EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the needed libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import plotly.graph_objects as go\n",
    "import datetime\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetching the data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shops = pd.read_csv(\"../data/shops.csv\")\n",
    "items = pd.read_csv(\"../data/items.csv\")\n",
    "item_categories = pd.read_csv(\"../data/item_categories.csv\")\n",
    "test = pd.read_csv(\"../data/test.csv\")\n",
    "sales_train = pd.read_csv(\"../data/sales_train.csv\")\n",
    "sample_submission = pd.read_csv(\"../data/sample_submission.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing the info and few data from each data table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 60 entries, 0 to 59\n",
      "Data columns (total 2 columns):\n",
      "shop_name    60 non-null object\n",
      "shop_id      60 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 1.0+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>shop_name</th>\n",
       "      <th>shop_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>!Якутск Орджоникидзе, 56 фран</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!Якутск ТЦ \"Центральный\" фран</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Адыгея ТЦ \"Мега\"</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Балашиха ТРК \"Октябрь-Киномир\"</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Волжский ТЦ \"Волга Молл\"</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        shop_name  shop_id\n",
       "0   !Якутск Орджоникидзе, 56 фран        0\n",
       "1   !Якутск ТЦ \"Центральный\" фран        1\n",
       "2                Адыгея ТЦ \"Мега\"        2\n",
       "3  Балашиха ТРК \"Октябрь-Киномир\"        3\n",
       "4        Волжский ТЦ \"Волга Молл\"        4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(shops.info())\n",
    "shops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 22170 entries, 0 to 22169\n",
      "Data columns (total 3 columns):\n",
      "item_name           22170 non-null object\n",
      "item_id             22170 non-null int64\n",
      "item_category_id    22170 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 519.7+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>!ABBYY FineReader 12 Professional Edition Full...</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>***В ЛУЧАХ СЛАВЫ   (UNV)                    D</td>\n",
       "      <td>2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>***ГОЛУБАЯ ВОЛНА  (Univ)                      D</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>***КОРОБКА (СТЕКЛО)                       D</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           item_name  item_id  \\\n",
       "0          ! ВО ВЛАСТИ НАВАЖДЕНИЯ (ПЛАСТ.)         D        0   \n",
       "1  !ABBYY FineReader 12 Professional Edition Full...        1   \n",
       "2      ***В ЛУЧАХ СЛАВЫ   (UNV)                    D        2   \n",
       "3    ***ГОЛУБАЯ ВОЛНА  (Univ)                      D        3   \n",
       "4        ***КОРОБКА (СТЕКЛО)                       D        4   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                40  \n",
       "3                40  \n",
       "4                40  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(items.info())\n",
    "items.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 84 entries, 0 to 83\n",
      "Data columns (total 2 columns):\n",
      "item_category_name    84 non-null object\n",
      "item_category_id      84 non-null int64\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 1.4+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_category_name</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC - Гарнитуры/Наушники</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Аксессуары - PS2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Аксессуары - PS3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Аксессуары - PS4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Аксессуары - PSP</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        item_category_name  item_category_id\n",
       "0  PC - Гарнитуры/Наушники                 0\n",
       "1         Аксессуары - PS2                 1\n",
       "2         Аксессуары - PS3                 2\n",
       "3         Аксессуары - PS4                 3\n",
       "4         Аксессуары - PSP                 4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(item_categories.info())\n",
    "item_categories.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214200 entries, 0 to 214199\n",
      "Data columns (total 3 columns):\n",
      "ID         214200 non-null int64\n",
      "shop_id    214200 non-null int64\n",
      "item_id    214200 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 4.9 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  shop_id  item_id\n",
       "0   0        5     5037\n",
       "1   1        5     5320\n",
       "2   2        5     5233\n",
       "3   3        5     5232\n",
       "4   4        5     5268"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(test.info())\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2935849 entries, 0 to 2935848\n",
      "Data columns (total 6 columns):\n",
      "date              object\n",
      "date_block_num    int64\n",
      "shop_id           int64\n",
      "item_id           int64\n",
      "item_price        float64\n",
      "item_cnt_day      float64\n",
      "dtypes: float64(2), int64(3), object(1)\n",
      "memory usage: 134.4+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>22154</td>\n",
       "      <td>999.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>03.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2552</td>\n",
       "      <td>899.00</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2554</td>\n",
       "      <td>1709.05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15.01.2013</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>2555</td>\n",
       "      <td>1099.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  date_block_num  shop_id  item_id  item_price  item_cnt_day\n",
       "0  02.01.2013               0       59    22154      999.00           1.0\n",
       "1  03.01.2013               0       25     2552      899.00           1.0\n",
       "2  05.01.2013               0       25     2552      899.00          -1.0\n",
       "3  06.01.2013               0       25     2554     1709.05           1.0\n",
       "4  15.01.2013               0       25     2555     1099.00           1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sales_train.info())\n",
    "sales_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 214200 entries, 0 to 214199\n",
      "Data columns (total 2 columns):\n",
      "ID                214200 non-null int64\n",
      "item_cnt_month    214200 non-null float64\n",
      "dtypes: float64(1), int64(1)\n",
      "memory usage: 3.3 MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>item_cnt_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  item_cnt_month\n",
       "0   0             0.5\n",
       "1   1             0.5\n",
       "2   2             0.5\n",
       "3   3             0.5\n",
       "4   4             0.5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sample_submission.info())\n",
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting `sales_train['date']` to `datetime` format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2935849 entries, 0 to 2935848\n",
      "Data columns (total 6 columns):\n",
      "date              datetime64[ns]\n",
      "date_block_num    int64\n",
      "shop_id           int64\n",
      "item_id           int64\n",
      "item_price        float64\n",
      "item_cnt_day      float64\n",
      "dtypes: datetime64[ns](1), float64(2), int64(3)\n",
      "memory usage: 134.4 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "sales_train['date'] = pd.to_datetime(sales_train['date'], format='%d.%m.%Y')\n",
    "print(sales_train.info())"
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  },
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     "data": {
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       "array([2169., 1000.,  669.,  637.,  624.,  539.,  533.,  512.,  508.,\n",
       "        504.,  502.,  501.,  500.,  500.,  480.,  412.,  405.,  401.,\n",
       "        401.,  343.,  325.,  313.,  313.,  300.,  299.,  288.,  264.,\n",
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\"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"total entries for each `shop_id` in training set\", \"xref\": \"paper\"}, \"xaxis\": {\"title\": {\"text\": \"shop_id\"}}, \"yaxis\": {\"title\": {\"text\": \"entry counts\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('63aa1075-5102-41f6-8494-983ac38ce19f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layout = go.Layout(\n",
    "    title=go.layout.Title(\n",
    "        text='total entries for each `shop_id` in training set',\n",
    "        xref='paper',\n",
    "    ),\n",
    "    xaxis=go.layout.XAxis(\n",
    "        title=go.layout.xaxis.Title(\n",
    "            text='shop_id',\n",
    "        )\n",
    "    ),\n",
    "    yaxis=go.layout.YAxis(\n",
    "        title=go.layout.yaxis.Title(\n",
    "            text='entry counts',\n",
    "        )\n",
    "    ),\n",
    "    \n",
    "    \n",
    ")\n",
    "\n",
    "fig = go.Figure(data=[go.Bar(y=sales_train['shop_id'].value_counts(normalize=False), x=sales_train['shop_id'].unique())], layout=layout)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "layout = go.Layout(\n",
    "    title='`item_id` histogram'\n",
    ")\n",
    "fig = go.Figure(data=[go.Bar(y=sales_train['item_id'].value_counts(normalize=False))], layout=layout)\n",
    "fig.show()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting time periods for `data_blocks_num` in `sales_train`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = go.Figure()\n",
    "for date_block in sales_train['date_block_num'].unique():\n",
    "    data = sales_train[sales_train['date_block_num'] == date_block]['date']\n",
    "    fig.add_trace(go.Scatter(x=[min(data), max(data)], y=[date_block, date_block],\n",
    "            mode='lines+markers',\n",
    "            name='date_block: '+ str(date_block)))\n",
    "fig.update_layout(legend_orientation=\"h\", width=1000, height= 800, title=\"date block time periods\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group the `sales_train` df by `shop_id` and `date_block_num`\n",
    "### Plotting `item_cnt_day` vs `date_block_num` for each `shop_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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        "title": {
         "text": "Plotting total 'item_cnt_day' amount across a 'date_block_num' for each 'shop_id'"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "grouped_sales_train = pd.DataFrame(sales_train.groupby(['shop_id', 'date_block_num'])['item_cnt_day'].sum().reset_index())\n",
    "\n",
    "fig = go.Figure()\n",
    "for shop_id in grouped_sales_train['shop_id'].unique():\n",
    "        data = grouped_sales_train[grouped_sales_train['shop_id'] == shop_id]\n",
    "        fig.add_trace(go.Scatter(x=data['date_block_num'], y=data['item_cnt_day'],\n",
    "                    mode='lines+markers',\n",
    "                    name='shop_id: '+ str(shop_id)))\n",
    "fig.update_layout(legend_orientation=\"h\", width=1600, height=1000, title=\"Plotting total 'item_cnt_day' amount across a 'date_block_num' for each 'shop_id'\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see a peak in the sales count at datablocks `11` and `23` which correspond to `December` month of `2013` and `2014` respectively"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Joining `sales_train` with `items`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>date</th>\n",
       "      <th>date_block_num</th>\n",
       "      <th>shop_id</th>\n",
       "      <th>item_price</th>\n",
       "      <th>item_cnt_day</th>\n",
       "      <th>item_category_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>20</td>\n",
       "      <td>54</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-04-04</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-04-02</td>\n",
       "      <td>15</td>\n",
       "      <td>55</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-07-06</td>\n",
       "      <td>18</td>\n",
       "      <td>55</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2014-08-04</td>\n",
       "      <td>19</td>\n",
       "      <td>55</td>\n",
       "      <td>4490.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item_id       date  date_block_num  shop_id  item_price  item_cnt_day  \\\n",
       "0        0 2014-09-01              20       54        58.0           1.0   \n",
       "1        1 2014-04-04              15       55      4490.0           1.0   \n",
       "2        1 2014-04-02              15       55      4490.0           1.0   \n",
       "3        1 2014-07-06              18       55      4490.0           1.0   \n",
       "4        1 2014-08-04              19       55      4490.0           1.0   \n",
       "\n",
       "   item_category_id  \n",
       "0                40  \n",
       "1                76  \n",
       "2                76  \n",
       "3                76  \n",
       "4                76  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train = sales_train.set_index('item_id').join(items.set_index('item_id')).drop('item_name', axis=1).reset_index()\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting `item_cnt_day` vs `date_block_num` for each `item_category_id`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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       "                        {\"responsive\": true}\n",
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       "var gd = document.getElementById('b0ed5f4e-2fa9-456f-b1a5-1a8ee2cc8795');\n",
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       "            Plotly.purge(gd);\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_sales_train = pd.DataFrame(train.groupby(['item_category_id', 'date_block_num'])['item_cnt_day'].sum().reset_index())\n",
    "fig = go.Figure()\n",
    "for item_cat in grouped_sales_train['item_category_id'].unique():\n",
    "        data = grouped_sales_train[grouped_sales_train['item_category_id'] == item_cat]\n",
    "        fig.add_trace(go.Scatter(x=data['date_block_num'], y=data['item_cnt_day'],\n",
    "                    mode='lines+markers',\n",
    "                    name='item cat: '+ str(item_cat)))\n",
    "fig.update_layout(legend_orientation=\"h\", width=1600, height=1000, title=\"Plotting total 'item_cnt_day' amount across a 'date_block_num' for each 'item_category_id'\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for outliers in `sales_train`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1207f1080>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "\n",
    "f, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.boxplot(x=sales_train['item_cnt_day'])"
   ]
  },
  {
   "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": "3.6.5"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}