ExpDataAnalysis (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "InstaCart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "aisles = pd.read_csv('aisles.csv')\n",
    "departments = pd.read_csv('departments.csv')\n",
    "order_products_prior = pd.read_csv('order_products__prior.csv')\n",
    "order_products_train = pd.read_csv('order_products__train.csv')\n",
    "orders = pd.read_csv('orders.csv')\n",
    "products = pd.read_csv('products.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exploring data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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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       "        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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aisles.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "departments.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>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",
       "</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",
       "\n",
       "   department_id  \n",
       "0             19  \n",
       "1             13  \n",
       "2              7  \n",
       "3              1  \n",
       "4             13  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
       "  </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"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "  </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"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concating order prior and train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33819106, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order_products = pd.concat([order_products_prior,order_products_train])\n",
    "order_products.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating test, trian, prior set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>38</th>\n",
       "      <td>2774568</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>329954</td>\n",
       "      <td>4</td>\n",
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       "      <td>22.0</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
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       "      <td>11</td>\n",
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       "      <td>12</td>\n",
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       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20</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  order_hour_of_day  \\\n",
       "38    2774568        3     test            13          5                 15   \n",
       "44     329954        4     test             6          3                 12   \n",
       "53    1528013        6     test             4          3                 16   \n",
       "96    1376945       11     test             8          6                 11   \n",
       "102   1356845       12     test             6          1                 20   \n",
       "\n",
       "     days_since_prior_order  \n",
       "38                     11.0  \n",
       "44                     30.0  \n",
       "53                     22.0  \n",
       "96                      8.0  \n",
       "102                    30.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = orders[orders['eval_set'] == 'test']\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "    order_id  user_id eval_set  order_number  order_dow  order_hour_of_day  \\\n",
       "10   1187899        1    train            11          4                  8   \n",
       "25   1492625        2    train            15          1                 11   \n",
       "49   2196797        5    train             5          0                 11   \n",
       "74    525192        7    train            21          2                 11   \n",
       "78    880375        8    train             4          1                 14   \n",
       "\n",
       "    days_since_prior_order  \n",
       "10                    14.0  \n",
       "25                    30.0  \n",
       "49                     6.0  \n",
       "74                     6.0  \n",
       "78                    10.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train = orders[orders['eval_set'] == 'train']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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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": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "prior = orders[orders['eval_set'] == 'prior']\n",
    "prior.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating final data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.merge(orders, order_products, on='order_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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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   2539329        1    prior             1          2                  8   \n",
       "2   2539329        1    prior             1          2                  8   \n",
       "3   2539329        1    prior             1          2                  8   \n",
       "4   2539329        1    prior             1          2                  8   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_order  reordered  \n",
       "0                     NaN         196                  1          0  \n",
       "1                     NaN       14084                  2          0  \n",
       "2                     NaN       12427                  3          0  \n",
       "3                     NaN       26088                  4          0  \n",
       "4                     NaN       26405                  5          0  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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  product_id  add_to_cart_order  reordered  \\\n",
       "0                     NaN         196                  1          0   \n",
       "1                    15.0         196                  1          1   \n",
       "2                    21.0         196                  1          1   \n",
       "3                    29.0         196                  1          1   \n",
       "4                    28.0         196                  1          1   \n",
       "\n",
       "  product_name  aisle_id  department_id  \n",
       "0         Soda        77              7  \n",
       "1         Soda        77              7  \n",
       "2         Soda        77              7  \n",
       "3         Soda        77              7  \n",
       "4         Soda        77              7  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(data, products, on='product_id')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>prior</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>77</td>\n",
       "      <td>7</td>\n",
       "      <td>soft drinks</td>\n",
       "      <td>beverages</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  product_id  add_to_cart_order  reordered  \\\n",
       "0                     NaN         196                  1          0   \n",
       "1                    15.0         196                  1          1   \n",
       "2                    21.0         196                  1          1   \n",
       "3                    29.0         196                  1          1   \n",
       "4                    28.0         196                  1          1   \n",
       "\n",
       "  product_name  aisle_id  department_id        aisle department  \n",
       "0         Soda        77              7  soft drinks  beverages  \n",
       "1         Soda        77              7  soft drinks  beverages  \n",
       "2         Soda        77              7  soft drinks  beverages  \n",
       "3         Soda        77              7  soft drinks  beverages  \n",
       "4         Soda        77              7  soft drinks  beverages  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(data, aisles, on='aisle_id')\n",
    "data = pd.merge(data, departments, on='department_id')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explanatory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataTransformerRegistry.enable('custom')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from altair import pipe, limit_rows, to_values\n",
    "t = lambda data: pipe(data, limit_rows(max_rows=1000000000), to_values)\n",
    "alt.data_transformers.register('custom', t)\n",
    "alt.data_transformers.enable('custom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#How many orders, items, and users are in each eval set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eval_set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prior</th>\n",
       "      <td>3214874</td>\n",
       "      <td>prior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>75000</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>131209</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_number      i\n",
       "eval_set                     \n",
       "prior          3214874  prior\n",
       "test             75000   test\n",
       "train           131209  train"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('eval_set')['order_number'].count()) \n",
    "count['i'] = count.index\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-1\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-1\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-59673407d41387f29e7bdb4ca729119e\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"i\", \"legend\": null}, \"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Evalution set\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of Orders\"}}, \"height\": 350, \"title\": \"Orders as per Evaluation Set\", \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-59673407d41387f29e7bdb4ca729119e\": [{\"order_number\": 3214874, \"i\": \"prior\"}, {\"order_number\": 75000, \"i\": \"test\"}, {\"order_number\": 131209, \"i\": \"train\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = alt.Chart(count, height = 350, width = 450,title = 'Orders as per Evaluation Set').mark_bar().encode(\n",
    "    x='i',\n",
    "    y='order_number',\n",
    "    color = alt.Color('i', legend=None)\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize=24)\n",
    "\n",
    "\n",
    "n.encoding.y.title = 'Number of Orders'\n",
    "n.encoding.x.title = 'Evalution set'\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eval_set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prior</th>\n",
       "      <td>32434489</td>\n",
       "      <td>prior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>1384617</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          order_number      i\n",
       "eval_set                     \n",
       "prior         32434489  prior\n",
       "train          1384617  train"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(data.groupby('eval_set')['order_number'].count()) \n",
    "count['i'] = count.index\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-2\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-2\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-7697f56b3844dcb2e501fe155d8577f2\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"i\", \"legend\": null}, \"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Evalution set\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of Orders\"}}, \"height\": 350, \"title\": \"Orders as per Evaluation Set\", \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-7697f56b3844dcb2e501fe155d8577f2\": [{\"order_number\": 32434489, \"i\": \"prior\"}, {\"order_number\": 1384617, \"i\": \"train\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = alt.Chart(count, height = 350, width = 450,title = 'Orders as per Evaluation Set').mark_bar().encode(\n",
    "    x='i',\n",
    "    y='order_number',\n",
    "    color = alt.Color('i', legend=None)\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize=24)\n",
    "\n",
    "\n",
    "n.encoding.y.title = 'Number of Orders'\n",
    "n.encoding.x.title = 'Evalution set'\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eval_set</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>prior</th>\n",
       "      <td>206209</td>\n",
       "      <td>prior</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>test</th>\n",
       "      <td>75000</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>train</th>\n",
       "      <td>131209</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id      i\n",
       "eval_set                \n",
       "prior      206209  prior\n",
       "test        75000   test\n",
       "train      131209  train"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('eval_set')['user_id'].nunique())\n",
    "count['i'] = count.index\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-3\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-3\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-51c836e5109249301b943e538c0800b8\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"type\": \"nominal\", \"field\": \"i\", \"legend\": null}, \"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Evalution set\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"user_id\", \"title\": \"Users\"}}, \"height\": 350, \"title\": \"Number of Users\", \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-51c836e5109249301b943e538c0800b8\": [{\"user_id\": 206209, \"i\": \"prior\"}, {\"user_id\": 75000, \"i\": \"test\"}, {\"user_id\": 131209, \"i\": \"train\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = alt.Chart(count, height = 350, width = 450,title = 'Number of Users').mark_bar().encode(\n",
    "    x='i',\n",
    "    y='user_id',\n",
    "    color = alt.Color('i', legend=None)\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize=24)\n",
    "\n",
    "\n",
    "n.encoding.y.title = 'Users'\n",
    "n.encoding.x.title = 'Evalution set'\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#How many users in prior appear in train? How many users in prior or train appear in test?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.Series(list(set(prior['user_id']) & set(test['user_id']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "131209"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.Series(list(set(prior['user_id']) & set(train['user_id']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.Series(list(set(train['user_id']) & set(test['user_id']))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#When are orders made?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_dow</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600905</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>587478</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>467260</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>436972</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>426339</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>453368</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>448761</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_number  i\n",
       "order_dow                 \n",
       "0                600905  1\n",
       "1                587478  2\n",
       "2                467260  3\n",
       "3                436972  4\n",
       "4                426339  5\n",
       "5                453368  6\n",
       "6                448761  7"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('order_dow')['order_number'].count()) \n",
    "count['i'] = count.index + 1\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-4\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-4\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-d2281a6acd22ffb65e9929fabc9197c9\"}, \"mark\": {\"type\": \"bar\", \"size\": 25}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"i\", \"title\": \"Day of the Week\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of order\"}}, \"height\": 350, \"title\": \"Number of Orders on Each day of Week\", \"width\": 350, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-d2281a6acd22ffb65e9929fabc9197c9\": [{\"order_number\": 600905, \"i\": 1}, {\"order_number\": 587478, \"i\": 2}, {\"order_number\": 467260, \"i\": 3}, {\"order_number\": 436972, \"i\": 4}, {\"order_number\": 426339, \"i\": 5}, {\"order_number\": 453368, \"i\": 6}, {\"order_number\": 448761, \"i\": 7}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = alt.Chart(count, height = 350, width = 350,title = 'Number of Orders on Each day of Week').mark_bar(size=25).encode(\n",
    "    x='i',\n",
    "    y='order_number'\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize=24)\n",
    "\n",
    "\n",
    "n.encoding.y.title = 'Number of order'\n",
    "n.encoding.x.title = 'Day of the Week'\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12398</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7539</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5474</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5527</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   order_number  i\n",
       "order_hour_of_day                 \n",
       "0                         22758  1\n",
       "1                         12398  2\n",
       "2                          7539  3\n",
       "3                          5474  4\n",
       "4                          5527  5"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('order_hour_of_day')['order_number'].count()) \n",
    "count['i'] = count.index + 1\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-5\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-5\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-43e70bc44017addeeb090f22a9c5135e\"}, \"mark\": {\"type\": \"bar\", \"size\": 20}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"i\", \"title\": \"Day of the Week\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of order\"}}, \"height\": 350, \"title\": \"Number of Orders on Each day of Week\", \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-43e70bc44017addeeb090f22a9c5135e\": [{\"order_number\": 22758, \"i\": 1}, {\"order_number\": 12398, \"i\": 2}, {\"order_number\": 7539, \"i\": 3}, {\"order_number\": 5474, \"i\": 4}, {\"order_number\": 5527, \"i\": 5}, {\"order_number\": 9569, \"i\": 6}, {\"order_number\": 30529, \"i\": 7}, {\"order_number\": 91868, \"i\": 8}, {\"order_number\": 178201, \"i\": 9}, {\"order_number\": 257812, \"i\": 10}, {\"order_number\": 288418, \"i\": 11}, {\"order_number\": 284728, \"i\": 12}, {\"order_number\": 272841, \"i\": 13}, {\"order_number\": 277999, \"i\": 14}, {\"order_number\": 283042, \"i\": 15}, {\"order_number\": 283639, \"i\": 16}, {\"order_number\": 272553, \"i\": 17}, {\"order_number\": 228795, \"i\": 18}, {\"order_number\": 182912, \"i\": 19}, {\"order_number\": 140569, \"i\": 20}, {\"order_number\": 104292, \"i\": 21}, {\"order_number\": 78109, \"i\": 22}, {\"order_number\": 61468, \"i\": 23}, {\"order_number\": 40043, \"i\": 24}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = alt.Chart(count, height = 350, width = 750,title = 'Number of Orders on Each day of Week').mark_bar(size=20).encode(\n",
    "    x='i',\n",
    "    y='order_number'\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize=24)\n",
    "\n",
    "\n",
    "n.encoding.y.title = 'Number of order'\n",
    "n.encoding.x.title = 'Day of the Week'\n",
    "\n",
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>67755</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>145247</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>193206</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>217005</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>221696</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        order_number    i\n",
       "days_since_prior_order                   \n",
       "0.0                            67755  1.0\n",
       "1.0                           145247  2.0\n",
       "2.0                           193206  3.0\n",
       "3.0                           217005  4.0\n",
       "4.0                           221696  5.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('days_since_prior_order')['order_number'].count()) \n",
    "count['i'] = count.index + 1\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-6\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-6\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
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       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-fba8f32bb1030d33122f1f5e490e323d\"}, \"mark\": {\"type\": \"bar\", \"size\": 20}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"i\", \"title\": \"Date\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of Order\"}}, \"height\": 350, \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-fba8f32bb1030d33122f1f5e490e323d\": [{\"order_number\": 67755, \"i\": 1.0}, {\"order_number\": 145247, \"i\": 2.0}, {\"order_number\": 193206, \"i\": 3.0}, {\"order_number\": 217005, \"i\": 4.0}, {\"order_number\": 221696, \"i\": 5.0}, {\"order_number\": 214503, \"i\": 6.0}, {\"order_number\": 240013, \"i\": 7.0}, {\"order_number\": 320608, \"i\": 8.0}, {\"order_number\": 181717, \"i\": 9.0}, {\"order_number\": 118188, \"i\": 10.0}, {\"order_number\": 95186, \"i\": 11.0}, {\"order_number\": 80970, \"i\": 12.0}, {\"order_number\": 76146, \"i\": 13.0}, {\"order_number\": 83214, \"i\": 14.0}, {\"order_number\": 100230, \"i\": 15.0}, {\"order_number\": 66579, \"i\": 16.0}, {\"order_number\": 46941, \"i\": 17.0}, {\"order_number\": 39245, \"i\": 18.0}, {\"order_number\": 35881, \"i\": 19.0}, {\"order_number\": 34384, \"i\": 20.0}, {\"order_number\": 38527, \"i\": 21.0}, {\"order_number\": 45470, \"i\": 22.0}, {\"order_number\": 32012, \"i\": 23.0}, {\"order_number\": 23885, \"i\": 24.0}, {\"order_number\": 20712, \"i\": 25.0}, {\"order_number\": 19234, \"i\": 26.0}, {\"order_number\": 19016, \"i\": 27.0}, {\"order_number\": 22013, \"i\": 28.0}, {\"order_number\": 26777, \"i\": 29.0}, {\"order_number\": 19191, \"i\": 30.0}, {\"order_number\": 369323, \"i\": 31.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(count, width = 750, height = 350).mark_bar(size = 20).encode(\n",
    "    alt.X(\"i\", title = 'Date'),\n",
    "    alt.Y(\"order_number\", title='Number of Order')\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16,\n",
    "    grid = False\n",
    ").configure_title(fontSize = 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>order_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>18346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>13392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>10501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>8532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>6087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_dow  order_hour_of_day  order_number\n",
       "0            0                  0          3936\n",
       "1            0                  1          2398\n",
       "2            0                  2          1409\n",
       "3            0                  3           963\n",
       "4            0                  4           813\n",
       "..         ...                ...           ...\n",
       "163          6                 19         18346\n",
       "164          6                 20         13392\n",
       "165          6                 21         10501\n",
       "166          6                 22          8532\n",
       "167          6                 23          6087\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hourDow = orders.groupby(['order_dow', 'order_hour_of_day'])['order_number'].aggregate('count').reset_index()\n",
    "hourDow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-7\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-7\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-2aaa61283a3ef9858f260917f5fd34b0\"}, \"mark\": \"rect\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"field\": \"order_number\"}, \"x\": {\"type\": \"ordinal\", \"field\": \"order_hour_of_day\", \"title\": \"Hour of the Day\"}, \"y\": {\"type\": \"ordinal\", \"field\": \"order_dow\", \"title\": \"Day of Week\"}}, \"height\": 450, \"title\": \"No of order day of week vs hour of day\", \"width\": 450, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-2aaa61283a3ef9858f260917f5fd34b0\": [{\"order_dow\": 0, \"order_hour_of_day\": 0, \"order_number\": 3936}, {\"order_dow\": 0, \"order_hour_of_day\": 1, \"order_number\": 2398}, {\"order_dow\": 0, \"order_hour_of_day\": 2, \"order_number\": 1409}, {\"order_dow\": 0, \"order_hour_of_day\": 3, \"order_number\": 963}, {\"order_dow\": 0, \"order_hour_of_day\": 4, \"order_number\": 813}, {\"order_dow\": 0, \"order_hour_of_day\": 5, \"order_number\": 1168}, {\"order_dow\": 0, \"order_hour_of_day\": 6, \"order_number\": 3329}, {\"order_dow\": 0, \"order_hour_of_day\": 7, \"order_number\": 12410}, {\"order_dow\": 0, \"order_hour_of_day\": 8, \"order_number\": 28108}, {\"order_dow\": 0, \"order_hour_of_day\": 9, \"order_number\": 40798}, {\"order_dow\": 0, \"order_hour_of_day\": 10, \"order_number\": 48465}, {\"order_dow\": 0, \"order_hour_of_day\": 11, \"order_number\": 51035}, {\"order_dow\": 0, \"order_hour_of_day\": 12, \"order_number\": 51443}, {\"order_dow\": 0, \"order_hour_of_day\": 13, \"order_number\": 53849}, {\"order_dow\": 0, \"order_hour_of_day\": 14, \"order_number\": 54552}, {\"order_dow\": 0, \"order_hour_of_day\": 15, \"order_number\": 53954}, {\"order_dow\": 0, \"order_hour_of_day\": 16, \"order_number\": 49463}, {\"order_dow\": 0, \"order_hour_of_day\": 17, \"order_number\": 39753}, {\"order_dow\": 0, \"order_hour_of_day\": 18, \"order_number\": 29572}, {\"order_dow\": 0, \"order_hour_of_day\": 19, \"order_number\": 22654}, {\"order_dow\": 0, \"order_hour_of_day\": 20, \"order_number\": 18277}, {\"order_dow\": 0, \"order_hour_of_day\": 21, \"order_number\": 14423}, {\"order_dow\": 0, \"order_hour_of_day\": 22, \"order_number\": 11246}, {\"order_dow\": 0, \"order_hour_of_day\": 23, \"order_number\": 6887}, {\"order_dow\": 1, \"order_hour_of_day\": 0, \"order_number\": 3674}, {\"order_dow\": 1, \"order_hour_of_day\": 1, \"order_number\": 1830}, {\"order_dow\": 1, \"order_hour_of_day\": 2, \"order_number\": 1105}, {\"order_dow\": 1, \"order_hour_of_day\": 3, \"order_number\": 748}, {\"order_dow\": 1, \"order_hour_of_day\": 4, \"order_number\": 809}, {\"order_dow\": 1, \"order_hour_of_day\": 5, \"order_number\": 1607}, {\"order_dow\": 1, \"order_hour_of_day\": 6, \"order_number\": 5370}, {\"order_dow\": 1, \"order_hour_of_day\": 7, \"order_number\": 16571}, {\"order_dow\": 1, \"order_hour_of_day\": 8, \"order_number\": 34116}, {\"order_dow\": 1, \"order_hour_of_day\": 9, \"order_number\": 51908}, {\"order_dow\": 1, \"order_hour_of_day\": 10, \"order_number\": 55671}, {\"order_dow\": 1, \"order_hour_of_day\": 11, \"order_number\": 51584}, {\"order_dow\": 1, \"order_hour_of_day\": 12, \"order_number\": 47079}, {\"order_dow\": 1, \"order_hour_of_day\": 13, \"order_number\": 46728}, {\"order_dow\": 1, \"order_hour_of_day\": 14, \"order_number\": 46764}, {\"order_dow\": 1, \"order_hour_of_day\": 15, \"order_number\": 46403}, {\"order_dow\": 1, \"order_hour_of_day\": 16, \"order_number\": 44761}, {\"order_dow\": 1, \"order_hour_of_day\": 17, \"order_number\": 36792}, {\"order_dow\": 1, \"order_hour_of_day\": 18, \"order_number\": 28977}, {\"order_dow\": 1, \"order_hour_of_day\": 19, \"order_number\": 22145}, {\"order_dow\": 1, \"order_hour_of_day\": 20, \"order_number\": 16281}, {\"order_dow\": 1, \"order_hour_of_day\": 21, \"order_number\": 11943}, {\"order_dow\": 1, \"order_hour_of_day\": 22, \"order_number\": 8992}, {\"order_dow\": 1, \"order_hour_of_day\": 23, \"order_number\": 5620}, {\"order_dow\": 2, \"order_hour_of_day\": 0, \"order_number\": 3059}, {\"order_dow\": 2, \"order_hour_of_day\": 1, \"order_number\": 1572}, {\"order_dow\": 2, \"order_hour_of_day\": 2, \"order_number\": 943}, {\"order_dow\": 2, \"order_hour_of_day\": 3, \"order_number\": 719}, {\"order_dow\": 2, \"order_hour_of_day\": 4, \"order_number\": 744}, {\"order_dow\": 2, \"order_hour_of_day\": 5, \"order_number\": 1399}, {\"order_dow\": 2, \"order_hour_of_day\": 6, \"order_number\": 4758}, {\"order_dow\": 2, \"order_hour_of_day\": 7, \"order_number\": 13245}, {\"order_dow\": 2, \"order_hour_of_day\": 8, \"order_number\": 24635}, {\"order_dow\": 2, \"order_hour_of_day\": 9, \"order_number\": 36314}, {\"order_dow\": 2, \"order_hour_of_day\": 10, \"order_number\": 39230}, {\"order_dow\": 2, \"order_hour_of_day\": 11, \"order_number\": 38128}, {\"order_dow\": 2, \"order_hour_of_day\": 12, \"order_number\": 35780}, {\"order_dow\": 2, \"order_hour_of_day\": 13, \"order_number\": 36650}, {\"order_dow\": 2, \"order_hour_of_day\": 14, \"order_number\": 37173}, {\"order_dow\": 2, \"order_hour_of_day\": 15, \"order_number\": 37469}, {\"order_dow\": 2, \"order_hour_of_day\": 16, \"order_number\": 37541}, {\"order_dow\": 2, \"order_hour_of_day\": 17, \"order_number\": 32151}, {\"order_dow\": 2, \"order_hour_of_day\": 18, \"order_number\": 26470}, {\"order_dow\": 2, \"order_hour_of_day\": 19, \"order_number\": 20084}, {\"order_dow\": 2, \"order_hour_of_day\": 20, \"order_number\": 15039}, {\"order_dow\": 2, \"order_hour_of_day\": 21, \"order_number\": 10653}, {\"order_dow\": 2, \"order_hour_of_day\": 22, \"order_number\": 8146}, {\"order_dow\": 2, \"order_hour_of_day\": 23, \"order_number\": 5358}, {\"order_dow\": 3, \"order_hour_of_day\": 0, \"order_number\": 2952}, {\"order_dow\": 3, \"order_hour_of_day\": 1, \"order_number\": 1495}, {\"order_dow\": 3, \"order_hour_of_day\": 2, \"order_number\": 953}, {\"order_dow\": 3, \"order_hour_of_day\": 3, \"order_number\": 654}, {\"order_dow\": 3, \"order_hour_of_day\": 4, \"order_number\": 719}, {\"order_dow\": 3, \"order_hour_of_day\": 5, \"order_number\": 1355}, {\"order_dow\": 3, \"order_hour_of_day\": 6, \"order_number\": 4562}, {\"order_dow\": 3, \"order_hour_of_day\": 7, \"order_number\": 12396}, {\"order_dow\": 3, \"order_hour_of_day\": 8, \"order_number\": 22553}, {\"order_dow\": 3, \"order_hour_of_day\": 9, \"order_number\": 32312}, {\"order_dow\": 3, \"order_hour_of_day\": 10, \"order_number\": 36040}, {\"order_dow\": 3, \"order_hour_of_day\": 11, \"order_number\": 35215}, {\"order_dow\": 3, \"order_hour_of_day\": 12, \"order_number\": 33455}, {\"order_dow\": 3, \"order_hour_of_day\": 13, \"order_number\": 34161}, {\"order_dow\": 3, \"order_hour_of_day\": 14, \"order_number\": 34773}, {\"order_dow\": 3, \"order_hour_of_day\": 15, \"order_number\": 35990}, {\"order_dow\": 3, \"order_hour_of_day\": 16, \"order_number\": 35273}, {\"order_dow\": 3, \"order_hour_of_day\": 17, \"order_number\": 30368}, {\"order_dow\": 3, \"order_hour_of_day\": 18, \"order_number\": 25001}, {\"order_dow\": 3, \"order_hour_of_day\": 19, \"order_number\": 19249}, {\"order_dow\": 3, \"order_hour_of_day\": 20, \"order_number\": 13795}, {\"order_dow\": 3, \"order_hour_of_day\": 21, \"order_number\": 10278}, {\"order_dow\": 3, \"order_hour_of_day\": 22, \"order_number\": 8242}, {\"order_dow\": 3, \"order_hour_of_day\": 23, \"order_number\": 5181}, {\"order_dow\": 4, \"order_hour_of_day\": 0, \"order_number\": 2642}, {\"order_dow\": 4, \"order_hour_of_day\": 1, \"order_number\": 1512}, {\"order_dow\": 4, \"order_hour_of_day\": 2, \"order_number\": 899}, {\"order_dow\": 4, \"order_hour_of_day\": 3, \"order_number\": 686}, {\"order_dow\": 4, \"order_hour_of_day\": 4, \"order_number\": 730}, {\"order_dow\": 4, \"order_hour_of_day\": 5, \"order_number\": 1330}, {\"order_dow\": 4, \"order_hour_of_day\": 6, \"order_number\": 4401}, {\"order_dow\": 4, \"order_hour_of_day\": 7, \"order_number\": 12493}, {\"order_dow\": 4, \"order_hour_of_day\": 8, \"order_number\": 21814}, {\"order_dow\": 4, \"order_hour_of_day\": 9, \"order_number\": 31409}, {\"order_dow\": 4, \"order_hour_of_day\": 10, \"order_number\": 35034}, {\"order_dow\": 4, \"order_hour_of_day\": 11, \"order_number\": 33857}, {\"order_dow\": 4, \"order_hour_of_day\": 12, \"order_number\": 32249}, {\"order_dow\": 4, \"order_hour_of_day\": 13, \"order_number\": 32751}, {\"order_dow\": 4, \"order_hour_of_day\": 14, \"order_number\": 33625}, {\"order_dow\": 4, \"order_hour_of_day\": 15, \"order_number\": 34222}, {\"order_dow\": 4, \"order_hour_of_day\": 16, \"order_number\": 34093}, {\"order_dow\": 4, \"order_hour_of_day\": 17, \"order_number\": 29378}, {\"order_dow\": 4, \"order_hour_of_day\": 18, \"order_number\": 24425}, {\"order_dow\": 4, \"order_hour_of_day\": 19, \"order_number\": 19350}, {\"order_dow\": 4, \"order_hour_of_day\": 20, \"order_number\": 14186}, {\"order_dow\": 4, \"order_hour_of_day\": 21, \"order_number\": 10796}, {\"order_dow\": 4, \"order_hour_of_day\": 22, \"order_number\": 8812}, {\"order_dow\": 4, \"order_hour_of_day\": 23, \"order_number\": 5645}, {\"order_dow\": 5, \"order_hour_of_day\": 0, \"order_number\": 3189}, {\"order_dow\": 5, \"order_hour_of_day\": 1, \"order_number\": 1672}, {\"order_dow\": 5, \"order_hour_of_day\": 2, \"order_number\": 1016}, {\"order_dow\": 5, \"order_hour_of_day\": 3, \"order_number\": 841}, {\"order_dow\": 5, \"order_hour_of_day\": 4, \"order_number\": 910}, {\"order_dow\": 5, \"order_hour_of_day\": 5, \"order_number\": 1574}, {\"order_dow\": 5, \"order_hour_of_day\": 6, \"order_number\": 4866}, {\"order_dow\": 5, \"order_hour_of_day\": 7, \"order_number\": 13434}, {\"order_dow\": 5, \"order_hour_of_day\": 8, \"order_number\": 24015}, {\"order_dow\": 5, \"order_hour_of_day\": 9, \"order_number\": 34232}, {\"order_dow\": 5, \"order_hour_of_day\": 10, \"order_number\": 38313}, {\"order_dow\": 5, \"order_hour_of_day\": 11, \"order_number\": 37915}, {\"order_dow\": 5, \"order_hour_of_day\": 12, \"order_number\": 35714}, {\"order_dow\": 5, \"order_hour_of_day\": 13, \"order_number\": 36296}, {\"order_dow\": 5, \"order_hour_of_day\": 14, \"order_number\": 37407}, {\"order_dow\": 5, \"order_hour_of_day\": 15, \"order_number\": 37508}, {\"order_dow\": 5, \"order_hour_of_day\": 16, \"order_number\": 35860}, {\"order_dow\": 5, \"order_hour_of_day\": 17, \"order_number\": 29955}, {\"order_dow\": 5, \"order_hour_of_day\": 18, \"order_number\": 24310}, {\"order_dow\": 5, \"order_hour_of_day\": 19, \"order_number\": 18741}, {\"order_dow\": 5, \"order_hour_of_day\": 20, \"order_number\": 13322}, {\"order_dow\": 5, \"order_hour_of_day\": 21, \"order_number\": 9515}, {\"order_dow\": 5, \"order_hour_of_day\": 22, \"order_number\": 7498}, {\"order_dow\": 5, \"order_hour_of_day\": 23, \"order_number\": 5265}, {\"order_dow\": 6, \"order_hour_of_day\": 0, \"order_number\": 3306}, {\"order_dow\": 6, \"order_hour_of_day\": 1, \"order_number\": 1919}, {\"order_dow\": 6, \"order_hour_of_day\": 2, \"order_number\": 1214}, {\"order_dow\": 6, \"order_hour_of_day\": 3, \"order_number\": 863}, {\"order_dow\": 6, \"order_hour_of_day\": 4, \"order_number\": 802}, {\"order_dow\": 6, \"order_hour_of_day\": 5, \"order_number\": 1136}, {\"order_dow\": 6, \"order_hour_of_day\": 6, \"order_number\": 3243}, {\"order_dow\": 6, \"order_hour_of_day\": 7, \"order_number\": 11319}, {\"order_dow\": 6, \"order_hour_of_day\": 8, \"order_number\": 22960}, {\"order_dow\": 6, \"order_hour_of_day\": 9, \"order_number\": 30839}, {\"order_dow\": 6, \"order_hour_of_day\": 10, \"order_number\": 35665}, {\"order_dow\": 6, \"order_hour_of_day\": 11, \"order_number\": 36994}, {\"order_dow\": 6, \"order_hour_of_day\": 12, \"order_number\": 37121}, {\"order_dow\": 6, \"order_hour_of_day\": 13, \"order_number\": 37564}, {\"order_dow\": 6, \"order_hour_of_day\": 14, \"order_number\": 38748}, {\"order_dow\": 6, \"order_hour_of_day\": 15, \"order_number\": 38093}, {\"order_dow\": 6, \"order_hour_of_day\": 16, \"order_number\": 35562}, {\"order_dow\": 6, \"order_hour_of_day\": 17, \"order_number\": 30398}, {\"order_dow\": 6, \"order_hour_of_day\": 18, \"order_number\": 24157}, {\"order_dow\": 6, \"order_hour_of_day\": 19, \"order_number\": 18346}, {\"order_dow\": 6, \"order_hour_of_day\": 20, \"order_number\": 13392}, {\"order_dow\": 6, \"order_hour_of_day\": 21, \"order_number\": 10501}, {\"order_dow\": 6, \"order_hour_of_day\": 22, \"order_number\": 8532}, {\"order_dow\": 6, \"order_hour_of_day\": 23, \"order_number\": 6087}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(hourDow, title='No of order day of week vs hour of day').mark_rect().encode(\n",
    "    x = alt.X('order_hour_of_day:O', title='Hour of the Day'),\n",
    "    y = alt.Y('order_dow:O', title='Day of Week'),\n",
    "    color = 'order_number:Q'\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 450,\n",
    "    width = 450\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count of Users by Number of Prior Orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206204</th>\n",
       "      <td>206205</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206205</th>\n",
       "      <td>206206</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>206207</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>206208</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>206209</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206209 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id  order_number\n",
       "0             1            11\n",
       "1             2            15\n",
       "2             3            13\n",
       "3             4             6\n",
       "4             5             5\n",
       "...         ...           ...\n",
       "206204   206205             4\n",
       "206205   206206            68\n",
       "206206   206207            17\n",
       "206207   206208            50\n",
       "206208   206209            14\n",
       "\n",
       "[206209 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(orders.groupby('user_id')['order_number'].aggregate('max').reset_index()) \n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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_number</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23986</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19590</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16165</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>13850</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11700</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_number  i\n",
       "4         23986  4\n",
       "5         19590  5\n",
       "6         16165  6\n",
       "7         13850  7\n",
       "8         11700  8"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = pd.DataFrame(count['order_number'].value_counts())\n",
    "new['i'] = new.index\n",
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-8\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-8\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"bar\": {\"color\": \"#7AAD80\"}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-27844ce7c2a1575747fff5f853073b84\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"i\", \"title\": \"Number of Prior Orders\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"order_number\", \"title\": \"Number of Users\"}}, \"height\": 400, \"title\": \"Count of users by number of prior orders\", \"width\": 700, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-27844ce7c2a1575747fff5f853073b84\": [{\"order_number\": 23986, \"i\": 4}, {\"order_number\": 19590, \"i\": 5}, {\"order_number\": 16165, \"i\": 6}, {\"order_number\": 13850, \"i\": 7}, {\"order_number\": 11700, \"i\": 8}, {\"order_number\": 10190, \"i\": 9}, {\"order_number\": 9032, \"i\": 10}, {\"order_number\": 7815, \"i\": 11}, {\"order_number\": 6952, \"i\": 12}, {\"order_number\": 6236, \"i\": 13}, {\"order_number\": 5613, \"i\": 14}, {\"order_number\": 5191, \"i\": 15}, {\"order_number\": 4593, \"i\": 16}, {\"order_number\": 4139, \"i\": 17}, {\"order_number\": 3836, \"i\": 18}, {\"order_number\": 3390, \"i\": 19}, {\"order_number\": 3200, \"i\": 20}, {\"order_number\": 2921, \"i\": 21}, {\"order_number\": 2706, \"i\": 22}, {\"order_number\": 2605, \"i\": 23}, {\"order_number\": 2288, \"i\": 24}, {\"order_number\": 2186, \"i\": 25}, {\"order_number\": 2060, \"i\": 26}, {\"order_number\": 1908, \"i\": 27}, {\"order_number\": 1779, \"i\": 28}, {\"order_number\": 1601, \"i\": 29}, {\"order_number\": 1494, \"i\": 30}, {\"order_number\": 1474, \"i\": 31}, {\"order_number\": 1386, \"i\": 32}, {\"order_number\": 1374, \"i\": 100}, {\"order_number\": 1273, \"i\": 33}, {\"order_number\": 1186, \"i\": 34}, {\"order_number\": 1157, \"i\": 35}, {\"order_number\": 1048, \"i\": 37}, {\"order_number\": 1015, \"i\": 36}, {\"order_number\": 960, \"i\": 38}, {\"order_number\": 904, \"i\": 39}, {\"order_number\": 885, \"i\": 41}, {\"order_number\": 878, \"i\": 40}, {\"order_number\": 814, \"i\": 42}, {\"order_number\": 743, \"i\": 43}, {\"order_number\": 724, \"i\": 44}, {\"order_number\": 707, \"i\": 45}, {\"order_number\": 675, \"i\": 47}, {\"order_number\": 657, \"i\": 46}, {\"order_number\": 654, \"i\": 48}, {\"order_number\": 582, \"i\": 51}, {\"order_number\": 579, \"i\": 49}, {\"order_number\": 554, \"i\": 50}, {\"order_number\": 527, \"i\": 52}, {\"order_number\": 524, \"i\": 53}, {\"order_number\": 499, \"i\": 54}, {\"order_number\": 425, \"i\": 55}, {\"order_number\": 396, \"i\": 57}, {\"order_number\": 390, \"i\": 56}, {\"order_number\": 343, \"i\": 58}, {\"order_number\": 332, \"i\": 59}, {\"order_number\": 324, \"i\": 61}, {\"order_number\": 303, \"i\": 60}, {\"order_number\": 276, \"i\": 62}, {\"order_number\": 267, \"i\": 63}, {\"order_number\": 254, \"i\": 64}, {\"order_number\": 239, \"i\": 65}, {\"order_number\": 238, \"i\": 66}, {\"order_number\": 204, \"i\": 67}, {\"order_number\": 189, \"i\": 68}, {\"order_number\": 185, \"i\": 69}, {\"order_number\": 178, \"i\": 70}, {\"order_number\": 166, \"i\": 72}, {\"order_number\": 162, \"i\": 74}, {\"order_number\": 162, \"i\": 75}, {\"order_number\": 154, \"i\": 71}, {\"order_number\": 138, \"i\": 73}, {\"order_number\": 134, \"i\": 76}, {\"order_number\": 127, \"i\": 78}, {\"order_number\": 127, \"i\": 79}, {\"order_number\": 120, \"i\": 77}, {\"order_number\": 119, \"i\": 82}, {\"order_number\": 116, \"i\": 80}, {\"order_number\": 108, \"i\": 81}, {\"order_number\": 100, \"i\": 85}, {\"order_number\": 99, \"i\": 84}, {\"order_number\": 93, \"i\": 86}, {\"order_number\": 86, \"i\": 83}, {\"order_number\": 85, \"i\": 90}, {\"order_number\": 79, \"i\": 88}, {\"order_number\": 78, \"i\": 87}, {\"order_number\": 77, \"i\": 89}, {\"order_number\": 69, \"i\": 92}, {\"order_number\": 67, \"i\": 93}, {\"order_number\": 67, \"i\": 96}, {\"order_number\": 64, \"i\": 95}, {\"order_number\": 57, \"i\": 94}, {\"order_number\": 56, \"i\": 91}, {\"order_number\": 54, \"i\": 97}, {\"order_number\": 50, \"i\": 98}, {\"order_number\": 47, \"i\": 99}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(new, title='Count of users by number of prior orders').mark_bar().encode(\n",
    "    alt.X('i', title = 'Number of Prior Orders'),\n",
    "    alt.Y('order_number', title = 'Number of Users')\n",
    ").configure_axis(\n",
    "    labelFontSize = 16,\n",
    "    titleFontSize = 16,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 400,\n",
    "    width = 700\n",
    ").configure_bar(\n",
    "    color = '#7AAD80'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribution of basket size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>add_to_cart_order</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>237225</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>236383</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>230299</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>228547</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>215060</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>113 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     add_to_cart_order    i\n",
       "5               237225    5\n",
       "6               236383    6\n",
       "4               230299    4\n",
       "7               228547    7\n",
       "3               215060    3\n",
       "..                 ...  ...\n",
       "112                  1  112\n",
       "105                  1  105\n",
       "94                   1   94\n",
       "90                   1   90\n",
       "145                  1  145\n",
       "\n",
       "[113 rows x 2 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OrderSize = data.reset_index().groupby(['user_id', 'order_number'])['add_to_cart_order'].aggregate('max')\n",
    "new = pd.DataFrame(OrderSize.value_counts())\n",
    "new['i'] = new.index\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    const outputDiv = document.getElementById(\"altair-viz-9\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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       "\n",
       "    function loadScript(lib) {\n",
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       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
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       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"bar\": {\"color\": \"#FC5959\"}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-6638fbb0c4c468589d7a490996fb9a3c\"}, \"mark\": {\"type\": \"bar\", \"size\": 4}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"i\", \"title\": \"Number of Items in Basket\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"add_to_cart_order\", \"title\": \"Number of Users\"}}, \"height\": 350, \"title\": \"Count of users by number of Items in Basket\", \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-6638fbb0c4c468589d7a490996fb9a3c\": [{\"add_to_cart_order\": 237225, \"i\": 5}, {\"add_to_cart_order\": 236383, \"i\": 6}, {\"add_to_cart_order\": 230299, \"i\": 4}, {\"add_to_cart_order\": 228547, \"i\": 7}, {\"add_to_cart_order\": 215060, \"i\": 3}, {\"add_to_cart_order\": 211357, \"i\": 8}, {\"add_to_cart_order\": 194361, \"i\": 2}, {\"add_to_cart_order\": 191564, \"i\": 9}, {\"add_to_cart_order\": 172103, \"i\": 10}, {\"add_to_cart_order\": 163593, \"i\": 1}, {\"add_to_cart_order\": 153495, \"i\": 11}, {\"add_to_cart_order\": 136963, \"i\": 12}, {\"add_to_cart_order\": 121714, \"i\": 13}, {\"add_to_cart_order\": 108077, \"i\": 14}, {\"add_to_cart_order\": 95475, \"i\": 15}, {\"add_to_cart_order\": 84714, \"i\": 16}, {\"add_to_cart_order\": 74468, \"i\": 17}, {\"add_to_cart_order\": 65348, \"i\": 18}, {\"add_to_cart_order\": 57290, \"i\": 19}, {\"add_to_cart_order\": 50198, \"i\": 20}, {\"add_to_cart_order\": 43720, \"i\": 21}, {\"add_to_cart_order\": 38049, \"i\": 22}, {\"add_to_cart_order\": 33134, \"i\": 23}, {\"add_to_cart_order\": 28357, \"i\": 24}, {\"add_to_cart_order\": 24692, \"i\": 25}, {\"add_to_cart_order\": 21269, \"i\": 26}, {\"add_to_cart_order\": 18348, \"i\": 27}, {\"add_to_cart_order\": 15781, \"i\": 28}, {\"add_to_cart_order\": 13667, \"i\": 29}, {\"add_to_cart_order\": 11804, \"i\": 30}, {\"add_to_cart_order\": 10017, \"i\": 31}, {\"add_to_cart_order\": 8438, \"i\": 32}, {\"add_to_cart_order\": 7337, \"i\": 33}, {\"add_to_cart_order\": 6356, \"i\": 34}, {\"add_to_cart_order\": 5444, \"i\": 35}, {\"add_to_cart_order\": 4617, \"i\": 36}, {\"add_to_cart_order\": 3874, \"i\": 37}, {\"add_to_cart_order\": 3347, \"i\": 38}, {\"add_to_cart_order\": 2795, \"i\": 39}, {\"add_to_cart_order\": 2371, \"i\": 40}, {\"add_to_cart_order\": 2068, \"i\": 41}, {\"add_to_cart_order\": 1730, \"i\": 42}, {\"add_to_cart_order\": 1487, \"i\": 43}, {\"add_to_cart_order\": 1306, \"i\": 44}, {\"add_to_cart_order\": 1112, \"i\": 45}, {\"add_to_cart_order\": 943, \"i\": 46}, {\"add_to_cart_order\": 792, \"i\": 47}, {\"add_to_cart_order\": 640, \"i\": 48}, {\"add_to_cart_order\": 589, \"i\": 49}, {\"add_to_cart_order\": 522, \"i\": 50}, {\"add_to_cart_order\": 418, \"i\": 51}, {\"add_to_cart_order\": 371, \"i\": 52}, {\"add_to_cart_order\": 306, \"i\": 53}, {\"add_to_cart_order\": 290, \"i\": 54}, {\"add_to_cart_order\": 236, \"i\": 55}, {\"add_to_cart_order\": 185, \"i\": 56}, {\"add_to_cart_order\": 170, \"i\": 58}, {\"add_to_cart_order\": 165, \"i\": 57}, {\"add_to_cart_order\": 129, \"i\": 60}, {\"add_to_cart_order\": 123, \"i\": 59}, {\"add_to_cart_order\": 103, \"i\": 61}, {\"add_to_cart_order\": 82, \"i\": 62}, {\"add_to_cart_order\": 72, \"i\": 63}, {\"add_to_cart_order\": 61, \"i\": 64}, {\"add_to_cart_order\": 56, \"i\": 65}, {\"add_to_cart_order\": 51, \"i\": 66}, {\"add_to_cart_order\": 45, \"i\": 67}, {\"add_to_cart_order\": 41, \"i\": 68}, {\"add_to_cart_order\": 37, \"i\": 70}, {\"add_to_cart_order\": 30, \"i\": 71}, {\"add_to_cart_order\": 26, \"i\": 74}, {\"add_to_cart_order\": 25, \"i\": 72}, {\"add_to_cart_order\": 24, \"i\": 69}, {\"add_to_cart_order\": 22, \"i\": 73}, {\"add_to_cart_order\": 16, \"i\": 77}, {\"add_to_cart_order\": 13, \"i\": 76}, {\"add_to_cart_order\": 10, \"i\": 84}, {\"add_to_cart_order\": 10, \"i\": 75}, {\"add_to_cart_order\": 9, \"i\": 78}, {\"add_to_cart_order\": 9, \"i\": 80}, {\"add_to_cart_order\": 9, \"i\": 92}, {\"add_to_cart_order\": 9, \"i\": 82}, {\"add_to_cart_order\": 7, \"i\": 79}, {\"add_to_cart_order\": 7, \"i\": 88}, {\"add_to_cart_order\": 7, \"i\": 86}, {\"add_to_cart_order\": 5, \"i\": 85}, {\"add_to_cart_order\": 5, \"i\": 81}, {\"add_to_cart_order\": 4, \"i\": 83}, {\"add_to_cart_order\": 4, \"i\": 87}, {\"add_to_cart_order\": 4, \"i\": 89}, {\"add_to_cart_order\": 4, \"i\": 100}, {\"add_to_cart_order\": 4, \"i\": 91}, {\"add_to_cart_order\": 4, \"i\": 93}, {\"add_to_cart_order\": 4, \"i\": 98}, {\"add_to_cart_order\": 4, \"i\": 95}, {\"add_to_cart_order\": 3, \"i\": 102}, {\"add_to_cart_order\": 3, \"i\": 96}, {\"add_to_cart_order\": 2, \"i\": 99}, {\"add_to_cart_order\": 2, \"i\": 101}, {\"add_to_cart_order\": 2, \"i\": 104}, {\"add_to_cart_order\": 2, \"i\": 108}, {\"add_to_cart_order\": 2, \"i\": 109}, 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       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(new, title='Count of users by number of Items in Basket').mark_bar(size = 4).encode(\n",
    "    alt.X('i', title = 'Number of Items in Basket'),\n",
    "    alt.Y('add_to_cart_order', title = 'Number of Users')\n",
    ").configure_axis(\n",
    "    labelFontSize = 16,\n",
    "    titleFontSize = 16,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 750\n",
    ").configure_bar(\n",
    "    color = '#FC5959'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Popular Products"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>eval_set</th>\n",
       "      <th>prior</th>\n",
       "      <th>train</th>\n",
       "      <th>total</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Banana</th>\n",
       "      <td>472565</td>\n",
       "      <td>18726</td>\n",
       "      <td>491291</td>\n",
       "      <td>Banana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bag of Organic Bananas</th>\n",
       "      <td>379450</td>\n",
       "      <td>15480</td>\n",
       "      <td>394930</td>\n",
       "      <td>Bag of Organic Bananas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic Strawberries</th>\n",
       "      <td>264683</td>\n",
       "      <td>10894</td>\n",
       "      <td>275577</td>\n",
       "      <td>Organic Strawberries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic Baby Spinach</th>\n",
       "      <td>241921</td>\n",
       "      <td>9784</td>\n",
       "      <td>251705</td>\n",
       "      <td>Organic Baby Spinach</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic Hass Avocado</th>\n",
       "      <td>213584</td>\n",
       "      <td>7293</td>\n",
       "      <td>220877</td>\n",
       "      <td>Organic Hass Avocado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic Avocado</th>\n",
       "      <td>176815</td>\n",
       "      <td>7409</td>\n",
       "      <td>184224</td>\n",
       "      <td>Organic Avocado</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Large Lemon</th>\n",
       "      <td>152657</td>\n",
       "      <td>8135</td>\n",
       "      <td>160792</td>\n",
       "      <td>Large Lemon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strawberries</th>\n",
       "      <td>142951</td>\n",
       "      <td>6494</td>\n",
       "      <td>149445</td>\n",
       "      <td>Strawberries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Limes</th>\n",
       "      <td>140627</td>\n",
       "      <td>6033</td>\n",
       "      <td>146660</td>\n",
       "      <td>Limes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Organic Whole Milk</th>\n",
       "      <td>137905</td>\n",
       "      <td>4908</td>\n",
       "      <td>142813</td>\n",
       "      <td>Organic Whole Milk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eval_set                 prior  train   total                       i\n",
       "product_name                                                         \n",
       "Banana                  472565  18726  491291                  Banana\n",
       "Bag of Organic Bananas  379450  15480  394930  Bag of Organic Bananas\n",
       "Organic Strawberries    264683  10894  275577    Organic Strawberries\n",
       "Organic Baby Spinach    241921   9784  251705    Organic Baby Spinach\n",
       "Organic Hass Avocado    213584   7293  220877    Organic Hass Avocado\n",
       "Organic Avocado         176815   7409  184224         Organic Avocado\n",
       "Large Lemon             152657   8135  160792             Large Lemon\n",
       "Strawberries            142951   6494  149445            Strawberries\n",
       "Limes                   140627   6033  146660                   Limes\n",
       "Organic Whole Milk      137905   4908  142813      Organic Whole Milk"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countProducts = pd.crosstab(data.product_name, data.eval_set)\n",
    "countProducts['total'] = countProducts.prior + countProducts.train\n",
    "count = countProducts.sort_values(by='total', ascending=False).head(25)\n",
    "count['i'] = count.index\n",
    "count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 14, \"titleFontSize\": 14}, \"title\": {\"fontSize\": 16}}, \"hconcat\": [{\"mark\": {\"type\": \"bar\", \"size\": 7}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Product Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"prior\", \"title\": \"Count of Orders\"}}, \"height\": 350, \"title\": \"Count of Order of prior 25 Most Ordered Product\", \"width\": 350}, {\"mark\": {\"type\": \"bar\", \"size\": 7}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Product Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"train\", \"title\": \"Count of Orders\"}}, \"height\": 350, \"title\": \"Count of Order of train 25 Most Ordered Product\", \"width\": 350}], \"data\": {\"name\": \"data-aae6a56e0232a0771a2b403d1477a415\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-aae6a56e0232a0771a2b403d1477a415\": [{\"prior\": 472565, \"train\": 18726, \"total\": 491291, \"i\": \"Banana\"}, {\"prior\": 379450, \"train\": 15480, \"total\": 394930, \"i\": \"Bag of Organic Bananas\"}, {\"prior\": 264683, \"train\": 10894, \"total\": 275577, \"i\": \"Organic Strawberries\"}, {\"prior\": 241921, \"train\": 9784, \"total\": 251705, \"i\": \"Organic Baby Spinach\"}, {\"prior\": 213584, \"train\": 7293, \"total\": 220877, \"i\": \"Organic Hass Avocado\"}, {\"prior\": 176815, \"train\": 7409, \"total\": 184224, \"i\": \"Organic Avocado\"}, {\"prior\": 152657, \"train\": 8135, \"total\": 160792, \"i\": \"Large Lemon\"}, {\"prior\": 142951, \"train\": 6494, \"total\": 149445, \"i\": \"Strawberries\"}, {\"prior\": 140627, \"train\": 6033, \"total\": 146660, \"i\": \"Limes\"}, {\"prior\": 137905, \"train\": 4908, \"total\": 142813, \"i\": \"Organic Whole Milk\"}, {\"prior\": 137057, \"train\": 5546, \"total\": 142603, \"i\": \"Organic Raspberries\"}, {\"prior\": 113426, \"train\": 4290, \"total\": 117716, \"i\": \"Organic Yellow Onion\"}, {\"prior\": 109778, \"train\": 4158, \"total\": 113936, \"i\": \"Organic Garlic\"}, {\"prior\": 104823, \"train\": 4589, \"total\": 109412, \"i\": \"Organic Zucchini\"}, {\"prior\": 100060, \"train\": 4966, \"total\": 105026, \"i\": \"Organic Blueberries\"}, {\"prior\": 97315, \"train\": 2413, \"total\": 99728, \"i\": \"Cucumber Kirby\"}, {\"prior\": 89632, \"train\": 3257, \"total\": 92889, \"i\": \"Organic Fuji Apple\"}, {\"prior\": 87746, \"train\": 3505, \"total\": 91251, \"i\": \"Organic Lemon\"}, {\"prior\": 84255, \"train\": 3823, \"total\": 88078, \"i\": \"Organic Grape Tomatoes\"}, {\"prior\": 85020, \"train\": 2252, \"total\": 87272, \"i\": \"Apple Honeycrisp Organic\"}, {\"prior\": 82689, \"train\": 4059, \"total\": 86748, \"i\": \"Seedless Red Grapes\"}, {\"prior\": 80392, \"train\": 4613, \"total\": 85005, \"i\": \"Organic Cucumber\"}, {\"prior\": 79769, \"train\": 3551, \"total\": 83320, \"i\": \"Honeycrisp Apple\"}, {\"prior\": 76896, \"train\": 3597, \"total\": 80493, \"i\": \"Organic Baby Carrots\"}, {\"prior\": 75886, \"train\": 3359, \"total\": 79245, \"i\": \"Sparkling Water Grapefruit\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = alt.Chart(count, title=\"Count of Order of prior 25 Most Ordered Product\").mark_bar(size = 7).encode(\n",
    "    alt.X('i', title = 'Product Name'),\n",
    "    alt.Y('prior', title = 'Count of Orders')\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 350\n",
    ")\n",
    "n = alt.Chart(count, title=\"Count of Order of train 25 Most Ordered Product\").mark_bar(size = 7).encode(\n",
    "    alt.X('i', title = 'Product Name'),\n",
    "    alt.Y('train', title = 'Count of Orders')\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 350\n",
    ")\n",
    "(m|n).configure_axis(\n",
    "    labelFontSize = 14,\n",
    "    titleFontSize = 14,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 16\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>eval_set</th>\n",
       "      <th>prior</th>\n",
       "      <th>train</th>\n",
       "      <th>total</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aisle</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fresh fruits</th>\n",
       "      <td>3642188</td>\n",
       "      <td>150473</td>\n",
       "      <td>3792661</td>\n",
       "      <td>fresh fruits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fresh vegetables</th>\n",
       "      <td>3418021</td>\n",
       "      <td>150609</td>\n",
       "      <td>3568630</td>\n",
       "      <td>fresh vegetables</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packaged vegetables fruits</th>\n",
       "      <td>1765313</td>\n",
       "      <td>78493</td>\n",
       "      <td>1843806</td>\n",
       "      <td>packaged vegetables fruits</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yogurt</th>\n",
       "      <td>1452343</td>\n",
       "      <td>55240</td>\n",
       "      <td>1507583</td>\n",
       "      <td>yogurt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>packaged cheese</th>\n",
       "      <td>979763</td>\n",
       "      <td>41699</td>\n",
       "      <td>1021462</td>\n",
       "      <td>packaged cheese</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eval_set                      prior   train    total  \\\n",
       "aisle                                                  \n",
       "fresh fruits                3642188  150473  3792661   \n",
       "fresh vegetables            3418021  150609  3568630   \n",
       "packaged vegetables fruits  1765313   78493  1843806   \n",
       "yogurt                      1452343   55240  1507583   \n",
       "packaged cheese              979763   41699  1021462   \n",
       "\n",
       "eval_set                                             i  \n",
       "aisle                                                   \n",
       "fresh fruits                              fresh fruits  \n",
       "fresh vegetables                      fresh vegetables  \n",
       "packaged vegetables fruits  packaged vegetables fruits  \n",
       "yogurt                                          yogurt  \n",
       "packaged cheese                        packaged cheese  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countAisles = pd.crosstab(data.aisle, data.eval_set)\n",
    "countAisles['total'] = countAisles.prior + countAisles.train\n",
    "count = countAisles.sort_values(by='total', ascending=False).head(15)\n",
    "count['i'] = count.index\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-11\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-11\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 14, \"titleFontSize\": 14}, \"title\": {\"fontSize\": 15}}, \"hconcat\": [{\"mark\": {\"type\": \"bar\", \"size\": 10}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Product Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"prior\", \"title\": \"Count of Orders\"}}, \"height\": 350, \"title\": \"Count of Order of prior 15 Most Ordered from Aisles\", \"width\": 350}, {\"mark\": {\"type\": \"bar\", \"size\": 10}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Product Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"train\", \"title\": \"Count of Orders\"}}, \"height\": 350, \"title\": \"Count of Order of train 15 Most Ordered from Aisles\", \"width\": 350}], \"data\": {\"name\": \"data-f4bc6b74605280406dc076da0b35ba6e\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-f4bc6b74605280406dc076da0b35ba6e\": [{\"prior\": 3642188, \"train\": 150473, \"total\": 3792661, \"i\": \"fresh fruits\"}, {\"prior\": 3418021, \"train\": 150609, \"total\": 3568630, \"i\": \"fresh vegetables\"}, {\"prior\": 1765313, \"train\": 78493, \"total\": 1843806, \"i\": \"packaged vegetables fruits\"}, {\"prior\": 1452343, \"train\": 55240, \"total\": 1507583, \"i\": \"yogurt\"}, {\"prior\": 979763, \"train\": 41699, \"total\": 1021462, \"i\": \"packaged cheese\"}, {\"prior\": 891015, \"train\": 32644, \"total\": 923659, \"i\": \"milk\"}, {\"prior\": 841533, \"train\": 36617, \"total\": 878150, \"i\": \"water seltzer sparkling water\"}, {\"prior\": 722470, \"train\": 31269, \"total\": 753739, \"i\": \"chips pretzels\"}, {\"prior\": 638253, \"train\": 26240, \"total\": 664493, \"i\": \"soy lactosefree\"}, {\"prior\": 584834, \"train\": 23635, \"total\": 608469, \"i\": \"bread\"}, {\"prior\": 575881, \"train\": 23228, \"total\": 599109, \"i\": \"refrigerated\"}, {\"prior\": 522654, \"train\": 22453, \"total\": 545107, \"i\": \"frozen produce\"}, {\"prior\": 498425, \"train\": 22676, \"total\": 521101, \"i\": \"ice cream ice\"}, {\"prior\": 458838, \"train\": 19592, \"total\": 478430, \"i\": \"crackers\"}, {\"prior\": 456386, \"train\": 17449, \"total\": 473835, \"i\": \"energy granola bars\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = alt.Chart(count, title=\"Count of Order of prior 15 Most Ordered from Aisles\").mark_bar(size = 10).encode(\n",
    "    alt.X('i', title = 'Product Name'),\n",
    "    alt.Y('prior', title = 'Count of Orders')\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 350\n",
    ")\n",
    "n = alt.Chart(count, title=\"Count of Order of train 15 Most Ordered from Aisles\").mark_bar(size = 10).encode(\n",
    "    alt.X('i', title = 'Product Name'),\n",
    "    alt.Y('train', title = 'Count of Orders')\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 350\n",
    ")\n",
    "(m|n).configure_axis(\n",
    "    labelFontSize = 14,\n",
    "    titleFontSize = 14,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 15\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>eval_set</th>\n",
       "      <th>prior</th>\n",
       "      <th>train</th>\n",
       "      <th>total</th>\n",
       "      <th>%</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</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>produce</th>\n",
       "      <td>9479291</td>\n",
       "      <td>409087</td>\n",
       "      <td>9888378</td>\n",
       "      <td>0.292390</td>\n",
       "      <td>produce</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy eggs</th>\n",
       "      <td>5414016</td>\n",
       "      <td>217051</td>\n",
       "      <td>5631067</td>\n",
       "      <td>0.166505</td>\n",
       "      <td>dairy eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snacks</th>\n",
       "      <td>2887550</td>\n",
       "      <td>118862</td>\n",
       "      <td>3006412</td>\n",
       "      <td>0.088897</td>\n",
       "      <td>snacks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beverages</th>\n",
       "      <td>2690129</td>\n",
       "      <td>114046</td>\n",
       "      <td>2804175</td>\n",
       "      <td>0.082917</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frozen</th>\n",
       "      <td>2236432</td>\n",
       "      <td>100426</td>\n",
       "      <td>2336858</td>\n",
       "      <td>0.069099</td>\n",
       "      <td>frozen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "eval_set      prior   train    total         %           i\n",
       "department                                                \n",
       "produce     9479291  409087  9888378  0.292390     produce\n",
       "dairy eggs  5414016  217051  5631067  0.166505  dairy eggs\n",
       "snacks      2887550  118862  3006412  0.088897      snacks\n",
       "beverages   2690129  114046  2804175  0.082917   beverages\n",
       "frozen      2236432  100426  2336858  0.069099      frozen"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ratio of Departments\n",
    "countDepartment = pd.crosstab(data.department, data.eval_set)\n",
    "countDepartment['total'] = countDepartment.prior + countDepartment.train\n",
    "countDepartment['%'] = countDepartment.total / countDepartment.total.sum()\n",
    "count = countDepartment.sort_values(by='total', ascending=False)\n",
    "count['i'] = count.index\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-12\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-12\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 14, \"titleFontSize\": 14}, \"title\": {\"fontSize\": 15}}, \"layer\": [{\"mark\": \"point\", \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Department Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"%\", \"title\": \"Total Percentage\"}}, \"title\": \"Proportion of Items Sold from Each Department\"}, {\"mark\": {\"type\": \"text\", \"align\": \"left\", \"baseline\": \"middle\", \"dy\": 7, \"fontSize\": 12}, \"encoding\": {\"text\": {\"type\": \"nominal\", \"field\": \"i\"}, \"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Department Name\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"%\", \"title\": \"Total Percentage\"}}, \"title\": \"Proportion of Items Sold from Each Department\"}], \"data\": {\"name\": \"data-74397a4054ce6cc75323c13b97e49728\"}, \"height\": 600, \"width\": 600, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-74397a4054ce6cc75323c13b97e49728\": [{\"prior\": 9479291, \"train\": 409087, \"total\": 9888378, \"%\": 0.2923902837644496, \"i\": \"produce\"}, {\"prior\": 5414016, \"train\": 217051, \"total\": 5631067, \"%\": 0.16650549544390678, \"i\": \"dairy eggs\"}, {\"prior\": 2887550, \"train\": 118862, \"total\": 3006412, \"%\": 0.08889685020059371, \"i\": \"snacks\"}, {\"prior\": 2690129, \"train\": 114046, \"total\": 2804175, \"%\": 0.08291688727667727, \"i\": \"beverages\"}, {\"prior\": 2236432, \"train\": 100426, \"total\": 2336858, \"%\": 0.0690987514572384, \"i\": \"frozen\"}, {\"prior\": 1875577, \"train\": 81242, \"total\": 1956819, \"%\": 0.0578613461869749, \"i\": \"pantry\"}, {\"prior\": 1176787, \"train\": 48394, \"total\": 1225181, \"%\": 0.03622748040708113, \"i\": \"bakery\"}, {\"prior\": 1068058, \"train\": 46799, \"total\": 1114857, \"%\": 0.032965300738582504, \"i\": \"canned goods\"}, {\"prior\": 1051249, \"train\": 44291, \"total\": 1095540, \"%\": 0.03239411473502582, \"i\": \"deli\"}, {\"prior\": 866627, \"train\": 38713, \"total\": 905340, \"%\": 0.02677007488015798, \"i\": \"dry goods pasta\"}, {\"prior\": 738666, \"train\": 35986, \"total\": 774652, \"%\": 0.02290575037672492, \"i\": \"household\"}, {\"prior\": 708931, \"train\": 30307, \"total\": 739238, \"%\": 0.0218585908214132, \"i\": \"meat seafood\"}, {\"prior\": 709569, \"train\": 29500, \"total\": 739069, \"%\": 0.02185359364614783, \"i\": \"breakfast\"}, {\"prior\": 447123, \"train\": 21570, \"total\": 468693, \"%\": 0.01385882287958765, \"i\": \"personal care\"}, {\"prior\": 423802, \"train\": 14941, \"total\": 438743, \"%\": 0.012973228801494634, \"i\": \"babies\"}, {\"prior\": 269253, \"train\": 11902, \"total\": 281155, \"%\": 0.008313495927420434, \"i\": \"international\"}, {\"prior\": 153696, \"train\": 5598, \"total\": 159294, \"%\": 0.004710177732078429, \"i\": \"alcohol\"}, {\"prior\": 97724, \"train\": 4497, \"total\": 102221, \"%\": 0.0030225813775207424, \"i\": \"pets\"}, {\"prior\": 69145, \"train\": 8251, \"total\": 77396, \"%\": 0.0022885288570312886, \"i\": \"missing\"}, {\"prior\": 36291, \"train\": 1795, \"total\": 38086, \"%\": 0.001126168148856448, \"i\": \"other\"}, {\"prior\": 34573, \"train\": 1359, \"total\": 35932, \"%\": 0.001062476341036336, \"i\": \"bulk\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = alt.Chart(count, title='Proportion of Items Sold from Each Department').mark_point().encode(\n",
    "    x=alt.X('i', title='Department Name'),\n",
    "    y=alt.Y('%', title='Total Percentage')\n",
    ")\n",
    "t = p.mark_text(\n",
    "    align='left',\n",
    "    baseline='middle',\n",
    "    dy=7,\n",
    "    fontSize = 12\n",
    ").encode(\n",
    "    text='i'\n",
    ")\n",
    "(p+t).configure_axis(\n",
    "    labelFontSize = 14,\n",
    "    titleFontSize = 14,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 15\n",
    ").properties(\n",
    "    height = 600,\n",
    "    width = 600\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reorder ratio by departement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>reordered</th>\n",
       "      <th>i</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>0.571221</td>\n",
       "      <td>alcohol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>0.577680</td>\n",
       "      <td>babies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bakery</th>\n",
       "      <td>0.628381</td>\n",
       "      <td>bakery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beverages</th>\n",
       "      <td>0.653651</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breakfast</th>\n",
       "      <td>0.561351</td>\n",
       "      <td>breakfast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            reordered          i\n",
       "department                      \n",
       "alcohol      0.571221    alcohol\n",
       "babies       0.577680     babies\n",
       "bakery       0.628381     bakery\n",
       "beverages    0.653651  beverages\n",
       "breakfast    0.561351  breakfast"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = pd.DataFrame(data.groupby('department')['reordered'].mean())\n",
    "count['i'] = count.index\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-13\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-13\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-f1892feafdc687241172cf7151fed1cd\"}, \"mark\": {\"type\": \"bar\", \"size\": 25}, \"encoding\": {\"x\": {\"type\": \"nominal\", \"field\": \"i\", \"title\": \"Department\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"reordered\", \"title\": \"Ratio\"}}, \"height\": 350, \"width\": 750, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-f1892feafdc687241172cf7151fed1cd\": [{\"reordered\": 0.5712205105025927, \"i\": \"alcohol\"}, {\"reordered\": 0.5776798718156188, \"i\": \"babies\"}, {\"reordered\": 0.6283806229446914, \"i\": \"bakery\"}, {\"reordered\": 0.6536510738452486, \"i\": \"beverages\"}, {\"reordered\": 0.5613508346311373, \"i\": \"breakfast\"}, {\"reordered\": 0.5770900590003339, \"i\": \"bulk\"}, {\"reordered\": 0.4586390900357624, \"i\": \"canned goods\"}, {\"reordered\": 0.6701612678378716, \"i\": \"dairy eggs\"}, {\"reordered\": 0.6081302371433266, \"i\": \"deli\"}, {\"reordered\": 0.4622197185587735, \"i\": \"dry goods pasta\"}, {\"reordered\": 0.5426337415452714, \"i\": \"frozen\"}, {\"reordered\": 0.40333853136634257, \"i\": \"household\"}, {\"reordered\": 0.3696822037666056, \"i\": \"international\"}, {\"reordered\": 0.5686247189673691, \"i\": \"meat seafood\"}, {\"reordered\": 0.3943227040157114, \"i\": \"missing\"}, {\"reordered\": 0.40705246022160374, \"i\": \"other\"}, {\"reordered\": 0.3474000405760574, \"i\": \"pantry\"}, {\"reordered\": 0.3218631385576486, \"i\": \"personal care\"}, {\"reordered\": 0.6025572044883145, \"i\": \"pets\"}, {\"reordered\": 0.650520843762243, \"i\": \"produce\"}, {\"reordered\": 0.5744638459399444, \"i\": \"snacks\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(count).mark_bar(size = 25).encode(\n",
    "    alt.X('i', title = 'Department'),\n",
    "    alt.Y('reordered', title = 'Ratio')\n",
    ").configure_axis(\n",
    "    labelFontSize = 16,\n",
    "    titleFontSize = 16,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 750\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reorder ratio by the hour of the day or the day of the week?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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_dow</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.586483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.603672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.589720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.586289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.591123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.595902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.575244</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_dow  reordered\n",
       "0          0   0.586483\n",
       "1          1   0.603672\n",
       "2          2   0.589720\n",
       "3          3   0.586289\n",
       "4          4   0.591123\n",
       "5          5   0.595902\n",
       "6          6   0.575244"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = data.groupby(['order_dow'])['reordered'].aggregate('mean').reset_index()\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-14\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-14\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"bar\": {\"color\": \"#9CABFF\"}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-4b7cb8adf8661917ba22050e04d37cf1\"}, \"mark\": {\"type\": \"bar\", \"size\": 25}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"order_dow\", \"title\": \"Day of week\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"reordered\", \"title\": \"Reorder Proportion\"}}, \"height\": 350, \"title\": \"Reorder proportion on Each day of Week\", \"width\": 300, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-4b7cb8adf8661917ba22050e04d37cf1\": [{\"order_dow\": 0, \"reordered\": 0.5864831093966474}, {\"order_dow\": 1, \"reordered\": 0.6036718681079881}, {\"order_dow\": 2, \"reordered\": 0.5897198951205475}, {\"order_dow\": 3, \"reordered\": 0.5862894016703272}, {\"order_dow\": 4, \"reordered\": 0.5911234343200693}, {\"order_dow\": 5, \"reordered\": 0.5959015083519836}, {\"order_dow\": 6, \"reordered\": 0.5752438140761406}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(count, title='Reorder proportion on Each day of Week').mark_bar(size = 25).encode(\n",
    "    alt.X('order_dow', title = 'Day of week'),\n",
    "    alt.Y('reordered', title = 'Reorder Proportion')\n",
    ").configure_axis(\n",
    "    labelFontSize = 16,\n",
    "    titleFontSize = 16,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 300\n",
    ").configure_bar(\n",
    "    color = '#9CABFF'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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_hour_of_day</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.565726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.558149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.555574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.560501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.572370</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_hour_of_day  reordered\n",
       "0                  0   0.565726\n",
       "1                  1   0.558149\n",
       "2                  2   0.555574\n",
       "3                  3   0.560501\n",
       "4                  4   0.572370"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = data.groupby(['order_hour_of_day'])['reordered'].aggregate('mean').reset_index()\n",
    "count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div id=\"altair-viz-15\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  (function(spec, embedOpt){\n",
       "    const outputDiv = document.getElementById(\"altair-viz-15\");\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.0.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function loadScript(lib) {\n",
       "      return new Promise(function(resolve, reject) {\n",
       "        var s = document.createElement('script');\n",
       "        s.src = paths[lib];\n",
       "        s.async = true;\n",
       "        s.onload = () => resolve(paths[lib]);\n",
       "        s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
       "    } else {\n",
       "      loadScript(\"vega\")\n",
       "        .then(() => loadScript(\"vega-lite\"))\n",
       "        .then(() => loadScript(\"vega-embed\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"grid\": false, \"labelFontSize\": 16, \"titleFontSize\": 16}, \"bar\": {\"color\": \"#E2A4FF\"}, \"title\": {\"fontSize\": 24}}, \"data\": {\"name\": \"data-baa53375161c09fba7d57ee08c8d8a64\"}, \"mark\": {\"type\": \"bar\", \"size\": 20}, \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"order_hour_of_day\", \"title\": \"Order of day\"}, \"y\": {\"type\": \"quantitative\", \"field\": \"reordered\", \"title\": \"Reorder Proportion\"}}, \"height\": 350, \"title\": \"Reorder proportion on Each Hour of day\", \"width\": 650, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.0.json\", \"datasets\": {\"data-baa53375161c09fba7d57ee08c8d8a64\": [{\"order_hour_of_day\": 0, \"reordered\": 0.5657257127320409}, {\"order_hour_of_day\": 1, \"reordered\": 0.5581491121141238}, {\"order_hour_of_day\": 2, \"reordered\": 0.5555739058629232}, {\"order_hour_of_day\": 3, \"reordered\": 0.5605014974236872}, {\"order_hour_of_day\": 4, \"reordered\": 0.5723696018953943}, {\"order_hour_of_day\": 5, \"reordered\": 0.6085693457659206}, {\"order_hour_of_day\": 6, \"reordered\": 0.6370497155054486}, {\"order_hour_of_day\": 7, \"reordered\": 0.644669099229832}, {\"order_hour_of_day\": 8, \"reordered\": 0.6323150525440049}, {\"order_hour_of_day\": 9, \"reordered\": 0.6195891975476845}, {\"order_hour_of_day\": 10, \"reordered\": 0.6005412352756004}, {\"order_hour_of_day\": 11, \"reordered\": 0.5876083753607546}, {\"order_hour_of_day\": 12, \"reordered\": 0.5796306007577402}, {\"order_hour_of_day\": 13, \"reordered\": 0.5803058543858399}, {\"order_hour_of_day\": 14, \"reordered\": 0.5817629685391036}, {\"order_hour_of_day\": 15, \"reordered\": 0.5808483452732393}, {\"order_hour_of_day\": 16, \"reordered\": 0.5790519678437283}, {\"order_hour_of_day\": 17, \"reordered\": 0.5755455635244824}, {\"order_hour_of_day\": 18, \"reordered\": 0.5739414212762731}, {\"order_hour_of_day\": 19, \"reordered\": 0.5749474793104914}, {\"order_hour_of_day\": 20, \"reordered\": 0.5828560706826804}, {\"order_hour_of_day\": 21, \"reordered\": 0.5914028559294403}, {\"order_hour_of_day\": 22, \"reordered\": 0.5860406946271673}, {\"order_hour_of_day\": 23, \"reordered\": 0.5800707842272722}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(count, title='Reorder proportion on Each Hour of day').mark_bar(size = 20).encode(\n",
    "    alt.X('order_hour_of_day', title = 'Order of day'),\n",
    "    alt.Y('reordered', title = 'Reorder Proportion')\n",
    ").configure_axis(\n",
    "    labelFontSize = 16,\n",
    "    titleFontSize = 16,\n",
    "    grid = False\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 350,\n",
    "    width = 650\n",
    ").configure_bar(\n",
    "    color = '#E2A4FF'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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_dow</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.576550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.553474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.554843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.568533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.548804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.564884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>0.576726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0.588330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>0.587386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>0.581613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_dow  order_hour_of_day  reordered\n",
       "0            0                  0   0.576550\n",
       "1            0                  1   0.553474\n",
       "2            0                  2   0.554843\n",
       "3            0                  3   0.568533\n",
       "4            0                  4   0.548804\n",
       "..         ...                ...        ...\n",
       "163          6                 19   0.564884\n",
       "164          6                 20   0.576726\n",
       "165          6                 21   0.588330\n",
       "166          6                 22   0.587386\n",
       "167          6                 23   0.581613\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reorderHourDow = data.groupby(['order_dow', 'order_hour_of_day'])['reordered'].aggregate('mean').reset_index()\n",
    "reorderHourDow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alt.Chart(reorderHourDow, title='Reorder Proportion by Day of Week vs Hour of Day').mark_rect().encode(\n",
    "    x = alt.X('order_hour_of_day:O', title='Hour of the Day'),\n",
    "    y = alt.Y('order_dow:O', title='Day of Week'),\n",
    "    color=alt.Color('reordered:Q',scale=alt.Scale(scheme='greenblue'))\n",
    ").configure_axis(\n",
    "    labelFontSize=16,\n",
    "    titleFontSize=16\n",
    ").configure_title(\n",
    "    fontSize = 24\n",
    ").properties(\n",
    "    height = 450,\n",
    "    width = 450\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "It looks as though mornings, especially Sunday mornings, are prime time for reorders. On the other hand, late-night mid-week reorders are the least likely."
   ]
  }
 ],
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