Cheetah DA Take-Home.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "from scipy import stats\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.mode.chained_assignment = None  # default='warn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = pd.read_csv(\"orders.csv\")\n",
    "order_items = pd.read_csv(\"order_items.csv\")\n",
    "promo_codes = pd.read_csv(\"promo_codes.csv\")\n",
    "restaurants = pd.read_csv(\"restaurants.csv\")\n",
    "categories = pd.read_csv(\"categories.csv\")\n",
    "categories_products = pd.read_csv(\"categories_products.csv\")\n",
    "products = pd.read_csv(\"products.csv\")\n",
    "stores = pd.read_csv(\"stores.csv\")\n",
    "invoices = pd.read_csv(\"invoices.csv\")\n",
    "invoice_items = pd.read_csv(\"invoice_items.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Additional Considerations:**\n",
    "    \n",
    "Determining profitability on a per order basis:\n",
    "    Revenue/order-COGS/order($70)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, need to format table according to prompt's constraints:\n",
    "1. orders.order_status = 4 and 8 only\n",
    "2. orders for analysis are to be before 2017-10-01\n",
    "3. all prices are stored in cents and to be represented in dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders = orders[orders['status'].isin([4, 8])]\n",
    "\n",
    "orders = orders[(orders['created_at'] < '2017-10-01')]\n",
    "\n",
    "orders['total'] = orders['total'].div(100)\n",
    "orders['sub_total'] = orders['sub_total'].div(100)\n",
    "orders['cart_total'] = orders['cart_total'].div(100)\n",
    "orders['charged'] = orders['charged'].div(100)\n",
    "orders['credit_card_charge'] = orders['credit_card_charge'].div(100)\n",
    "\n",
    "invoices['total'] = invoices['total'].div(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peek to see if reformatting happened."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>edit_up_to</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>status</th>\n",
       "      <th>total</th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>sub_total</th>\n",
       "      <th>delivery_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>delivery_time_end</th>\n",
       "      <th>source</th>\n",
       "      <th>credit_card_charge</th>\n",
       "      <th>deleted_at</th>\n",
       "      <th>fill_in</th>\n",
       "      <th>has_frozen_item</th>\n",
       "      <th>has_alcohol_item</th>\n",
       "      <th>store_id</th>\n",
       "      <th>manual_fill_in</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>2015-12-28 17:08:47.393476</td>\n",
       "      <td>2017-03-07 16:41:40.138347</td>\n",
       "      <td>2015-12-29 08:00:00</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>8</td>\n",
       "      <td>142.65</td>\n",
       "      <td>54</td>\n",
       "      <td>142.65</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16:41:40.13079</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11861</td>\n",
       "      <td>2017-03-09 09:30:12.024129</td>\n",
       "      <td>2017-03-14 20:44:22.002984</td>\n",
       "      <td>2017-03-13 08:30:00</td>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>8</td>\n",
       "      <td>684.17</td>\n",
       "      <td>403</td>\n",
       "      <td>692.50</td>\n",
       "      <td>7000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9696</td>\n",
       "      <td>2017-01-25 09:32:09.913808</td>\n",
       "      <td>2017-08-22 20:42:35.288515</td>\n",
       "      <td>2017-03-23 08:30:00</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>8</td>\n",
       "      <td>533.72</td>\n",
       "      <td>304</td>\n",
       "      <td>520.68</td>\n",
       "      <td>4500</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20:42:35.255945</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>26219</td>\n",
       "      <td>2017-08-24 08:37:33.23127</td>\n",
       "      <td>2017-11-29 20:07:46.71471</td>\n",
       "      <td>2017-11-29 09:30:00</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>4</td>\n",
       "      <td>547.38</td>\n",
       "      <td>439</td>\n",
       "      <td>547.38</td>\n",
       "      <td>5000</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>22.327701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>245</td>\n",
       "      <td>2016-02-01 19:44:19.954347</td>\n",
       "      <td>2016-07-25 14:35:05.767401</td>\n",
       "      <td>2016-02-09 08:00:00</td>\n",
       "      <td>2016-02-09</td>\n",
       "      <td>4</td>\n",
       "      <td>577.59</td>\n",
       "      <td>104</td>\n",
       "      <td>577.59</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14:35:05.764617</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
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      "text/plain": [
       "       id                  created_at                  updated_at  \\\n",
       "1     101  2015-12-28 17:08:47.393476  2017-03-07 16:41:40.138347   \n",
       "4   11861  2017-03-09 09:30:12.024129  2017-03-14 20:44:22.002984   \n",
       "6    9696  2017-01-25 09:32:09.913808  2017-08-22 20:42:35.288515   \n",
       "31  26219   2017-08-24 08:37:33.23127   2017-11-29 20:07:46.71471   \n",
       "32    245  2016-02-01 19:44:19.954347  2016-07-25 14:35:05.767401   \n",
       "\n",
       "             edit_up_to delivery_date  status   total  restaurant_id  \\\n",
       "1   2015-12-29 08:00:00    2015-12-29       8  142.65             54   \n",
       "4   2017-03-13 08:30:00    2017-03-13       8  684.17            403   \n",
       "6   2017-03-23 08:30:00    2017-03-23       8  533.72            304   \n",
       "31  2017-11-29 09:30:00    2017-11-29       4  547.38            439   \n",
       "32  2016-02-09 08:00:00    2016-02-09       4  577.59            104   \n",
       "\n",
       "    sub_total  delivery_fee  ... delivery_time_end  source  \\\n",
       "1      142.65          7500  ...                17     NaN   \n",
       "4      692.50          7000  ...                18     0.0   \n",
       "6      520.68          4500  ...                18     0.0   \n",
       "31     547.38          5000  ...                18     0.0   \n",
       "32     577.59          7500  ...                17     NaN   \n",
       "\n",
       "    credit_card_charge       deleted_at  fill_in  has_frozen_item  \\\n",
       "1                  0.0   16:41:40.13079    False            False   \n",
       "4                  0.0              NaN    False            False   \n",
       "6                  0.0  20:42:35.255945    False             True   \n",
       "31                 0.0              NaN    False            False   \n",
       "32                 0.0  14:35:05.764617    False            False   \n",
       "\n",
       "    has_alcohol_item  store_id manual_fill_in     volume  \n",
       "1              False       NaN          False   0.000000  \n",
       "4              False       1.0          False   0.000000  \n",
       "6              False       1.0          False  10.000000  \n",
       "31             False       4.0          False  22.327701  \n",
       "32             False       NaN          False   0.000000  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>status</th>\n",
       "      <th>signed_at</th>\n",
       "      <th>paid_at</th>\n",
       "      <th>payment_method</th>\n",
       "      <th>total</th>\n",
       "      <th>to_pay_to_vendor</th>\n",
       "      <th>...</th>\n",
       "      <th>paid_to_cheetah</th>\n",
       "      <th>paid_to_vendor</th>\n",
       "      <th>deleted_at</th>\n",
       "      <th>charging_platform</th>\n",
       "      <th>verified</th>\n",
       "      <th>updated_by_id</th>\n",
       "      <th>charged_at</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2016-07-28 11:57:35.306967</td>\n",
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       "      <td>1</td>\n",
       "      <td>1605</td>\n",
       "      <td>3452</td>\n",
       "      <td>2016-07-28 11:57:35.428423</td>\n",
       "      <td>2016-07-28 11:57:35.428423</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2</td>\n",
       "      <td>1606</td>\n",
       "      <td>3451</td>\n",
       "      <td>2016-07-28 11:57:35.624098</td>\n",
       "      <td>2016-07-28 11:57:35.624098</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1607</td>\n",
       "      <td>3450</td>\n",
       "      <td>2016-07-28 11:57:35.897542</td>\n",
       "      <td>2016-07-28 11:57:35.897542</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "      <td>4</td>\n",
       "      <td>1608</td>\n",
       "      <td>3449</td>\n",
       "      <td>2016-07-28 11:57:36.104266</td>\n",
       "      <td>2016-07-28 11:57:36.104266</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "     id  order_id                  created_at                  updated_at  \\\n",
       "0  1604      3453  2016-07-28 11:57:35.306967  2016-07-28 11:57:35.306967   \n",
       "1  1605      3452  2016-07-28 11:57:35.428423  2016-07-28 11:57:35.428423   \n",
       "2  1606      3451  2016-07-28 11:57:35.624098  2016-07-28 11:57:35.624098   \n",
       "3  1607      3450  2016-07-28 11:57:35.897542  2016-07-28 11:57:35.897542   \n",
       "4  1608      3449  2016-07-28 11:57:36.104266  2016-07-28 11:57:36.104266   \n",
       "\n",
       "   status signed_at paid_at  payment_method  total  to_pay_to_vendor  ...  \\\n",
       "0       0       NaN     NaN             NaN    0.0               NaN  ...   \n",
       "1       0       NaN     NaN             NaN    0.0               NaN  ...   \n",
       "2       0       NaN     NaN             NaN    0.0               NaN  ...   \n",
       "3       0       NaN     NaN             NaN    0.0               NaN  ...   \n",
       "4       0       NaN     NaN             NaN    0.0               NaN  ...   \n",
       "\n",
       "   paid_to_cheetah  paid_to_vendor  deleted_at charging_platform  verified  \\\n",
       "0              0.0             0.0         NaN               NaN       NaN   \n",
       "1              0.0             0.0         NaN               NaN       NaN   \n",
       "2              0.0             0.0         NaN               NaN       NaN   \n",
       "3              0.0             0.0         NaN               NaN       NaN   \n",
       "4              0.0             0.0         NaN               NaN       NaN   \n",
       "\n",
       "  updated_by_id  charged_at failed_at charged_payment_method_id  \\\n",
       "0           NaN         NaN       NaN                       NaN   \n",
       "1           NaN         NaN       NaN                       NaN   \n",
       "2           NaN         NaN       NaN                       NaN   \n",
       "3           NaN         NaN       NaN                       NaN   \n",
       "4           NaN         NaN       NaN                       NaN   \n",
       "\n",
       "   disable_direct_payments  \n",
       "0                    False  \n",
       "1                    False  \n",
       "2                    False  \n",
       "3                    False  \n",
       "4                    False  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "invoices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks good!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Back to considering profitability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders[\"profit\"] = orders.charged-70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>edit_up_to</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>status</th>\n",
       "      <th>total</th>\n",
       "      <th>restaurant_id</th>\n",
       "      <th>sub_total</th>\n",
       "      <th>delivery_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>credit_card_charge</th>\n",
       "      <th>deleted_at</th>\n",
       "      <th>fill_in</th>\n",
       "      <th>has_frozen_item</th>\n",
       "      <th>has_alcohol_item</th>\n",
       "      <th>store_id</th>\n",
       "      <th>manual_fill_in</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>101</td>\n",
       "      <td>2015-12-28 17:08:47.393476</td>\n",
       "      <td>2017-03-07 16:41:40.138347</td>\n",
       "      <td>2015-12-29 08:00:00</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>8</td>\n",
       "      <td>142.65</td>\n",
       "      <td>54</td>\n",
       "      <td>142.65</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16:41:40.13079</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11861</td>\n",
       "      <td>2017-03-09 09:30:12.024129</td>\n",
       "      <td>2017-03-14 20:44:22.002984</td>\n",
       "      <td>2017-03-13 08:30:00</td>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>8</td>\n",
       "      <td>684.17</td>\n",
       "      <td>403</td>\n",
       "      <td>692.50</td>\n",
       "      <td>7000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>614.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>9696</td>\n",
       "      <td>2017-01-25 09:32:09.913808</td>\n",
       "      <td>2017-08-22 20:42:35.288515</td>\n",
       "      <td>2017-03-23 08:30:00</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>8</td>\n",
       "      <td>533.72</td>\n",
       "      <td>304</td>\n",
       "      <td>520.68</td>\n",
       "      <td>4500</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20:42:35.255945</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>463.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>26219</td>\n",
       "      <td>2017-08-24 08:37:33.23127</td>\n",
       "      <td>2017-11-29 20:07:46.71471</td>\n",
       "      <td>2017-11-29 09:30:00</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>4</td>\n",
       "      <td>547.38</td>\n",
       "      <td>439</td>\n",
       "      <td>547.38</td>\n",
       "      <td>5000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>22.327701</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>245</td>\n",
       "      <td>2016-02-01 19:44:19.954347</td>\n",
       "      <td>2016-07-25 14:35:05.767401</td>\n",
       "      <td>2016-02-09 08:00:00</td>\n",
       "      <td>2016-02-09</td>\n",
       "      <td>4</td>\n",
       "      <td>577.59</td>\n",
       "      <td>104</td>\n",
       "      <td>577.59</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14:35:05.764617</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>463.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                  created_at                  updated_at  \\\n",
       "1     101  2015-12-28 17:08:47.393476  2017-03-07 16:41:40.138347   \n",
       "4   11861  2017-03-09 09:30:12.024129  2017-03-14 20:44:22.002984   \n",
       "6    9696  2017-01-25 09:32:09.913808  2017-08-22 20:42:35.288515   \n",
       "31  26219   2017-08-24 08:37:33.23127   2017-11-29 20:07:46.71471   \n",
       "32    245  2016-02-01 19:44:19.954347  2016-07-25 14:35:05.767401   \n",
       "\n",
       "             edit_up_to delivery_date  status   total  restaurant_id  \\\n",
       "1   2015-12-29 08:00:00    2015-12-29       8  142.65             54   \n",
       "4   2017-03-13 08:30:00    2017-03-13       8  684.17            403   \n",
       "6   2017-03-23 08:30:00    2017-03-23       8  533.72            304   \n",
       "31  2017-11-29 09:30:00    2017-11-29       4  547.38            439   \n",
       "32  2016-02-09 08:00:00    2016-02-09       4  577.59            104   \n",
       "\n",
       "    sub_total  delivery_fee  ... source  credit_card_charge       deleted_at  \\\n",
       "1      142.65          7500  ...    NaN                 0.0   16:41:40.13079   \n",
       "4      692.50          7000  ...    0.0                 0.0              NaN   \n",
       "6      520.68          4500  ...    0.0                 0.0  20:42:35.255945   \n",
       "31     547.38          5000  ...    0.0                 0.0              NaN   \n",
       "32     577.59          7500  ...    NaN                 0.0  14:35:05.764617   \n",
       "\n",
       "    fill_in  has_frozen_item  has_alcohol_item  store_id  manual_fill_in  \\\n",
       "1     False            False             False       NaN           False   \n",
       "4     False            False             False       1.0           False   \n",
       "6     False             True             False       1.0           False   \n",
       "31    False            False             False       4.0           False   \n",
       "32    False            False             False       NaN           False   \n",
       "\n",
       "       volume  profit  \n",
       "1    0.000000     NaN  \n",
       "4    0.000000  614.17  \n",
       "6   10.000000  463.72  \n",
       "31  22.327701     NaN  \n",
       "32   0.000000  463.48  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just from a sneak preview of the profit column, we see that there was not profit made all the time. Let us see the overall picture by seeing the trend over time along with comparing it with revenue across time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before trending, let us take a look at how many gaps we have in the data since profit column has some NaN's:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "created_at                 0\n",
       "updated_at                 0\n",
       "edit_up_to               545\n",
       "delivery_date             41\n",
       "status                     0\n",
       "total                      0\n",
       "restaurant_id              0\n",
       "sub_total                  0\n",
       "delivery_fee               0\n",
       "last_time_modified         0\n",
       "cart_total                 0\n",
       "same_day_charge            0\n",
       "error_code                 0\n",
       "taxes                      0\n",
       "billed                     0\n",
       "last_job_id              549\n",
       "manual                     0\n",
       "uuid                       0\n",
       "charged                  170\n",
       "delivery_time_start        0\n",
       "delivery_time_end          0\n",
       "source                   331\n",
       "credit_card_charge         0\n",
       "deleted_at             28078\n",
       "fill_in                    0\n",
       "has_frozen_item            0\n",
       "has_alcohol_item           0\n",
       "store_id                  32\n",
       "manual_fill_in             0\n",
       "volume                     0\n",
       "profit                   170\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28165, 32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 170 NaN's in charged column (and profit column) out of 28165 rows of remaining data. That amounts to about 0.6% of the dataset, not even 1% of the dataset. The data gap is not a problem at this point then."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders.created_at = pd.to_datetime(orders.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders2 = orders[['created_at','charged', 'profit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x146848e9c48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders2.set_index('created_at').plot(figsize=(10,5), grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without charged graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x146b8597648>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "orders2.plot(x='created_at', y='profit', figsize=(10,5), grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get granularity of data points, getting most popular values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-70.00      84\n",
       " 309.80      5\n",
       "-42.91       5\n",
       " 64.29       4\n",
       "-60.00       4\n",
       "            ..\n",
       " 1134.26     1\n",
       " 10.29       1\n",
       " 2237.98     1\n",
       "-57.14       1\n",
       "-0.25        1\n",
       "Name: profit, Length: 25881, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders2.profit.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see here that profit was not always in the positive. The most occurring value was actually -70 dollars. So most charges were 0 dollars. This is something the business can work on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us look at how orders are broken up by customer types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_prom = pd.merge(orders, promo_codes, left_on='id', right_on= 'order_id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonthlySubscription    722\n",
       "FirstDeliveryPromo     176\n",
       "PercentageDiscount      16\n",
       "LapsedCustomerPromo      1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom.type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_types = orders_prom.type.value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cust_types.index, cust_types.values, alpha=0.8, color=color[1])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Customer type', fontsize=12)\n",
    "plt.title('Count of orders from each type of customer', fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NaN's are not included. Need to include them in order to represent the rest of the a la carte customers. NaN's need to be recoded as 'no_promo'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_prom['type'].fillna('no_promo', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_promo               27250\n",
       "MonthlySubscription      722\n",
       "FirstDeliveryPromo       176\n",
       "PercentageDiscount        16\n",
       "LapsedCustomerPromo        1\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom.type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cust_types = orders_prom.type.value_counts()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(cust_types.index, cust_types.values, alpha=0.8, color=color[1])\n",
    "plt.ylabel('Number of Occurrences', fontsize=12)\n",
    "plt.xlabel('Customer type', fontsize=12)\n",
    "plt.title('Count of orders from each type of customer', fontsize=15)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No_promo customers/a la carte customers and then Monthly Subscribers have the highest order volumes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's get a visualization of customer types' dollar spent per order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "FirstDeliveryPromo      778.360000\n",
       "LapsedCustomerPromo    1059.150000\n",
       "MonthlySubscription     836.657064\n",
       "PercentageDiscount      743.980000\n",
       "no_promo                809.572466\n",
       "Name: cart_total, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = orders_prom.groupby('type')['cart_total'].mean()\n",
    "df"
   ]
  },
  {
   "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>type</th>\n",
       "      <th>average_dollar__per_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>FirstDeliveryPromo</td>\n",
       "      <td>778.360000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>LapsedCustomerPromo</td>\n",
       "      <td>1059.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>MonthlySubscription</td>\n",
       "      <td>836.657064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>PercentageDiscount</td>\n",
       "      <td>743.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>no_promo</td>\n",
       "      <td>809.572466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  type  average_dollar__per_order\n",
       "0   FirstDeliveryPromo                 778.360000\n",
       "1  LapsedCustomerPromo                1059.150000\n",
       "2  MonthlySubscription                 836.657064\n",
       "3   PercentageDiscount                 743.980000\n",
       "4             no_promo                 809.572466"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'type': ['FirstDeliveryPromo', 'LapsedCustomerPromo','MonthlySubscription','PercentageDiscount','no_promo'], 'average_dollar__per_order': [778.360000, 1059.150000, 836.657064, 743.980000, 809.572466]}\n",
    "df = pd.DataFrame(data=d)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Dollar Per Order by Customer Type')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({'type':['FirstDeliveryPromo', 'LapsedCustomerPromo','MonthlySubscription','PercentageDiscount','no_promo'], 'av_dollar_per_order':[778.360000, 1059.150000, 836.657064, 743.980000, 809.572466]})\n",
    "ax = df.plot.bar(x='type', y='av_dollar_per_order', rot=0, figsize=(10,5))\n",
    "plt.title(\"Average Dollar Per Order by Customer Type\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The customer who seems to not reorder on time is spending the most per order. Looks like that LapsedCustomerPromo is working out well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get a visualization of customer types' revenue per order to see how Cheetah is benefitting from these promotional offers/ customer types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type\n",
       "FirstDeliveryPromo      769.601193\n",
       "LapsedCustomerPromo            NaN\n",
       "MonthlySubscription    1166.043670\n",
       "PercentageDiscount      763.562000\n",
       "no_promo                861.854900\n",
       "Name: charged, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = orders_prom.groupby('type')['charged'].mean()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Revenue Per Order by Customer Type')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'type':['FirstDeliveryPromo', 'LapsedCustomerPromo','MonthlySubscription','PercentageDiscount','no_promo'], 'av_rev_per_order':[769.601193, 0, 1166.043670,  763.562000, 861.854900]})\n",
    "ax = df2.plot.bar(x='type', y='av_rev_per_order', rot=0, figsize=(10,5))\n",
    "plt.title(\"Average Revenue Per Order by Customer Type\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monthly Subscribers are helping the business a lot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us see which region/market is dominating in orders assuming that each order is being fulfilled by a local Restaurant Depot/Sam's Club."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_prom_stores = pd.merge(orders_prom, stores, left_on='store_id', right_on= 'id', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_x</th>\n",
       "      <th>created_at_x</th>\n",
       "      <th>updated_at_x</th>\n",
       "      <th>edit_up_to</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>status</th>\n",
       "      <th>total</th>\n",
       "      <th>restaurant_id_x</th>\n",
       "      <th>sub_total</th>\n",
       "      <th>delivery_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>restaurant_id_y</th>\n",
       "      <th>expiration_date</th>\n",
       "      <th>price</th>\n",
       "      <th>renewed</th>\n",
       "      <th>period</th>\n",
       "      <th>renew_to_default</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>created_at</th>\n",
       "      <th>updated_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>101</td>\n",
       "      <td>2015-12-28 17:08:47.393476</td>\n",
       "      <td>2017-03-07 16:41:40.138347</td>\n",
       "      <td>2015-12-29 08:00:00</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>8</td>\n",
       "      <td>142.65</td>\n",
       "      <td>54</td>\n",
       "      <td>142.65</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>11861</td>\n",
       "      <td>2017-03-09 09:30:12.024129</td>\n",
       "      <td>2017-03-14 20:44:22.002984</td>\n",
       "      <td>2017-03-13 08:30:00</td>\n",
       "      <td>2017-03-13</td>\n",
       "      <td>8</td>\n",
       "      <td>684.17</td>\n",
       "      <td>403</td>\n",
       "      <td>692.50</td>\n",
       "      <td>7000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RD San Francisco</td>\n",
       "      <td>2015-08-17 23:46:24.891441</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>9696</td>\n",
       "      <td>2017-01-25 09:32:09.913808</td>\n",
       "      <td>2017-08-22 20:42:35.288515</td>\n",
       "      <td>2017-03-23 08:30:00</td>\n",
       "      <td>2017-03-23</td>\n",
       "      <td>8</td>\n",
       "      <td>533.72</td>\n",
       "      <td>304</td>\n",
       "      <td>520.68</td>\n",
       "      <td>4500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>RD San Francisco</td>\n",
       "      <td>2015-08-17 23:46:24.891441</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>26219</td>\n",
       "      <td>2017-08-24 08:37:33.231270</td>\n",
       "      <td>2017-11-29 20:07:46.71471</td>\n",
       "      <td>2017-11-29 09:30:00</td>\n",
       "      <td>2017-11-29</td>\n",
       "      <td>4</td>\n",
       "      <td>547.38</td>\n",
       "      <td>439</td>\n",
       "      <td>547.38</td>\n",
       "      <td>5000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RD Oakland</td>\n",
       "      <td>2017-09-20 04:23:23.829306</td>\n",
       "      <td>2017-11-29 13:14:35.884335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>245</td>\n",
       "      <td>2016-02-01 19:44:19.954347</td>\n",
       "      <td>2016-07-25 14:35:05.767401</td>\n",
       "      <td>2016-02-09 08:00:00</td>\n",
       "      <td>2016-02-09</td>\n",
       "      <td>4</td>\n",
       "      <td>577.59</td>\n",
       "      <td>104</td>\n",
       "      <td>577.59</td>\n",
       "      <td>7500</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id_x               created_at_x                updated_at_x  \\\n",
       "0    101 2015-12-28 17:08:47.393476  2017-03-07 16:41:40.138347   \n",
       "1  11861 2017-03-09 09:30:12.024129  2017-03-14 20:44:22.002984   \n",
       "2   9696 2017-01-25 09:32:09.913808  2017-08-22 20:42:35.288515   \n",
       "3  26219 2017-08-24 08:37:33.231270   2017-11-29 20:07:46.71471   \n",
       "4    245 2016-02-01 19:44:19.954347  2016-07-25 14:35:05.767401   \n",
       "\n",
       "            edit_up_to delivery_date  status   total  restaurant_id_x  \\\n",
       "0  2015-12-29 08:00:00    2015-12-29       8  142.65               54   \n",
       "1  2017-03-13 08:30:00    2017-03-13       8  684.17              403   \n",
       "2  2017-03-23 08:30:00    2017-03-23       8  533.72              304   \n",
       "3  2017-11-29 09:30:00    2017-11-29       4  547.38              439   \n",
       "4  2016-02-09 08:00:00    2016-02-09       4  577.59              104   \n",
       "\n",
       "   sub_total  delivery_fee  ... restaurant_id_y  expiration_date  price  \\\n",
       "0     142.65          7500  ...             NaN              NaN    NaN   \n",
       "1     692.50          7000  ...             NaN              NaN    NaN   \n",
       "2     520.68          4500  ...             NaN              NaN    NaN   \n",
       "3     547.38          5000  ...             NaN              NaN    NaN   \n",
       "4     577.59          7500  ...             NaN              NaN    NaN   \n",
       "\n",
       "   renewed  period  renew_to_default   id              name  \\\n",
       "0      NaN     NaN               NaN  NaN               NaN   \n",
       "1      NaN     NaN               NaN  1.0  RD San Francisco   \n",
       "2      NaN     NaN               NaN  1.0  RD San Francisco   \n",
       "3      NaN     NaN               NaN  4.0        RD Oakland   \n",
       "4      NaN     NaN               NaN  NaN               NaN   \n",
       "\n",
       "                   created_at                  updated_at  \n",
       "0                         NaN                         NaN  \n",
       "1  2015-08-17 23:46:24.891441  2017-11-29 13:14:02.788332  \n",
       "2  2015-08-17 23:46:24.891441  2017-11-29 13:14:02.788332  \n",
       "3  2017-09-20 04:23:23.829306  2017-11-29 13:14:35.884335  \n",
       "4                         NaN                         NaN  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom_stores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RD San Francisco      24132\n",
       "RD Van Nuys            3172\n",
       "RD Oakland              781\n",
       "Sam's Club Concord       48\n",
       "Name: name, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom_stores.name.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us look at suppliers relative to each other to get an idea where most of the fulfillments are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "temp_series = orders_prom_stores['name'].value_counts()\n",
    "labels = (np.array(temp_series.index))\n",
    "sizes = (np.array((temp_series / temp_series.sum())*100))\n",
    "plt.pie(sizes, labels=labels, \n",
    "        autopct='%1.1f%%', startangle=200)\n",
    "plt.title(\"Stores Distribution\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RD San Francisco reigns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us get a closer look at what sort of products are Monthly Subscribers ordering the most since they are the cause for most of the business' revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's subset the last merged dataset into a smaller DF with only 'MonthlySubscription' type customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub_df = orders_prom_stores.loc[(orders_prom_stores['type'] == 'MonthlySubscription')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_prom_stores_prod = pd.merge(sub_df, products, left_on='id', right_on= 'store_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peak at the merge result to see if product info made it in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_x</th>\n",
       "      <th>created_at_x</th>\n",
       "      <th>updated_at_x</th>\n",
       "      <th>edit_up_to</th>\n",
       "      <th>delivery_date</th>\n",
       "      <th>status</th>\n",
       "      <th>total</th>\n",
       "      <th>restaurant_id_x</th>\n",
       "      <th>sub_total</th>\n",
       "      <th>delivery_fee</th>\n",
       "      <th>...</th>\n",
       "      <th>updated_at</th>\n",
       "      <th>id_y</th>\n",
       "      <th>name_y</th>\n",
       "      <th>unit</th>\n",
       "      <th>pack</th>\n",
       "      <th>unit_orderable</th>\n",
       "      <th>unit_per_pack</th>\n",
       "      <th>store_id_y</th>\n",
       "      <th>items_per_unit</th>\n",
       "      <th>units_per_case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>3855</td>\n",
       "      <td>2016-08-12 08:30:48.215398</td>\n",
       "      <td>2017-01-05 18:10:50.673588</td>\n",
       "      <td>2017-01-04 09:30:00</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>8</td>\n",
       "      <td>698.84</td>\n",
       "      <td>69</td>\n",
       "      <td>459.52</td>\n",
       "      <td>30000</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "      <td>12391</td>\n",
       "      <td>Jack Cross Drk Rum 750ml - 12/CS</td>\n",
       "      <td>EA</td>\n",
       "      <td>CS</td>\n",
       "      <td>True</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>3855</td>\n",
       "      <td>2016-08-12 08:30:48.215398</td>\n",
       "      <td>2017-01-05 18:10:50.673588</td>\n",
       "      <td>2017-01-04 09:30:00</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>8</td>\n",
       "      <td>698.84</td>\n",
       "      <td>69</td>\n",
       "      <td>459.52</td>\n",
       "      <td>30000</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "      <td>12523</td>\n",
       "      <td>Exec Red Chef Coat Sm</td>\n",
       "      <td>EA</td>\n",
       "      <td>CS</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3855</td>\n",
       "      <td>2016-08-12 08:30:48.215398</td>\n",
       "      <td>2017-01-05 18:10:50.673588</td>\n",
       "      <td>2017-01-04 09:30:00</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>8</td>\n",
       "      <td>698.84</td>\n",
       "      <td>69</td>\n",
       "      <td>459.52</td>\n",
       "      <td>30000</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "      <td>8707</td>\n",
       "      <td>Libbey Rocks Glasses 9oz (36 CT)</td>\n",
       "      <td>EA</td>\n",
       "      <td>CS</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3855</td>\n",
       "      <td>2016-08-12 08:30:48.215398</td>\n",
       "      <td>2017-01-05 18:10:50.673588</td>\n",
       "      <td>2017-01-04 09:30:00</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>8</td>\n",
       "      <td>698.84</td>\n",
       "      <td>69</td>\n",
       "      <td>459.52</td>\n",
       "      <td>30000</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "      <td>1951</td>\n",
       "      <td>Live Blu Claw Crab R/W</td>\n",
       "      <td>EA</td>\n",
       "      <td>CS</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>3855</td>\n",
       "      <td>2016-08-12 08:30:48.215398</td>\n",
       "      <td>2017-01-05 18:10:50.673588</td>\n",
       "      <td>2017-01-04 09:30:00</td>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>8</td>\n",
       "      <td>698.84</td>\n",
       "      <td>69</td>\n",
       "      <td>459.52</td>\n",
       "      <td>30000</td>\n",
       "      <td>...</td>\n",
       "      <td>2017-11-29 13:14:02.788332</td>\n",
       "      <td>23559</td>\n",
       "      <td>Zzwndsr Sld Fork</td>\n",
       "      <td>EA</td>\n",
       "      <td>CS</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_x               created_at_x                updated_at_x  \\\n",
       "0  3855 2016-08-12 08:30:48.215398  2017-01-05 18:10:50.673588   \n",
       "1  3855 2016-08-12 08:30:48.215398  2017-01-05 18:10:50.673588   \n",
       "2  3855 2016-08-12 08:30:48.215398  2017-01-05 18:10:50.673588   \n",
       "3  3855 2016-08-12 08:30:48.215398  2017-01-05 18:10:50.673588   \n",
       "4  3855 2016-08-12 08:30:48.215398  2017-01-05 18:10:50.673588   \n",
       "\n",
       "            edit_up_to delivery_date  status   total  restaurant_id_x  \\\n",
       "0  2017-01-04 09:30:00    2017-01-04       8  698.84               69   \n",
       "1  2017-01-04 09:30:00    2017-01-04       8  698.84               69   \n",
       "2  2017-01-04 09:30:00    2017-01-04       8  698.84               69   \n",
       "3  2017-01-04 09:30:00    2017-01-04       8  698.84               69   \n",
       "4  2017-01-04 09:30:00    2017-01-04       8  698.84               69   \n",
       "\n",
       "   sub_total  delivery_fee  ...                  updated_at   id_y  \\\n",
       "0     459.52         30000  ...  2017-11-29 13:14:02.788332  12391   \n",
       "1     459.52         30000  ...  2017-11-29 13:14:02.788332  12523   \n",
       "2     459.52         30000  ...  2017-11-29 13:14:02.788332   8707   \n",
       "3     459.52         30000  ...  2017-11-29 13:14:02.788332   1951   \n",
       "4     459.52         30000  ...  2017-11-29 13:14:02.788332  23559   \n",
       "\n",
       "                             name_y  unit  pack  unit_orderable  \\\n",
       "0  Jack Cross Drk Rum 750ml - 12/CS    EA    CS            True   \n",
       "1             Exec Red Chef Coat Sm    EA    CS            True   \n",
       "2  Libbey Rocks Glasses 9oz (36 CT)    EA    CS            True   \n",
       "3            Live Blu Claw Crab R/W    EA    CS            True   \n",
       "4                  Zzwndsr Sld Fork    EA    CS            True   \n",
       "\n",
       "   unit_per_pack  store_id_y items_per_unit  units_per_case  \n",
       "0             12           1            1.0            12.0  \n",
       "1              1           1            1.0             0.0  \n",
       "2              1           1           36.0             0.0  \n",
       "3              1           1            NaN             NaN  \n",
       "4              1           1            1.0             0.0  \n",
       "\n",
       "[5 rows x 59 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom_stores_prod.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes! Name_y made it in. Now let us check to see that only MonthlySubcription customers are in the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MonthlySubscription    9161556\n",
       "Name: type, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders_prom_stores_prod.type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_name</th>\n",
       "      <th>frequency_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Do Not Use</td>\n",
       "      <td>2378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Plate Fm 10\" Wht (500 CT)</td>\n",
       "      <td>1630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Winco Stainless Steel Flat Solid Cover for Six...</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Alegacy Optima III Natural Finish Fry Pan 7 inch</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Winco Stainless Steel Perforated Steampan Half...</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Sugar Gran Ex Fine</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Strainer Double Mesh 10.25\"</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Pd Flwr Xmas Tree</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Kumamoto Oysters (60CT)</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Coffeemate 1.58qt Original W/Pump Coffee-MateÂ...</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Chef's Quality Ground Oregano 3 LB</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Fresh Shucked Pacific Northwest Oysters 1/2GAL...</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Coat Chef Cool Vent White Small</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Pd Root Rutabaga</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>Frfsh Halibut H&amp;G</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Cont Sqr Blk 48z (100 CT)</td>\n",
       "      <td>1398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Pd Caul Green 12ct</td>\n",
       "      <td>1398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Xx-Iceu220ha Ice Maker</td>\n",
       "      <td>1392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>Pd Proc Onion Red - 4/CS</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>Pd Pineapple 7ct</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         product_name  frequency_count\n",
       "0                                          Do Not Use             2378\n",
       "1                           Plate Fm 10\" Wht (500 CT)             1630\n",
       "2   Winco Stainless Steel Flat Solid Cover for Six...             1444\n",
       "3    Alegacy Optima III Natural Finish Fry Pan 7 inch             1444\n",
       "4   Winco Stainless Steel Perforated Steampan Half...             1444\n",
       "5                                  Sugar Gran Ex Fine             1444\n",
       "6                         Strainer Double Mesh 10.25\"             1444\n",
       "7                                   Pd Flwr Xmas Tree             1444\n",
       "8                             Kumamoto Oysters (60CT)             1444\n",
       "9   Coffeemate 1.58qt Original W/Pump Coffee-MateÂ...             1444\n",
       "10                 Chef's Quality Ground Oregano 3 LB             1444\n",
       "11  Fresh Shucked Pacific Northwest Oysters 1/2GAL...             1444\n",
       "12                    Coat Chef Cool Vent White Small             1444\n",
       "13                                   Pd Root Rutabaga             1444\n",
       "14                                  Frfsh Halibut H&G             1444\n",
       "15                          Cont Sqr Blk 48z (100 CT)             1398\n",
       "16                                 Pd Caul Green 12ct             1398\n",
       "17                             Xx-Iceu220ha Ice Maker             1392\n",
       "18                           Pd Proc Onion Red - 4/CS             1212\n",
       "19                                   Pd Pineapple 7ct             1212"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt_srs = orders_prom_stores_prod['name_y'].value_counts().reset_index().head(20)\n",
    "cnt_srs.columns = ['product_name', 'frequency_count']\n",
    "cnt_srs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data table had Do Not Use label frequently. So this was not the most bought, but styrofoam plates along with some steel flat covers and frying pans were the most popular amongst Monthly Subscribers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion:\n",
    "    \n",
    "    Though Monthly Subscribers only make up a small percentage of the business, they are making up a huge portion of the company's revenue. This is in line with the 80%, 20% rule where most business' revenue comes from a small percentage of the customer base. So after looking at customer behavior across all customer groups, in order to optimize revenue across all different types of customers, it would be a good idea to invest in monthly subscriptions, lapsed customer promotions as well as invest in these customers purchase habits--such as the types of products they order. Also, if time permitted, there could have also been a look at when these different customers make their orders as well as the sequence in which the products they order are placed in their checkout cart to see which products are on their priority list. These are some of the things to keep in mind when looking at ordering behavior in an attempt to optimize revenue across all customer groups."
   ]
  }
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