Inventory-Planner-EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6caae57",
   "metadata": {},
   "source": [
    "# 1. Import libraries & setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "e55f7fe9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.8.3\r\n"
     ]
    }
   ],
   "source": [
    "!python --version\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from itertools import cycle\n",
    "\n",
    "pd.set_option('max_columns', 50)\n",
    "plt.style.use('bmh')\n",
    "color_pal=plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "color_cycle=cycle(plt.rcParams['axes.prop_cycle'].by_key()['color'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bd179ca",
   "metadata": {},
   "source": [
    "# 2. Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac094d7f",
   "metadata": {},
   "source": [
    "**Below files are available**\n",
    "- calendar.csv - Contains information about the dates on which the products are sold.\n",
    "- sales_train_validation.csv - Contains the historical daily unit sales data per product and store [d_1 - d_1913]\n",
    "- sample_submission.csv - The correct format for submissions. Reference the Evaluation tab for more info.\n",
    "- sell_prices.csv - Contains information about the price of the products sold per store and date.\n",
    "- sales_train_evaluation.csv - Includes sales [d_1 - d_1941] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850ba191",
   "metadata": {},
   "source": [
    "## 2.1. Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "0ce55791",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales=pd.read_csv('sales_train_validation.csv')\n",
    "cal=pd.read_csv('calendar.csv')\n",
    "price=pd.read_csv('sell_prices.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736d464b",
   "metadata": {},
   "source": [
    "# 3. Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bf5bcd5",
   "metadata": {},
   "source": [
    "## 3.1 Analyse given datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fcd3e1b",
   "metadata": {},
   "source": [
    "### 3.1.1. Describe the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "fa8da4e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30490, 1919)\n",
      "(1969, 10)\n",
      "(6841121, 4)\n"
     ]
    }
   ],
   "source": [
    "# print shape\n",
    "\n",
    "print(sales.shape)\n",
    "print(cal.shape)\n",
    "print(price.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "666cde2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id', 'd_1',\n",
      "       'd_2', 'd_3', 'd_4',\n",
      "       ...\n",
      "       'd_1904', 'd_1905', 'd_1906', 'd_1907', 'd_1908', 'd_1909', 'd_1910',\n",
      "       'd_1911', 'd_1912', 'd_1913'],\n",
      "      dtype='object', length=1919)\n",
      "Index(['date', 'wm_yr_wk', 'd', 'event_name_1', 'event_type_1', 'event_name_2',\n",
      "       'event_type_2', 'snap_CA', 'snap_TX', 'snap_WI'],\n",
      "      dtype='object')\n",
      "Index(['store_id', 'item_id', 'wm_yr_wk', 'sell_price'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# print column names in all 3 dataframes\n",
    "\n",
    "print(sales.columns)\n",
    "print(cal.columns)\n",
    "print(price.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9c04c3e",
   "metadata": {},
   "source": [
    "### 3.1.2. Look for unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "b2fe8caa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 3 categories\n",
      "There are 7 departments\n",
      "There are 3 unique states\n",
      "There are 10 stores\n",
      "There are 3049 items\n",
      "unique states are: ['CA' 'TX' 'WI']\n",
      "unique store ids are: ['CA_1' 'CA_2' 'CA_3' 'CA_4' 'TX_1' 'TX_2' 'TX_3' 'WI_1' 'WI_2' 'WI_3']\n",
      "unique category IDs are: ['HOBBIES' 'HOUSEHOLD' 'FOODS']\n",
      "unique department IDs are: ['HOBBIES_1' 'HOBBIES_2' 'HOUSEHOLD_1' 'HOUSEHOLD_2' 'FOODS_1' 'FOODS_2'\n",
      " 'FOODS_3']\n"
     ]
    }
   ],
   "source": [
    "# look for unique counts and values of main columns\n",
    "\n",
    "print(f\"There are {sales['cat_id'].nunique()} categories\")\n",
    "\n",
    "print(f\"There are {sales['dept_id'].nunique()} departments\")\n",
    "print(f\"There are {sales['state_id'].nunique()} unique states\")\n",
    "print(f\"There are {sales['store_id'].nunique()} stores\")\n",
    "print(f\"There are {sales['item_id'].nunique()} items\")\n",
    "\n",
    "print(f\"unique states are: {sales.state_id.unique()}\")\n",
    "print(f\"unique store ids are: {sales.store_id.unique()}\")\n",
    "print(f\"unique category IDs are: {sales.cat_id.unique()}\")\n",
    "print(f\"unique department IDs are: {sales.dept_id.unique()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b527478",
   "metadata": {},
   "source": [
    "### 3.1.3. Look for NA values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "2080852a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id          0\n",
      "item_id     0\n",
      "dept_id     0\n",
      "cat_id      0\n",
      "store_id    0\n",
      "           ..\n",
      "d_1909      0\n",
      "d_1910      0\n",
      "d_1911      0\n",
      "d_1912      0\n",
      "d_1913      0\n",
      "Length: 1919, dtype: int64\n",
      "---------------------------------------------------------\n",
      "date               0\n",
      "wm_yr_wk           0\n",
      "weekday            0\n",
      "wday               0\n",
      "month              0\n",
      "year               0\n",
      "d                  0\n",
      "event_name_1    1807\n",
      "event_type_1    1807\n",
      "event_name_2    1964\n",
      "event_type_2    1964\n",
      "snap_CA            0\n",
      "snap_TX            0\n",
      "snap_WI            0\n",
      "dtype: int64\n",
      "---------------------------------------------------------\n",
      "store_id      0\n",
      "item_id       0\n",
      "wm_yr_wk      0\n",
      "sell_price    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Checking for any na values\n",
    "print(sales.isna().sum())\n",
    "print(\"---------------------------------------------------------\")\n",
    "print(cal.isna().sum())\n",
    "print(\"---------------------------------------------------------\")\n",
    "print(price.isna().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f3060e0",
   "metadata": {},
   "source": [
    "**Observations about datasets:**\n",
    "\n",
    "In the data, there are 3 states, 10 stores, 3 categories, 7 departments, and 3049 items.\n",
    "\n",
    "- States are (CA, TX, WI)\n",
    "- 10 stores are:\n",
    "    - 4 in CA: (CA_1, CA_2, CA_3, CA_4) \n",
    "    - 3 stores in TX: (TX_1, TX_2, TX_3)\n",
    "    - 3 stores in WI: (WI_1, WI_2, WI_3)\n",
    "- 3 Categories are:\n",
    "     - HOBBIES, \n",
    "     - HOUSEHOLD, \n",
    "     - FOODS\n",
    "- Departments are:\n",
    "    - HOBBIES:'HOBBIES_1', 'HOBBIES_2', \n",
    "    - HOUSEHOLD:'HOUSEHOLD_1', 'HOUSEHOLD_2', \n",
    "    - FOOD:'FOODS_1','FOODS_2','FOODS_3'\n",
    "    \n",
    "- Null Values:\n",
    "    - There are null values in calendar dataset under event_name and event_type categories."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e15f39",
   "metadata": {},
   "source": [
    "## 3.2. Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2acb6a34",
   "metadata": {},
   "source": [
    "### 3.2.1. Visualizing the distribution of items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "9aafaa1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "branchvalues": "total",
         "customdata": [
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           216
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           398
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           823
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           416
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           149
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           532
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           515
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           614.0494084899095
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           345.58761061946905
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           523.6380133715377
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ],
          [
           533.2551656280748
          ]
         ],
         "domain": {
          "x": [
           0,
           1
          ],
          "y": [
           0,
           1
          ]
         },
         "hovertemplate": "labels=%{label}<br>item_id_sum=%{value}<br>parent=%{parent}<br>id=%{id}<br>item_id=%{color}<extra></extra>",
         "ids": [
          "USA/CA/CA_1/FOODS/FOODS_1",
          "USA/CA/CA_2/FOODS/FOODS_1",
          "USA/CA/CA_3/FOODS/FOODS_1",
          "USA/CA/CA_4/FOODS/FOODS_1",
          "USA/TX/TX_1/FOODS/FOODS_1",
          "USA/TX/TX_2/FOODS/FOODS_1",
          "USA/TX/TX_3/FOODS/FOODS_1",
          "USA/WI/WI_1/FOODS/FOODS_1",
          "USA/WI/WI_2/FOODS/FOODS_1",
          "USA/WI/WI_3/FOODS/FOODS_1",
          "USA/CA/CA_1/FOODS/FOODS_2",
          "USA/CA/CA_2/FOODS/FOODS_2",
          "USA/CA/CA_3/FOODS/FOODS_2",
          "USA/CA/CA_4/FOODS/FOODS_2",
          "USA/TX/TX_1/FOODS/FOODS_2",
          "USA/TX/TX_2/FOODS/FOODS_2",
          "USA/TX/TX_3/FOODS/FOODS_2",
          "USA/WI/WI_1/FOODS/FOODS_2",
          "USA/WI/WI_2/FOODS/FOODS_2",
          "USA/WI/WI_3/FOODS/FOODS_2",
          "USA/CA/CA_1/FOODS/FOODS_3",
          "USA/CA/CA_2/FOODS/FOODS_3",
          "USA/CA/CA_3/FOODS/FOODS_3",
          "USA/CA/CA_4/FOODS/FOODS_3",
          "USA/TX/TX_1/FOODS/FOODS_3",
          "USA/TX/TX_2/FOODS/FOODS_3",
          "USA/TX/TX_3/FOODS/FOODS_3",
          "USA/WI/WI_1/FOODS/FOODS_3",
          "USA/WI/WI_2/FOODS/FOODS_3",
          "USA/WI/WI_3/FOODS/FOODS_3",
          "USA/CA/CA_1/HOBBIES/HOBBIES_1",
          "USA/CA/CA_2/HOBBIES/HOBBIES_1",
          "USA/CA/CA_3/HOBBIES/HOBBIES_1",
          "USA/CA/CA_4/HOBBIES/HOBBIES_1",
          "USA/TX/TX_1/HOBBIES/HOBBIES_1",
          "USA/TX/TX_2/HOBBIES/HOBBIES_1",
          "USA/TX/TX_3/HOBBIES/HOBBIES_1",
          "USA/WI/WI_1/HOBBIES/HOBBIES_1",
          "USA/WI/WI_2/HOBBIES/HOBBIES_1",
          "USA/WI/WI_3/HOBBIES/HOBBIES_1",
          "USA/CA/CA_1/HOBBIES/HOBBIES_2",
          "USA/CA/CA_2/HOBBIES/HOBBIES_2",
          "USA/CA/CA_3/HOBBIES/HOBBIES_2",
          "USA/CA/CA_4/HOBBIES/HOBBIES_2",
          "USA/TX/TX_1/HOBBIES/HOBBIES_2",
          "USA/TX/TX_2/HOBBIES/HOBBIES_2",
          "USA/TX/TX_3/HOBBIES/HOBBIES_2",
          "USA/WI/WI_1/HOBBIES/HOBBIES_2",
          "USA/WI/WI_2/HOBBIES/HOBBIES_2",
          "USA/WI/WI_3/HOBBIES/HOBBIES_2",
          "USA/CA/CA_1/HOUSEHOLD/HOUSEHOLD_1",
          "USA/CA/CA_2/HOUSEHOLD/HOUSEHOLD_1",
          "USA/CA/CA_3/HOUSEHOLD/HOUSEHOLD_1",
          "USA/CA/CA_4/HOUSEHOLD/HOUSEHOLD_1",
          "USA/TX/TX_1/HOUSEHOLD/HOUSEHOLD_1",
          "USA/TX/TX_2/HOUSEHOLD/HOUSEHOLD_1",
          "USA/TX/TX_3/HOUSEHOLD/HOUSEHOLD_1",
          "USA/WI/WI_1/HOUSEHOLD/HOUSEHOLD_1",
          "USA/WI/WI_2/HOUSEHOLD/HOUSEHOLD_1",
          "USA/WI/WI_3/HOUSEHOLD/HOUSEHOLD_1",
          "USA/CA/CA_1/HOUSEHOLD/HOUSEHOLD_2",
          "USA/CA/CA_2/HOUSEHOLD/HOUSEHOLD_2",
          "USA/CA/CA_3/HOUSEHOLD/HOUSEHOLD_2",
          "USA/CA/CA_4/HOUSEHOLD/HOUSEHOLD_2",
          "USA/TX/TX_1/HOUSEHOLD/HOUSEHOLD_2",
          "USA/TX/TX_2/HOUSEHOLD/HOUSEHOLD_2",
          "USA/TX/TX_3/HOUSEHOLD/HOUSEHOLD_2",
          "USA/WI/WI_1/HOUSEHOLD/HOUSEHOLD_2",
          "USA/WI/WI_2/HOUSEHOLD/HOUSEHOLD_2",
          "USA/WI/WI_3/HOUSEHOLD/HOUSEHOLD_2",
          "USA/CA/CA_1/FOODS",
          "USA/CA/CA_2/FOODS",
          "USA/CA/CA_3/FOODS",
          "USA/CA/CA_4/FOODS",
          "USA/TX/TX_1/FOODS",
          "USA/TX/TX_2/FOODS",
          "USA/TX/TX_3/FOODS",
          "USA/WI/WI_1/FOODS",
          "USA/WI/WI_2/FOODS",
          "USA/WI/WI_3/FOODS",
          "USA/CA/CA_1/HOBBIES",
          "USA/CA/CA_2/HOBBIES",
          "USA/CA/CA_3/HOBBIES",
          "USA/CA/CA_4/HOBBIES",
          "USA/TX/TX_1/HOBBIES",
          "USA/TX/TX_2/HOBBIES",
          "USA/TX/TX_3/HOBBIES",
          "USA/WI/WI_1/HOBBIES",
          "USA/WI/WI_2/HOBBIES",
          "USA/WI/WI_3/HOBBIES",
          "USA/CA/CA_1/HOUSEHOLD",
          "USA/CA/CA_2/HOUSEHOLD",
          "USA/CA/CA_3/HOUSEHOLD",
          "USA/CA/CA_4/HOUSEHOLD",
          "USA/TX/TX_1/HOUSEHOLD",
          "USA/TX/TX_2/HOUSEHOLD",
          "USA/TX/TX_3/HOUSEHOLD",
          "USA/WI/WI_1/HOUSEHOLD",
          "USA/WI/WI_2/HOUSEHOLD",
          "USA/WI/WI_3/HOUSEHOLD",
          "USA/CA/CA_1",
          "USA/CA/CA_2",
          "USA/CA/CA_3",
          "USA/CA/CA_4",
          "USA/TX/TX_1",
          "USA/TX/TX_2",
          "USA/TX/TX_3",
          "USA/WI/WI_1",
          "USA/WI/WI_2",
          "USA/WI/WI_3",
          "USA/CA",
          "USA/TX",
          "USA/WI",
          "USA"
         ],
         "labels": [
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_1",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_2",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "FOODS_3",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_1",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOBBIES_2",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_1",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "HOUSEHOLD_2",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "FOODS",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOBBIES",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "HOUSEHOLD",
          "CA_1",
          "CA_2",
          "CA_3",
          "CA_4",
          "TX_1",
          "TX_2",
          "TX_3",
          "WI_1",
          "WI_2",
          "WI_3",
          "CA",
          "TX",
          "WI",
          "USA"
         ],
         "marker": {
          "coloraxis": "coloraxis",
          "colors": [
           216,
           216,
           216,
           216,
           216,
           216,
           216,
           216,
           216,
           216,
           398,
           398,
           398,
           398,
           398,
           398,
           398,
           398,
           398,
           398,
           823,
           823,
           823,
           823,
           823,
           823,
           823,
           823,
           823,
           823,
           416,
           416,
           416,
           416,
           416,
           416,
           416,
           416,
           416,
           416,
           149,
           149,
           149,
           149,
           149,
           149,
           149,
           149,
           149,
           149,
           532,
           532,
           532,
           532,
           532,
           532,
           532,
           532,
           532,
           532,
           515,
           515,
           515,
           515,
           515,
           515,
           515,
           515,
           515,
           515,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           614.0494084899095,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           345.58761061946905,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           523.6380133715377,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748,
           533.2551656280748
          ]
         },
         "name": "",
         "parents": [
          "USA/CA/CA_1/FOODS",
          "USA/CA/CA_2/FOODS",
          "USA/CA/CA_3/FOODS",
          "USA/CA/CA_4/FOODS",
          "USA/TX/TX_1/FOODS",
          "USA/TX/TX_2/FOODS",
          "USA/TX/TX_3/FOODS",
          "USA/WI/WI_1/FOODS",
          "USA/WI/WI_2/FOODS",
          "USA/WI/WI_3/FOODS",
          "USA/CA/CA_1/FOODS",
          "USA/CA/CA_2/FOODS",
          "USA/CA/CA_3/FOODS",
          "USA/CA/CA_4/FOODS",
          "USA/TX/TX_1/FOODS",
          "USA/TX/TX_2/FOODS",
          "USA/TX/TX_3/FOODS",
          "USA/WI/WI_1/FOODS",
          "USA/WI/WI_2/FOODS",
          "USA/WI/WI_3/FOODS",
          "USA/CA/CA_1/FOODS",
          "USA/CA/CA_2/FOODS",
          "USA/CA/CA_3/FOODS",
          "USA/CA/CA_4/FOODS",
          "USA/TX/TX_1/FOODS",
          "USA/TX/TX_2/FOODS",
          "USA/TX/TX_3/FOODS",
          "USA/WI/WI_1/FOODS",
          "USA/WI/WI_2/FOODS",
          "USA/WI/WI_3/FOODS",
          "USA/CA/CA_1/HOBBIES",
          "USA/CA/CA_2/HOBBIES",
          "USA/CA/CA_3/HOBBIES",
          "USA/CA/CA_4/HOBBIES",
          "USA/TX/TX_1/HOBBIES",
          "USA/TX/TX_2/HOBBIES",
          "USA/TX/TX_3/HOBBIES",
          "USA/WI/WI_1/HOBBIES",
          "USA/WI/WI_2/HOBBIES",
          "USA/WI/WI_3/HOBBIES",
          "USA/CA/CA_1/HOBBIES",
          "USA/CA/CA_2/HOBBIES",
          "USA/CA/CA_3/HOBBIES",
          "USA/CA/CA_4/HOBBIES",
          "USA/TX/TX_1/HOBBIES",
          "USA/TX/TX_2/HOBBIES",
          "USA/TX/TX_3/HOBBIES",
          "USA/WI/WI_1/HOBBIES",
          "USA/WI/WI_2/HOBBIES",
          "USA/WI/WI_3/HOBBIES",
          "USA/CA/CA_1/HOUSEHOLD",
          "USA/CA/CA_2/HOUSEHOLD",
          "USA/CA/CA_3/HOUSEHOLD",
          "USA/CA/CA_4/HOUSEHOLD",
          "USA/TX/TX_1/HOUSEHOLD",
          "USA/TX/TX_2/HOUSEHOLD",
          "USA/TX/TX_3/HOUSEHOLD",
          "USA/WI/WI_1/HOUSEHOLD",
          "USA/WI/WI_2/HOUSEHOLD",
          "USA/WI/WI_3/HOUSEHOLD",
          "USA/CA/CA_1/HOUSEHOLD",
          "USA/CA/CA_2/HOUSEHOLD",
          "USA/CA/CA_3/HOUSEHOLD",
          "USA/CA/CA_4/HOUSEHOLD",
          "USA/TX/TX_1/HOUSEHOLD",
          "USA/TX/TX_2/HOUSEHOLD",
          "USA/TX/TX_3/HOUSEHOLD",
          "USA/WI/WI_1/HOUSEHOLD",
          "USA/WI/WI_2/HOUSEHOLD",
          "USA/WI/WI_3/HOUSEHOLD",
          "USA/CA/CA_1",
          "USA/CA/CA_2",
          "USA/CA/CA_3",
          "USA/CA/CA_4",
          "USA/TX/TX_1",
          "USA/TX/TX_2",
          "USA/TX/TX_3",
          "USA/WI/WI_1",
          "USA/WI/WI_2",
          "USA/WI/WI_3",
          "USA/CA/CA_1",
          "USA/CA/CA_2",
          "USA/CA/CA_3",
          "USA/CA/CA_4",
          "USA/TX/TX_1",
          "USA/TX/TX_2",
          "USA/TX/TX_3",
          "USA/WI/WI_1",
          "USA/WI/WI_2",
          "USA/WI/WI_3",
          "USA/CA/CA_1",
          "USA/CA/CA_2",
          "USA/CA/CA_3",
          "USA/CA/CA_4",
          "USA/TX/TX_1",
          "USA/TX/TX_2",
          "USA/TX/TX_3",
          "USA/WI/WI_1",
          "USA/WI/WI_2",
          "USA/WI/WI_3",
          "USA/CA",
          "USA/CA",
          "USA/CA",
          "USA/CA",
          "USA/TX",
          "USA/TX",
          "USA/TX",
          "USA/WI",
          "USA/WI",
          "USA/WI",
          "USA",
          "USA",
          "USA",
          ""
         ],
         "type": "treemap",
         "values": [
          216,
          216,
          216,
          216,
          216,
          216,
          216,
          216,
          216,
          216,
          398,
          398,
          398,
          398,
          398,
          398,
          398,
          398,
          398,
          398,
          823,
          823,
          823,
          823,
          823,
          823,
          823,
          823,
          823,
          823,
          416,
          416,
          416,
          416,
          416,
          416,
          416,
          416,
          416,
          416,
          149,
          149,
          149,
          149,
          149,
          149,
          149,
          149,
          149,
          149,
          532,
          532,
          532,
          532,
          532,
          532,
          532,
          532,
          532,
          532,
          515,
          515,
          515,
          515,
          515,
          515,
          515,
          515,
          515,
          515,
          1437,
          1437,
          1437,
          1437,
          1437,
          1437,
          1437,
          1437,
          1437,
          1437,
          565,
          565,
          565,
          565,
          565,
          565,
          565,
          565,
          565,
          565,
          1047,
          1047,
          1047,
          1047,
          1047,
          1047,
          1047,
          1047,
          1047,
          1047,
          3049,
          3049,
          3049,
          3049,
          3049,
          3049,
          3049,
          3049,
          3049,
          3049,
          12196,
          9147,
          9147,
          30490
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "colorbar": {
          "title": {
           "text": "item_id"
          }
         },
         "colorscale": [
          [
           0,
           "rgb(103,0,31)"
          ],
          [
           0.1,
           "rgb(178,24,43)"
          ],
          [
           0.2,
           "rgb(214,96,77)"
          ],
          [
           0.3,
           "rgb(244,165,130)"
          ],
          [
           0.4,
           "rgb(253,219,199)"
          ],
          [
           0.5,
           "rgb(247,247,247)"
          ],
          [
           0.6,
           "rgb(209,229,240)"
          ],
          [
           0.7,
           "rgb(146,197,222)"
          ],
          [
           0.8,
           "rgb(67,147,195)"
          ],
          [
           0.9,
           "rgb(33,102,172)"
          ],
          [
           1,
           "rgb(5,48,97)"
          ]
         ]
        },
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#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": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Walmart: Distribution of items"
        },
        "treemapcolorway": [
         "pink",
         "lightgray"
        ]
       }
      },
      "text/html": [
       "<div>                            <div id=\"c42fb56d-13de-4015-acd9-6550c74305d7\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c42fb56d-13de-4015-acd9-6550c74305d7\")) {                    Plotly.newPlot(                        \"c42fb56d-13de-4015-acd9-6550c74305d7\",                        [{\"branchvalues\": \"total\", \"customdata\": [[216.0], [216.0], [216.0], [216.0], [216.0], [216.0], [216.0], [216.0], [216.0], [216.0], [398.0], [398.0], [398.0], [398.0], [398.0], [398.0], [398.0], [398.0], [398.0], [398.0], [823.0], [823.0], [823.0], [823.0], [823.0], [823.0], [823.0], [823.0], [823.0], [823.0], [416.0], [416.0], [416.0], [416.0], [416.0], [416.0], [416.0], [416.0], [416.0], [416.0], [149.0], [149.0], [149.0], [149.0], [149.0], [149.0], [149.0], [149.0], [149.0], [149.0], [532.0], [532.0], [532.0], [532.0], [532.0], [532.0], [532.0], [532.0], [532.0], [532.0], [515.0], [515.0], [515.0], [515.0], [515.0], [515.0], [515.0], [515.0], [515.0], [515.0], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [614.0494084899095], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [345.58761061946905], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [523.6380133715377], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748], [533.2551656280748]], \"domain\": {\"x\": [0.0, 1.0], \"y\": [0.0, 1.0]}, \"hovertemplate\": \"labels=%{label}<br>item_id_sum=%{value}<br>parent=%{parent}<br>id=%{id}<br>item_id=%{color}<extra></extra>\", \"ids\": [\"USA/CA/CA_1/FOODS/FOODS_1\", \"USA/CA/CA_2/FOODS/FOODS_1\", \"USA/CA/CA_3/FOODS/FOODS_1\", \"USA/CA/CA_4/FOODS/FOODS_1\", \"USA/TX/TX_1/FOODS/FOODS_1\", \"USA/TX/TX_2/FOODS/FOODS_1\", \"USA/TX/TX_3/FOODS/FOODS_1\", \"USA/WI/WI_1/FOODS/FOODS_1\", \"USA/WI/WI_2/FOODS/FOODS_1\", \"USA/WI/WI_3/FOODS/FOODS_1\", \"USA/CA/CA_1/FOODS/FOODS_2\", \"USA/CA/CA_2/FOODS/FOODS_2\", \"USA/CA/CA_3/FOODS/FOODS_2\", \"USA/CA/CA_4/FOODS/FOODS_2\", \"USA/TX/TX_1/FOODS/FOODS_2\", \"USA/TX/TX_2/FOODS/FOODS_2\", \"USA/TX/TX_3/FOODS/FOODS_2\", \"USA/WI/WI_1/FOODS/FOODS_2\", \"USA/WI/WI_2/FOODS/FOODS_2\", \"USA/WI/WI_3/FOODS/FOODS_2\", \"USA/CA/CA_1/FOODS/FOODS_3\", \"USA/CA/CA_2/FOODS/FOODS_3\", \"USA/CA/CA_3/FOODS/FOODS_3\", \"USA/CA/CA_4/FOODS/FOODS_3\", \"USA/TX/TX_1/FOODS/FOODS_3\", \"USA/TX/TX_2/FOODS/FOODS_3\", \"USA/TX/TX_3/FOODS/FOODS_3\", \"USA/WI/WI_1/FOODS/FOODS_3\", \"USA/WI/WI_2/FOODS/FOODS_3\", \"USA/WI/WI_3/FOODS/FOODS_3\", \"USA/CA/CA_1/HOBBIES/HOBBIES_1\", \"USA/CA/CA_2/HOBBIES/HOBBIES_1\", \"USA/CA/CA_3/HOBBIES/HOBBIES_1\", \"USA/CA/CA_4/HOBBIES/HOBBIES_1\", \"USA/TX/TX_1/HOBBIES/HOBBIES_1\", \"USA/TX/TX_2/HOBBIES/HOBBIES_1\", \"USA/TX/TX_3/HOBBIES/HOBBIES_1\", \"USA/WI/WI_1/HOBBIES/HOBBIES_1\", \"USA/WI/WI_2/HOBBIES/HOBBIES_1\", \"USA/WI/WI_3/HOBBIES/HOBBIES_1\", \"USA/CA/CA_1/HOBBIES/HOBBIES_2\", \"USA/CA/CA_2/HOBBIES/HOBBIES_2\", \"USA/CA/CA_3/HOBBIES/HOBBIES_2\", \"USA/CA/CA_4/HOBBIES/HOBBIES_2\", \"USA/TX/TX_1/HOBBIES/HOBBIES_2\", \"USA/TX/TX_2/HOBBIES/HOBBIES_2\", \"USA/TX/TX_3/HOBBIES/HOBBIES_2\", \"USA/WI/WI_1/HOBBIES/HOBBIES_2\", \"USA/WI/WI_2/HOBBIES/HOBBIES_2\", \"USA/WI/WI_3/HOBBIES/HOBBIES_2\", \"USA/CA/CA_1/HOUSEHOLD/HOUSEHOLD_1\", \"USA/CA/CA_2/HOUSEHOLD/HOUSEHOLD_1\", \"USA/CA/CA_3/HOUSEHOLD/HOUSEHOLD_1\", \"USA/CA/CA_4/HOUSEHOLD/HOUSEHOLD_1\", \"USA/TX/TX_1/HOUSEHOLD/HOUSEHOLD_1\", \"USA/TX/TX_2/HOUSEHOLD/HOUSEHOLD_1\", \"USA/TX/TX_3/HOUSEHOLD/HOUSEHOLD_1\", \"USA/WI/WI_1/HOUSEHOLD/HOUSEHOLD_1\", \"USA/WI/WI_2/HOUSEHOLD/HOUSEHOLD_1\", \"USA/WI/WI_3/HOUSEHOLD/HOUSEHOLD_1\", \"USA/CA/CA_1/HOUSEHOLD/HOUSEHOLD_2\", \"USA/CA/CA_2/HOUSEHOLD/HOUSEHOLD_2\", \"USA/CA/CA_3/HOUSEHOLD/HOUSEHOLD_2\", \"USA/CA/CA_4/HOUSEHOLD/HOUSEHOLD_2\", \"USA/TX/TX_1/HOUSEHOLD/HOUSEHOLD_2\", \"USA/TX/TX_2/HOUSEHOLD/HOUSEHOLD_2\", \"USA/TX/TX_3/HOUSEHOLD/HOUSEHOLD_2\", \"USA/WI/WI_1/HOUSEHOLD/HOUSEHOLD_2\", \"USA/WI/WI_2/HOUSEHOLD/HOUSEHOLD_2\", \"USA/WI/WI_3/HOUSEHOLD/HOUSEHOLD_2\", \"USA/CA/CA_1/FOODS\", \"USA/CA/CA_2/FOODS\", \"USA/CA/CA_3/FOODS\", \"USA/CA/CA_4/FOODS\", \"USA/TX/TX_1/FOODS\", \"USA/TX/TX_2/FOODS\", \"USA/TX/TX_3/FOODS\", \"USA/WI/WI_1/FOODS\", \"USA/WI/WI_2/FOODS\", \"USA/WI/WI_3/FOODS\", \"USA/CA/CA_1/HOBBIES\", \"USA/CA/CA_2/HOBBIES\", \"USA/CA/CA_3/HOBBIES\", \"USA/CA/CA_4/HOBBIES\", \"USA/TX/TX_1/HOBBIES\", \"USA/TX/TX_2/HOBBIES\", \"USA/TX/TX_3/HOBBIES\", \"USA/WI/WI_1/HOBBIES\", \"USA/WI/WI_2/HOBBIES\", \"USA/WI/WI_3/HOBBIES\", \"USA/CA/CA_1/HOUSEHOLD\", \"USA/CA/CA_2/HOUSEHOLD\", \"USA/CA/CA_3/HOUSEHOLD\", \"USA/CA/CA_4/HOUSEHOLD\", \"USA/TX/TX_1/HOUSEHOLD\", \"USA/TX/TX_2/HOUSEHOLD\", \"USA/TX/TX_3/HOUSEHOLD\", \"USA/WI/WI_1/HOUSEHOLD\", \"USA/WI/WI_2/HOUSEHOLD\", \"USA/WI/WI_3/HOUSEHOLD\", \"USA/CA/CA_1\", \"USA/CA/CA_2\", \"USA/CA/CA_3\", \"USA/CA/CA_4\", \"USA/TX/TX_1\", \"USA/TX/TX_2\", \"USA/TX/TX_3\", \"USA/WI/WI_1\", \"USA/WI/WI_2\", \"USA/WI/WI_3\", \"USA/CA\", \"USA/TX\", \"USA/WI\", \"USA\"], \"labels\": [\"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_1\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_2\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"FOODS_3\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_1\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOBBIES_2\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_1\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"HOUSEHOLD_2\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"FOODS\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOBBIES\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"HOUSEHOLD\", \"CA_1\", \"CA_2\", \"CA_3\", \"CA_4\", \"TX_1\", \"TX_2\", \"TX_3\", \"WI_1\", \"WI_2\", \"WI_3\", \"CA\", \"TX\", \"WI\", \"USA\"], \"marker\": {\"coloraxis\": \"coloraxis\", \"colors\": [216.0, 216.0, 216.0, 216.0, 216.0, 216.0, 216.0, 216.0, 216.0, 216.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 398.0, 823.0, 823.0, 823.0, 823.0, 823.0, 823.0, 823.0, 823.0, 823.0, 823.0, 416.0, 416.0, 416.0, 416.0, 416.0, 416.0, 416.0, 416.0, 416.0, 416.0, 149.0, 149.0, 149.0, 149.0, 149.0, 149.0, 149.0, 149.0, 149.0, 149.0, 532.0, 532.0, 532.0, 532.0, 532.0, 532.0, 532.0, 532.0, 532.0, 532.0, 515.0, 515.0, 515.0, 515.0, 515.0, 515.0, 515.0, 515.0, 515.0, 515.0, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 614.0494084899095, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 345.58761061946905, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 523.6380133715377, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748, 533.2551656280748]}, \"name\": \"\", \"parents\": [\"USA/CA/CA_1/FOODS\", \"USA/CA/CA_2/FOODS\", \"USA/CA/CA_3/FOODS\", \"USA/CA/CA_4/FOODS\", \"USA/TX/TX_1/FOODS\", \"USA/TX/TX_2/FOODS\", \"USA/TX/TX_3/FOODS\", \"USA/WI/WI_1/FOODS\", \"USA/WI/WI_2/FOODS\", \"USA/WI/WI_3/FOODS\", \"USA/CA/CA_1/FOODS\", \"USA/CA/CA_2/FOODS\", \"USA/CA/CA_3/FOODS\", \"USA/CA/CA_4/FOODS\", \"USA/TX/TX_1/FOODS\", \"USA/TX/TX_2/FOODS\", \"USA/TX/TX_3/FOODS\", \"USA/WI/WI_1/FOODS\", \"USA/WI/WI_2/FOODS\", \"USA/WI/WI_3/FOODS\", \"USA/CA/CA_1/FOODS\", \"USA/CA/CA_2/FOODS\", \"USA/CA/CA_3/FOODS\", \"USA/CA/CA_4/FOODS\", \"USA/TX/TX_1/FOODS\", \"USA/TX/TX_2/FOODS\", \"USA/TX/TX_3/FOODS\", \"USA/WI/WI_1/FOODS\", \"USA/WI/WI_2/FOODS\", \"USA/WI/WI_3/FOODS\", \"USA/CA/CA_1/HOBBIES\", \"USA/CA/CA_2/HOBBIES\", \"USA/CA/CA_3/HOBBIES\", \"USA/CA/CA_4/HOBBIES\", \"USA/TX/TX_1/HOBBIES\", \"USA/TX/TX_2/HOBBIES\", \"USA/TX/TX_3/HOBBIES\", \"USA/WI/WI_1/HOBBIES\", \"USA/WI/WI_2/HOBBIES\", \"USA/WI/WI_3/HOBBIES\", \"USA/CA/CA_1/HOBBIES\", \"USA/CA/CA_2/HOBBIES\", \"USA/CA/CA_3/HOBBIES\", \"USA/CA/CA_4/HOBBIES\", \"USA/TX/TX_1/HOBBIES\", \"USA/TX/TX_2/HOBBIES\", \"USA/TX/TX_3/HOBBIES\", \"USA/WI/WI_1/HOBBIES\", \"USA/WI/WI_2/HOBBIES\", \"USA/WI/WI_3/HOBBIES\", \"USA/CA/CA_1/HOUSEHOLD\", \"USA/CA/CA_2/HOUSEHOLD\", \"USA/CA/CA_3/HOUSEHOLD\", \"USA/CA/CA_4/HOUSEHOLD\", \"USA/TX/TX_1/HOUSEHOLD\", \"USA/TX/TX_2/HOUSEHOLD\", \"USA/TX/TX_3/HOUSEHOLD\", \"USA/WI/WI_1/HOUSEHOLD\", \"USA/WI/WI_2/HOUSEHOLD\", \"USA/WI/WI_3/HOUSEHOLD\", \"USA/CA/CA_1/HOUSEHOLD\", \"USA/CA/CA_2/HOUSEHOLD\", \"USA/CA/CA_3/HOUSEHOLD\", \"USA/CA/CA_4/HOUSEHOLD\", \"USA/TX/TX_1/HOUSEHOLD\", \"USA/TX/TX_2/HOUSEHOLD\", \"USA/TX/TX_3/HOUSEHOLD\", \"USA/WI/WI_1/HOUSEHOLD\", \"USA/WI/WI_2/HOUSEHOLD\", \"USA/WI/WI_3/HOUSEHOLD\", \"USA/CA/CA_1\", \"USA/CA/CA_2\", \"USA/CA/CA_3\", \"USA/CA/CA_4\", \"USA/TX/TX_1\", \"USA/TX/TX_2\", \"USA/TX/TX_3\", \"USA/WI/WI_1\", \"USA/WI/WI_2\", \"USA/WI/WI_3\", \"USA/CA/CA_1\", \"USA/CA/CA_2\", \"USA/CA/CA_3\", \"USA/CA/CA_4\", \"USA/TX/TX_1\", \"USA/TX/TX_2\", \"USA/TX/TX_3\", \"USA/WI/WI_1\", \"USA/WI/WI_2\", \"USA/WI/WI_3\", \"USA/CA/CA_1\", \"USA/CA/CA_2\", \"USA/CA/CA_3\", \"USA/CA/CA_4\", \"USA/TX/TX_1\", \"USA/TX/TX_2\", \"USA/TX/TX_3\", \"USA/WI/WI_1\", \"USA/WI/WI_2\", \"USA/WI/WI_3\", \"USA/CA\", \"USA/CA\", \"USA/CA\", \"USA/CA\", \"USA/TX\", \"USA/TX\", \"USA/TX\", \"USA/WI\", \"USA/WI\", \"USA/WI\", \"USA\", \"USA\", \"USA\", \"\"], \"type\": \"treemap\", \"values\": [216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 398, 398, 398, 398, 398, 398, 398, 398, 398, 398, 823, 823, 823, 823, 823, 823, 823, 823, 823, 823, 416, 416, 416, 416, 416, 416, 416, 416, 416, 416, 149, 149, 149, 149, 149, 149, 149, 149, 149, 149, 532, 532, 532, 532, 532, 532, 532, 532, 532, 532, 515, 515, 515, 515, 515, 515, 515, 515, 515, 515, 1437, 1437, 1437, 1437, 1437, 1437, 1437, 1437, 1437, 1437, 565, 565, 565, 565, 565, 565, 565, 565, 565, 565, 1047, 1047, 1047, 1047, 1047, 1047, 1047, 1047, 1047, 1047, 3049, 3049, 3049, 3049, 3049, 3049, 3049, 3049, 3049, 3049, 12196, 9147, 9147, 30490]}],                        {\"coloraxis\": {\"colorbar\": {\"title\": {\"text\": \"item_id\"}}, \"colorscale\": [[0.0, \"rgb(103,0,31)\"], [0.1, \"rgb(178,24,43)\"], [0.2, \"rgb(214,96,77)\"], [0.3, \"rgb(244,165,130)\"], [0.4, \"rgb(253,219,199)\"], [0.5, \"rgb(247,247,247)\"], [0.6, \"rgb(209,229,240)\"], [0.7, \"rgb(146,197,222)\"], [0.8, \"rgb(67,147,195)\"], [0.9, \"rgb(33,102,172)\"], [1.0, \"rgb(5,48,97)\"]]}, \"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"contour\"}], \"contourcarpet\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"contourcarpet\"}], \"heatmap\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmap\"}], \"heatmapgl\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"heatmapgl\"}], \"histogram\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"histogram\"}], \"histogram2d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2d\"}], \"histogram2dcontour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"histogram2dcontour\"}], \"mesh3d\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"mesh3d\"}], \"parcoords\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"parcoords\"}], \"pie\": [{\"automargin\": true, \"type\": \"pie\"}], \"scatter\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter\"}], \"scatter3d\": [{\"line\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatter3d\"}], \"scattercarpet\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattercarpet\"}], \"scattergeo\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergeo\"}], \"scattergl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattergl\"}], \"scattermapbox\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scattermapbox\"}], \"scatterpolar\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolar\"}], \"scatterpolargl\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterpolargl\"}], \"scatterternary\": [{\"marker\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"type\": \"scatterternary\"}], \"surface\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"autotypenumbers\": \"strict\", \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#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\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Walmart: Distribution of items\"}, \"treemapcolorway\": [\"pink\", \"lightgray\"]},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('c42fb56d-13de-4015-acd9-6550c74305d7');\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",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "gp = sales.groupby(['state_id','store_id','cat_id','dept_id'],as_index=False)\n",
    "gp = gp['item_id'].count().dropna()\n",
    "gp['USA'] = 'USA'\n",
    "\n",
    "fig = px.treemap(gp, path=['USA', 'state_id', 'store_id', 'cat_id', 'dept_id'], values='item_id',\n",
    "                  color='item_id',\n",
    "                  color_continuous_scale = 'RdBu',\n",
    "                  title='Walmart: Distribution of items')\n",
    "            \n",
    "fig.update_layout(treemapcolorway = [\"pink\", \"lightgray\"])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac91d863",
   "metadata": {},
   "source": [
    "## 3.3. EDA on Sales dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "9d601e7b",
   "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>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>d_1</th>\n",
       "      <th>d_2</th>\n",
       "      <th>d_3</th>\n",
       "      <th>d_4</th>\n",
       "      <th>d_5</th>\n",
       "      <th>d_6</th>\n",
       "      <th>d_7</th>\n",
       "      <th>d_8</th>\n",
       "      <th>d_9</th>\n",
       "      <th>d_10</th>\n",
       "      <th>d_11</th>\n",
       "      <th>d_12</th>\n",
       "      <th>d_13</th>\n",
       "      <th>d_14</th>\n",
       "      <th>d_15</th>\n",
       "      <th>d_16</th>\n",
       "      <th>d_17</th>\n",
       "      <th>d_18</th>\n",
       "      <th>d_19</th>\n",
       "      <th>...</th>\n",
       "      <th>d_1889</th>\n",
       "      <th>d_1890</th>\n",
       "      <th>d_1891</th>\n",
       "      <th>d_1892</th>\n",
       "      <th>d_1893</th>\n",
       "      <th>d_1894</th>\n",
       "      <th>d_1895</th>\n",
       "      <th>d_1896</th>\n",
       "      <th>d_1897</th>\n",
       "      <th>d_1898</th>\n",
       "      <th>d_1899</th>\n",
       "      <th>d_1900</th>\n",
       "      <th>d_1901</th>\n",
       "      <th>d_1902</th>\n",
       "      <th>d_1903</th>\n",
       "      <th>d_1904</th>\n",
       "      <th>d_1905</th>\n",
       "      <th>d_1906</th>\n",
       "      <th>d_1907</th>\n",
       "      <th>d_1908</th>\n",
       "      <th>d_1909</th>\n",
       "      <th>d_1910</th>\n",
       "      <th>d_1911</th>\n",
       "      <th>d_1912</th>\n",
       "      <th>d_1913</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_002_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_002</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_003_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_003</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_004_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_004</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_005_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_005</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1919 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              id        item_id    dept_id   cat_id store_id  \\\n",
       "0  HOBBIES_1_001_CA_1_validation  HOBBIES_1_001  HOBBIES_1  HOBBIES     CA_1   \n",
       "1  HOBBIES_1_002_CA_1_validation  HOBBIES_1_002  HOBBIES_1  HOBBIES     CA_1   \n",
       "2  HOBBIES_1_003_CA_1_validation  HOBBIES_1_003  HOBBIES_1  HOBBIES     CA_1   \n",
       "3  HOBBIES_1_004_CA_1_validation  HOBBIES_1_004  HOBBIES_1  HOBBIES     CA_1   \n",
       "4  HOBBIES_1_005_CA_1_validation  HOBBIES_1_005  HOBBIES_1  HOBBIES     CA_1   \n",
       "\n",
       "  state_id  d_1  d_2  d_3  d_4  d_5  d_6  d_7  d_8  d_9  d_10  d_11  d_12  \\\n",
       "0       CA    0    0    0    0    0    0    0    0    0     0     0     0   \n",
       "1       CA    0    0    0    0    0    0    0    0    0     0     0     0   \n",
       "2       CA    0    0    0    0    0    0    0    0    0     0     0     0   \n",
       "3       CA    0    0    0    0    0    0    0    0    0     0     0     0   \n",
       "4       CA    0    0    0    0    0    0    0    0    0     0     0     0   \n",
       "\n",
       "   d_13  d_14  d_15  d_16  d_17  d_18  d_19  ...  d_1889  d_1890  d_1891  \\\n",
       "0     0     0     0     0     0     0     0  ...       0       0       0   \n",
       "1     0     0     0     0     0     0     0  ...       0       0       0   \n",
       "2     0     0     0     0     0     0     0  ...       0       0       0   \n",
       "3     0     0     0     0     0     0     0  ...       0       3       1   \n",
       "4     0     0     0     0     0     0     0  ...       4       0       1   \n",
       "\n",
       "   d_1892  d_1893  d_1894  d_1895  d_1896  d_1897  d_1898  d_1899  d_1900  \\\n",
       "0       1       0       4       2       3       0       1       2       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       1       0       0       0       1       0       0       0       0   \n",
       "3       2       1       3       1       0       2       5       4       2   \n",
       "4       4       0       1       0       1       0       1       1       2   \n",
       "\n",
       "   d_1901  d_1902  d_1903  d_1904  d_1905  d_1906  d_1907  d_1908  d_1909  \\\n",
       "0       0       0       1       1       3       0       1       1       1   \n",
       "1       0       0       0       0       0       0       0       0       1   \n",
       "2       0       1       2       2       1       2       1       1       1   \n",
       "3       0       3       0       1       0       5       4       1       0   \n",
       "4       0       1       1       2       1       1       0       1       1   \n",
       "\n",
       "   d_1910  d_1911  d_1912  d_1913  \n",
       "0       3       0       1       1  \n",
       "1       0       0       0       0  \n",
       "2       0       1       1       1  \n",
       "3       1       3       7       2  \n",
       "4       2       2       2       4  \n",
       "\n",
       "[5 rows x 1919 columns]"
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print first 5 rows of sales dataset\n",
    "sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "c679925a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    12196\n",
       "TX     9147\n",
       "WI     9147\n",
       "Name: state_id, dtype: int64"
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total items per state\n",
    "total_items = sales['state_id'].value_counts()\n",
    "total_items "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "id": "01991de3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales['item_total']=sales.iloc[:,6:1947].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "fc512282",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_state = sales.groupby(['state_id'])['item_total'].sum()\n",
    "total_cat = sales.groupby(['cat_id'])['item_total'].sum()\n",
    "total_cat_by_state = sales.groupby(['state_id','cat_id'])['item_total'].sum()\n",
    "total_store_by_state = sales.groupby(['store_id','state_id'])['item_total'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86f1d699",
   "metadata": {},
   "source": [
    "### 3.3.1. Visualize Sales data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "id": "140894cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'total items sold by state and category'}, xlabel='state_id,cat_id'>"
      ]
     },
     "execution_count": 339,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for i in [0, 1, 2]:\n",
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(20,3))\n",
    "\n",
    "# Total items sold by state \n",
    "total_state.plot(kind='bar', title='Total items sold by State', color='grey', ax=ax1)\n",
    "\n",
    "# Total items sold by state and category\n",
    "sales.groupby(['state_id','cat_id'])['item_total'].sum().plot.bar(title='total items sold by state and category', color='seagreen', ax=ax2)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28b68f8",
   "metadata": {},
   "source": [
    "**Observations:**\n",
    "\n",
    "- FOODS category has the highest number of items.\n",
    "- The number of items in descending order are FOODs, HOUSEHOLD, and HOBBIES."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "d3f35ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total items sold by Stores'}, xlabel='store_id,state_id'>"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2,figsize=(20,3))\n",
    "    \n",
    "# Sales over time by store ID\n",
    "sales.groupby('dept_id').count()['id'].sort_values().plot(kind='barh', title='Count of Items by store', ax=ax1)\n",
    "    \n",
    "# Total items sold by stores\n",
    "sales.groupby(['store_id','state_id'])['item_total'].sum().plot.bar(title='Total items sold by Stores', ax=ax2)\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3d4e5ad",
   "metadata": {},
   "source": [
    "*From above analysis, it can be seen that the most frequent item that sells is:*\n",
    "\n",
    "- Category: Food\n",
    "- State: California\n",
    "- Department: Foods_3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2ea396f",
   "metadata": {},
   "source": [
    "### 3.3.2. Visualize data for a single item that sells frequently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "3fe8f3fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_cols=[c for c in sales.columns if 'd_' in c]\n",
    "\n",
    "# chaining the following operations below:\n",
    "# 1. Select a particular item\n",
    "# 2. Set id as index, & keep only sales data column starting with d_\n",
    "# 3. Transpose so that dataframe is a column\n",
    "# 4. Plot the data\n",
    "sales.loc[sales['id']=='FOODS_3_090_CA_3_validation'] \\\n",
    ".set_index('id')[d_cols] \\\n",
    ".T \\\n",
    ".plot(figsize=(15,5), title='FOODS_3_090_CA_3 sales by \"d\" number', xlabel='day_numbers', ylabel='sales', color='cornflowerblue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3adba3de",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "\n",
    "- The sales curve is very inconsistent, which implies that there are so many factors that affect the sales on a given day.\n",
    "- For some days the sales curve is flatline which means the item is unavailable.\n",
    "- Item is unavailable for many number of days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "9c461b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean sales for different stores\n",
    "store_list = price['store_id'].unique()\n",
    "past_sales_item = sales.set_index('id')[d_cols].T \\\n",
    ".merge(cal_data.set_index('d')['date'], left_index=True, right_index=True).set_index('date')\n",
    "\n",
    "mean_value_store = []\n",
    "for s in store_list:\n",
    "    store_items = [c for c in past_sales_item if s in c]\n",
    "    mean_value_store.append(np.mean(past_sales_item[store_items].sum(axis=1)))\n",
    "\n",
    "plt.figure(figsize=(15,7))\n",
    "bplot = plt.bar(store_list,mean_value_store)\n",
    "\n",
    "#fill different colors for bar plot\n",
    "colors = [next(color_cycle) for i in range(0,len(store_list))]\n",
    "for i, b in enumerate(bplot):\n",
    "    b.set_color(colors[i])\n",
    "    \n",
    "plt.xlabel('Stores')\n",
    "plt.ylabel('Mean sales')\n",
    "plt.title('Mean sales for different stores')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "id": "8d3c1c32",
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = ['FOODS_3_090_CA_3_validation', 'HOBBIES_1_234_CA_3_validation', 'HOUSEHOLD_1_118_CA_3_validation', 'HOBBIES_1_001_CA_1_validation']\n",
    "examples = []\n",
    "\n",
    "# General function to extract specific item Id values from sales_train_evaluation dataset\n",
    "for i in range(len(ids)):\n",
    "    # Merge calendar date with sales_train_evaluation date\n",
    "    examples.append(sales.loc[sales['id']== ids[i]][d_cols].T) # Fetch a specific Id and transform it as column\n",
    "    examples[i] = examples[i].rename(columns={examples[i].columns.values[0] : ids[i]}) # Rename column e.g 8412 (location Id) to the proper name\n",
    "    examples[i] = examples[i].reset_index().rename(columns={'index':'d'}) # Reset index so, d_cols are not index and then, rename index as d\n",
    "    examples[i] = examples[i].merge(cal) # Merge two dataframe on the bases of column named d\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "78b75900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in [0, 1, 2]:\n",
    "    fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(20,3))\n",
    "    # Groupby wday colum and do mean or count etc. to values of other columns & then, extract the specific sales Id column  \n",
    "    examples[i].groupby('wday').mean()[ids[i]] \\\n",
    "    .plot(kind='line', title='avg sale: day of week', color=color_pal[0], ax=ax1)\n",
    "    \n",
    "    examples[i].groupby('month').mean()[ids[i]] \\\n",
    "    .plot(kind='line', title='avg sale: month', color=color_pal[1], ax=ax2)\n",
    "    \n",
    "    examples[i].groupby('year').mean()[ids[i]] \\\n",
    "    .plot(kind='line', title='avg sale: year', color=color_pal[2], ax=ax3)\n",
    "    \n",
    "    fig.suptitle(f'Trends for item: {ids[i]}', size=20, y=1.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9d7e49",
   "metadata": {},
   "source": [
    "### EDA of sells price data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "157bcb82",
   "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>store_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>sell_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11325</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11326</td>\n",
       "      <td>9.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11327</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11328</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA_1</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>11329</td>\n",
       "      <td>8.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  store_id        item_id  wm_yr_wk  sell_price\n",
       "0     CA_1  HOBBIES_1_001     11325        9.58\n",
       "1     CA_1  HOBBIES_1_001     11326        9.58\n",
       "2     CA_1  HOBBIES_1_001     11327        8.26\n",
       "3     CA_1  HOBBIES_1_001     11328        8.26\n",
       "4     CA_1  HOBBIES_1_001     11329        8.26"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "c48d6d21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a category column to price data \n",
    "price['Category'] = price['item_id'].apply(lambda x: x.split('_')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "510fe22d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,3,figsize=(15,5))\n",
    "ax_id=0\n",
    "\n",
    "for cat, d in price.groupby('Category'):\n",
    "    # Do the log of values of sell_price columns & plot it's histogram distribution\n",
    "    d['sell_price'].apply(np.log1p).plot(kind='hist', bins=50,ax=axs[ax_id], color=next(color_cycle), title=f\"Distribution of {cat} prices\")\n",
    "    ax_id+=1\n",
    "        \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5e7554",
   "metadata": {},
   "source": [
    "### EDA of Calendar data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "397ea777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1969, 14)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "69850f45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "162\n",
      "5\n",
      "162\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "print(cal['event_name_1'].count())\n",
    "print(cal['event_name_2'].count())\n",
    "print(cal['event_type_1'].count())\n",
    "print(cal['event_type_2'].count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "fc2c0f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique count of event_names_1: 30\n",
      "unique events of event_names_1: [nan 'SuperBowl' 'ValentinesDay' 'PresidentsDay' 'LentStart' 'LentWeek2'\n",
      " 'StPatricksDay' 'Purim End' 'OrthodoxEaster' 'Pesach End' 'Cinco De Mayo'\n",
      " \"Mother's day\" 'MemorialDay' 'NBAFinalsStart' 'NBAFinalsEnd'\n",
      " \"Father's day\" 'IndependenceDay' 'Ramadan starts' 'Eid al-Fitr'\n",
      " 'LaborDay' 'ColumbusDay' 'Halloween' 'EidAlAdha' 'VeteransDay'\n",
      " 'Thanksgiving' 'Christmas' 'Chanukah End' 'NewYear' 'OrthodoxChristmas'\n",
      " 'MartinLutherKingDay' 'Easter']\n",
      "------------------------------------------------------\n",
      "unique count of event_names_2: 4\n",
      "unique events of event_names_2: [nan 'Easter' 'Cinco De Mayo' 'OrthodoxEaster' \"Father's day\"]\n",
      "------------------------------------------------------\n",
      "unique count of event_type_1: 4\n",
      "unique events of event_type_1: [nan 'Sporting' 'Cultural' 'National' 'Religious']\n",
      "------------------------------------------------------\n",
      "unique count of event_type_2: 2\n",
      "unique events of event_type_2: [nan 'Cultural' 'Religious']\n"
     ]
    }
   ],
   "source": [
    "#print unique counts of events and event names:\n",
    "\n",
    "print(\"unique count of event_names_1:\", cal['event_name_1'].nunique())\n",
    "print(\"unique events of event_names_1:\", cal['event_name_1'].unique())\n",
    "print('------------------------------------------------------')\n",
    "print(\"unique count of event_names_2:\", cal['event_name_2'].nunique())\n",
    "print(\"unique events of event_names_2:\",cal['event_name_2'].unique())\n",
    "print('------------------------------------------------------')\n",
    "print(\"unique count of event_type_1:\", cal['event_type_1'].nunique())\n",
    "print(\"unique events of event_type_1:\", cal['event_type_1'].unique())\n",
    "print('------------------------------------------------------')\n",
    "print(\"unique count of event_type_2:\", cal['event_type_2'].nunique())\n",
    "print(\"unique events of event_type_2:\", cal['event_type_2'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "4c4da527",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "{nan, 'LaborDay', 'Chanukah End', 'VeteransDay', 'SuperBowl', 'OrthodoxChristmas', \"Mother's day\", 'Purim End', \"Father's day\", 'OrthodoxEaster', 'Easter', 'Pesach End', 'Thanksgiving', 'ValentinesDay', 'Eid al-Fitr', 'NBAFinalsStart', 'MartinLutherKingDay', 'EidAlAdha', 'NBAFinalsEnd', 'ColumbusDay', 'Cinco De Mayo', 'Halloween', 'NewYear', 'LentStart', 'PresidentsDay', 'IndependenceDay', 'StPatricksDay', 'Christmas', 'Ramadan starts', 'LentWeek2', 'MemorialDay'}\n"
     ]
    }
   ],
   "source": [
    "# unique event names\n",
    "en1=set(cal['event_name_1'])\n",
    "en2=set(cal['event_name_2'])\n",
    "print(len(en1.union(en2))-1)\n",
    "print(en1.union(en2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "61952678",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "{nan, 'National', 'Religious', 'Sporting', 'Cultural'}\n"
     ]
    }
   ],
   "source": [
    "# unique event types\n",
    "et1=set(cal['event_type_1'])\n",
    "et2=set(cal['event_type_2'])\n",
    "print(len(et1.union(et2))-1)\n",
    "print(et1.union(et2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d33dc08",
   "metadata": {},
   "source": [
    "**Observation**\n",
    "\n",
    "- There are 30 unique events\n",
    "\n",
    "- There are 4 unique event types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0af9cf43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# finding not null values for determining event in the calendar dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "a565584f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total not null values in event_name_1 are 162\n",
      "total not null values in event_name_2 are 5\n"
     ]
    }
   ],
   "source": [
    "en1=len(cal.loc[cal['event_name_1'].notnull()])\n",
    "en2=len(cal.loc[cal['event_name_2'].notnull()])\n",
    "print(\"total not null values in event_name_1 are\" ,en1)\n",
    "print(\"total not null values in event_name_2 are\" ,en2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "d75c1a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'wm_yr_wk', 'weekday', 'wday', 'month', 'year', 'd',\n",
       "       'event_name_1', 'event_type_1', 'event_name_2', 'event_type_2',\n",
       "       'snap_CA', 'snap_TX', 'snap_WI'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "f3a874ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 ... 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(cal['snap_CA'].values)\n",
    "#print(cal['snap_TX'])\n",
    "#print(cal['snap_WI'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c79f6b1",
   "metadata": {},
   "source": [
    "Observations from calendar data:\n",
    "- SNAP follows a diiferent pattern in different state.\n",
    "- SNAP is allowed on the first ten days in CA, it follows the pattern 101-011 in TX and follows the pattern 011 in WI.\n",
    "- There are total 30 unique events which belong to 5 unique types.\n",
    "- There are total 162 not null values in event_name_1 and 5 not null values in event_name_2 for approx 4.5 years, which results these events occur every year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdf2c443",
   "metadata": {},
   "source": [
    "# Downcasting and reducing memory usage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ff86c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def reduce_mem_usage(df, verbose=True):\n",
    "#     numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "#     start_mem = df.memory_usage().sum() / 1024**2    \n",
    "#     for col in df.columns:\n",
    "#         col_type = df[col].dtypes\n",
    "#         if col_type in numerics:\n",
    "#             c_min = df[col].min()\n",
    "#             c_max = df[col].max()\n",
    "#             if str(col_type)[:3] == 'int':\n",
    "#                 if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "#                     df[col] = df[col].astype(np.int8)\n",
    "#                 elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "#                     df[col] = df[col].astype(np.int16)\n",
    "#                 elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "#                     df[col] = df[col].astype(np.int32)\n",
    "#                 elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "#                     df[col] = df[col].astype(np.int64)  \n",
    "#             else:\n",
    "#                 if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "#                     df[col] = df[col].astype(np.float16)\n",
    "#                 elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "#                     df[col] = df[col].astype(np.float32)\n",
    "#                 else:\n",
    "#                     df[col] = df[col].astype(np.float64)    \n",
    "#     end_mem = df.memory_usage().sum() / 1024**2\n",
    "#     if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))\n",
    "#     return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "68380fee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reduce_mem_usage(df, verbose=True):\n",
    "\n",
    "    cols = df.dtypes.index.tolist()\n",
    "    types = df.dtypes.values.tolist()\n",
    "\n",
    "    for i,t in enumerate(types):\n",
    "\n",
    "        if 'int' in str(t):\n",
    "            if df[cols[i]].min() > np.iinfo(np.int8).min and df[cols[i]].max() < np.iinfo(np.int8).max:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.int8)\n",
    "            elif df[cols[i]].min() > np.iinfo(np.int16).min and df[cols[i]].max() < np.iinfo(np.int16).max:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.int16)\n",
    "            elif df[cols[i]].min() > np.iinfo(np.int32).min and df[cols[i]].max() < np.iinfo(np.int32).max:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.int32)\n",
    "            else:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.int64)\n",
    "\n",
    "        elif 'float' in str(t):\n",
    "            if df[cols[i]].min() > np.finfo(np.float16).min and df[cols[i]].max() < np.finfo(np.float16).max:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.float16)\n",
    "            elif df[cols[i]].min() > np.finfo(np.float32).min and df[cols[i]].max() < np.finfo(np.float32).max:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.float32)\n",
    "            else:\n",
    "                df[cols[i]] = df[cols[i]].astype(np.float64)\n",
    "\n",
    "        elif t == object:\n",
    "            if cols[i] == 'date':\n",
    "                df[cols[i]] = pd.to_datetime(df[cols[i]], format='%Y-%m-%d')\n",
    "            else:\n",
    "                df[cols[i]] = df[cols[i]].astype('category')\n",
    "    return df  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "a2d36c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_b_d=sales.memory_usage().sum()\n",
    "sales_b_d=np.round(sales_b_d/(1024*1024),1)\n",
    "\n",
    "cal_b_d=cal.memory_usage().sum()\n",
    "cal_b_d=np.round(cal_b_d/(1024*1024),1)\n",
    "\n",
    "price_b_d=price.memory_usage().sum()\n",
    "price_b_d=np.round(price_b_d/(1024*1024),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "220d9dd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales = reduce_mem_usage(sales)\n",
    "cal = reduce_mem_usage(cal)\n",
    "price = reduce_mem_usage(price)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "a48f9e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_a_d=sales.memory_usage().sum()\n",
    "sales_a_d=np.round(sales_a_d/(1024*1024),1)\n",
    "\n",
    "cal_a_d=cal.memory_usage().sum()\n",
    "cal_a_d=np.round(cal_a_d/(1024*1024),1)\n",
    "\n",
    "price_a_d=price.memory_usage().sum()\n",
    "price_a_d=np.round(price_a_d/(1024*1024),1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "04e7bdf7",
   "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>df</th>\n",
       "      <th>Before downcasting</th>\n",
       "      <th>After downcasting</th>\n",
       "      <th>memory_decrease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sales</td>\n",
       "      <td>446.4</td>\n",
       "      <td>95.2</td>\n",
       "      <td>78.673835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>calendar</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>price</td>\n",
       "      <td>208.8</td>\n",
       "      <td>45.8</td>\n",
       "      <td>78.065134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         df  Before downcasting  After downcasting  memory_decrease\n",
       "0     sales               446.4               95.2        78.673835\n",
       "1  calendar                 0.2                0.1        50.000000\n",
       "2     price               208.8               45.8        78.065134"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'df':['sales','calendar','price'],\n",
    "       'Before downcasting':[sales_b_d,cal_b_d,price_b_d],\n",
    "       'After downcasting':[sales_a_d,cal_a_d,price_a_d]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "memory_decrease=(df[\"Before downcasting\"]-df[\"After downcasting\"])/df[\"Before downcasting\"]\n",
    "memory_decrease=memory_decrease*100\n",
    "\n",
    "df[\"memory_decrease\"] = memory_decrease\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6232d9c",
   "metadata": {},
   "source": [
    "## Data transformation and Merge Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d38d3e",
   "metadata": {},
   "source": [
    "#### Since it is a time-series forecasting problem,\n",
    "\n",
    "Let's follow below approach to process existing datasets into a supervised learning single dataset:\n",
    "1) Convert wide data format to a long data format\n",
    "2) Merge all the datasets ie. sales data, calendar data, and sell prices data to a single dataframe on “d_” (day number).\n",
    "\n",
    "Once datasets are merged, handled the missing values as it comes from sell price data, through mean imputation techniques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "aeba2079",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['date', 'weekday', 'd', 'event_name_1', 'event_type_1', 'event_name_2', 'event_type_2']\n",
      "['store_id', 'item_id']\n",
      "['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id']\n"
     ]
    }
   ],
   "source": [
    "# Find categorical columns in all 3 dataframes\n",
    "catcols_cal = cal.select_dtypes(include=['object']).columns.tolist()\n",
    "print (catcols_cal)\n",
    "catcols_price = price.select_dtypes(include=['object']).columns.tolist()\n",
    "print(catcols_price)\n",
    "catcols_sales=sales.select_dtypes(include=['object']).columns.tolist()\n",
    "print(catcols_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "2fae642c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cal[\"date\"] = pd.to_datetime(cal[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "ab32648a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop some calendar features\n",
    "cal.drop(['weekday', 'wday', 'month', 'year'], inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "cc56e8c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt the data\n",
    "# Unpivot a DataFrame from wide to long format\n",
    "# This function pd.melt() is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), \n",
    "#while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.\n",
    "\n",
    "wide_to_long_dataframe = pd.melt(sales, id_vars=['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'], var_name='d', value_name='sales')\n",
    "wide_to_long_dataframe = wide_to_long_dataframe.dropna()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "aa344dc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-270-f987d85821a1>:26: DeprecationWarning:\n",
      "\n",
      "`np.object` is a deprecated alias for the builtin `object`. To silence this warning, use `object` by itself. Doing this will not modify any behavior and is safe. \n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "\n"
     ]
    },
    {
     "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>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>d</th>\n",
       "      <th>sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_002_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_002</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_003_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_003</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_004_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_004</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_005_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_005</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327365</th>\n",
       "      <td>FOODS_3_823_WI_3_validation</td>\n",
       "      <td>FOODS_3_823</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327366</th>\n",
       "      <td>FOODS_3_824_WI_3_validation</td>\n",
       "      <td>FOODS_3_824</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327367</th>\n",
       "      <td>FOODS_3_825_WI_3_validation</td>\n",
       "      <td>FOODS_3_825</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327368</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>FOODS_3_826</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327369</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>FOODS_3_827</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58327370 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id        item_id    dept_id   cat_id  \\\n",
       "0         HOBBIES_1_001_CA_1_validation  HOBBIES_1_001  HOBBIES_1  HOBBIES   \n",
       "1         HOBBIES_1_002_CA_1_validation  HOBBIES_1_002  HOBBIES_1  HOBBIES   \n",
       "2         HOBBIES_1_003_CA_1_validation  HOBBIES_1_003  HOBBIES_1  HOBBIES   \n",
       "3         HOBBIES_1_004_CA_1_validation  HOBBIES_1_004  HOBBIES_1  HOBBIES   \n",
       "4         HOBBIES_1_005_CA_1_validation  HOBBIES_1_005  HOBBIES_1  HOBBIES   \n",
       "...                                 ...            ...        ...      ...   \n",
       "58327365    FOODS_3_823_WI_3_validation    FOODS_3_823    FOODS_3    FOODS   \n",
       "58327366    FOODS_3_824_WI_3_validation    FOODS_3_824    FOODS_3    FOODS   \n",
       "58327367    FOODS_3_825_WI_3_validation    FOODS_3_825    FOODS_3    FOODS   \n",
       "58327368    FOODS_3_826_WI_3_validation    FOODS_3_826    FOODS_3    FOODS   \n",
       "58327369    FOODS_3_827_WI_3_validation    FOODS_3_827    FOODS_3    FOODS   \n",
       "\n",
       "         store_id state_id       d  sales  \n",
       "0            CA_1       CA     d_1      0  \n",
       "1            CA_1       CA     d_1      0  \n",
       "2            CA_1       CA     d_1      0  \n",
       "3            CA_1       CA     d_1      0  \n",
       "4            CA_1       CA     d_1      0  \n",
       "...           ...      ...     ...    ...  \n",
       "58327365     WI_3       WI  d_1913      1  \n",
       "58327366     WI_3       WI  d_1913      0  \n",
       "58327367     WI_3       WI  d_1913      0  \n",
       "58327368     WI_3       WI  d_1913      3  \n",
       "58327369     WI_3       WI  d_1913      0  \n",
       "\n",
       "[58327370 rows x 8 columns]"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_to_long_dataframe = reduce_mem_usage(wide_to_long_dataframe)\n",
    "wide_to_long_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "37c58fd0",
   "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>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>d</th>\n",
       "      <th>sales</th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>event_name_2</th>\n",
       "      <th>event_type_2</th>\n",
       "      <th>snap_CA</th>\n",
       "      <th>snap_TX</th>\n",
       "      <th>snap_WI</th>\n",
       "      <th>sell_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_002_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_002</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_003_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_003</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_004_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_004</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_005_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_005</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327365</th>\n",
       "      <td>FOODS_3_823_WI_3_validation</td>\n",
       "      <td>FOODS_3_823</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327366</th>\n",
       "      <td>FOODS_3_824_WI_3_validation</td>\n",
       "      <td>FOODS_3_824</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327367</th>\n",
       "      <td>FOODS_3_825_WI_3_validation</td>\n",
       "      <td>FOODS_3_825</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327368</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>FOODS_3_826</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327369</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>FOODS_3_827</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58327370 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id        item_id    dept_id   cat_id  \\\n",
       "0         HOBBIES_1_001_CA_1_validation  HOBBIES_1_001  HOBBIES_1  HOBBIES   \n",
       "1         HOBBIES_1_002_CA_1_validation  HOBBIES_1_002  HOBBIES_1  HOBBIES   \n",
       "2         HOBBIES_1_003_CA_1_validation  HOBBIES_1_003  HOBBIES_1  HOBBIES   \n",
       "3         HOBBIES_1_004_CA_1_validation  HOBBIES_1_004  HOBBIES_1  HOBBIES   \n",
       "4         HOBBIES_1_005_CA_1_validation  HOBBIES_1_005  HOBBIES_1  HOBBIES   \n",
       "...                                 ...            ...        ...      ...   \n",
       "58327365    FOODS_3_823_WI_3_validation    FOODS_3_823    FOODS_3    FOODS   \n",
       "58327366    FOODS_3_824_WI_3_validation    FOODS_3_824    FOODS_3    FOODS   \n",
       "58327367    FOODS_3_825_WI_3_validation    FOODS_3_825    FOODS_3    FOODS   \n",
       "58327368    FOODS_3_826_WI_3_validation    FOODS_3_826    FOODS_3    FOODS   \n",
       "58327369    FOODS_3_827_WI_3_validation    FOODS_3_827    FOODS_3    FOODS   \n",
       "\n",
       "         store_id state_id       d  sales       date  wm_yr_wk event_name_1  \\\n",
       "0            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "1            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "2            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "3            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "4            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "...           ...      ...     ...    ...        ...       ...          ...   \n",
       "58327365     WI_3       WI  d_1913      1 2016-04-24     11613          NaN   \n",
       "58327366     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "58327367     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "58327368     WI_3       WI  d_1913      3 2016-04-24     11613          NaN   \n",
       "58327369     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "\n",
       "         event_type_1 event_name_2 event_type_2  snap_CA  snap_TX  snap_WI  \\\n",
       "0                 NaN          NaN          NaN        0        0        0   \n",
       "1                 NaN          NaN          NaN        0        0        0   \n",
       "2                 NaN          NaN          NaN        0        0        0   \n",
       "3                 NaN          NaN          NaN        0        0        0   \n",
       "4                 NaN          NaN          NaN        0        0        0   \n",
       "...               ...          ...          ...      ...      ...      ...   \n",
       "58327365          NaN          NaN          NaN        0        0        0   \n",
       "58327366          NaN          NaN          NaN        0        0        0   \n",
       "58327367          NaN          NaN          NaN        0        0        0   \n",
       "58327368          NaN          NaN          NaN        0        0        0   \n",
       "58327369          NaN          NaN          NaN        0        0        0   \n",
       "\n",
       "          sell_price  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "...              ...  \n",
       "58327365        2.98  \n",
       "58327366        2.48  \n",
       "58327367        3.98  \n",
       "58327368        1.28  \n",
       "58327369        1.00  \n",
       "\n",
       "[58327370 rows x 18 columns]"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge all 3 datasets to a single dataframe on \"d_\" (day number)\n",
    "\n",
    "final_df = pd.merge(wide_to_long_dataframe , cal, on='d', how='left')\n",
    "final_df = pd.merge(final_df, price, on=['store_id','item_id','wm_yr_wk'], how='left')\n",
    "\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "705bfc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fil the missing sell price values by mean imputaion\n",
    "final_df[\"sell_price\"].fillna(final_df.groupby(\"id\")[\"sell_price\"].transform(\"mean\"), inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "a79c2e6c",
   "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>item_id</th>\n",
       "      <th>dept_id</th>\n",
       "      <th>cat_id</th>\n",
       "      <th>store_id</th>\n",
       "      <th>state_id</th>\n",
       "      <th>d</th>\n",
       "      <th>sales</th>\n",
       "      <th>date</th>\n",
       "      <th>wm_yr_wk</th>\n",
       "      <th>event_name_1</th>\n",
       "      <th>event_type_1</th>\n",
       "      <th>event_name_2</th>\n",
       "      <th>event_type_2</th>\n",
       "      <th>snap_CA</th>\n",
       "      <th>snap_TX</th>\n",
       "      <th>snap_WI</th>\n",
       "      <th>sell_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HOBBIES_1_001_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_001</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.280059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HOBBIES_1_002_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_002</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HOBBIES_1_003_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_003</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HOBBIES_1_004_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_004</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.524824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HOBBIES_1_005_CA_1_validation</td>\n",
       "      <td>HOBBIES_1_005</td>\n",
       "      <td>HOBBIES_1</td>\n",
       "      <td>HOBBIES</td>\n",
       "      <td>CA_1</td>\n",
       "      <td>CA</td>\n",
       "      <td>d_1</td>\n",
       "      <td>0</td>\n",
       "      <td>2011-01-29</td>\n",
       "      <td>11101</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.942965</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327365</th>\n",
       "      <td>FOODS_3_823_WI_3_validation</td>\n",
       "      <td>FOODS_3_823</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327366</th>\n",
       "      <td>FOODS_3_824_WI_3_validation</td>\n",
       "      <td>FOODS_3_824</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327367</th>\n",
       "      <td>FOODS_3_825_WI_3_validation</td>\n",
       "      <td>FOODS_3_825</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327368</th>\n",
       "      <td>FOODS_3_826_WI_3_validation</td>\n",
       "      <td>FOODS_3_826</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58327369</th>\n",
       "      <td>FOODS_3_827_WI_3_validation</td>\n",
       "      <td>FOODS_3_827</td>\n",
       "      <td>FOODS_3</td>\n",
       "      <td>FOODS</td>\n",
       "      <td>WI_3</td>\n",
       "      <td>WI</td>\n",
       "      <td>d_1913</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-24</td>\n",
       "      <td>11613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58327370 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     id        item_id    dept_id   cat_id  \\\n",
       "0         HOBBIES_1_001_CA_1_validation  HOBBIES_1_001  HOBBIES_1  HOBBIES   \n",
       "1         HOBBIES_1_002_CA_1_validation  HOBBIES_1_002  HOBBIES_1  HOBBIES   \n",
       "2         HOBBIES_1_003_CA_1_validation  HOBBIES_1_003  HOBBIES_1  HOBBIES   \n",
       "3         HOBBIES_1_004_CA_1_validation  HOBBIES_1_004  HOBBIES_1  HOBBIES   \n",
       "4         HOBBIES_1_005_CA_1_validation  HOBBIES_1_005  HOBBIES_1  HOBBIES   \n",
       "...                                 ...            ...        ...      ...   \n",
       "58327365    FOODS_3_823_WI_3_validation    FOODS_3_823    FOODS_3    FOODS   \n",
       "58327366    FOODS_3_824_WI_3_validation    FOODS_3_824    FOODS_3    FOODS   \n",
       "58327367    FOODS_3_825_WI_3_validation    FOODS_3_825    FOODS_3    FOODS   \n",
       "58327368    FOODS_3_826_WI_3_validation    FOODS_3_826    FOODS_3    FOODS   \n",
       "58327369    FOODS_3_827_WI_3_validation    FOODS_3_827    FOODS_3    FOODS   \n",
       "\n",
       "         store_id state_id       d  sales       date  wm_yr_wk event_name_1  \\\n",
       "0            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "1            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "2            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "3            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "4            CA_1       CA     d_1      0 2011-01-29     11101          NaN   \n",
       "...           ...      ...     ...    ...        ...       ...          ...   \n",
       "58327365     WI_3       WI  d_1913      1 2016-04-24     11613          NaN   \n",
       "58327366     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "58327367     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "58327368     WI_3       WI  d_1913      3 2016-04-24     11613          NaN   \n",
       "58327369     WI_3       WI  d_1913      0 2016-04-24     11613          NaN   \n",
       "\n",
       "         event_type_1 event_name_2 event_type_2  snap_CA  snap_TX  snap_WI  \\\n",
       "0                 NaN          NaN          NaN        0        0        0   \n",
       "1                 NaN          NaN          NaN        0        0        0   \n",
       "2                 NaN          NaN          NaN        0        0        0   \n",
       "3                 NaN          NaN          NaN        0        0        0   \n",
       "4                 NaN          NaN          NaN        0        0        0   \n",
       "...               ...          ...          ...      ...      ...      ...   \n",
       "58327365          NaN          NaN          NaN        0        0        0   \n",
       "58327366          NaN          NaN          NaN        0        0        0   \n",
       "58327367          NaN          NaN          NaN        0        0        0   \n",
       "58327368          NaN          NaN          NaN        0        0        0   \n",
       "58327369          NaN          NaN          NaN        0        0        0   \n",
       "\n",
       "          sell_price  \n",
       "0           8.280059  \n",
       "1           3.970000  \n",
       "2           2.970000  \n",
       "3           4.524824  \n",
       "4           2.942965  \n",
       "...              ...  \n",
       "58327365    2.980000  \n",
       "58327366    2.480000  \n",
       "58327367    3.980000  \n",
       "58327368    1.280000  \n",
       "58327369    1.000000  \n",
       "\n",
       "[58327370 rows x 18 columns]"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27893bea",
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}