Data Description and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Description and Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('max_columns', 150)\n",
    "\n",
    "import gc\n",
    "import os\n",
    "\n",
    "# matplotlib and seaborn for plotting\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.dpi'] = 120 #resolution\n",
    "matplotlib.rcParams['figure.figsize'] = (8,6) #figure size\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set_style('darkgrid')\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "\n",
    "root = 'C:/Data/instacart-market-basket-analysis/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains relational set of files describing customers' orders over time. For each user, 4 to 100 orders are provided with the sequence of products purchased in each order. The data of the order's week and hour of the day as well as a relative measure of time between orders is provided.\n",
    "\n",
    "**Files in the Dataset:** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aisles.csv',\n",
       " 'departments.csv',\n",
       " 'orders.csv',\n",
       " 'order_products__prior.csv',\n",
       " 'order_products__train.csv',\n",
       " 'products.csv',\n",
       " 'sample_submission.csv']"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(root)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "aisles = pd.read_csv(root + 'aisles.csv')\n",
    "departments = pd.read_csv(root + 'departments.csv')\n",
    "orders = pd.read_csv(root + 'orders.csv')\n",
    "order_products_prior = pd.read_csv(root + 'order_products__prior.csv')\n",
    "order_products_train = pd.read_csv(root + 'order_products__train.csv')\n",
    "products = pd.read_csv(root + 'products.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aisles: \n",
    "This file contains different aisles and there are total 134 unique aisles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
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       "    }\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>aisle_id</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>prepared soups salads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>specialty cheeses</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>energy granola bars</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>instant foods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>marinades meat preparation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   aisle_id                       aisle\n",
       "0         1       prepared soups salads\n",
       "1         2           specialty cheeses\n",
       "2         3         energy granola bars\n",
       "3         4               instant foods\n",
       "4         5  marinades meat preparation"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>aisle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>hot cereal pancake mixes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>dry pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>beauty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>muscles joints pain relief</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>specialty wines champagnes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     aisle_id                       aisle\n",
       "129       130    hot cereal pancake mixes\n",
       "130       131                   dry pasta\n",
       "131       132                      beauty\n",
       "132       133  muscles joints pain relief\n",
       "133       134  specialty wines champagnes"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(aisles.aisle.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['prepared soups salads', 'specialty cheeses',\n",
       "       'energy granola bars', 'instant foods',\n",
       "       'marinades meat preparation', 'other', 'packaged meat',\n",
       "       'bakery desserts', 'pasta sauce', 'kitchen supplies',\n",
       "       'cold flu allergy', 'fresh pasta', 'prepared meals',\n",
       "       'tofu meat alternatives', 'packaged seafood', 'fresh herbs',\n",
       "       'baking ingredients', 'bulk dried fruits vegetables',\n",
       "       'oils vinegars', 'oral hygiene', 'packaged cheese', 'hair care',\n",
       "       'popcorn jerky', 'fresh fruits', 'soap', 'coffee', 'beers coolers',\n",
       "       'red wines', 'honeys syrups nectars', 'latino foods',\n",
       "       'refrigerated', 'packaged produce', 'kosher foods',\n",
       "       'frozen meat seafood', 'poultry counter', 'butter',\n",
       "       'ice cream ice', 'frozen meals', 'seafood counter',\n",
       "       'dog food care', 'cat food care', 'frozen vegan vegetarian',\n",
       "       'buns rolls', 'eye ear care', 'candy chocolate', 'mint gum',\n",
       "       'vitamins supplements', 'breakfast bars pastries',\n",
       "       'packaged poultry', 'fruit vegetable snacks',\n",
       "       'preserved dips spreads', 'frozen breakfast', 'cream',\n",
       "       'paper goods', 'shave needs', 'diapers wipes', 'granola',\n",
       "       'frozen breads doughs', 'canned meals beans', 'trash bags liners',\n",
       "       'cookies cakes', 'white wines', 'grains rice dried goods',\n",
       "       'energy sports drinks', 'protein meal replacements', 'asian foods',\n",
       "       'fresh dips tapenades', 'bulk grains rice dried goods',\n",
       "       'soup broth bouillon', 'digestion',\n",
       "       'refrigerated pudding desserts', 'condiments', 'facial care',\n",
       "       'dish detergents', 'laundry', 'indian foods', 'soft drinks',\n",
       "       'crackers', 'frozen pizza', 'deodorants',\n",
       "       'canned jarred vegetables', 'baby accessories', 'fresh vegetables',\n",
       "       'milk', 'food storage', 'eggs', 'more household', 'spreads',\n",
       "       'salad dressing toppings', 'cocoa drink mixes', 'soy lactosefree',\n",
       "       'baby food formula', 'breakfast bakery', 'tea',\n",
       "       'canned meat seafood', 'lunch meat', 'baking supplies decor',\n",
       "       'juice nectars', 'canned fruit applesauce', 'missing',\n",
       "       'air fresheners candles', 'baby bath body care',\n",
       "       'ice cream toppings', 'spices seasonings',\n",
       "       'doughs gelatins bake mixes', 'hot dogs bacon sausage',\n",
       "       'chips pretzels', 'other creams cheeses', 'skin care',\n",
       "       'pickled goods olives', 'plates bowls cups flatware', 'bread',\n",
       "       'frozen juice', 'cleaning products',\n",
       "       'water seltzer sparkling water', 'frozen produce',\n",
       "       'nuts seeds dried fruit', 'first aid', 'frozen dessert', 'yogurt',\n",
       "       'cereal', 'meat counter', 'packaged vegetables fruits', 'spirits',\n",
       "       'trail mix snack mix', 'feminine care', 'body lotions soap',\n",
       "       'tortillas flat bread', 'frozen appetizers sides',\n",
       "       'hot cereal pancake mixes', 'dry pasta', 'beauty',\n",
       "       'muscles joints pain relief', 'specialty wines champagnes'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles.aisle.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### departments: \n",
    "This file contains different departments and there are total 21 unique departments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_id</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>alcohol</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   department_id department\n",
       "0              1     frozen\n",
       "1              2      other\n",
       "2              3     bakery\n",
       "3              4    produce\n",
       "4              5    alcohol"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>department_id</th>\n",
       "      <th>department</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>household</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>babies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>deli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    department_id department\n",
       "16             17  household\n",
       "17             18     babies\n",
       "18             19     snacks\n",
       "19             20       deli\n",
       "20             21    missing"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(departments.department.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['frozen', 'other', 'bakery', 'produce', 'alcohol', 'international',\n",
       "       'beverages', 'pets', 'dry goods pasta', 'bulk', 'personal care',\n",
       "       'meat seafood', 'pantry', 'breakfast', 'canned goods',\n",
       "       'dairy eggs', 'household', 'babies', 'snacks', 'deli', 'missing'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments.department.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### orders: \n",
    "This file contains all the orders made by different users. From below analysis, we can conclude following:\n",
    "\n",
    "- There are total 3421083 orders made by total 206209 users.\n",
    "- There are three sets of orders: Prior, Train and Test. The distributions of orders in Train and Test sets are similar whereas the distribution of orders in Prior set is different.\n",
    "- The total orders per customer ranges from 0 to 100. \n",
    "- Based on the plot of 'Orders VS Day of Week' we can map 0 and 1 as Saturday and Sunday respectively based on the assumption that most of the people buy groceries on weekends.\n",
    "- Majority of the orders are made during the day time.\n",
    "- Customers order once in a week which is supported by peaks at 7, 14, 21 and 30 in 'Orders VS Days since prior order' graph.\n",
    "- Based on the heatmap between 'Day of Week' and 'Hour of Day,' we can say that Saturday afternoons and Sunday mornings are prime time for orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
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       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
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       "      <td>3</td>\n",
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       "      <td>431534</td>\n",
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       "      <td>prior</td>\n",
       "      <td>5</td>\n",
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       "      <td>28.0</td>\n",
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       "      <td>3367565</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
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       "      <td>550135</td>\n",
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       "      <td>14</td>\n",
       "      <td>14.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2295261</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2550362</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1187899</td>\n",
       "      <td>1</td>\n",
       "      <td>train</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2168274</td>\n",
       "      <td>2</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "0    2539329        1    prior             1          2                  8   \n",
       "1    2398795        1    prior             2          3                  7   \n",
       "2     473747        1    prior             3          3                 12   \n",
       "3    2254736        1    prior             4          4                  7   \n",
       "4     431534        1    prior             5          4                 15   \n",
       "5    3367565        1    prior             6          2                  7   \n",
       "6     550135        1    prior             7          1                  9   \n",
       "7    3108588        1    prior             8          1                 14   \n",
       "8    2295261        1    prior             9          1                 16   \n",
       "9    2550362        1    prior            10          4                  8   \n",
       "10   1187899        1    train            11          4                  8   \n",
       "11   2168274        2    prior             1          2                 11   \n",
       "\n",
       "    days_since_prior_order  \n",
       "0                      NaN  \n",
       "1                     15.0  \n",
       "2                     21.0  \n",
       "3                     29.0  \n",
       "4                     28.0  \n",
       "5                     19.0  \n",
       "6                     20.0  \n",
       "7                     14.0  \n",
       "8                      0.0  \n",
       "9                     30.0  \n",
       "10                    14.0  \n",
       "11                     NaN  "
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>eval_set</th>\n",
       "      <th>order_number</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3421078</th>\n",
       "      <td>2266710</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421079</th>\n",
       "      <td>1854736</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421080</th>\n",
       "      <td>626363</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421081</th>\n",
       "      <td>2977660</td>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3421082</th>\n",
       "      <td>272231</td>\n",
       "      <td>206209</td>\n",
       "      <td>train</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  user_id eval_set  order_number  order_dow  \\\n",
       "3421078   2266710   206209    prior            10          5   \n",
       "3421079   1854736   206209    prior            11          4   \n",
       "3421080    626363   206209    prior            12          1   \n",
       "3421081   2977660   206209    prior            13          1   \n",
       "3421082    272231   206209    train            14          6   \n",
       "\n",
       "         order_hour_of_day  days_since_prior_order  \n",
       "3421078                 18                    29.0  \n",
       "3421079                 10                    30.0  \n",
       "3421080                 12                    18.0  \n",
       "3421081                 12                     7.0  \n",
       "3421082                 14                    30.0  "
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3421083 entries, 0 to 3421082\n",
      "Data columns (total 7 columns):\n",
      "order_id                  int64\n",
      "user_id                   int64\n",
      "eval_set                  object\n",
      "order_number              int64\n",
      "order_dow                 int64\n",
      "order_hour_of_day         int64\n",
      "days_since_prior_order    float64\n",
      "dtypes: float64(1), int64(5), object(1)\n",
      "memory usage: 182.7+ MB\n"
     ]
    }
   ],
   "source": [
    "orders.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3421083"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders.order_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206209"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(orders.user_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prior    3214874\n",
       "train     131209\n",
       "test       75000\n",
       "Name: eval_set, dtype: int64"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.eval_set.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3421083.00\n",
       "mean          17.15\n",
       "std           17.73\n",
       "min            1.00\n",
       "25%            5.00\n",
       "50%           11.00\n",
       "75%           23.00\n",
       "max          100.00\n",
       "Name: order_number, dtype: object"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.order_number.describe().apply(lambda x: format(x, '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "order_number = orders.groupby('user_id')['order_number'].max()\n",
    "order_number = order_number.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "ax = sns.barplot(x = order_number.index, y = order_number.values, color = color[3])\n",
    "ax.set_xlabel('Orders per customer')\n",
    "ax.set_ylabel('Count')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize=10)\n",
    "ax.set_title('Frequency of Total Orders by Customers')\n",
    "fig.savefig('Frequency of Total Orders by Customers.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,4))\n",
    "ax = sns.kdeplot(orders.order_number[orders.eval_set == 'prior'], label = \"Prior set\", lw = 1)\n",
    "ax = sns.kdeplot(orders.order_number[orders.eval_set == 'train'], label = \"Train set\", lw = 1)\n",
    "ax = sns.kdeplot(orders.order_number[orders.eval_set == 'test'], label = \"Test set\", lw = 1)\n",
    "ax.set_xlabel('Order Number')\n",
    "ax.set_ylabel('Count')\n",
    "ax.tick_params(axis = 'both', labelsize = 10)\n",
    "ax.set_title('Distribution of Orders in Various Sets')\n",
    "fig.savefig('Distribution of Orders in Various Sets.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (5,3))\n",
    "ax = sns.countplot(orders.order_dow)\n",
    "ax.set_xlabel('Day of Week', size = 10)\n",
    "ax.set_ylabel('Orders', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.set_title('Total Orders per Day of Week')\n",
    "fig.savefig('Total Orders per Day of Week.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = orders.groupby('order_dow')['user_id'].nunique()\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (5,3))\n",
    "ax = sns.barplot(x = temp_df.index, y = temp_df.values)\n",
    "ax.set_xlabel('Day of Week', size = 10)\n",
    "ax.set_ylabel('Total Unique Users', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.set_title('Total Unique Users per Day of Week')\n",
    "fig.savefig('Total Unique Users per Day of Week.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax = sns.countplot(orders.order_hour_of_day, color = color[2])\n",
    "ax.set_xlabel('Hour of Day', size = 10 )\n",
    "ax.set_ylabel('Orders', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.set_title('Total Orders per Hour of Day')\n",
    "fig.savefig('Total Orders per Hour of Day.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax = sns.countplot(orders.days_since_prior_order, color = color[2])\n",
    "ax.set_xlabel('Days since prior order', size = 10)\n",
    "ax.set_ylabel('Orders', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.set_title('Orders VS Days since prior order')\n",
    "fig.savefig('Orders VS Days since prior order.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "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>order_hour_of_day</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_dow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3936</td>\n",
       "      <td>2398</td>\n",
       "      <td>1409</td>\n",
       "      <td>963</td>\n",
       "      <td>813</td>\n",
       "      <td>1168</td>\n",
       "      <td>3329</td>\n",
       "      <td>12410</td>\n",
       "      <td>28108</td>\n",
       "      <td>40798</td>\n",
       "      <td>48465</td>\n",
       "      <td>51035</td>\n",
       "      <td>51443</td>\n",
       "      <td>53849</td>\n",
       "      <td>54552</td>\n",
       "      <td>53954</td>\n",
       "      <td>49463</td>\n",
       "      <td>39753</td>\n",
       "      <td>29572</td>\n",
       "      <td>22654</td>\n",
       "      <td>18277</td>\n",
       "      <td>14423</td>\n",
       "      <td>11246</td>\n",
       "      <td>6887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3674</td>\n",
       "      <td>1830</td>\n",
       "      <td>1105</td>\n",
       "      <td>748</td>\n",
       "      <td>809</td>\n",
       "      <td>1607</td>\n",
       "      <td>5370</td>\n",
       "      <td>16571</td>\n",
       "      <td>34116</td>\n",
       "      <td>51908</td>\n",
       "      <td>55671</td>\n",
       "      <td>51584</td>\n",
       "      <td>47079</td>\n",
       "      <td>46728</td>\n",
       "      <td>46764</td>\n",
       "      <td>46403</td>\n",
       "      <td>44761</td>\n",
       "      <td>36792</td>\n",
       "      <td>28977</td>\n",
       "      <td>22145</td>\n",
       "      <td>16281</td>\n",
       "      <td>11943</td>\n",
       "      <td>8992</td>\n",
       "      <td>5620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3059</td>\n",
       "      <td>1572</td>\n",
       "      <td>943</td>\n",
       "      <td>719</td>\n",
       "      <td>744</td>\n",
       "      <td>1399</td>\n",
       "      <td>4758</td>\n",
       "      <td>13245</td>\n",
       "      <td>24635</td>\n",
       "      <td>36314</td>\n",
       "      <td>39230</td>\n",
       "      <td>38128</td>\n",
       "      <td>35780</td>\n",
       "      <td>36650</td>\n",
       "      <td>37173</td>\n",
       "      <td>37469</td>\n",
       "      <td>37541</td>\n",
       "      <td>32151</td>\n",
       "      <td>26470</td>\n",
       "      <td>20084</td>\n",
       "      <td>15039</td>\n",
       "      <td>10653</td>\n",
       "      <td>8146</td>\n",
       "      <td>5358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2952</td>\n",
       "      <td>1495</td>\n",
       "      <td>953</td>\n",
       "      <td>654</td>\n",
       "      <td>719</td>\n",
       "      <td>1355</td>\n",
       "      <td>4562</td>\n",
       "      <td>12396</td>\n",
       "      <td>22553</td>\n",
       "      <td>32312</td>\n",
       "      <td>36040</td>\n",
       "      <td>35215</td>\n",
       "      <td>33455</td>\n",
       "      <td>34161</td>\n",
       "      <td>34773</td>\n",
       "      <td>35990</td>\n",
       "      <td>35273</td>\n",
       "      <td>30368</td>\n",
       "      <td>25001</td>\n",
       "      <td>19249</td>\n",
       "      <td>13795</td>\n",
       "      <td>10278</td>\n",
       "      <td>8242</td>\n",
       "      <td>5181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2642</td>\n",
       "      <td>1512</td>\n",
       "      <td>899</td>\n",
       "      <td>686</td>\n",
       "      <td>730</td>\n",
       "      <td>1330</td>\n",
       "      <td>4401</td>\n",
       "      <td>12493</td>\n",
       "      <td>21814</td>\n",
       "      <td>31409</td>\n",
       "      <td>35034</td>\n",
       "      <td>33857</td>\n",
       "      <td>32249</td>\n",
       "      <td>32751</td>\n",
       "      <td>33625</td>\n",
       "      <td>34222</td>\n",
       "      <td>34093</td>\n",
       "      <td>29378</td>\n",
       "      <td>24425</td>\n",
       "      <td>19350</td>\n",
       "      <td>14186</td>\n",
       "      <td>10796</td>\n",
       "      <td>8812</td>\n",
       "      <td>5645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "order_hour_of_day    0     1     2    3    4     5     6      7      8   \\\n",
       "order_dow                                                                 \n",
       "0                  3936  2398  1409  963  813  1168  3329  12410  28108   \n",
       "1                  3674  1830  1105  748  809  1607  5370  16571  34116   \n",
       "2                  3059  1572   943  719  744  1399  4758  13245  24635   \n",
       "3                  2952  1495   953  654  719  1355  4562  12396  22553   \n",
       "4                  2642  1512   899  686  730  1330  4401  12493  21814   \n",
       "\n",
       "order_hour_of_day     9      10     11     12     13     14     15     16  \\\n",
       "order_dow                                                                   \n",
       "0                  40798  48465  51035  51443  53849  54552  53954  49463   \n",
       "1                  51908  55671  51584  47079  46728  46764  46403  44761   \n",
       "2                  36314  39230  38128  35780  36650  37173  37469  37541   \n",
       "3                  32312  36040  35215  33455  34161  34773  35990  35273   \n",
       "4                  31409  35034  33857  32249  32751  33625  34222  34093   \n",
       "\n",
       "order_hour_of_day     17     18     19     20     21     22    23  \n",
       "order_dow                                                          \n",
       "0                  39753  29572  22654  18277  14423  11246  6887  \n",
       "1                  36792  28977  22145  16281  11943   8992  5620  \n",
       "2                  32151  26470  20084  15039  10653   8146  5358  \n",
       "3                  30368  25001  19249  13795  10278   8242  5181  \n",
       "4                  29378  24425  19350  14186  10796   8812  5645  "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = orders.groupby([\"order_dow\", \"order_hour_of_day\"])[\"order_number\"].aggregate(\"count\").reset_index()\n",
    "temp_df = temp_df.pivot('order_dow', 'order_hour_of_day', 'order_number')\n",
    "temp_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 840x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplots(figsize=(7,3))\n",
    "ax = sns.heatmap(temp_df, cmap=\"YlGnBu\", linewidths=.5)\n",
    "ax.set_title(\"Frequency of Day of week Vs Hour of day\", size = 12)\n",
    "ax.set_xlabel(\"Hour of Day\", size = 10)\n",
    "ax.set_ylabel(\"Day of Week\", size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.ax.tick_params(labelsize=10)\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"Frequency of Day of week Vs Hour of day.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### order_products_prior:\n",
    "\n",
    "This file gives information about which products were ordered and in which order they were added in the cart. It also tells us that if the product was reordered or not. \n",
    "\n",
    "- In this file there is an information of total 3214874 orders through which total 49677 products were ordered. \n",
    "- From the 'Count VS Items in cart' plot, we can say that most of the people buy 1-15 items in an order and there were a maximum of 145 items in an order.\n",
    "- The percentage of reorder items in this set is 58.97%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>17794</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>40141</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>1819</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>43668</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3</td>\n",
       "      <td>33754</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         2       33120                  1          1\n",
       "1         2       28985                  2          1\n",
       "2         2        9327                  3          0\n",
       "3         2       45918                  4          1\n",
       "4         2       30035                  5          0\n",
       "5         2       17794                  6          1\n",
       "6         2       40141                  7          1\n",
       "7         2        1819                  8          1\n",
       "8         2       43668                  9          0\n",
       "9         3       33754                  1          1"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32434484</th>\n",
       "      <td>3421083</td>\n",
       "      <td>39678</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434485</th>\n",
       "      <td>3421083</td>\n",
       "      <td>11352</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434486</th>\n",
       "      <td>3421083</td>\n",
       "      <td>4600</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434487</th>\n",
       "      <td>3421083</td>\n",
       "      <td>24852</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434488</th>\n",
       "      <td>3421083</td>\n",
       "      <td>5020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id  product_id  add_to_cart_order  reordered\n",
       "32434484   3421083       39678                  6          1\n",
       "32434485   3421083       11352                  7          0\n",
       "32434486   3421083        4600                  8          0\n",
       "32434487   3421083       24852                  9          1\n",
       "32434488   3421083        5020                 10          1"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3214874"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(order_products_prior.order_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49677"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(order_products_prior.product_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_to_cart_order_prior = order_products_prior.groupby('order_id')['add_to_cart_order'].count()\n",
    "add_to_cart_order_prior = add_to_cart_order_prior.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    228330\n",
       "6    227675\n",
       "4    222081\n",
       "7    220006\n",
       "3    207027\n",
       "Name: add_to_cart_order, dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    1\n",
       "105    1\n",
       "94     1\n",
       "90     1\n",
       "145    1\n",
       "Name: add_to_cart_order, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_prior.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_prior.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15,8))\n",
    "ax = sns.barplot(x = add_to_cart_order_prior.index, y = add_to_cart_order_prior.values, color = color[3])\n",
    "ax.set_xlabel('Items in cart')\n",
    "ax.set_ylabel('Count')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize = 9)\n",
    "ax.set_title('Frequency of Items in Cart in Prior set', size = 15)\n",
    "fig.savefig('Frequency of Items in Cart in Prior set.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3,3))\n",
    "ax = sns.barplot(x = order_products_prior.reordered.value_counts().index, \n",
    "                y = order_products_prior.reordered.value_counts().values, color = color[3])\n",
    "ax.set_xlabel('Reorder', size = 10)\n",
    "ax.set_ylabel('Count', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.ticklabel_format(style='plain', axis='y')\n",
    "ax.set_title('Reorder Frequency in Prior Set')\n",
    "fig.savefig('Reorder Frequency in Prior Set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of reorder in prior set: 58.97\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of reorder in prior set:',\n",
    "      format(order_products_prior[order_products_prior.reordered == 1].shape[0]*100/order_products_prior.shape[0], '.2f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### order_products_train:\n",
    "\n",
    "This file gives information about which products were ordered and in which order they were added in the cart. It also tells us that if the product was reordered or not. \n",
    "\n",
    "- In this file there is an information of total 131209 orders through which total 39123 products were ordered. \n",
    "- From the 'Count VS Items in cart' plot, we can say that most of the people buy 1-15 items in an order and there were a maximum of 145 items in an order.\n",
    "- The percentage of reorder items in this set is 59.86%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>49302</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>11109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>10246</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>49683</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43633</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>13176</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>47209</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>22035</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36</td>\n",
       "      <td>39612</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>36</td>\n",
       "      <td>19660</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered\n",
       "0         1       49302                  1          1\n",
       "1         1       11109                  2          1\n",
       "2         1       10246                  3          0\n",
       "3         1       49683                  4          0\n",
       "4         1       43633                  5          1\n",
       "5         1       13176                  6          0\n",
       "6         1       47209                  7          0\n",
       "7         1       22035                  8          1\n",
       "8        36       39612                  1          0\n",
       "9        36       19660                  2          1"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1384612</th>\n",
       "      <td>3421063</td>\n",
       "      <td>14233</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384613</th>\n",
       "      <td>3421063</td>\n",
       "      <td>35548</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384614</th>\n",
       "      <td>3421070</td>\n",
       "      <td>35951</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384615</th>\n",
       "      <td>3421070</td>\n",
       "      <td>16953</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1384616</th>\n",
       "      <td>3421070</td>\n",
       "      <td>4724</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         order_id  product_id  add_to_cart_order  reordered\n",
       "1384612   3421063       14233                  3          1\n",
       "1384613   3421063       35548                  4          1\n",
       "1384614   3421070       35951                  1          1\n",
       "1384615   3421070       16953                  2          1\n",
       "1384616   3421070        4724                  3          1"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131209"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(order_products_train.order_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39123"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(order_products_train.product_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_to_cart_order_train = order_products_prior.groupby('order_id')['add_to_cart_order'].count()\n",
    "add_to_cart_order_train = add_to_cart_order_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    228330\n",
       "6    227675\n",
       "4    222081\n",
       "7    220006\n",
       "3    207027\n",
       "Name: add_to_cart_order, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112    1\n",
       "105    1\n",
       "94     1\n",
       "90     1\n",
       "145    1\n",
       "Name: add_to_cart_order, dtype: int64"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_to_cart_order_train.index.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (15,8))\n",
    "ax = sns.barplot(x = add_to_cart_order_train.index, y = add_to_cart_order_train.values, color = color[2])\n",
    "ax.set_xlabel('Items in cart')\n",
    "ax.set_ylabel('Count')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize = 8)\n",
    "ax.set_title('Frequency of Items in Cart in Train set', size = 15)\n",
    "fig.savefig('Frequency of Items in Cart in Train set.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(3,3))\n",
    "ax = sns.barplot(x = order_products_train.reordered.value_counts().index, \n",
    "                y = order_products_train.reordered.value_counts().values, color = color[2])\n",
    "ax.set_xlabel('Reorder', size = 10)\n",
    "ax.set_ylabel('Count', size = 10)\n",
    "ax.tick_params(axis = 'both', labelsize = 8)\n",
    "ax.set_title('Reorder Frequency in Train Set')\n",
    "fig.savefig('Reorder Frequency in Train Set')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of reorder in train set: 59.86\n"
     ]
    }
   ],
   "source": [
    "print('Percentage of reorder in train set:',\n",
    "      format(order_products_train[order_products_train.reordered == 1].shape[0]*100/order_products_train.shape[0], '.2f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### products:\n",
    "\n",
    "This file contains the list of total 49688 products and their aisle as well as department. The number of products in different aisles and different departments are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>All-Seasons Salt</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Robust Golden Unsweetened Oolong Tea</td>\n",
       "      <td>94</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Green Chile Anytime Sauce</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Dry Nose Oil</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Pure Coconut Water With Orange</td>\n",
       "      <td>98</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Cut Russet Potatoes Steam N' Mash</td>\n",
       "      <td>116</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Light Strawberry Blueberry Yogurt</td>\n",
       "      <td>120</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Sparkling Orange Juice &amp; Prickly Pear Beverage</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id                                       product_name  aisle_id  \\\n",
       "0           1                         Chocolate Sandwich Cookies        61   \n",
       "1           2                                   All-Seasons Salt       104   \n",
       "2           3               Robust Golden Unsweetened Oolong Tea        94   \n",
       "3           4  Smart Ones Classic Favorites Mini Rigatoni Wit...        38   \n",
       "4           5                          Green Chile Anytime Sauce         5   \n",
       "5           6                                       Dry Nose Oil        11   \n",
       "6           7                     Pure Coconut Water With Orange        98   \n",
       "7           8                  Cut Russet Potatoes Steam N' Mash       116   \n",
       "8           9                  Light Strawberry Blueberry Yogurt       120   \n",
       "9          10     Sparkling Orange Juice & Prickly Pear Beverage       115   \n",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  \n",
       "5             11  \n",
       "6              7  \n",
       "7              1  \n",
       "8             16  \n",
       "9              7  "
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49683</th>\n",
       "      <td>49684</td>\n",
       "      <td>Vodka, Triple Distilled, Twist of Vanilla</td>\n",
       "      <td>124</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49684</th>\n",
       "      <td>49685</td>\n",
       "      <td>En Croute Roast Hazelnut Cranberry</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49685</th>\n",
       "      <td>49686</td>\n",
       "      <td>Artisan Baguette</td>\n",
       "      <td>112</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49686</th>\n",
       "      <td>49687</td>\n",
       "      <td>Smartblend Healthy Metabolism Dry Cat Food</td>\n",
       "      <td>41</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49687</th>\n",
       "      <td>49688</td>\n",
       "      <td>Fresh Foaming Cleanser</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       product_id                                product_name  aisle_id  \\\n",
       "49683       49684   Vodka, Triple Distilled, Twist of Vanilla       124   \n",
       "49684       49685          En Croute Roast Hazelnut Cranberry        42   \n",
       "49685       49686                            Artisan Baguette       112   \n",
       "49686       49687  Smartblend Healthy Metabolism Dry Cat Food        41   \n",
       "49687       49688                      Fresh Foaming Cleanser        73   \n",
       "\n",
       "       department_id  \n",
       "49683              5  \n",
       "49684              1  \n",
       "49685              3  \n",
       "49686              8  \n",
       "49687             11  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49688"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(products.product_name.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(products.aisle_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(products.department_id.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = products.groupby('aisle_id')['product_id'].count()\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (15,6))\n",
    "ax = sns.barplot(x = temp_df.index, y = temp_df.values, color = color[3])\n",
    "ax.set_xlabel('Aisle Id')\n",
    "ax.set_ylabel('Total products in aisle')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize = 7)\n",
    "ax.set_title('Total Products in Aisle VS Aisle ID', size = 12)\n",
    "fig.savefig('Total Products in Aisle VS Aisle ID.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = products.groupby('department_id')['product_id'].count()\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,5))\n",
    "ax = sns.barplot(x = temp_df.index, y = temp_df.values, color = color[2])\n",
    "ax.set_xlabel('Department Id')\n",
    "ax.set_ylabel('Total products in department')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize = 9)\n",
    "ax.set_title('Total Products in Department VS Department ID', size = 10)\n",
    "fig.savefig('Total Products in Department VS Department ID.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temp_df = products.groupby('department_id')['aisle_id'].nunique()\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,5))\n",
    "ax = sns.barplot(x = temp_df.index, y = temp_df.values)\n",
    "ax.set_xlabel('Department Id')\n",
    "ax.set_ylabel('Total Aisles in department')\n",
    "ax.xaxis.set_tick_params(rotation=90, labelsize = 9)\n",
    "ax.set_title('Total Aisles in Department VS Department ID', size = 10)\n",
    "fig.savefig('Total Aisles in Department VS Department ID.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}