12_2023_Instacart_Basket_Analysis/03_Scripts/Step6_IC_Derivations_and_Aggregations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7673b8d",
   "metadata": {},
   "source": [
    "# Project: 12-2023 Instacart Basket Analysis\n",
    "## Author: Nadia Ordonez\n",
    "## Step 6 IC Derivations and Aggregations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcdb603",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [1. Introduction](#1.-Introduction)\n",
    "* [2. Importing data](#2.-Importing-data)\n",
    "    * [2.1 Importing libraries](#2.1-Importing-libraries)\n",
    "    * [2.2 Importing data](#2.2-Importing-data)\n",
    "* [3. Price range products](#3.-Price-range-products)\n",
    "* [4. Busiest days](#4.-Busiest-days)\n",
    "* [5. Busiest period of day](#5.-Busiest-period-of-day)\n",
    "* [6. Region](#6.-Region)\n",
    "* [7. Loyalty flag](#7.-Loyalty-flag)\n",
    "* [8. Type of spender](#8.-Type-of-spender)\n",
    "* [9. Usage frequency](#9.-Usage-frequency)\n",
    "* [10. Exporting data](#10.-Exporting-data) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cb1625",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e09786",
   "metadata": {},
   "source": [
    "* To answer Instacart research question, all dataframes were combined into a single dataframe that excludes customers with less than 5 placed orders as requested by CFO. Here, aggregations and derived variables will be created to enrich our analyses as requested by different stakeholders.\n",
    "* These variables will be created out of existing variables in our dataframe:\n",
    "    * \"price_range_products\" containing labels for high, middle and low range products.\n",
    "    * \"busiest_days\" containing labels busiest, regular and least busiest days.\n",
    "    * \"busiest_period_of_day\" containing labels most, average, fewest orders.\n",
    "    * \"region\" grouping states into regions.\n",
    "    * \"loyalty_flag\" containing labels loyal, regular, new customer.\n",
    "* These variables will be created after calculating an aggregate variable from an existing variable:\n",
    "    * \"type_of_spender\" containing labels high and low spenders.\n",
    "    * \"usage_frequency\" containing labels frequent, regular and non-frequent. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f08d26",
   "metadata": {},
   "source": [
    "# 2. Importing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806c6cb4",
   "metadata": {},
   "source": [
    "## 2.1 Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd860302",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import analytical libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100286fa",
   "metadata": {},
   "source": [
    "## 2.2 Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e6c249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Project folder path into a string to easily retrieve data\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73e4f76",
   "metadata": {},
   "source": [
    "### Order products final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "18ba291b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import “orders_products_final_step5.pkl”\n",
    "#See \"Step 5 IC Orders products final\" to check for merging details\n",
    "orders_products_final = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared data', 'orders_products_final_step5.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "555b8908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 21)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check df size\n",
    "orders_products_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a427098",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>gender</th>\n",
       "      <th>state</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>max_order_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  department_id  prices  gender    state age  date_joined  \\\n",
       "0         Soda  ...              7     9.0  Female  Alabama  31   2019-02-17   \n",
       "1         Soda  ...              7     9.0  Female  Alabama  31   2019-02-17   \n",
       "2         Soda  ...              7     9.0  Female  Alabama  31   2019-02-17   \n",
       "3         Soda  ...              7     9.0  Female  Alabama  31   2019-02-17   \n",
       "4         Soda  ...              7     9.0  Female  Alabama  31   2019-02-17   \n",
       "\n",
       "  number_of_dependants  family_status income  max_order_number  \n",
       "0                    3        married  40423                10  \n",
       "1                    3        married  40423                10  \n",
       "2                    3        married  40423                10  \n",
       "3                    3        married  40423                10  \n",
       "4                    3        married  40423                10  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check headers\n",
    "orders_products_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5752afe6",
   "metadata": {},
   "source": [
    "# 3. Price range products"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d21fd55b",
   "metadata": {},
   "source": [
    "* Different marketing strategies are applied when selling a 2 dollar candy bar than a 15 dollar box of Belgian chocolates! It makes sense to categorize products into price range groups for easy filtering. The following conditions are applied to \"prices\" to create the variable \"price_range_products\":\n",
    "    * If the item’s price is lower than or equal to 5 dollars, it will be labeled a “low-range”.\n",
    "    * If the item’s price is above 5 dollars but lower than or equal to 15 dollars, it will be labeled a “mid-range”.\n",
    "    * If the item’s price is above 15 dollars, it will be labeled a “high-range”."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5b5be2",
   "metadata": {},
   "source": [
    "## Variable evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "515fb4bd",
   "metadata": {},
   "source": [
    "The \"prices\" variable would be used to create the \"price_range_products\" labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "556d210d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.098809e+07\n",
       "mean     7.795331e+00\n",
       "std      4.241884e+00\n",
       "min      1.000000e+00\n",
       "25%      4.200000e+00\n",
       "50%      7.400000e+00\n",
       "75%      1.130000e+01\n",
       "max      2.500000e+01\n",
       "Name: prices, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"prices\"\n",
    "orders_products_final['prices'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "001cd794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4877"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing values\n",
    "#Missing values are expected (Step1 IC Data Import, Wrangling and Consistency checks)\n",
    "orders_products_final['prices'].isnull().sum()\n",
    "#NaN corresponds to two products that contained odd values (outliers) and were converted to NaN\n",
    "#In Step 1, following deliverations with stakeholders, it was advised to convert odd price values to NaN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae68be15",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da1025d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\1128871956.py:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'high-range' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  orders_products_final.loc[orders_products_final['prices'] > 15, 'price_range_products'] = 'high-range'\n"
     ]
    }
   ],
   "source": [
    "orders_products_final.loc[orders_products_final['prices'] > 15, 'price_range_products'] = 'high-range'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c203978e",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_products_final.loc[(orders_products_final['prices'] <= 15) & (orders_products_final['prices'] > 5), 'price_range_products'] = 'mid-range' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1dda3649",
   "metadata": {},
   "outputs": [],
   "source": [
    "orders_products_final.loc[orders_products_final['prices'] <= 5, 'price_range_products'] = 'low-range'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "561438ac",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f85a8141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range_products\n",
       "mid-range     20920614\n",
       "low-range      9674395\n",
       "high-range      393080\n",
       "NaN               4877\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count values\n",
    "orders_products_final['price_range_products'].value_counts(dropna = False)\n",
    "#NaN corresponds to two items that contained odd values (outliers) that were converted to NaN\n",
    "#For details see (Step1 IC Data Import, Wrangling and Consistency checks)\n",
    "#In Step 1, following deliverations with stakeholders, it was advised to convert odd values to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "177788a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>min</th>\n",
       "      <th>max</th>\n",
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       "    <tr>\n",
       "      <th>price_range_products</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high-range</th>\n",
       "      <td>15.1</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low-range</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mid-range</th>\n",
       "      <td>5.1</td>\n",
       "      <td>15.0</td>\n",
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      "text/plain": [
       "                     prices      \n",
       "                        min   max\n",
       "price_range_products             \n",
       "high-range             15.1  25.0\n",
       "low-range               1.0   5.0\n",
       "mid-range               5.1  15.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "orders_products_final.groupby('price_range_products').agg({'prices' : ['min', 'max']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcda4ab3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>mid-range</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>9.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12.5</td>\n",
       "      <td>mid-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.4</td>\n",
       "      <td>low-range</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    prices price_range_products\n",
       "0      9.0            mid-range\n",
       "1      9.0            mid-range\n",
       "2      9.0            mid-range\n",
       "3      9.0            mid-range\n",
       "4      9.0            mid-range\n",
       "5      9.0            mid-range\n",
       "6      9.0            mid-range\n",
       "7      9.0            mid-range\n",
       "8      9.0            mid-range\n",
       "9      9.0            mid-range\n",
       "10    12.5            mid-range\n",
       "11     4.4            low-range\n",
       "12     4.4            low-range\n",
       "13     4.4            low-range\n",
       "14     4.4            low-range\n",
       "15     4.4            low-range\n",
       "16     4.4            low-range\n",
       "17     4.4            low-range\n",
       "18     4.4            low-range\n",
       "19     4.4            low-range"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check results\n",
    "orders_products_final[['prices', 'price_range_products']] .head(20)\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3e0ca90",
   "metadata": {},
   "source": [
    "The conditions were correctly assigned to all products EXCEPT for two products. NOTE: \"price_range_products\" contains NaN coming from two products (See Step 1 IC Data Import, Wrangling and Consistency checks)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2778c58",
   "metadata": {},
   "source": [
    "# 4. Busiest days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab2c0db",
   "metadata": {},
   "source": [
    "* Our stakeholders want to create a “busiest_days” variable based on the frequency of orders in the \"orders_day_of_week\". The following conditions will be applied:\n",
    "    * “Busiest days” corresponding to the two busiest days of the week. \n",
    "    * At the same time, our stakeholders also like to know the two slowest days, labeled as \"Least busiest days\".\n",
    "    * The reamining days should be labeled as \"Regular days\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6f153c7",
   "metadata": {},
   "source": [
    "## Variable evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4efdfdfc",
   "metadata": {},
   "source": [
    "The variable \"busiest_days\" will be created based on the frequency of orders observed in the \"orders_day_of_week\" variable. After deliverations with stakeholders, it was agreed to evaluate the frequency of orders taking into account the volumen of orders per user, instead of single orders per user. In our current dataframe, a single order_id is repeated several items in the \"order_id\" variable depending on how many products were purchased within an order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4af3d780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.099297e+07\n",
       "mean     2.741350e+00\n",
       "std      2.089228e+00\n",
       "min      0.000000e+00\n",
       "25%      1.000000e+00\n",
       "50%      3.000000e+00\n",
       "75%      5.000000e+00\n",
       "max      6.000000e+00\n",
       "Name: orders_day_of_week, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"orders_day_of_week\"\n",
    "orders_products_final['orders_day_of_week'].describe()\n",
    "#7 days of the week exists, starting at 0 as Saturday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6df97f52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orders_day_of_week\n",
       "0    5913859\n",
       "1    5417064\n",
       "6    4294962\n",
       "5    4037350\n",
       "2    4028494\n",
       "3    3672954\n",
       "4    3628283\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column.\n",
    "orders_products_final['orders_day_of_week'].value_counts(dropna = False)\n",
    "#the value 0 means Saturday\n",
    "#0 and 1 are the \"Busiest days\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "221f3541",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3d058b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The folllowing conditions are applied:\n",
    "#If the value in \"orders_day_of_week\" equals to 0 or 1, a “Busiest days” string is appended to the result list. \n",
    "#If the value is equal to 4 and 3, a “Least busiest days” string is appended. \n",
    "#If neither of these conditions has been met (the value is neither 0, 1, 3 nor 4), then a “Regular days” is appended.\n",
    "\n",
    "result = []\n",
    "\n",
    "for value in orders_products_final[\"orders_day_of_week\"]:\n",
    "  if value == 0 or value == 1:\n",
    "    result.append(\"Busiest days\")\n",
    "    #busiest days = 0 and 1 Saturday and Sunday\n",
    "  elif value == 4 or value == 3:\n",
    "    result.append(\"Least busiest days\")\n",
    "    #least busy days = 4 and 3 Wednesday and Tuesday\n",
    "  else:\n",
    "    result.append(\"Regular days\")\n",
    "    # 2, 5, and 6 Monday, Thrusday and Friday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "59f3316e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Regular days',\n",
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       " 'Least busiest days',\n",
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       " 'Regular days',\n",
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       " 'Regular days',\n",
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       " 'Busiest days',\n",
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       " 'Least busiest days',\n",
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       " 'Least busiest days',\n",
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       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Regular days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Least busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Regular days',\n",
       " 'Busiest days',\n",
       " 'Regular days',\n",
       " 'Regular days',\n",
       " 'Regular days',\n",
       " 'Least busiest days',\n",
       " 'Regular days',\n",
       " ...]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2654bde3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the results to your dataframe\n",
    "orders_products_final['busiest_days'] = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46bb6d34",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32d77502",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
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       "      <th>price_range_products</th>\n",
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       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>Female</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  gender    state  age date_joined number_of_dependants  \\\n",
       "0         Soda  ...  Female  Alabama   31  2019-02-17                    3   \n",
       "1         Soda  ...  Female  Alabama   31  2019-02-17                    3   \n",
       "2         Soda  ...  Female  Alabama   31  2019-02-17                    3   \n",
       "3         Soda  ...  Female  Alabama   31  2019-02-17                    3   \n",
       "4         Soda  ...  Female  Alabama   31  2019-02-17                    3   \n",
       "\n",
       "   family_status income  max_order_number price_range_products  \\\n",
       "0        married  40423                10            mid-range   \n",
       "1        married  40423                10            mid-range   \n",
       "2        married  40423                10            mid-range   \n",
       "3        married  40423                10            mid-range   \n",
       "4        married  40423                10            mid-range   \n",
       "\n",
       "         busiest_days  \n",
       "0        Regular days  \n",
       "1  Least busiest days  \n",
       "2  Least busiest days  \n",
       "3  Least busiest days  \n",
       "4  Least busiest days  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()\n",
    "#variable \"busiest_days\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a128fdea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "busiest_days\n",
       "Regular days          12360806\n",
       "Busiest days          11330923\n",
       "Least busiest days     7301237\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting values within new column\n",
    "orders_products_final['busiest_days'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0a581a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orders_day_of_week\n",
       "0          Busiest days\n",
       "1          Busiest days\n",
       "2          Regular days\n",
       "3    Least busiest days\n",
       "4    Least busiest days\n",
       "5          Regular days\n",
       "6          Regular days\n",
       "Name: busiest_days, dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "orders_products_final.groupby(['orders_day_of_week'])['busiest_days'].max()\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23640a06",
   "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>orders_day_of_week</th>\n",
       "      <th>busiest_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Regular days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>Regular days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>Regular days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>Regular days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4</td>\n",
       "      <td>Least busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2</td>\n",
       "      <td>Regular days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>Busiest days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    orders_day_of_week        busiest_days\n",
       "0                    2        Regular days\n",
       "1                    3  Least busiest days\n",
       "2                    3  Least busiest days\n",
       "3                    4  Least busiest days\n",
       "4                    4  Least busiest days\n",
       "5                    2        Regular days\n",
       "6                    1        Busiest days\n",
       "7                    1        Busiest days\n",
       "8                    1        Busiest days\n",
       "9                    4  Least busiest days\n",
       "10                   2        Regular days\n",
       "11                   2        Regular days\n",
       "12                   3  Least busiest days\n",
       "13                   3  Least busiest days\n",
       "14                   4  Least busiest days\n",
       "15                   4  Least busiest days\n",
       "16                   2        Regular days\n",
       "17                   1        Busiest days\n",
       "18                   1        Busiest days\n",
       "19                   1        Busiest days"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check results\n",
    "orders_products_final[['orders_day_of_week', 'busiest_days']].head(20)\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf44ce83",
   "metadata": {},
   "source": [
    "The conditions were correctly assigned to all days of the week. Regular days are the most predominant days. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ababb32",
   "metadata": {},
   "source": [
    "# 5. Busiest period of day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81a3a210",
   "metadata": {},
   "source": [
    "* When too many users make Instacart orders at the same time, the app freezes. The senior technical officer at Instacart wants to identify the busiest hours of the day. Rather than by hour, they want periods of time labeled “Most orders” “Average orders” and “Fewest orders”. After deliverations with the senrior technical officer, the following conditions were used to create a “busiest_period_of_day” variable based on the frequency of orders in the \"order_hour_of_day\":\n",
    "    * Since the app freezes usually when is processing more than 2.5M orders at a time, then hour of days with more than 2.5M will be labeled as \"Most orders\".\n",
    "    * “Fewest orders\" are those with < 100 thousand orders, since the app does not experience any problems at all with this volumen of orders.\n",
    "    * The reamining hours of the day should be labeled as \"Average orders\"."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c07da6",
   "metadata": {},
   "source": [
    "## Variable evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34f19263",
   "metadata": {},
   "source": [
    "The variable \"busiest_period_of_day\" will be created based on the frequency of orders observed in the \"order_hour_of_day\" variable. After deliverations with the senior technical officer, it was agreed to evaluate the frequency of orders taking into account the volumen of orders per user, instead of single orders per user. In our current dataframe, a single order_id is repeated several items in the \"order_id\" variable depending on how many products were purchased within an order. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a744639",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.099297e+07\n",
       "mean     1.341081e+01\n",
       "std      4.248825e+00\n",
       "min      0.000000e+00\n",
       "25%      1.000000e+01\n",
       "50%      1.300000e+01\n",
       "75%      1.600000e+01\n",
       "max      2.300000e+01\n",
       "Name: order_hour_of_day, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"orders_hour_of_day\"\n",
    "orders_products_final['order_hour_of_day'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e26712f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_hour_of_day\n",
       "10    2652317\n",
       "11    2617229\n",
       "14    2570213\n",
       "13    2541527\n",
       "15    2538253\n",
       "12    2497997\n",
       "16    2415839\n",
       "9     2366037\n",
       "17    1984069\n",
       "8     1660891\n",
       "18    1552540\n",
       "19    1194918\n",
       "20     931746\n",
       "7      864069\n",
       "21     763813\n",
       "22     607550\n",
       "23     384838\n",
       "6      281307\n",
       "0      208214\n",
       "1      109705\n",
       "5       84514\n",
       "2       65743\n",
       "4       50802\n",
       "3       48835\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column\n",
    "orders_products_final['order_hour_of_day'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d06816",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d8a0e940",
   "metadata": {},
   "outputs": [],
   "source": [
    "#The folllowing conditions are applied:\n",
    "#Since the app usually freezes when is processing more than 2.5M orders at a time, \n",
    "#then hour of days with more than 2.5M will be labeled as \"Most orders\"\n",
    "#“Fewest orders\" are those with < 100 thousand orders. The app does not experience any problems with this volumen of orders\n",
    "#The reamining hours of the day should be labeled as \"Average orders\".\n",
    "\n",
    "result = []\n",
    "\n",
    "for value in orders_products_final[\"order_hour_of_day\"]:\n",
    "  if value in [15, 13, 14, 11, 10]:\n",
    "    result.append(\"Most orders\")\n",
    "  elif value in [5, 2, 4, 3]:\n",
    "    result.append(\"Fewest orders\")\n",
    "  else:\n",
    "    result.append(\"Average orders\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4cc4fad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
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       " 'Average orders',\n",
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       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
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       " 'Average orders',\n",
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       " 'Average orders',\n",
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       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
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       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
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       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
       " 'Most orders',\n",
       " 'Most orders',\n",
       " 'Average orders',\n",
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       " 'Average orders',\n",
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       " 'Average orders',\n",
       " 'Average orders',\n",
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       " 'Fewest orders',\n",
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aa57570f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the results to the df\n",
    "orders_products_final['busiest_period_of_day'] = result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cf17758",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6214791a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>state</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>2539329</td>\n",
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       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
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       "      <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>...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...    state  age  date_joined number_of_dependants  \\\n",
       "0         Soda  ...  Alabama   31   2019-02-17                    3   \n",
       "1         Soda  ...  Alabama   31   2019-02-17                    3   \n",
       "2         Soda  ...  Alabama   31   2019-02-17                    3   \n",
       "3         Soda  ...  Alabama   31   2019-02-17                    3   \n",
       "4         Soda  ...  Alabama   31   2019-02-17                    3   \n",
       "\n",
       "  family_status  income max_order_number  price_range_products  \\\n",
       "0       married   40423               10             mid-range   \n",
       "1       married   40423               10             mid-range   \n",
       "2       married   40423               10             mid-range   \n",
       "3       married   40423               10             mid-range   \n",
       "4       married   40423               10             mid-range   \n",
       "\n",
       "         busiest_days  busiest_period_of_day  \n",
       "0        Regular days         Average orders  \n",
       "1  Least busiest days         Average orders  \n",
       "2  Least busiest days         Average orders  \n",
       "3  Least busiest days         Average orders  \n",
       "4  Least busiest days            Most orders  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()\n",
    "#variable \"busiest_days\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5ef254ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "busiest_period_of_day\n",
       "Average orders    17823533\n",
       "Most orders       12919539\n",
       "Fewest orders       249894\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting values within new column\n",
    "orders_products_final['busiest_period_of_day'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "290ac93c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_hour_of_day\n",
       "0     Average orders\n",
       "1     Average orders\n",
       "2      Fewest orders\n",
       "3      Fewest orders\n",
       "4      Fewest orders\n",
       "5      Fewest orders\n",
       "6     Average orders\n",
       "7     Average orders\n",
       "8     Average orders\n",
       "9     Average orders\n",
       "10       Most orders\n",
       "11       Most orders\n",
       "12    Average orders\n",
       "13       Most orders\n",
       "14       Most orders\n",
       "15       Most orders\n",
       "16    Average orders\n",
       "17    Average orders\n",
       "18    Average orders\n",
       "19    Average orders\n",
       "20    Average orders\n",
       "21    Average orders\n",
       "22    Average orders\n",
       "23    Average orders\n",
       "Name: busiest_period_of_day, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "orders_products_final.groupby(['order_hour_of_day'])['busiest_period_of_day'].max()\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ae50cb49",
   "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>busiest_period_of_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "      <td>Most orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>Most orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Most orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>9</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14</td>\n",
       "      <td>Most orders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>16</td>\n",
       "      <td>Average orders</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_hour_of_day busiest_period_of_day\n",
       "0                   8        Average orders\n",
       "1                   7        Average orders\n",
       "2                  12        Average orders\n",
       "3                   7        Average orders\n",
       "4                  15           Most orders\n",
       "5                   7        Average orders\n",
       "6                   9        Average orders\n",
       "7                  14           Most orders\n",
       "8                  16        Average orders\n",
       "9                   8        Average orders\n",
       "10                  8        Average orders\n",
       "11                  8        Average orders\n",
       "12                  7        Average orders\n",
       "13                 12        Average orders\n",
       "14                  7        Average orders\n",
       "15                 15           Most orders\n",
       "16                  7        Average orders\n",
       "17                  9        Average orders\n",
       "18                 14           Most orders\n",
       "19                 16        Average orders"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check results\n",
    "orders_products_final[['order_hour_of_day', 'busiest_period_of_day']].head(20)\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f533ad3e",
   "metadata": {},
   "source": [
    "The conditions were correctly assigned to all hours of the day. Regular days are the most predominant days. Most orders are placed between 10:00 to 15:00."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bdce710",
   "metadata": {},
   "source": [
    "# 6. Region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77031cc3",
   "metadata": {},
   "source": [
    "* The Instacart officers are interested in comparing customer behavior in different geographic areas. Here, a regional segmentation of the data will be created.The “region” column will be based on the states present in the “state” column. The regional segmentation will be created following the criteria on this link:\n",
    "    * List of regions of the United States (https://simple.wikipedia.org/wiki/List_of_regions_of_the_United_States). Only regions will be created, not divisions. A dictionary will be created to map states to regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e496a067",
   "metadata": {},
   "source": [
    "## Variable evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21b10eb0",
   "metadata": {},
   "source": [
    "The variable \"state\" will be used to create the \"region\" variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bd2f0add",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of unique values in 'state': 51\n"
     ]
    }
   ],
   "source": [
    "# Identify the number of unique values in 'state'\n",
    "unique_states = orders_products_final['state'].nunique()\n",
    "print(f\"The number of unique values in 'state': {unique_states}\")\n",
    "#There are 51 states listed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cbce5297",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Names of unique states:\n",
      "Alabama\n",
      "Indiana\n",
      "Louisiana\n",
      "Montana\n",
      "Oklahoma\n",
      "Tennessee\n",
      "Iowa\n",
      "Ohio\n",
      "Virginia\n",
      "Connecticut\n",
      "Oregon\n",
      "South Dakota\n",
      "Kentucky\n",
      "Michigan\n",
      "New Jersey\n",
      "Hawaii\n",
      "Minnesota\n",
      "Illinois\n",
      "Idaho\n",
      "Kansas\n",
      "North Carolina\n",
      "Vermont\n",
      "Delaware\n",
      "Nebraska\n",
      "Nevada\n",
      "Arkansas\n",
      "Georgia\n",
      "District of Columbia\n",
      "Utah\n",
      "Alaska\n",
      "Colorado\n",
      "Florida\n",
      "Rhode Island\n",
      "Maine\n",
      "South Carolina\n",
      "Washington\n",
      "Mississippi\n",
      "California\n",
      "Texas\n",
      "Arizona\n",
      "New Mexico\n",
      "Maryland\n",
      "Missouri\n",
      "Wyoming\n",
      "New Hampshire\n",
      "Massachusetts\n",
      "Pennsylvania\n",
      "Wisconsin\n",
      "West Virginia\n",
      "North Dakota\n",
      "New York\n"
     ]
    }
   ],
   "source": [
    "#Printing names of unique states\n",
    "unique_states_names = orders_products_final['state'].unique()\n",
    "print(\"Names of unique states:\")\n",
    "for state_name in unique_states_names:\n",
    "    print(state_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7a45ee",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "30be91cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a dictionary mapping each state to its corresponding region\n",
    "state_to_region = {\n",
    "    'Alabama': 'South',\n",
    "    'Indiana': 'Midwest',\n",
    "    'Louisiana': 'South',\n",
    "    'Montana': 'West',\n",
    "    'Oklahoma': 'South',\n",
    "    'Tennessee': 'South',\n",
    "    'Iowa': 'Midwest',\n",
    "    'Ohio': 'Midwest',\n",
    "    'Virginia': 'South',\n",
    "    'Connecticut': 'Northeast',\n",
    "    'Oregon': 'West',\n",
    "    'South Dakota': 'Midwest',\n",
    "    'Kentucky': 'South',\n",
    "    'Michigan': 'Midwest',\n",
    "    'New Jersey': 'Northeast',\n",
    "    'Hawaii': 'West',\n",
    "    'Minnesota': 'Midwest',\n",
    "    'Illinois': 'Midwest',\n",
    "    'Idaho': 'West',\n",
    "    'Kansas': 'Midwest',\n",
    "    'North Carolina': 'South',\n",
    "    'Vermont': 'Northeast',\n",
    "    'Delaware': 'South',\n",
    "    'Nebraska': 'Midwest',\n",
    "    'Nevada': 'West',\n",
    "    'Arkansas': 'South',\n",
    "    'Georgia': 'South',\n",
    "    'District of Columbia': 'South',\n",
    "    'Utah': 'West',\n",
    "    'Alaska': 'West',\n",
    "    'Colorado': 'West',\n",
    "    'Florida': 'South',\n",
    "    'Rhode Island': 'Northeast',\n",
    "    'Maine': 'Northeast',\n",
    "    'South Carolina': 'South',\n",
    "    'Washington': 'West',\n",
    "    'Mississippi': 'South',\n",
    "    'California': 'West',\n",
    "    'Texas': 'South',\n",
    "    'Arizona': 'West',\n",
    "    'New Mexico': 'West',\n",
    "    'Maryland': 'South',\n",
    "    'Missouri': 'Midwest',\n",
    "    'Wyoming': 'West',\n",
    "    'New Hampshire': 'Northeast',\n",
    "    'Massachusetts': 'Northeast',\n",
    "    'Pennsylvania': 'Northeast',\n",
    "    'Wisconsin': 'Midwest',\n",
    "    'West Virginia': 'Midwest',\n",
    "    'North Dakota': 'Midwest',\n",
    "    'New York': 'Northeast'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a5ebd1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding the 'region' column to the df based on the mapping\n",
    "orders_products_final['region'] = orders_products_final['state'].map(state_to_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e0500e61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-17</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  age  date_joined  number_of_dependants family_status  \\\n",
       "0         Soda  ...   31   2019-02-17                     3       married   \n",
       "1         Soda  ...   31   2019-02-17                     3       married   \n",
       "2         Soda  ...   31   2019-02-17                     3       married   \n",
       "3         Soda  ...   31   2019-02-17                     3       married   \n",
       "4         Soda  ...   31   2019-02-17                     3       married   \n",
       "\n",
       "  income  max_order_number price_range_products        busiest_days  \\\n",
       "0  40423                10            mid-range        Regular days   \n",
       "1  40423                10            mid-range  Least busiest days   \n",
       "2  40423                10            mid-range  Least busiest days   \n",
       "3  40423                10            mid-range  Least busiest days   \n",
       "4  40423                10            mid-range  Least busiest days   \n",
       "\n",
       "  busiest_period_of_day  region  \n",
       "0        Average orders   South  \n",
       "1        Average orders   South  \n",
       "2        Average orders   South  \n",
       "3        Average orders   South  \n",
       "4           Most orders   South  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()\n",
    "#\"region\" variable was added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e787a383",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "64e9def3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region\n",
       "South        9737281\n",
       "West         7934470\n",
       "Midwest      7851135\n",
       "Northeast    5470080\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting values within new column\n",
    "orders_products_final['region'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b9422d2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\2633492751.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  orders_products_final.groupby(['state'])['region'].max()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "state\n",
       "Alabama                     South\n",
       "Alaska                       West\n",
       "Arizona                      West\n",
       "Arkansas                    South\n",
       "California                   West\n",
       "Colorado                     West\n",
       "Connecticut             Northeast\n",
       "Delaware                    South\n",
       "District of Columbia        South\n",
       "Florida                     South\n",
       "Georgia                     South\n",
       "Hawaii                       West\n",
       "Idaho                        West\n",
       "Illinois                  Midwest\n",
       "Indiana                   Midwest\n",
       "Iowa                      Midwest\n",
       "Kansas                    Midwest\n",
       "Kentucky                    South\n",
       "Louisiana                   South\n",
       "Maine                   Northeast\n",
       "Maryland                    South\n",
       "Massachusetts           Northeast\n",
       "Michigan                  Midwest\n",
       "Minnesota                 Midwest\n",
       "Mississippi                 South\n",
       "Missouri                  Midwest\n",
       "Montana                      West\n",
       "Nebraska                  Midwest\n",
       "Nevada                       West\n",
       "New Hampshire           Northeast\n",
       "New Jersey              Northeast\n",
       "New Mexico                   West\n",
       "New York                Northeast\n",
       "North Carolina              South\n",
       "North Dakota              Midwest\n",
       "Ohio                      Midwest\n",
       "Oklahoma                    South\n",
       "Oregon                       West\n",
       "Pennsylvania            Northeast\n",
       "Rhode Island            Northeast\n",
       "South Carolina              South\n",
       "South Dakota              Midwest\n",
       "Tennessee                   South\n",
       "Texas                       South\n",
       "Utah                         West\n",
       "Vermont                 Northeast\n",
       "Virginia                    South\n",
       "Washington                   West\n",
       "West Virginia             Midwest\n",
       "Wisconsin                 Midwest\n",
       "Wyoming                      West\n",
       "Name: region, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "orders_products_final.groupby(['state'])['region'].max()\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00da958f",
   "metadata": {},
   "source": [
    "Using a dictionary, all states in our dataframe were correctly mapped to a region. Most of orders are coming from states in the South region."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accbdbfa",
   "metadata": {},
   "source": [
    "## 7. Loyalty flag"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4745fc",
   "metadata": {},
   "source": [
    "* The business strategy team would like to employ some kind of bonus point program for loyal Instacart customers. These are customers that come back time and time again and use the service or buy the product often. The \"max_order_number\" variable will be used to create the \"loyalty_flag\" following these conditions:\n",
    "    * If the maximum orders the user has made is over 40, then the customer will be labeled a “Loyal”.\n",
    "    * If the maximum orders the user has made is over 10 but less than or equal to 40, then the customer will be labeled “Regular”.\n",
    "    * If the maximum orders the user has made is less than or equal to 10, then the customer will be labeled “New”."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2f4e81f",
   "metadata": {},
   "source": [
    "## Variable evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ff0e814",
   "metadata": {},
   "source": [
    "The variable \"loyalty_flag\" will be created based on the values observed in the \"max_order_number\" variable. See Step 5 IC Final dataset after exclusion flag to find out details how the \"max_order_number\" variable was created out of an aggregation from the \"order_number\" variable.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29ee8d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.099297e+07\n",
       "mean     3.442599e+01\n",
       "std      2.489448e+01\n",
       "min      5.000000e+00\n",
       "25%      1.400000e+01\n",
       "50%      2.800000e+01\n",
       "75%      4.800000e+01\n",
       "max      9.900000e+01\n",
       "Name: max_order_number, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"max_order_number\"\n",
    "orders_products_final['max_order_number'].describe()\n",
    "#The min of 5 orders was set in Step 5 IC Final dataset after exclusion flag\n",
    "#CFO is not interested in data analysis coming from users that placed less than 5 orders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e37c096",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f0831e3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\3252715431.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'Loyal' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  orders_products_final.loc[orders_products_final['max_order_number'] > 40, 'loyalty_flag'] = 'Loyal'\n"
     ]
    }
   ],
   "source": [
    "#If the maximum orders the user has made is over 40, then the customer will be labeled a “Loyal”\n",
    "orders_products_final.loc[orders_products_final['max_order_number'] > 40, 'loyalty_flag'] = 'Loyal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "8a2e26c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the maximum orders the user has made is over 10 but less than or equal to 40, then the customer will be labeled “Regular”\n",
    "orders_products_final.loc[(orders_products_final['max_order_number'] <= 40) & (orders_products_final['max_order_number'] > 10), 'loyalty_flag'] = 'Regular'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d25c1d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the maximum orders the user has made is less than or equal to 10, then the customer will be labeled “New”\n",
    "orders_products_final.loc[orders_products_final['max_order_number'] <= 10, 'loyalty_flag'] = 'New'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a1086fc",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ab902d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loyalty_flag\n",
       "Regular    15891172\n",
       "Loyal      10293959\n",
       "New         4807835\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the frequency of “loyalty_flag” \n",
    "orders_products_final['loyalty_flag'].value_counts(dropna = False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fb5a836a",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">max_order_number</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Loyal</th>\n",
       "      <td>41</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular</th>\n",
       "      <td>11</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             max_order_number    \n",
       "                          min max\n",
       "loyalty_flag                     \n",
       "Loyal                      41  99\n",
       "New                         5  10\n",
       "Regular                    11  40"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "orders_products_final.groupby('loyalty_flag').agg({'max_order_number' : ['min', 'max']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f5f82706",
   "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>loyalty_flag</th>\n",
       "      <th>max_order_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  loyalty_flag  max_order_number\n",
       "0          New                10\n",
       "1          New                10\n",
       "2          New                10\n",
       "3          New                10\n",
       "4          New                10"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final[['loyalty_flag', 'max_order_number']].head()\n",
    "#variable is added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0e8708c4",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>max_order_number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32434480</th>\n",
       "      <td>Regular</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434481</th>\n",
       "      <td>Regular</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434482</th>\n",
       "      <td>Regular</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434483</th>\n",
       "      <td>Regular</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434484</th>\n",
       "      <td>Regular</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         loyalty_flag  max_order_number\n",
       "32434480      Regular                26\n",
       "32434481      Regular                26\n",
       "32434482      Regular                26\n",
       "32434483      Regular                26\n",
       "32434484      Regular                26"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final[['loyalty_flag', 'max_order_number']].tail()\n",
    "#variable is added"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6310f8b",
   "metadata": {},
   "source": [
    "The conditions were correctly assigned to all our users. Loyal users corresponds to 33.2 % of our total users."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfcf1ed3",
   "metadata": {},
   "source": [
    "# 8. Type of spender"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c623ba",
   "metadata": {},
   "source": [
    "* Instacart wants to target different types of spenders in their marketing campaigns. This can be achieved by looking at the prices of the items people are buying. A spending flag for each user will be created based on the average price across all their orders using the following criteria:\n",
    "    * If the mean of the prices of products purchased by a user is lower than 10, then they will labeled “Low spender”.\n",
    "    * If the mean of the prices of products purchased by a user is higher than or equal to 10, then they will be labeled “High spender”."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dda2d329",
   "metadata": {},
   "source": [
    "## Aggregate variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9249f9",
   "metadata": {},
   "source": [
    "To create the variable \"type_of_spender\" firstly, the mean of the prices of products purchased by a user should be calculated. Here, an aggregate variable based on the \"prices\" variable will be created to assign a mean under the \"mean_price_per_user\" variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "46d1e286",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\1571174399.py:2: FutureWarning: The provided callable <function mean at 0x000001C92C736E80> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
      "  orders_products_final['mean_price_per_user'] = orders_products_final.groupby(['user_id'])['prices'].transform(np.mean)\n"
     ]
    }
   ],
   "source": [
    "#Calculating the average price of products purchased per user = (\"mean_price_per_user\")\n",
    "orders_products_final['mean_price_per_user'] = orders_products_final.groupby(['user_id'])['prices'].transform(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a816a2da",
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price_per_user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
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       "      <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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  number_of_dependants  family_status  income  \\\n",
       "0         Soda  ...                     3        married   40423   \n",
       "1         Soda  ...                     3        married   40423   \n",
       "2         Soda  ...                     3        married   40423   \n",
       "3         Soda  ...                     3        married   40423   \n",
       "4         Soda  ...                     3        married   40423   \n",
       "\n",
       "  max_order_number price_range_products        busiest_days  \\\n",
       "0               10            mid-range        Regular days   \n",
       "1               10            mid-range  Least busiest days   \n",
       "2               10            mid-range  Least busiest days   \n",
       "3               10            mid-range  Least busiest days   \n",
       "4               10            mid-range  Least busiest days   \n",
       "\n",
       "  busiest_period_of_day  region loyalty_flag  mean_price_per_user  \n",
       "0        Average orders   South          New             6.367797  \n",
       "1        Average orders   South          New             6.367797  \n",
       "2        Average orders   South          New             6.367797  \n",
       "3        Average orders   South          New             6.367797  \n",
       "4           Most orders   South          New             6.367797  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()\n",
    "# \"mean_price_per_user\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "32b6ac72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.099297e+07\n",
       "mean     7.795333e+00\n",
       "std      7.216319e-01\n",
       "min      1.000000e+00\n",
       "25%      7.389744e+00\n",
       "50%      7.816998e+00\n",
       "75%      8.228163e+00\n",
       "max      2.011429e+01\n",
       "Name: mean_price_per_user, dtype: float64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"mean_price_per_user\"\n",
    "orders_products_final['mean_price_per_user'].describe()\n",
    "#Average price per product purchased per user ranges from 1 to 20.11 dollars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aa3cc50c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">prices</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.367797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>14.8</td>\n",
       "      <td>7.515897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.3</td>\n",
       "      <td>14.4</td>\n",
       "      <td>8.197727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>14.6</td>\n",
       "      <td>8.205556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.1</td>\n",
       "      <td>23.2</td>\n",
       "      <td>7.466990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206203</th>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.138655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>1.1</td>\n",
       "      <td>14.8</td>\n",
       "      <td>7.646667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>1.2</td>\n",
       "      <td>24.7</td>\n",
       "      <td>7.313453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>1.1</td>\n",
       "      <td>21.6</td>\n",
       "      <td>8.366617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206209</th>\n",
       "      <td>1.1</td>\n",
       "      <td>18.8</td>\n",
       "      <td>7.058915</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        prices                \n",
       "           min   max      mean\n",
       "user_id                       \n",
       "1          1.0  14.0  6.367797\n",
       "2          1.3  14.8  7.515897\n",
       "3          1.3  14.4  8.197727\n",
       "4          1.4  14.6  8.205556\n",
       "7          1.1  23.2  7.466990\n",
       "...        ...   ...       ...\n",
       "206203     1.0  25.0  7.138655\n",
       "206206     1.1  14.8  7.646667\n",
       "206207     1.2  24.7  7.313453\n",
       "206208     1.1  21.6  8.366617\n",
       "206209     1.1  18.8  7.058915\n",
       "\n",
       "[162633 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check aggregations\n",
    "#Snap view of prices per user\n",
    "orders_products_final.groupby('user_id').agg({'prices' : ['min', 'max', 'mean']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc76362",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2c85141c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\2405728393.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'High spender' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  orders_products_final.loc[orders_products_final['mean_price_per_user'] >= 10, 'type_of_spender'] = 'High spender'\n"
     ]
    }
   ],
   "source": [
    "#If the mean of the prices of products purchased by a user is higher than or equal to 10, then they will be labeled “High spender”\n",
    "orders_products_final.loc[orders_products_final['mean_price_per_user'] >= 10, 'type_of_spender'] = 'High spender'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4e7aaeb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the mean of the prices of products purchased by a user is lower than 10, then they will labeled “Low spender”\n",
    "orders_products_final.loc[orders_products_final['mean_price_per_user'] < 10, 'type_of_spender'] = 'Low spender'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76943a46",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "aca17f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type_of_spender\n",
       "Low spender     30894578\n",
       "High spender       98388\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the frequency of “type_of_spender” \n",
    "orders_products_final['type_of_spender'].value_counts(dropna = False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6673dfa1",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mean_price_per_user</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type_of_spender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High spender</th>\n",
       "      <td>10.0</td>\n",
       "      <td>20.114286</td>\n",
       "      <td>10.563224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low spender</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.998305</td>\n",
       "      <td>7.786518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean_price_per_user                      \n",
       "                                min        max       mean\n",
       "type_of_spender                                          \n",
       "High spender                   10.0  20.114286  10.563224\n",
       "Low spender                     1.0   9.998305   7.786518"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check aggregations\n",
    "#Snap view of prices per user\n",
    "orders_products_final.groupby('type_of_spender').agg({'mean_price_per_user' : ['min', 'max', 'mean']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "85f5ccce",
   "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>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price_per_user</th>\n",
       "      <th>type_of_spender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <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>...</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</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>...</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  family_status  income  max_order_number  \\\n",
       "0         Soda  ...        married   40423                10   \n",
       "1         Soda  ...        married   40423                10   \n",
       "2         Soda  ...        married   40423                10   \n",
       "3         Soda  ...        married   40423                10   \n",
       "4         Soda  ...        married   40423                10   \n",
       "\n",
       "  price_range_products        busiest_days  busiest_period_of_day region  \\\n",
       "0            mid-range        Regular days         Average orders  South   \n",
       "1            mid-range  Least busiest days         Average orders  South   \n",
       "2            mid-range  Least busiest days         Average orders  South   \n",
       "3            mid-range  Least busiest days         Average orders  South   \n",
       "4            mid-range  Least busiest days            Most orders  South   \n",
       "\n",
       "   loyalty_flag mean_price_per_user  type_of_spender  \n",
       "0           New            6.367797      Low spender  \n",
       "1           New            6.367797      Low spender  \n",
       "2           New            6.367797      Low spender  \n",
       "3           New            6.367797      Low spender  \n",
       "4           New            6.367797      Low spender  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1292ff9d",
   "metadata": {},
   "source": [
    "Most of our users (99.68%) are of \"Low spender\" type. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deae1fd0",
   "metadata": {},
   "source": [
    "# 9. Usage frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3964ea9",
   "metadata": {},
   "source": [
    "* In order to send relevant notifications to users within the app (for instance, asking users if they want to buy the same item again), the Instacart team wants to determine frequent versus non-frequent customers. It was agreed that this frequency flag that marks the regularity of a user’s ordering behavior will be based on the median in the “days_since_prior_order” variable. The criteria for the flag should be as follows:\n",
    "    * If the median of “days_since_prior_order” is higher than 20, then the customer should be labeled a “Non-frequent customer”.\n",
    "    * If the median is higher than 10 and lower than or equal to 20, then the customer should be labeled a “Regular customer”.\n",
    "    * If the median is lower than or equal to 10, then the customer should be labeled a “Frequent customer”."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f587fc3f",
   "metadata": {},
   "source": [
    "## Aggregate variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44cc3dc6",
   "metadata": {},
   "source": [
    "To create the variable \"usage_frequency\" firstly, the median of the \"days_since_prior_order\" variable per user should be calculated. Here, an aggregate variable based on the \"days_since_prior_order\" variable will be created to assign a media per user under the \"median_days_since_prior_order\" variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f374b4a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\649786976.py:2: FutureWarning: The provided callable <function median at 0x000001C92C87FE20> is currently using SeriesGroupBy.median. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"median\" instead.\n",
      "  orders_products_final['median_days_since_prior_order'] = orders_products_final.groupby(['user_id'])['days_since_prior_order'].transform(np.median)\n"
     ]
    }
   ],
   "source": [
    "#Calculating the median per user = (\"median_days_since_prior_order\") that each user took before placing a follow-up order\n",
    "orders_products_final['median_days_since_prior_order'] = orders_products_final.groupby(['user_id'])['days_since_prior_order'].transform(np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9108560",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
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       "      <td>10</td>\n",
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       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
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       "      <td>28.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>40423</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  income  max_order_number  price_range_products  \\\n",
       "0         Soda  ...   40423                10             mid-range   \n",
       "1         Soda  ...   40423                10             mid-range   \n",
       "2         Soda  ...   40423                10             mid-range   \n",
       "3         Soda  ...   40423                10             mid-range   \n",
       "4         Soda  ...   40423                10             mid-range   \n",
       "\n",
       "         busiest_days busiest_period_of_day  region loyalty_flag  \\\n",
       "0        Regular days        Average orders   South          New   \n",
       "1  Least busiest days        Average orders   South          New   \n",
       "2  Least busiest days        Average orders   South          New   \n",
       "3  Least busiest days        Average orders   South          New   \n",
       "4  Least busiest days           Most orders   South          New   \n",
       "\n",
       "   mean_price_per_user type_of_spender  median_days_since_prior_order  \n",
       "0             6.367797     Low spender                           20.5  \n",
       "1             6.367797     Low spender                           20.5  \n",
       "2             6.367797     Low spender                           20.5  \n",
       "3             6.367797     Low spender                           20.5  \n",
       "4             6.367797     Low spender                           20.5  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()\n",
    "# the \"median_days_since_prior_order\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b744b7c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.099297e+07\n",
       "mean     9.957106e+00\n",
       "std      6.665469e+00\n",
       "min      0.000000e+00\n",
       "25%      6.000000e+00\n",
       "50%      7.000000e+00\n",
       "75%      1.250000e+01\n",
       "max      3.000000e+01\n",
       "Name: median_days_since_prior_order, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluate \"median_days_since_prior_order\"\n",
    "orders_products_final['median_days_since_prior_order'].describe()\n",
    "#the median ranges from 0 to 30 days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "35599570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">days_since_prior_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206203</th>\n",
       "      <td>27.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206206</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206207</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206208</th>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206209</th>\n",
       "      <td>3.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>162633 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        days_since_prior_order             \n",
       "                           min   max median\n",
       "user_id                                    \n",
       "1                          0.0  30.0   20.5\n",
       "2                          3.0  30.0   13.0\n",
       "3                          7.0  21.0   10.0\n",
       "4                          0.0  21.0   20.0\n",
       "7                          2.0  30.0    9.0\n",
       "...                        ...   ...    ...\n",
       "206203                    27.0  30.0   30.0\n",
       "206206                     0.0  15.0    3.0\n",
       "206207                     1.0  30.0   16.0\n",
       "206208                     0.0  20.0    7.0\n",
       "206209                     3.0  30.0   22.0\n",
       "\n",
       "[162633 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check aggregations\n",
    "#Snap view of days_since_prior_order per user\n",
    "orders_products_final.groupby('user_id').agg({'days_since_prior_order' : ['min', 'max', 'median']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d8fb13",
   "metadata": {},
   "source": [
    "## Conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8f626148",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_10992\\1833112232.py:2: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'Non-frequent customer' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  orders_products_final.loc[orders_products_final['median_days_since_prior_order'] > 20, 'usage_frequency'] = 'Non-frequent customer'\n"
     ]
    }
   ],
   "source": [
    "#If the median of “days_since_prior_order” is higher than 20, then the customer should be labeled a “Non-frequent customer”\n",
    "orders_products_final.loc[orders_products_final['median_days_since_prior_order'] > 20, 'usage_frequency'] = 'Non-frequent customer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1155352a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the median is lower than or equal to 10, then the customer should be labeled a “Frequent customer”\n",
    "orders_products_final.loc[orders_products_final['median_days_since_prior_order'] <= 10, 'usage_frequency'] = 'Frequent customer'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f886b939",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If the median is higher than 10 and lower than or equal to 20, then the customer should be labeled a “Regular customer”\n",
    "orders_products_final.loc[(orders_products_final['median_days_since_prior_order'] > 10) & (orders_products_final['median_days_since_prior_order'] <= 20) , 'usage_frequency'] = 'Regular customer'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed8abde3",
   "metadata": {},
   "source": [
    "## Output evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b35aff0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "usage_frequency\n",
       "Frequent customer        21222034\n",
       "Regular customer          6873531\n",
       "Non-frequent customer     2897401\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Print the frequency of “usage_frequency” \n",
    "orders_products_final['usage_frequency'].value_counts(dropna = False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "aa9d6130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">median_days_since_prior_order</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usage_frequency</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Frequent customer</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-frequent customer</th>\n",
       "      <td>20.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Regular customer</th>\n",
       "      <td>10.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      median_days_since_prior_order             \n",
       "                                                min   max median\n",
       "usage_frequency                                                 \n",
       "Frequent customer                               0.0  10.0    7.0\n",
       "Non-frequent customer                          20.5  30.0   28.0\n",
       "Regular customer                               10.5  20.0   14.0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check aggregations\n",
    "#Snap view of usage_frequency variable\n",
    "orders_products_final.groupby('usage_frequency').agg({'median_days_since_prior_order' : ['min', 'max', 'median']})\n",
    "#nothing odd on the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7c33b395",
   "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>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price_per_user</th>\n",
       "      <th>type_of_spender</th>\n",
       "      <th>median_days_since_prior_order</th>\n",
       "      <th>usage_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</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>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  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  ...  max_order_number  price_range_products  \\\n",
       "0         Soda  ...                10             mid-range   \n",
       "1         Soda  ...                10             mid-range   \n",
       "2         Soda  ...                10             mid-range   \n",
       "3         Soda  ...                10             mid-range   \n",
       "4         Soda  ...                10             mid-range   \n",
       "\n",
       "         busiest_days busiest_period_of_day region  loyalty_flag  \\\n",
       "0        Regular days        Average orders  South           New   \n",
       "1  Least busiest days        Average orders  South           New   \n",
       "2  Least busiest days        Average orders  South           New   \n",
       "3  Least busiest days        Average orders  South           New   \n",
       "4  Least busiest days           Most orders  South           New   \n",
       "\n",
       "  mean_price_per_user  type_of_spender median_days_since_prior_order  \\\n",
       "0            6.367797      Low spender                          20.5   \n",
       "1            6.367797      Low spender                          20.5   \n",
       "2            6.367797      Low spender                          20.5   \n",
       "3            6.367797      Low spender                          20.5   \n",
       "4            6.367797      Low spender                          20.5   \n",
       "\n",
       "         usage_frequency  \n",
       "0  Non-frequent customer  \n",
       "1  Non-frequent customer  \n",
       "2  Non-frequent customer  \n",
       "3  Non-frequent customer  \n",
       "4  Non-frequent customer  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "orders_products_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d17e9896",
   "metadata": {},
   "source": [
    "Most of our users (68.38%) are 'Frequent customers' placing a follow-up order within 10 days of a previous order. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98393134",
   "metadata": {},
   "source": [
    "# 10. Exporting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ec74acc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 30)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check size before exporting\n",
    "orders_products_final.shape\n",
    "#9 new variables were added on the initially imported dataframe\n",
    "#the number or rows were not altered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "1e08b78d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting to prepared data folder\n",
    "#The pickle format is preferred for large df. This df contains 31M rows\n",
    "orders_products_final.to_pickle(os.path.join(path, '02 Data','Prepared data', 'orders_products_der_step6.pkl'))"
   ]
  }
 ],
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