",
       "      <td>44.990002</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Вода питьевая минеральная BonAqua газированная...</td>\n",
       "      <td>2530</td>\n",
       "      <td>BonAqua</td>\n",
       "      <td>77.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29244246</td>\n",
       "      <td>65.900002</td>\n",
       "      <td>6</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>Газированный напиток Mirinda Апельсин 1,5 л</td>\n",
       "      <td>3818486</td>\n",
       "      <td>Mirinda</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596975</th>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388143</td>\n",
       "      <td>34.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>Йогурт Epica гранат и малина 4,8% 130 г бзмж</td>\n",
       "      <td>71049</td>\n",
       "      <td>Epica</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596976</th>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388392</td>\n",
       "      <td>63.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>Шоколад Ritter Sport молочный с начинкой Клубн...</td>\n",
       "      <td>96217</td>\n",
       "      <td>Ritter Sport</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596977</th>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388690</td>\n",
       "      <td>63.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>82.500000</td>\n",
       "      <td>Шоколад Ritter Sport молочный с начинкой Карам...</td>\n",
       "      <td>94125</td>\n",
       "      <td>Ritter Sport</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596978</th>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388868</td>\n",
       "      <td>298.489990</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Филе цыпленка-бройлера Троекурово охлажденное ...</td>\n",
       "      <td>4235874</td>\n",
       "      <td>Троекурово</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596979</th>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173389498</td>\n",
       "      <td>210.589996</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Голень цыпленка-бройлера Троекурово охлажденна...</td>\n",
       "      <td>3850672</td>\n",
       "      <td>Троекурово</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59596980 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  order_id  line_item_id       price  quantity   discount  \\\n",
       "0             525   5354800      28381452  116.000000         4   0.000000   \n",
       "1             525   5354800      29242911   56.990002         2  13.080000   \n",
       "2             525   5354800      29242919   64.989998         2  40.020000   \n",
       "3             525   5354800      29243785   44.990002         6   0.000000   \n",
       "4             525   5354800      29244246   65.900002         6  30.990000   \n",
       "...           ...       ...           ...         ...       ...        ...   \n",
       "59596975  3142966  16654689     173388143   34.889999         1  17.700001   \n",
       "59596976  3142966  16654689     173388392   63.889999         1  73.000000   \n",
       "59596977  3142966  16654689     173388690   63.889999         1  82.500000   \n",
       "59596978  3142966  16654689     173388868  298.489990         1   0.000000   \n",
       "59596979  3142966  16654689     173389498  210.589996         1   0.000000   \n",
       "\n",
       "                                               product_name  product_id  \\\n",
       "0         Лазанья Золотой Петушок мясной слоеный пирог з...       37548   \n",
       "1         Хлеб Хлебный Дом Геркулес зерновой с отрубями ...        5636   \n",
       "2                 Тортилья Delicados оригинальная пшеничная       22107   \n",
       "3         Вода питьевая минеральная BonAqua газированная...        2530   \n",
       "4               Газированный напиток Mirinda Апельсин 1,5 л     3818486   \n",
       "...                                                     ...         ...   \n",
       "59596975       Йогурт Epica гранат и малина 4,8% 130 г бзмж       71049   \n",
       "59596976  Шоколад Ritter Sport молочный с начинкой Клубн...       96217   \n",
       "59596977  Шоколад Ritter Sport молочный с начинкой Карам...       94125   \n",
       "59596978  Филе цыпленка-бройлера Троекурово охлажденное ...     4235874   \n",
       "59596979  Голень цыпленка-бройлера Троекурово охлажденна...     3850672   \n",
       "\n",
       "               brand_name  master_category_id  parent_category_id  month  \\\n",
       "0         Золотой Петушок               101.0                  99      1   \n",
       "1             Хлебный Дом               596.0                  51      1   \n",
       "2               Delicados               596.0                  51      1   \n",
       "3                 BonAqua                77.0                  74      1   \n",
       "4                 Mirinda                76.0                  74      1   \n",
       "...                   ...                 ...                 ...    ...   \n",
       "59596975            Epica                21.0                  20      9   \n",
       "59596976     Ritter Sport                60.0                  56      9   \n",
       "59596977     Ritter Sport                60.0                  56      9   \n",
       "59596978       Троекурово               122.0                 119      9   \n",
       "59596979       Троекурово               122.0                 119      9   \n",
       "\n",
       "          moment  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  \n",
       "...          ...  \n",
       "59596975       1  \n",
       "59596976       1  \n",
       "59596977       1  \n",
       "59596978       1  \n",
       "59596979       1  \n",
       "\n",
       "[59596980 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-2b2309614abe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data_2.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'df2' is not defined"
     ]
    }
   ],
   "source": [
    "df2.to_csv('data_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame111 = pd.read_csv('data_2.csv')"
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>line_item_id</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "      <th>discount</th>\n",
       "      <th>product_name</th>\n",
       "      <th>product_id</th>\n",
       "      <th>brand_name</th>\n",
       "      <th>master_category_id</th>\n",
       "      <th>parent_category_id</th>\n",
       "      <th>month</th>\n",
       "      <th>moment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>28381452</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Лазанья Золотой Петушок мясной слоеный пирог з...</td>\n",
       "      <td>37548</td>\n",
       "      <td>Золотой Петушок</td>\n",
       "      <td>101.0</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29242911</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>2</td>\n",
       "      <td>13.080000</td>\n",
       "      <td>Хлеб Хлебный Дом Геркулес зерновой с отрубями ...</td>\n",
       "      <td>5636</td>\n",
       "      <td>Хлебный Дом</td>\n",
       "      <td>596.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29242919</td>\n",
       "      <td>64.989998</td>\n",
       "      <td>2</td>\n",
       "      <td>40.020000</td>\n",
       "      <td>Тортилья Delicados оригинальная пшеничная</td>\n",
       "      <td>22107</td>\n",
       "      <td>Delicados</td>\n",
       "      <td>596.0</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29243785</td>\n",
       "      <td>44.990002</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Вода питьевая минеральная BonAqua газированная...</td>\n",
       "      <td>2530</td>\n",
       "      <td>BonAqua</td>\n",
       "      <td>77.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>525</td>\n",
       "      <td>5354800</td>\n",
       "      <td>29244246</td>\n",
       "      <td>65.900002</td>\n",
       "      <td>6</td>\n",
       "      <td>30.990000</td>\n",
       "      <td>Газированный напиток Mirinda Апельсин 1,5 л</td>\n",
       "      <td>3818486</td>\n",
       "      <td>Mirinda</td>\n",
       "      <td>76.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596975</th>\n",
       "      <td>59596975</td>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388143</td>\n",
       "      <td>34.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>17.700001</td>\n",
       "      <td>Йогурт Epica гранат и малина 4,8% 130 г бзмж</td>\n",
       "      <td>71049</td>\n",
       "      <td>Epica</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596976</th>\n",
       "      <td>59596976</td>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388392</td>\n",
       "      <td>63.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>Шоколад Ritter Sport молочный с начинкой Клубн...</td>\n",
       "      <td>96217</td>\n",
       "      <td>Ritter Sport</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596977</th>\n",
       "      <td>59596977</td>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388690</td>\n",
       "      <td>63.889999</td>\n",
       "      <td>1</td>\n",
       "      <td>82.500000</td>\n",
       "      <td>Шоколад Ritter Sport молочный с начинкой Карам...</td>\n",
       "      <td>94125</td>\n",
       "      <td>Ritter Sport</td>\n",
       "      <td>60.0</td>\n",
       "      <td>56</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596978</th>\n",
       "      <td>59596978</td>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173388868</td>\n",
       "      <td>298.489990</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Филе цыпленка-бройлера Троекурово охлажденное ...</td>\n",
       "      <td>4235874</td>\n",
       "      <td>Троекурово</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59596979</th>\n",
       "      <td>59596979</td>\n",
       "      <td>3142966</td>\n",
       "      <td>16654689</td>\n",
       "      <td>173389498</td>\n",
       "      <td>210.589996</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Голень цыпленка-бройлера Троекурово охлажденна...</td>\n",
       "      <td>3850672</td>\n",
       "      <td>Троекурово</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59596980 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  user_id  order_id  line_item_id       price  quantity  \\\n",
       "0                  0      525   5354800      28381452  116.000000         4   \n",
       "1                  1      525   5354800      29242911   56.990002         2   \n",
       "2                  2      525   5354800      29242919   64.989998         2   \n",
       "3                  3      525   5354800      29243785   44.990002         6   \n",
       "4                  4      525   5354800      29244246   65.900002         6   \n",
       "...              ...      ...       ...           ...         ...       ...   \n",
       "59596975    59596975  3142966  16654689     173388143   34.889999         1   \n",
       "59596976    59596976  3142966  16654689     173388392   63.889999         1   \n",
       "59596977    59596977  3142966  16654689     173388690   63.889999         1   \n",
       "59596978    59596978  3142966  16654689     173388868  298.489990         1   \n",
       "59596979    59596979  3142966  16654689     173389498  210.589996         1   \n",
       "\n",
       "           discount                                       product_name  \\\n",
       "0          0.000000  Лазанья Золотой Петушок мясной слоеный пирог з...   \n",
       "1         13.080000  Хлеб Хлебный Дом Геркулес зерновой с отрубями ...   \n",
       "2         40.020000          Тортилья Delicados оригинальная пшеничная   \n",
       "3          0.000000  Вода питьевая минеральная BonAqua газированная...   \n",
       "4         30.990000        Газированный напиток Mirinda Апельсин 1,5 л   \n",
       "...             ...                                                ...   \n",
       "59596975  17.700001       Йогурт Epica гранат и малина 4,8% 130 г бзмж   \n",
       "59596976  73.000000  Шоколад Ritter Sport молочный с начинкой Клубн...   \n",
       "59596977  82.500000  Шоколад Ritter Sport молочный с начинкой Карам...   \n",
       "59596978   0.000000  Филе цыпленка-бройлера Троекурово охлажденное ...   \n",
       "59596979   0.000000  Голень цыпленка-бройлера Троекурово охлажденна...   \n",
       "\n",
       "          product_id       brand_name  master_category_id  parent_category_id  \\\n",
       "0              37548  Золотой Петушок               101.0                  99   \n",
       "1               5636      Хлебный Дом               596.0                  51   \n",
       "2              22107        Delicados               596.0                  51   \n",
       "3               2530          BonAqua                77.0                  74   \n",
       "4            3818486          Mirinda                76.0                  74   \n",
       "...              ...              ...                 ...                 ...   \n",
       "59596975       71049            Epica                21.0                  20   \n",
       "59596976       96217     Ritter Sport                60.0                  56   \n",
       "59596977       94125     Ritter Sport                60.0                  56   \n",
       "59596978     4235874       Троекурово               122.0                 119   \n",
       "59596979     3850672       Троекурово               122.0                 119   \n",
       "\n",
       "          month  moment  \n",
       "0             1       1  \n",
       "1             1       1  \n",
       "2             1       1  \n",
       "3             1       1  \n",
       "4             1       1  \n",
       "...         ...     ...  \n",
       "59596975      9       1  \n",
       "59596976      9       1  \n",
       "59596977      9       1  \n",
       "59596978      9       1  \n",
       "59596979      9       1  \n",
       "\n",
       "[59596980 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  14863,   19562,   63057, 3562687,     709,    9979, 3817489,\n",
       "                456,     661,    5501,     655,   54728,   54710,     562,\n",
       "            6005183,   57399, 3817538,   87007,   50190,     407,    2600,\n",
       "            3562686,   46762,   55049,   84364,   84365,   93822,   63724,\n",
       "              55133,   16219,       0,    5851,   64633, 3817487,   57398,\n",
       "              93825, 5481648,   64216,    7397,   16420,     487,   58839,\n",
       "              27471,   56900,   57383,   29788,   58780, 3817571, 7061902,\n",
       "            4830888],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame111[DataFrame111['user_id'] == 51]['product_id'].value_counts().index[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame222 = pd.read_csv('sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Id = DataFrame222['Id'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  14863,   19562,   63057, 3562687,     709,    9979, 3817489,\n",
       "                456,     661,    5501,     655,   54728,   54710,     562,\n",
       "            6005183,   57399, 3817538,   87007,   50190,     407,    2600,\n",
       "            3562686,   46762,   55049,   84364,   84365,   93822,   63724,\n",
       "              55133,   16219,       0,    5851,   64633, 3817487,   57398,\n",
       "              93825, 5481648,   64216,    7397,   16420,     487,   58839,\n",
       "              27471,   56900,   57383,   29788,   58780, 3817571, 7061902,\n",
       "            4830888],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DataFrame111[DataFrame111['user_id'] == 51]['product_id'].value_counts().index[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataa = pd.DataFrame(columns=['id']+['pr'+str(i+1) for i in range(50)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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</th>\n",
       "      <th>pr1</th>\n",
       "      <th>pr2</th>\n",
       "      <th>pr3</th>\n",
       "      <th>pr4</th>\n",
       "      <th>pr5</th>\n",
       "      <th>pr6</th>\n",
       "      <th>pr7</th>\n",
       "      <th>pr8</th>\n",
       "      <th>pr9</th>\n",
       "      <th>...</th>\n",
       "      <th>pr41</th>\n",
       "      <th>pr42</th>\n",
       "      <th>pr43</th>\n",
       "      <th>pr44</th>\n",
       "      <th>pr45</th>\n",
       "      <th>pr46</th>\n",
       "      <th>pr47</th>\n",
       "      <th>pr48</th>\n",
       "      <th>pr49</th>\n",
       "      <th>pr50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, pr1, pr2, pr3, pr4, pr5, pr6, pr7, pr8, pr9, pr10, pr11, pr12, pr13, pr14, pr15, pr16, pr17, pr18, pr19, pr20, pr21, pr22, pr23, pr24, pr25, pr26, pr27, pr28, pr29, pr30, pr31, pr32, pr33, pr34, pr35, pr36, pr37, pr38, pr39, pr40, pr41, pr42, pr43, pr44, pr45, pr46, pr47, pr48, pr49, pr50]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 51 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls=[]\n",
    "for i in Id:\n",
    "    \n",
    "    data = DataFrame111[DataFrame111['user_id'] == i]['product_id'].value_counts().index[:50]\n",
    "    \n",
    "    ls.append([i]+list(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataa = pd.DataFrame(ls, columns=['id']+['pr'+str(i+1) for i in range(50)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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</th>\n",
       "      <th>pr1</th>\n",
       "      <th>pr2</th>\n",
       "      <th>pr3</th>\n",
       "      <th>pr4</th>\n",
       "      <th>pr5</th>\n",
       "      <th>pr6</th>\n",
       "      <th>pr7</th>\n",
       "      <th>pr8</th>\n",
       "      <th>pr9</th>\n",
       "      <th>...</th>\n",
       "      <th>pr41</th>\n",
       "      <th>pr42</th>\n",
       "      <th>pr43</th>\n",
       "      <th>pr44</th>\n",
       "      <th>pr45</th>\n",
       "      <th>pr46</th>\n",
       "      <th>pr47</th>\n",
       "      <th>pr48</th>\n",
       "      <th>pr49</th>\n",
       "      <th>pr50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>14863</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>63057.0</td>\n",
       "      <td>3562687.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>9979.0</td>\n",
       "      <td>3817489.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>58839.0</td>\n",
       "      <td>27471.0</td>\n",
       "      <td>56900.0</td>\n",
       "      <td>57383.0</td>\n",
       "      <td>29788.0</td>\n",
       "      <td>58780.0</td>\n",
       "      <td>3817571.0</td>\n",
       "      <td>7061902.0</td>\n",
       "      <td>4830888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>54728</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>3817542.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>26062.0</td>\n",
       "      <td>18439.0</td>\n",
       "      <td>105764.0</td>\n",
       "      <td>77152.0</td>\n",
       "      <td>5541.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105514.0</td>\n",
       "      <td>67035.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>69471.0</td>\n",
       "      <td>5479568.0</td>\n",
       "      <td>37122.0</td>\n",
       "      <td>4830912.0</td>\n",
       "      <td>12792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>766</td>\n",
       "      <td>3043457</td>\n",
       "      <td>3041849.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3042629.0</td>\n",
       "      <td>4033676.0</td>\n",
       "      <td>3497419.0</td>\n",
       "      <td>3497629.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3075445.0</td>\n",
       "      <td>74609.0</td>\n",
       "      <td>5060917.0</td>\n",
       "      <td>38931.0</td>\n",
       "      <td>94343.0</td>\n",
       "      <td>64712.0</td>\n",
       "      <td>67694.0</td>\n",
       "      <td>74633.0</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>3850658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1132</td>\n",
       "      <td>95465</td>\n",
       "      <td>2599278.0</td>\n",
       "      <td>9970.0</td>\n",
       "      <td>91008.0</td>\n",
       "      <td>2629.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>26590.0</td>\n",
       "      <td>6316.0</td>\n",
       "      <td>6749935.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64017.0</td>\n",
       "      <td>26130.0</td>\n",
       "      <td>20499.0</td>\n",
       "      <td>6607636.0</td>\n",
       "      <td>7062038.0</td>\n",
       "      <td>16864.0</td>\n",
       "      <td>5073443.0</td>\n",
       "      <td>199461.0</td>\n",
       "      <td>4033580.0</td>\n",
       "      <td>5046391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1578</td>\n",
       "      <td>22161</td>\n",
       "      <td>19924.0</td>\n",
       "      <td>4513.0</td>\n",
       "      <td>45457.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>69131.0</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>39492.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80451.0</td>\n",
       "      <td>26335.0</td>\n",
       "      <td>49924.0</td>\n",
       "      <td>106262.0</td>\n",
       "      <td>78672.0</td>\n",
       "      <td>45929.0</td>\n",
       "      <td>59100.0</td>\n",
       "      <td>87064.0</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>5045462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107063</th>\n",
       "      <td>2997849</td>\n",
       "      <td>93412</td>\n",
       "      <td>94112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107064</th>\n",
       "      <td>2997853</td>\n",
       "      <td>60692</td>\n",
       "      <td>3818078.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>49921.0</td>\n",
       "      <td>3818498.0</td>\n",
       "      <td>96291.0</td>\n",
       "      <td>100804.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107065</th>\n",
       "      <td>2997873</td>\n",
       "      <td>7062001</td>\n",
       "      <td>6210170.0</td>\n",
       "      <td>30361.0</td>\n",
       "      <td>60116.0</td>\n",
       "      <td>24851.0</td>\n",
       "      <td>67520.0</td>\n",
       "      <td>68465.0</td>\n",
       "      <td>8016.0</td>\n",
       "      <td>46762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107066</th>\n",
       "      <td>2997988</td>\n",
       "      <td>6590</td>\n",
       "      <td>6692211.0</td>\n",
       "      <td>6003841.0</td>\n",
       "      <td>7392322.0</td>\n",
       "      <td>5479075.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6749607.0</td>\n",
       "      <td>6749608.0</td>\n",
       "      <td>9867.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107067</th>\n",
       "      <td>2998048</td>\n",
       "      <td>21757</td>\n",
       "      <td>76069.0</td>\n",
       "      <td>74016.0</td>\n",
       "      <td>6691979.0</td>\n",
       "      <td>29647.0</td>\n",
       "      <td>6692436.0</td>\n",
       "      <td>6692437.0</td>\n",
       "      <td>77079.0</td>\n",
       "      <td>62233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107068 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id      pr1        pr2        pr3        pr4        pr5  \\\n",
       "0            51    14863    19562.0    63057.0  3562687.0      709.0   \n",
       "1            65    54728    18450.0  3817542.0      709.0    26062.0   \n",
       "2           766  3043457  3041849.0        0.0  3042629.0  4033676.0   \n",
       "3          1132    95465  2599278.0     9970.0    91008.0     2629.0   \n",
       "4          1578    22161    19924.0     4513.0    45457.0      304.0   \n",
       "...         ...      ...        ...        ...        ...        ...   \n",
       "107063  2997849    93412    94112.0        NaN        NaN        NaN   \n",
       "107064  2997853    60692  3818078.0      407.0    49921.0  3818498.0   \n",
       "107065  2997873  7062001  6210170.0    30361.0    60116.0    24851.0   \n",
       "107066  2997988     6590  6692211.0  6003841.0  7392322.0  5479075.0   \n",
       "107067  2998048    21757    76069.0    74016.0  6691979.0    29647.0   \n",
       "\n",
       "              pr6        pr7        pr8        pr9  ...       pr41     pr42  \\\n",
       "0          9979.0  3817489.0      456.0      661.0  ...      487.0  58839.0   \n",
       "1         18439.0   105764.0    77152.0     5541.0  ...   105514.0  67035.0   \n",
       "2       3497419.0  3497629.0      981.0     1037.0  ...  3075445.0  74609.0   \n",
       "3          1199.0    26590.0     6316.0  6749935.0  ...    64017.0  26130.0   \n",
       "4          4689.0    69131.0    19562.0    39492.0  ...    80451.0  26335.0   \n",
       "...           ...        ...        ...        ...  ...        ...      ...   \n",
       "107063        NaN        NaN        NaN        NaN  ...        NaN      NaN   \n",
       "107064    96291.0   100804.0        0.0    26086.0  ...        NaN      NaN   \n",
       "107065    67520.0    68465.0     8016.0    46762.0  ...        NaN      NaN   \n",
       "107066      100.0  6749607.0  6749608.0     9867.0  ...        NaN      NaN   \n",
       "107067  6692436.0  6692437.0    77079.0    62233.0  ...        NaN      NaN   \n",
       "\n",
       "             pr43       pr44       pr45     pr46       pr47       pr48  \\\n",
       "0         27471.0    56900.0    57383.0  29788.0    58780.0  3817571.0   \n",
       "1           848.0     1037.0     2063.0  69471.0  5479568.0    37122.0   \n",
       "2       5060917.0    38931.0    94343.0  64712.0    67694.0    74633.0   \n",
       "3         20499.0  6607636.0  7062038.0  16864.0  5073443.0   199461.0   \n",
       "4         49924.0   106262.0    78672.0  45929.0    59100.0    87064.0   \n",
       "...           ...        ...        ...      ...        ...        ...   \n",
       "107063        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107064        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107065        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107066        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107067        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "\n",
       "             pr49       pr50  \n",
       "0       7061902.0  4830888.0  \n",
       "1       4830912.0    12792.0  \n",
       "2          9553.0  3850658.0  \n",
       "3       4033580.0  5046391.0  \n",
       "4          5154.0  5045462.0  \n",
       "...           ...        ...  \n",
       "107063        NaN        NaN  \n",
       "107064        NaN        NaN  \n",
       "107065        NaN        NaN  \n",
       "107066        NaN        NaN  \n",
       "107067        NaN        NaN  \n",
       "\n",
       "[107068 rows x 51 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataa.to_csv('to_submit.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_products =  list(DataFrame111['product_id'].value_counts().index[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3817489"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.choice(top_products)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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</th>\n",
       "      <th>pr1</th>\n",
       "      <th>pr2</th>\n",
       "      <th>pr3</th>\n",
       "      <th>pr4</th>\n",
       "      <th>pr5</th>\n",
       "      <th>pr6</th>\n",
       "      <th>pr7</th>\n",
       "      <th>pr8</th>\n",
       "      <th>pr9</th>\n",
       "      <th>...</th>\n",
       "      <th>pr41</th>\n",
       "      <th>pr42</th>\n",
       "      <th>pr43</th>\n",
       "      <th>pr44</th>\n",
       "      <th>pr45</th>\n",
       "      <th>pr46</th>\n",
       "      <th>pr47</th>\n",
       "      <th>pr48</th>\n",
       "      <th>pr49</th>\n",
       "      <th>pr50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>14863</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>63057.0</td>\n",
       "      <td>3562687.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>9979.0</td>\n",
       "      <td>3817489.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>58839.0</td>\n",
       "      <td>27471.0</td>\n",
       "      <td>56900.0</td>\n",
       "      <td>57383.0</td>\n",
       "      <td>29788.0</td>\n",
       "      <td>58780.0</td>\n",
       "      <td>3817571.0</td>\n",
       "      <td>7061902.0</td>\n",
       "      <td>4830888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>54728</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>3817542.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>26062.0</td>\n",
       "      <td>18439.0</td>\n",
       "      <td>105764.0</td>\n",
       "      <td>77152.0</td>\n",
       "      <td>5541.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105514.0</td>\n",
       "      <td>67035.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>69471.0</td>\n",
       "      <td>5479568.0</td>\n",
       "      <td>37122.0</td>\n",
       "      <td>4830912.0</td>\n",
       "      <td>12792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>766</td>\n",
       "      <td>3043457</td>\n",
       "      <td>3041849.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3042629.0</td>\n",
       "      <td>4033676.0</td>\n",
       "      <td>3497419.0</td>\n",
       "      <td>3497629.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3075445.0</td>\n",
       "      <td>74609.0</td>\n",
       "      <td>5060917.0</td>\n",
       "      <td>38931.0</td>\n",
       "      <td>94343.0</td>\n",
       "      <td>64712.0</td>\n",
       "      <td>67694.0</td>\n",
       "      <td>74633.0</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>3850658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1132</td>\n",
       "      <td>95465</td>\n",
       "      <td>2599278.0</td>\n",
       "      <td>9970.0</td>\n",
       "      <td>91008.0</td>\n",
       "      <td>2629.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>26590.0</td>\n",
       "      <td>6316.0</td>\n",
       "      <td>6749935.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64017.0</td>\n",
       "      <td>26130.0</td>\n",
       "      <td>20499.0</td>\n",
       "      <td>6607636.0</td>\n",
       "      <td>7062038.0</td>\n",
       "      <td>16864.0</td>\n",
       "      <td>5073443.0</td>\n",
       "      <td>199461.0</td>\n",
       "      <td>4033580.0</td>\n",
       "      <td>5046391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1578</td>\n",
       "      <td>22161</td>\n",
       "      <td>19924.0</td>\n",
       "      <td>4513.0</td>\n",
       "      <td>45457.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>69131.0</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>39492.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80451.0</td>\n",
       "      <td>26335.0</td>\n",
       "      <td>49924.0</td>\n",
       "      <td>106262.0</td>\n",
       "      <td>78672.0</td>\n",
       "      <td>45929.0</td>\n",
       "      <td>59100.0</td>\n",
       "      <td>87064.0</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>5045462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107063</th>\n",
       "      <td>2997849</td>\n",
       "      <td>93412</td>\n",
       "      <td>94112.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107064</th>\n",
       "      <td>2997853</td>\n",
       "      <td>60692</td>\n",
       "      <td>3818078.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>49921.0</td>\n",
       "      <td>3818498.0</td>\n",
       "      <td>96291.0</td>\n",
       "      <td>100804.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107065</th>\n",
       "      <td>2997873</td>\n",
       "      <td>7062001</td>\n",
       "      <td>6210170.0</td>\n",
       "      <td>30361.0</td>\n",
       "      <td>60116.0</td>\n",
       "      <td>24851.0</td>\n",
       "      <td>67520.0</td>\n",
       "      <td>68465.0</td>\n",
       "      <td>8016.0</td>\n",
       "      <td>46762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107066</th>\n",
       "      <td>2997988</td>\n",
       "      <td>6590</td>\n",
       "      <td>6692211.0</td>\n",
       "      <td>6003841.0</td>\n",
       "      <td>7392322.0</td>\n",
       "      <td>5479075.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6749607.0</td>\n",
       "      <td>6749608.0</td>\n",
       "      <td>9867.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107067</th>\n",
       "      <td>2998048</td>\n",
       "      <td>21757</td>\n",
       "      <td>76069.0</td>\n",
       "      <td>74016.0</td>\n",
       "      <td>6691979.0</td>\n",
       "      <td>29647.0</td>\n",
       "      <td>6692436.0</td>\n",
       "      <td>6692437.0</td>\n",
       "      <td>77079.0</td>\n",
       "      <td>62233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107068 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id      pr1        pr2        pr3        pr4        pr5  \\\n",
       "0            51    14863    19562.0    63057.0  3562687.0      709.0   \n",
       "1            65    54728    18450.0  3817542.0      709.0    26062.0   \n",
       "2           766  3043457  3041849.0        0.0  3042629.0  4033676.0   \n",
       "3          1132    95465  2599278.0     9970.0    91008.0     2629.0   \n",
       "4          1578    22161    19924.0     4513.0    45457.0      304.0   \n",
       "...         ...      ...        ...        ...        ...        ...   \n",
       "107063  2997849    93412    94112.0        NaN        NaN        NaN   \n",
       "107064  2997853    60692  3818078.0      407.0    49921.0  3818498.0   \n",
       "107065  2997873  7062001  6210170.0    30361.0    60116.0    24851.0   \n",
       "107066  2997988     6590  6692211.0  6003841.0  7392322.0  5479075.0   \n",
       "107067  2998048    21757    76069.0    74016.0  6691979.0    29647.0   \n",
       "\n",
       "              pr6        pr7        pr8        pr9  ...       pr41     pr42  \\\n",
       "0          9979.0  3817489.0      456.0      661.0  ...      487.0  58839.0   \n",
       "1         18439.0   105764.0    77152.0     5541.0  ...   105514.0  67035.0   \n",
       "2       3497419.0  3497629.0      981.0     1037.0  ...  3075445.0  74609.0   \n",
       "3          1199.0    26590.0     6316.0  6749935.0  ...    64017.0  26130.0   \n",
       "4          4689.0    69131.0    19562.0    39492.0  ...    80451.0  26335.0   \n",
       "...           ...        ...        ...        ...  ...        ...      ...   \n",
       "107063        NaN        NaN        NaN        NaN  ...        NaN      NaN   \n",
       "107064    96291.0   100804.0        0.0    26086.0  ...        NaN      NaN   \n",
       "107065    67520.0    68465.0     8016.0    46762.0  ...        NaN      NaN   \n",
       "107066      100.0  6749607.0  6749608.0     9867.0  ...        NaN      NaN   \n",
       "107067  6692436.0  6692437.0    77079.0    62233.0  ...        NaN      NaN   \n",
       "\n",
       "             pr43       pr44       pr45     pr46       pr47       pr48  \\\n",
       "0         27471.0    56900.0    57383.0  29788.0    58780.0  3817571.0   \n",
       "1           848.0     1037.0     2063.0  69471.0  5479568.0    37122.0   \n",
       "2       5060917.0    38931.0    94343.0  64712.0    67694.0    74633.0   \n",
       "3         20499.0  6607636.0  7062038.0  16864.0  5073443.0   199461.0   \n",
       "4         49924.0   106262.0    78672.0  45929.0    59100.0    87064.0   \n",
       "...           ...        ...        ...      ...        ...        ...   \n",
       "107063        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107064        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107065        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107066        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "107067        NaN        NaN        NaN      NaN        NaN        NaN   \n",
       "\n",
       "             pr49       pr50  \n",
       "0       7061902.0  4830888.0  \n",
       "1       4830912.0    12792.0  \n",
       "2          9553.0  3850658.0  \n",
       "3       4033580.0  5046391.0  \n",
       "4          5154.0  5045462.0  \n",
       "...           ...        ...  \n",
       "107063        NaN        NaN  \n",
       "107064        NaN        NaN  \n",
       "107065        NaN        NaN  \n",
       "107066        NaN        NaN  \n",
       "107067        NaN        NaN  \n",
       "\n",
       "[107068 rows x 51 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "New_data = dataa.fillna(top_products[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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</th>\n",
       "      <th>pr1</th>\n",
       "      <th>pr2</th>\n",
       "      <th>pr3</th>\n",
       "      <th>pr4</th>\n",
       "      <th>pr5</th>\n",
       "      <th>pr6</th>\n",
       "      <th>pr7</th>\n",
       "      <th>pr8</th>\n",
       "      <th>pr9</th>\n",
       "      <th>...</th>\n",
       "      <th>pr41</th>\n",
       "      <th>pr42</th>\n",
       "      <th>pr43</th>\n",
       "      <th>pr44</th>\n",
       "      <th>pr45</th>\n",
       "      <th>pr46</th>\n",
       "      <th>pr47</th>\n",
       "      <th>pr48</th>\n",
       "      <th>pr49</th>\n",
       "      <th>pr50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>14863</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>63057.0</td>\n",
       "      <td>3562687.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>9979.0</td>\n",
       "      <td>3817489.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>661.0</td>\n",
       "      <td>...</td>\n",
       "      <td>487.0</td>\n",
       "      <td>58839.0</td>\n",
       "      <td>27471.0</td>\n",
       "      <td>56900.0</td>\n",
       "      <td>57383.0</td>\n",
       "      <td>29788.0</td>\n",
       "      <td>58780.0</td>\n",
       "      <td>3817571.0</td>\n",
       "      <td>7061902.0</td>\n",
       "      <td>4830888.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65</td>\n",
       "      <td>54728</td>\n",
       "      <td>18450.0</td>\n",
       "      <td>3817542.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>26062.0</td>\n",
       "      <td>18439.0</td>\n",
       "      <td>105764.0</td>\n",
       "      <td>77152.0</td>\n",
       "      <td>5541.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105514.0</td>\n",
       "      <td>67035.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>2063.0</td>\n",
       "      <td>69471.0</td>\n",
       "      <td>5479568.0</td>\n",
       "      <td>37122.0</td>\n",
       "      <td>4830912.0</td>\n",
       "      <td>12792.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>766</td>\n",
       "      <td>3043457</td>\n",
       "      <td>3041849.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3042629.0</td>\n",
       "      <td>4033676.0</td>\n",
       "      <td>3497419.0</td>\n",
       "      <td>3497629.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>1037.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3075445.0</td>\n",
       "      <td>74609.0</td>\n",
       "      <td>5060917.0</td>\n",
       "      <td>38931.0</td>\n",
       "      <td>94343.0</td>\n",
       "      <td>64712.0</td>\n",
       "      <td>67694.0</td>\n",
       "      <td>74633.0</td>\n",
       "      <td>9553.0</td>\n",
       "      <td>3850658.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1132</td>\n",
       "      <td>95465</td>\n",
       "      <td>2599278.0</td>\n",
       "      <td>9970.0</td>\n",
       "      <td>91008.0</td>\n",
       "      <td>2629.0</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>26590.0</td>\n",
       "      <td>6316.0</td>\n",
       "      <td>6749935.0</td>\n",
       "      <td>...</td>\n",
       "      <td>64017.0</td>\n",
       "      <td>26130.0</td>\n",
       "      <td>20499.0</td>\n",
       "      <td>6607636.0</td>\n",
       "      <td>7062038.0</td>\n",
       "      <td>16864.0</td>\n",
       "      <td>5073443.0</td>\n",
       "      <td>199461.0</td>\n",
       "      <td>4033580.0</td>\n",
       "      <td>5046391.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1578</td>\n",
       "      <td>22161</td>\n",
       "      <td>19924.0</td>\n",
       "      <td>4513.0</td>\n",
       "      <td>45457.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>4689.0</td>\n",
       "      <td>69131.0</td>\n",
       "      <td>19562.0</td>\n",
       "      <td>39492.0</td>\n",
       "      <td>...</td>\n",
       "      <td>80451.0</td>\n",
       "      <td>26335.0</td>\n",
       "      <td>49924.0</td>\n",
       "      <td>106262.0</td>\n",
       "      <td>78672.0</td>\n",
       "      <td>45929.0</td>\n",
       "      <td>59100.0</td>\n",
       "      <td>87064.0</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>5045462.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107063</th>\n",
       "      <td>2997849</td>\n",
       "      <td>93412</td>\n",
       "      <td>94112.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107064</th>\n",
       "      <td>2997853</td>\n",
       "      <td>60692</td>\n",
       "      <td>3818078.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>49921.0</td>\n",
       "      <td>3818498.0</td>\n",
       "      <td>96291.0</td>\n",
       "      <td>100804.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107065</th>\n",
       "      <td>2997873</td>\n",
       "      <td>7062001</td>\n",
       "      <td>6210170.0</td>\n",
       "      <td>30361.0</td>\n",
       "      <td>60116.0</td>\n",
       "      <td>24851.0</td>\n",
       "      <td>67520.0</td>\n",
       "      <td>68465.0</td>\n",
       "      <td>8016.0</td>\n",
       "      <td>46762.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107066</th>\n",
       "      <td>2997988</td>\n",
       "      <td>6590</td>\n",
       "      <td>6692211.0</td>\n",
       "      <td>6003841.0</td>\n",
       "      <td>7392322.0</td>\n",
       "      <td>5479075.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6749607.0</td>\n",
       "      <td>6749608.0</td>\n",
       "      <td>9867.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107067</th>\n",
       "      <td>2998048</td>\n",
       "      <td>21757</td>\n",
       "      <td>76069.0</td>\n",
       "      <td>74016.0</td>\n",
       "      <td>6691979.0</td>\n",
       "      <td>29647.0</td>\n",
       "      <td>6692436.0</td>\n",
       "      <td>6692437.0</td>\n",
       "      <td>77079.0</td>\n",
       "      <td>62233.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107068 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id      pr1        pr2        pr3        pr4        pr5  \\\n",
       "0            51    14863    19562.0    63057.0  3562687.0      709.0   \n",
       "1            65    54728    18450.0  3817542.0      709.0    26062.0   \n",
       "2           766  3043457  3041849.0        0.0  3042629.0  4033676.0   \n",
       "3          1132    95465  2599278.0     9970.0    91008.0     2629.0   \n",
       "4          1578    22161    19924.0     4513.0    45457.0      304.0   \n",
       "...         ...      ...        ...        ...        ...        ...   \n",
       "107063  2997849    93412    94112.0        0.0        0.0        0.0   \n",
       "107064  2997853    60692  3818078.0      407.0    49921.0  3818498.0   \n",
       "107065  2997873  7062001  6210170.0    30361.0    60116.0    24851.0   \n",
       "107066  2997988     6590  6692211.0  6003841.0  7392322.0  5479075.0   \n",
       "107067  2998048    21757    76069.0    74016.0  6691979.0    29647.0   \n",
       "\n",
       "              pr6        pr7        pr8        pr9  ...       pr41     pr42  \\\n",
       "0          9979.0  3817489.0      456.0      661.0  ...      487.0  58839.0   \n",
       "1         18439.0   105764.0    77152.0     5541.0  ...   105514.0  67035.0   \n",
       "2       3497419.0  3497629.0      981.0     1037.0  ...  3075445.0  74609.0   \n",
       "3          1199.0    26590.0     6316.0  6749935.0  ...    64017.0  26130.0   \n",
       "4          4689.0    69131.0    19562.0    39492.0  ...    80451.0  26335.0   \n",
       "...           ...        ...        ...        ...  ...        ...      ...   \n",
       "107063        0.0        0.0        0.0        0.0  ...        0.0      0.0   \n",
       "107064    96291.0   100804.0        0.0    26086.0  ...        0.0      0.0   \n",
       "107065    67520.0    68465.0     8016.0    46762.0  ...        0.0      0.0   \n",
       "107066      100.0  6749607.0  6749608.0     9867.0  ...        0.0      0.0   \n",
       "107067  6692436.0  6692437.0    77079.0    62233.0  ...        0.0      0.0   \n",
       "\n",
       "             pr43       pr44       pr45     pr46       pr47       pr48  \\\n",
       "0         27471.0    56900.0    57383.0  29788.0    58780.0  3817571.0   \n",
       "1           848.0     1037.0     2063.0  69471.0  5479568.0    37122.0   \n",
       "2       5060917.0    38931.0    94343.0  64712.0    67694.0    74633.0   \n",
       "3         20499.0  6607636.0  7062038.0  16864.0  5073443.0   199461.0   \n",
       "4         49924.0   106262.0    78672.0  45929.0    59100.0    87064.0   \n",
       "...           ...        ...        ...      ...        ...        ...   \n",
       "107063        0.0        0.0        0.0      0.0        0.0        0.0   \n",
       "107064        0.0        0.0        0.0      0.0        0.0        0.0   \n",
       "107065        0.0        0.0        0.0      0.0        0.0        0.0   \n",
       "107066        0.0        0.0        0.0      0.0        0.0        0.0   \n",
       "107067        0.0        0.0        0.0      0.0        0.0        0.0   \n",
       "\n",
       "             pr49       pr50  \n",
       "0       7061902.0  4830888.0  \n",
       "1       4830912.0    12792.0  \n",
       "2          9553.0  3850658.0  \n",
       "3       4033580.0  5046391.0  \n",
       "4          5154.0  5045462.0  \n",
       "...           ...        ...  \n",
       "107063        0.0        0.0  \n",
       "107064        0.0        0.0  \n",
       "107065        0.0        0.0  \n",
       "107066        0.0        0.0  \n",
       "107067        0.0        0.0  \n",
       "\n",
       "[107068 rows x 51 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "New_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "New_data.to_csv('to_submit5.csv')"
   ]
  },
  {
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}