instacart_eda_v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib as plt\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<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>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
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       "      <td>prior</td>\n",
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       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</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",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders = pd.read_csv('orders.csv')\n",
    "df_orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3421083, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3421083"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206209"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <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",
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       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
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       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "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",
       "\n",
       "   days_since_prior_order  \n",
       "0                     NaN  \n",
       "1                    15.0  \n",
       "2                    21.0  \n",
       "3                    29.0  \n",
       "4                    28.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orders_prior = df_orders.loc[df_orders['eval_set']=='prior',:]\n",
    "df_orders_prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3214874, 7)"
      ]
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     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_orders_prior.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206209"
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     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_orders_prior['user_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "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",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products = pd.read_csv('products.csv')\n",
    "df_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49688, 4)"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_products.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>order_id</th>\n",
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      "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_order_products_prior = pd.read_csv('order_products__prior.csv')\n",
    "df_order_products_prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32434489, 4)"
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_order_products_prior.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "3214874"
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     "execution_count": 14,
     "metadata": {},
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    }
   ],
   "source": [
    "df_order_products_prior['order_id'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>prior</td>\n",
       "      <td>3</td>\n",
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       "      <td>8.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>9327</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>prior</td>\n",
       "      <td>3</td>\n",
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       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>prior</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
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       "      <td>5</td>\n",
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       "      <td>prior</td>\n",
       "      <td>3</td>\n",
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       "      <td>8.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   order_id  product_id  add_to_cart_order  reordered  user_id eval_set  \\\n",
       "0         2       33120                  1          1   202279    prior   \n",
       "1         2       28985                  2          1   202279    prior   \n",
       "2         2        9327                  3          0   202279    prior   \n",
       "3         2       45918                  4          1   202279    prior   \n",
       "4         2       30035                  5          0   202279    prior   \n",
       "\n",
       "   order_number  order_dow  order_hour_of_day  days_since_prior_order  \n",
       "0             3          5                  9                     8.0  \n",
       "1             3          5                  9                     8.0  \n",
       "2             3          5                  9                     8.0  \n",
       "3             3          5                  9                     8.0  \n",
       "4             3          5                  9                     8.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_1 = pd.merge(df_order_products_prior, df_orders_prior, on=['order_id'])\n",
    "result_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>reordered</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</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>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>28985</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9327</td>\n",
       "      <td>0</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>45918</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>30035</td>\n",
       "      <td>0</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  reordered  user_id  order_dow  order_hour_of_day\n",
       "0         2       33120          1   202279          5                  9\n",
       "1         2       28985          1   202279          5                  9\n",
       "2         2        9327          0   202279          5                  9\n",
       "3         2       45918          1   202279          5                  9\n",
       "4         2       30035          0   202279          5                  9"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_1_drop = result_1.drop(['add_to_cart_order', 'eval_set', 'order_number', 'days_since_prior_order'], axis=1)\n",
    "result_1_drop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>reordered</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</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>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>153404</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>23750</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>327</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>58707</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>390</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>166654</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  reordered  user_id  order_dow  order_hour_of_day  \\\n",
       "0         2       33120          1   202279          5                  9   \n",
       "1        26       33120          0   153404          0                 16   \n",
       "2       120       33120          0    23750          6                  8   \n",
       "3       327       33120          1    58707          6                  9   \n",
       "4       390       33120          1   166654          0                 12   \n",
       "\n",
       "         product_name  aisle_id  department_id  \n",
       "0  Organic Egg Whites        86             16  \n",
       "1  Organic Egg Whites        86             16  \n",
       "2  Organic Egg Whites        86             16  \n",
       "3  Organic Egg Whites        86             16  \n",
       "4  Organic Egg Whites        86             16  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_2 = pd.merge(result_1_drop, df_products, on=['product_id'])\n",
    "result_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gradient = np.linspace(0, 1, 20)\n",
    "color = plt.cm.plasma(gradient)\n",
    "\n",
    "#fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "result_2['product_name'].value_counts()[:20].plot(kind='bar', color=color, title=\"Number of produts ordered\").set\\\n",
    "                                        (xlabel=\"Products\", ylabel=\"Count\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "marker": {
          "color": [
           472565,
           379450,
           264683,
           241921,
           213584,
           176815,
           152657,
           142951,
           140627,
           137905,
           137057,
           113426,
           109778,
           104823,
           100060,
           97315,
           89632,
           87746,
           85020,
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('190acca3-b350-4fa9-8236-c3729937512b');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(\n",
    "    data = go.Bar(x=result_2['product_name'].value_counts().head(20).index, \n",
    "             y=result_2['product_name'].value_counts().head(20).values, \n",
    "                  marker={'color': result_2['product_name'].value_counts().head(20).values,\n",
    "            'colorscale': 'plasma'}, showlegend = False),\n",
    "    layout = go.Layout(\n",
    "        title = go.layout.Title(text = \"Number of produts ordered\", x = 0.5),\n",
    "        xaxis = go.layout.XAxis(title = 'Products')\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()\n",
    "\n",
    "#B = result_2['product_name'].value_counts(ascending=True).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Avec les department / aisles\n",
    "\n",
    "fig = make_subplots(2,1, subplot_titles=('Products ordered more often',\n",
    "                                        'Products ordered less often'))#,\n",
    "                   #horizontal_spacing=0.15)\n",
    "\n",
    "#fig = go.Figure(\n",
    "#    data = go.Bar(x=A.index, \n",
    "#             y=A.values, marker={'color': A.values,\n",
    "#            'colorscale': 'plasma'}, showlegend = False),\n",
    "#    layout = go.Layout(\n",
    "#        title = go.layout.Title(text = \"Number of produts ordered\", x = 0.5),\n",
    "#        xaxis = go.layout.XAxis(title = 'Products')\n",
    "#    )\n",
    "#)\n",
    "\n",
    "\n",
    "fig.add_trace(go.Bar(x=A.index, \n",
    "             y=A.values, marker={'color': A.values,\n",
    "            'colorscale': 'plasma'}, showlegend = False)\n",
    "              , row=1, col=1)\n",
    "fig.add_trace(go.Bar(x=B.index, \n",
    "             y=B.values, marker={'color': B.values,\n",
    "            'colorscale': 'plasma'}, showlegend = False)\n",
    "              , row=2, col=1)\n",
    "\n",
    "fig.layout.annotations[0].update(font= {'size': 9})\n",
    "fig.layout.annotations[1].update(font= {'size': 9})\n",
    "fig['layout']['xaxis']['title']='Products'\n",
    "fig['layout']['xaxis2']['title']='Products'\n",
    "fig['layout']['yaxis']['title']='Count'\n",
    "fig['layout']['yaxis2']['title']='Count'\n",
    "\n",
    "#fig.update_layout(height=400, width=600,\n",
    "#                  title_text=\"Side By Side Subplots\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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   ],
   "source": [
    "fig = go.Figure(\n",
    "    data = go.Bar(x=result_2['order_dow'].value_counts().index, \n",
    "             y=result_2['order_dow'].value_counts().values, \n",
    "            marker={'color': result_2['order_dow'].value_counts().values,\n",
    "            'colorscale': 'plasma'}, showlegend = False),\n",
    "    layout = go.Layout(\n",
    "        title = go.layout.Title(text = \"Products bought in each day of the week\", x = 0.5),\n",
    "        xaxis = go.layout.XAxis(title = 'Day of week')\n",
    "    )\n",
    ")\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
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{\"anchor\": \"y2\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Hour of the day\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.625, 1.0], \"title\": {\"text\": \"Count\"}}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 0.375], \"title\": {\"text\": \"Count\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('57bac3de-39b9-4703-90a0-e25e1f9449e9');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "\n",
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       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = make_subplots(2,1, subplot_titles=('Products bought in each day of the week',\n",
    "                                        'Products bought in each hour of the day'))#,\n",
    "                   #horizontal_spacing=0.15)\n",
    "\n",
    "fig.add_trace(go.Bar(x=['Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'], \n",
    "             y=result_2['order_dow'].value_counts().values, \n",
    "            marker={'color': result_2['order_dow'].value_counts().values,\n",
    "            'colorscale': 'plasma'}, showlegend = False),\n",
    "              row=1, col=1)\n",
    "fig.add_trace(go.Bar(x=result_2['order_hour_of_day'].value_counts().index, \n",
    "             y=D.values, marker={'color': result_2['order_hour_of_day'].value_counts().values,\n",
    "            'colorscale': 'plasma'}, showlegend = False)\n",
    "              , row=2, col=1)\n",
    "\n",
    "fig.layout.annotations[0].update(font= {'size': 9})\n",
    "fig.layout.annotations[1].update(font= {'size': 9})\n",
    "fig['layout']['xaxis']['title']='Day of week'\n",
    "fig['layout']['xaxis2']['title']='Hour of the day'\n",
    "fig['layout']['yaxis']['title']='Count'\n",
    "fig['layout']['yaxis2']['title']='Count'\n",
    "\n",
    "#fig.update_layout(height=400, width=600,\n",
    "#                  title_text=\"Side By Side Subplots\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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   "source": [
    "day_of_week = [0, 1, 2, 3, 4, 5, 6]\n",
    "\n",
    "fig = make_subplots(rows = 7, cols = 1, shared_xaxes = False, vertical_spacing = 0.03)\n",
    "\n",
    "for i in range(len(day_of_week)):\n",
    "    #order_dow = result_2.loc[result_2['order_dow']== day_of_week[i] ,:]\n",
    "    #order_dow = order_dow['product_name'].value_counts().head(20)\n",
    "    order_dow = result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20)\n",
    "    fig.add_trace(\n",
    "        go.Bar(x = order_dow.index,\n",
    "            y = order_dow.values, text= order_dow.index,\n",
    "               textposition=\"inside\", textangle=90,\n",
    "        name = str(day_of_week[i])),\n",
    "        row = i + 1,\n",
    "        col = 1\n",
    "    )\n",
    "\n",
    "fig.update_layout(\n",
    "    autosize=False, height=2000)\n",
    "fig.update_xaxes(showticklabels=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_of_week = [0, 1, 2, 3, 4, 5, 6]\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "for i in range(len(day_of_week)):\n",
    "    if i == 0:\n",
    "        fig.add_trace(\n",
    "            go.Bar(\n",
    "                x = result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).index,\n",
    "                y = result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).values, \n",
    "                text=result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).index,\n",
    "                textposition=\"inside\", textangle=90,\n",
    "                #nbinsx = dataset.loc[dataset['Year']==last_years[i], 'Date'].nunique(),\n",
    "                visible=True))\n",
    "    else:\n",
    "        fig.add_trace(\n",
    "            go.Bar(\n",
    "                x = result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).index,\n",
    "                y = result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).values, \n",
    "                text=result_2.loc[result_2['order_dow'] == day_of_week[i] ,'product_name'].value_counts().head(20).index,\n",
    "                textposition=\"inside\", textangle=90,\n",
    "                #nbinsx = dataset.loc[dataset['Year']==last_years[i], 'Date'].nunique(),\n",
    "                visible=False))\n",
    "\n",
    "\n",
    "fig.update_layout(\n",
    "        title = go.layout.Title(text = \"Daily observations for one year\", x = 0.5),\n",
    "        showlegend = False)\n",
    "fig.update_xaxes(showticklabels=False)\n",
    "\n",
    "fig.update_layout(\n",
    "    updatemenus = [go.layout.Updatemenu(\n",
    "        active = 0,\n",
    "        buttons = [\n",
    "                    go.layout.updatemenu.Button(\n",
    "                        label = \"Saturday\",\n",
    "                        method = \"update\",\n",
    "                        args = [{\"visible\" : [True, False, False, False, False, False, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Sunday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, True, False, False, False, False, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Monday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, False, True, False, False, False, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Tuesday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, False, False, True, False, False, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Wednesday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, False, False, False, True, False, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Thursday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, False, False, False, False, True, False]}]),\n",
    "                    go.layout.updatemenu.Button(\n",
    "                            label = \"Friday\",\n",
    "                            method = \"update\",\n",
    "                            args = [{\"visible\" : [False, False, False, False, False, False, True]}])\n",
    "                ]\n",
    "    )]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>reordered</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</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>2</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>153404</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>23750</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>327</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>58707</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>390</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>166654</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434484</th>\n",
       "      <td>3265099</td>\n",
       "      <td>43492</td>\n",
       "      <td>0</td>\n",
       "      <td>111311</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Gourmet Burger Seasoning</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434485</th>\n",
       "      <td>3361945</td>\n",
       "      <td>43492</td>\n",
       "      <td>0</td>\n",
       "      <td>142932</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Gourmet Burger Seasoning</td>\n",
       "      <td>104</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434486</th>\n",
       "      <td>3267201</td>\n",
       "      <td>33097</td>\n",
       "      <td>0</td>\n",
       "      <td>183867</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>Piquillo &amp; Jalapeno Bruschetta</td>\n",
       "      <td>81</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434487</th>\n",
       "      <td>3393151</td>\n",
       "      <td>38977</td>\n",
       "      <td>0</td>\n",
       "      <td>79056</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>Original Jerky</td>\n",
       "      <td>100</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434488</th>\n",
       "      <td>3400803</td>\n",
       "      <td>23624</td>\n",
       "      <td>0</td>\n",
       "      <td>152189</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>Flatbread Pizza All Natural</td>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32434489 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_id  product_id  reordered  user_id  order_dow  \\\n",
       "0                2       33120          1   202279          5   \n",
       "1               26       33120          0   153404          0   \n",
       "2              120       33120          0    23750          6   \n",
       "3              327       33120          1    58707          6   \n",
       "4              390       33120          1   166654          0   \n",
       "...            ...         ...        ...      ...        ...   \n",
       "32434484   3265099       43492          0   111311          3   \n",
       "32434485   3361945       43492          0   142932          3   \n",
       "32434486   3267201       33097          0   183867          3   \n",
       "32434487   3393151       38977          0    79056          4   \n",
       "32434488   3400803       23624          0   152189          3   \n",
       "\n",
       "          order_hour_of_day                    product_name  aisle_id  \\\n",
       "0                         9              Organic Egg Whites        86   \n",
       "1                        16              Organic Egg Whites        86   \n",
       "2                         8              Organic Egg Whites        86   \n",
       "3                         9              Organic Egg Whites        86   \n",
       "4                        12              Organic Egg Whites        86   \n",
       "...                     ...                             ...       ...   \n",
       "32434484                 10        Gourmet Burger Seasoning       104   \n",
       "32434485                 10        Gourmet Burger Seasoning       104   \n",
       "32434486                 18  Piquillo & Jalapeno Bruschetta        81   \n",
       "32434487                 13                  Original Jerky       100   \n",
       "32434488                 14     Flatbread Pizza All Natural        79   \n",
       "\n",
       "          department_id  \n",
       "0                    16  \n",
       "1                    16  \n",
       "2                    16  \n",
       "3                    16  \n",
       "4                    16  \n",
       "...                 ...  \n",
       "32434484             13  \n",
       "32434485             13  \n",
       "32434486             15  \n",
       "32434487             21  \n",
       "32434488              1  \n",
       "\n",
       "[32434489 rows x 9 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aisles = pd.read_csv('aisles.csv')\n",
    "df_aisles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>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",
       "</div>"
      ],
      "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_department = pd.read_csv('departments.csv')\n",
    "df_department.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>reordered</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>aisle</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>202279</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>153404</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>120</td>\n",
       "      <td>33120</td>\n",
       "      <td>0</td>\n",
       "      <td>23750</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>327</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>58707</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>390</td>\n",
       "      <td>33120</td>\n",
       "      <td>1</td>\n",
       "      <td>166654</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>Organic Egg Whites</td>\n",
       "      <td>86</td>\n",
       "      <td>16</td>\n",
       "      <td>eggs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  product_id  reordered  user_id  order_dow  order_hour_of_day  \\\n",
       "0         2       33120          1   202279          5                  9   \n",
       "1        26       33120          0   153404          0                 16   \n",
       "2       120       33120          0    23750          6                  8   \n",
       "3       327       33120          1    58707          6                  9   \n",
       "4       390       33120          1   166654          0                 12   \n",
       "\n",
       "         product_name  aisle_id  department_id aisle  \n",
       "0  Organic Egg Whites        86             16  eggs  \n",
       "1  Organic Egg Whites        86             16  eggs  \n",
       "2  Organic Egg Whites        86             16  eggs  \n",
       "3  Organic Egg Whites        86             16  eggs  \n",
       "4  Organic Egg Whites        86             16  eggs  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_3 = pd.merge(result_2, df_aisles, on=['aisle_id'])\n",
    "result_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_4 = pd.merge(result_3, df_department, on=['department_id'])\n",
    "result_5 = result_4.drop(['aisle_id', 'department_id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "product_name              aisle                     \n",
       "Banana                    fresh fruits                  96769\n",
       "Bag of Organic Bananas    fresh fruits                  71493\n",
       "Organic Baby Spinach      packaged vegetables fruits    54914\n",
       "Organic Strawberries      fresh fruits                  53831\n",
       "Organic Hass Avocado      fresh fruits                  43944\n",
       "Organic Avocado           fresh fruits                  39846\n",
       "Large Lemon               fresh fruits                  33605\n",
       "Limes                     fresh fruits                  30266\n",
       "Strawberries              fresh fruits                  27486\n",
       "Organic Raspberries       packaged vegetables fruits    26378\n",
       "Organic Whole Milk        milk                          26177\n",
       "Organic Yellow Onion      fresh vegetables              26011\n",
       "Organic Garlic            fresh vegetables              24845\n",
       "Organic Zucchini          fresh vegetables              23864\n",
       "Cucumber Kirby            fresh vegetables              22054\n",
       "Organic Grape Tomatoes    packaged vegetables fruits    19615\n",
       "Organic Blueberries       packaged vegetables fruits    19195\n",
       "Organic Lemon             fresh fruits                  17960\n",
       "Yellow Onions             fresh vegetables              17639\n",
       "Apple Honeycrisp Organic  fresh fruits                  17611\n",
       "dtype: int64"
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     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_5.loc[result_5['order_dow'] == 0 ,['product_name', 'aisle']].value_counts().head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Banana',\n",
       " 'Bag of Organic Bananas',\n",
       " 'Organic Baby Spinach',\n",
       " 'Organic Strawberries',\n",
       " 'Organic Hass Avocado',\n",
       " 'Organic Avocado',\n",
       " 'Large Lemon',\n",
       " 'Limes',\n",
       " 'Strawberries',\n",
       " 'Organic Raspberries',\n",
       " 'Organic Whole Milk',\n",
       " 'Organic Yellow Onion',\n",
       " 'Organic Garlic',\n",
       " 'Organic Zucchini',\n",
       " 'Cucumber Kirby',\n",
       " 'Organic Grape Tomatoes',\n",
       " 'Organic Blueberries',\n",
       " 'Organic Lemon',\n",
       " 'Yellow Onions',\n",
       " 'Apple Honeycrisp Organic']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_product = [a_tuple[0] for a_tuple in result_5.loc[result_5['order_dow'] == 0 ,['product_name', 'aisle']].value_counts().head(20).index]\n",
    "index_product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'packaged vegetables fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh fruits',\n",
       " 'packaged vegetables fruits',\n",
       " 'milk',\n",
       " 'fresh vegetables',\n",
       " 'fresh vegetables',\n",
       " 'fresh vegetables',\n",
       " 'fresh vegetables',\n",
       " 'packaged vegetables fruits',\n",
       " 'packaged vegetables fruits',\n",
       " 'fresh fruits',\n",
       " 'fresh vegetables',\n",
       " 'fresh fruits']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_aisle = [a_tuple[1] for a_tuple in result_5.loc[result_5['order_dow'] == 0 ,['product_name', 'aisle']].value_counts().head(20).index]\n",
    "index_aisle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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    "              #line_group = ”country”, #group rows of a column\n",
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       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
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       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
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       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
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    "fig = go.Figure(\n",
    "    data = go.Bar(x=[a_tuple[0] for a_tuple in result_5.loc[:,['product_name', 'aisle']].value_counts().head(20).index], \n",
    "             y=result_5.loc[:,['product_name', 'aisle']].value_counts().head(20).values, \n",
    "                  hovertext = [a_tuple[1] for a_tuple in result_5.loc[:,['product_name', 'aisle']].value_counts().head(20).index]), # showlegend = False\n",
    "    layout = go.Layout(\n",
    "        title = go.layout.Title(text = \"Top of produts ordered on Saturday\", x = 0.5),\n",
    "        xaxis = go.layout.XAxis(title = 'Products')\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
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       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('21bf7022-d583-45ec-945b-e9d532189563');\n",
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       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure(\n",
    "    data = go.Bar(x=result_5['department'].value_counts().head(20).index, \n",
    "             y=result_5['department'].value_counts().head(20).values, \n",
    "                  marker={'color': result_5['department'].value_counts().head(20).values,\n",
    "            'colorscale': 'plasma'}, showlegend = False),\n",
    "    layout = go.Layout(\n",
    "        title = go.layout.Title(text = \"Number of produts ordered by department\", x = 0.5),\n",
    "        xaxis = go.layout.XAxis(title = 'Department')\n",
    "    )\n",
    ")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
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   "source": [
    "fig = go.Figure(\n",
    "    data = go.Bar(x=result_5['aisle'].value_counts().head(20).index, \n",
    "             y=result_5['aisle'].value_counts().head(20).values, \n",
    "                  marker={'color': result_5['aisle'].value_counts().head(20).values,\n",
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    "        title = go.layout.Title(text = \"Number of produts ordered by aisle\", x = 0.5),\n",
    "        xaxis = go.layout.XAxis(title = 'Aisle')\n",
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    ")\n",
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    "fig.show()"
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