Deriving_New_Variables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2053a77",
   "metadata": {},
   "source": [
    "# Deriving New Variables\n",
    "## This script contains the following points;\n",
    "## 1. Importing data\n",
    "## 2. Creating New Variables\n",
    "    2.1 The Price Ranges\n",
    "    2.2 The Shopping Activity Categories Based on Weekdays\n",
    "    2.3 The Shopping Habit Categories Based on Hours of The Day\n",
    "## 3. Exporting data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd4a322",
   "metadata": {},
   "source": [
    "## 1. Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c0f3381d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8cd401c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:\\Users\\senja\\OneDrive\\Documents\\5. Learning\\4. Data Analytics Immersion\\Instacart Basket Analysis'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ad59f40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\senja\\\\OneDrive\\\\Documents\\\\5. Learning\\\\4. Data Analytics Immersion\\\\Instacart Basket Analysis'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "218bec3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import merged order product pkl file\n",
    "\n",
    "ords_prods_merged = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared Data', 'orders_products_merged.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a51e9cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</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>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>5.8</td>\n",
       "      <td>3139998</td>\n",
       "      <td>138</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1977647</td>\n",
       "      <td>138</td>\n",
       "      <td>30</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>5.8</td>\n",
       "      <td>389851</td>\n",
       "      <td>709</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>6.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>5.8</td>\n",
       "      <td>652770</td>\n",
       "      <td>764</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Chocolate Sandwich Cookies</td>\n",
       "      <td>61</td>\n",
       "      <td>19</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1813452</td>\n",
       "      <td>764</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>9.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   product_id                product_name  aisle_id  department_id  prices  \\\n",
       "0           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "1           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "2           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "3           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "4           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "\n",
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   3139998      138            28                   6                 11   \n",
       "1   1977647      138            30                   6                 17   \n",
       "2    389851      709             2                   0                 21   \n",
       "3    652770      764             1                   3                 13   \n",
       "4   1813452      764             3                   4                 17   \n",
       "\n",
       "   days_since_prior_order  add_to_cart_order  reordered _merge  \n",
       "0                     3.0                  5          0   both  \n",
       "1                    20.0                  1          1   both  \n",
       "2                     6.0                 20          0   both  \n",
       "3                     0.0                 10          0   both  \n",
       "4                     9.0                 11          1   both  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the columnns\n",
    "\n",
    "ords_prods_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c958034a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32434212, 14)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8772f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a subset \n",
    "\n",
    "df = ords_prods_merged[:1000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6c13fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 14)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e23f536",
   "metadata": {},
   "source": [
    "## 2. Creating New Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d59bd9ae",
   "metadata": {},
   "source": [
    "### 2.1 The Price Ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0a5a10c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the function \n",
    "\n",
    "def price_label(row):\n",
    "\n",
    "  if row['prices'] <= 5:\n",
    "    return 'Low-range product'\n",
    "  elif (row['prices'] > 5) and (row['prices'] <= 15):\n",
    "    return 'Mid-range product'\n",
    "  elif row['prices'] > 15:\n",
    "    return 'High range'\n",
    "  else: return 'Not enough data'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ef25b35",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-88b74d434f1b>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['price_range'] = df.apply(price_label, axis=1)\n"
     ]
    }
   ],
   "source": [
    "# apply the function into subset\n",
    "\n",
    "df['price_range'] = df.apply(price_label, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d7b7c8bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mid-range product    659373\n",
       "Low-range product    331283\n",
       "High range             9344\n",
       "Name: price_range, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "07f6ee71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.5"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['prices'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ae408ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\senja\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1599: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = infer_fill_value(value)\n",
      "C:\\Users\\senja\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "df.loc[df['prices'] > 15, 'price_range_loc'] = 'High-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09f13686",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\senja\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "df.loc[(df['prices'] <= 15) & (df['prices'] > 5), 'price_range_loc'] = 'Mid-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "411b6344",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\senja\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:1720: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(loc, value, pi)\n"
     ]
    }
   ],
   "source": [
    "df.loc[df['prices'] <= 5, 'price_range_loc'] = 'Low-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a848b940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mid-range product     659373\n",
       "Low-range product     331283\n",
       "High-range product      9344\n",
       "Name: price_range_loc, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range_loc'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73de0781",
   "metadata": {},
   "outputs": [],
   "source": [
    "ords_prods_merged.loc[ords_prods_merged['prices'] > 15, 'price_range_loc'] = 'High-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7d0eab8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ords_prods_merged.loc[(ords_prods_merged['prices'] <= 15) & (ords_prods_merged['prices'] > 5), 'price_range_loc'] = 'Mid-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e319098c",
   "metadata": {},
   "outputs": [],
   "source": [
    "ords_prods_merged.loc[ords_prods_merged['prices'] <=5, 'price_range_loc'] = 'Low-range product'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "71cc0eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mid-range product     21890146\n",
       "Low-range product     10126384\n",
       "High-range product      417682\n",
       "Name: price_range_loc, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged['price_range_loc'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68bff068",
   "metadata": {},
   "source": [
    "### 2.2 The Shopping Activity Categories based On Weekdays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "95388722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6209632\n",
       "1    5665830\n",
       "6    4500246\n",
       "2    4217766\n",
       "5    4209449\n",
       "3    3844096\n",
       "4    3787193\n",
       "Name: orders_day_of_week, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the values for orders days in merged dataframe\n",
    "\n",
    "ords_prods_merged['orders_day_of_week'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "183dcc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = []\n",
    "\n",
    "for value in ords_prods_merged['orders_day_of_week']:\n",
    "  if value == 0:\n",
    "    result.append('Busiest day')\n",
    "  elif value == 4:\n",
    "    result.append('Least busy')\n",
    "  else:\n",
    "    result.append('Regularly busy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a56d22ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "ords_prods_merged['Busiest day'] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "073abab0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Busiest day',\n",
       " 'Busiest day',\n",
       " 'Busiest day',\n",
       " 'Busiest day',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Least busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Busiest day',\n",
       " 'Busiest day',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Busiest day',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
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       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " 'Least busy',\n",
       " 'Regularly busy',\n",
       " ...]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23599935",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regularly busy    22437387\n",
       "Busiest day        6209632\n",
       "Least busy         3787193\n",
       "Name: Busiest day, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged['Busiest day'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "12206a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating new categories based on the two busiest and two slowest days\n",
    "\n",
    "result2 = []\n",
    "for value in ords_prods_merged['orders_day_of_week']:\n",
    "  if value == 0 or value == 1:\n",
    "    result2.append('Busiest days')\n",
    "  elif value == 3 or value == 4:\n",
    "    result2.append('Slowest days')\n",
    "  else:\n",
    "    result2.append('Regularly busy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bae1167c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Busiest days',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Busiest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Busiest days',\n",
       " 'Slowest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Busiest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Busiest days',\n",
       " 'Busiest days',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " 'Slowest days',\n",
       " 'Regularly busy',\n",
       " ...]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "046368b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ords_prods_merged['Busiest days'] = result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9401db2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Regularly busy    12927461\n",
       "Busiest days      11875462\n",
       "Slowest days       7631289\n",
       "Name: Busiest days, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged['Busiest days'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "32554cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['product_id', 'product_name', 'aisle_id', 'department_id', 'prices',\n",
       "       'order_id', 'user_id', 'order_number', 'orders_day_of_week',\n",
       "       'order_hour_of_day', 'days_since_prior_order', 'add_to_cart_order',\n",
       "       'reordered', '_merge', 'price_range_loc', 'Busiest day',\n",
       "       'Busiest days'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the output accuracy \n",
    "\n",
    "ords_prods_merged.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "557b4975",
   "metadata": {},
   "outputs": [
    {
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       "      <th>days_since_prior_order</th>\n",
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      "text/plain": [
       "   product_id                product_name  aisle_id  department_id  prices  \\\n",
       "0           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "1           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "2           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "3           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "4           1  Chocolate Sandwich Cookies        61             19     5.8   \n",
       "\n",
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   3139998      138            28                   6                 11   \n",
       "1   1977647      138            30                   6                 17   \n",
       "2    389851      709             2                   0                 21   \n",
       "3    652770      764             1                   3                 13   \n",
       "4   1813452      764             3                   4                 17   \n",
       "\n",
       "   days_since_prior_order  add_to_cart_order  reordered _merge  \\\n",
       "0                     3.0                  5          0   both   \n",
       "1                    20.0                  1          1   both   \n",
       "2                     6.0                 20          0   both   \n",
       "3                     0.0                 10          0   both   \n",
       "4                     9.0                 11          1   both   \n",
       "\n",
       "     price_range_loc     Busiest day    Busiest days  \n",
       "0  Mid-range product  Regularly busy  Regularly busy  \n",
       "1  Mid-range product  Regularly busy  Regularly busy  \n",
       "2  Mid-range product     Busiest day    Busiest days  \n",
       "3  Mid-range product  Regularly busy    Slowest days  \n",
       "4  Mid-range product      Least busy    Slowest days  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the columns \n",
    "\n",
    "ords_prods_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27d14bb3",
   "metadata": {},
   "source": [
    "### 2.3 The Shopping Habit Categories Based on Hours of The Day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9991202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    2764390\n",
       "11    2738585\n",
       "14    2691563\n",
       "15    2664522\n",
       "13    2663272\n",
       "12    2620800\n",
       "16    2537469\n",
       "9     2456661\n",
       "17    2089452\n",
       "8     1719952\n",
       "18    1637922\n",
       "19    1259382\n",
       "20     977017\n",
       "7      891928\n",
       "21     796362\n",
       "22     634737\n",
       "23     402612\n",
       "6      290770\n",
       "0      218942\n",
       "1      115786\n",
       "5       88057\n",
       "2       69431\n",
       "4       53283\n",
       "3       51317\n",
       "Name: order_hour_of_day, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the values for hours in merged dataframe\n",
    "\n",
    "ords_prods_merged['order_hour_of_day'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a4d8877d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product_id</th>\n",
       "      <th>aisle_id</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</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>add_to_cart_order</th>\n",
       "      <th>reordered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "      <td>3.243421e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.557778e+04</td>\n",
       "      <td>7.121649e+01</td>\n",
       "      <td>9.922231e+00</td>\n",
       "      <td>1.198114e+01</td>\n",
       "      <td>1.710748e+06</td>\n",
       "      <td>1.029374e+05</td>\n",
       "      <td>1.714192e+01</td>\n",
       "      <td>2.738810e+00</td>\n",
       "      <td>1.342500e+01</td>\n",
       "      <td>1.039271e+01</td>\n",
       "      <td>8.351176e+00</td>\n",
       "      <td>5.896888e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.409614e+04</td>\n",
       "      <td>3.820429e+01</td>\n",
       "      <td>6.281190e+00</td>\n",
       "      <td>4.954311e+02</td>\n",
       "      <td>9.872996e+05</td>\n",
       "      <td>5.946649e+04</td>\n",
       "      <td>1.753489e+01</td>\n",
       "      <td>2.090047e+00</td>\n",
       "      <td>4.246351e+00</td>\n",
       "      <td>8.917726e+00</td>\n",
       "      <td>7.126688e+00</td>\n",
       "      <td>4.918902e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.353500e+04</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>4.200000e+00</td>\n",
       "      <td>8.559450e+05</td>\n",
       "      <td>5.142000e+04</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.525600e+04</td>\n",
       "      <td>8.300000e+01</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>7.400000e+00</td>\n",
       "      <td>1.711049e+06</td>\n",
       "      <td>1.026125e+05</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.793500e+04</td>\n",
       "      <td>1.070000e+02</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.130000e+01</td>\n",
       "      <td>2.565510e+06</td>\n",
       "      <td>1.543910e+05</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.968800e+04</td>\n",
       "      <td>1.340000e+02</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>9.999900e+04</td>\n",
       "      <td>3.421083e+06</td>\n",
       "      <td>2.062090e+05</td>\n",
       "      <td>9.900000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>3.000000e+01</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         product_id      aisle_id  department_id        prices      order_id  \\\n",
       "count  3.243421e+07  3.243421e+07   3.243421e+07  3.243421e+07  3.243421e+07   \n",
       "mean   2.557778e+04  7.121649e+01   9.922231e+00  1.198114e+01  1.710748e+06   \n",
       "std    1.409614e+04  3.820429e+01   6.281190e+00  4.954311e+02  9.872996e+05   \n",
       "min    1.000000e+00  1.000000e+00   1.000000e+00  1.000000e+00  2.000000e+00   \n",
       "25%    1.353500e+04  3.100000e+01   4.000000e+00  4.200000e+00  8.559450e+05   \n",
       "50%    2.525600e+04  8.300000e+01   9.000000e+00  7.400000e+00  1.711049e+06   \n",
       "75%    3.793500e+04  1.070000e+02   1.600000e+01  1.130000e+01  2.565510e+06   \n",
       "max    4.968800e+04  1.340000e+02   2.100000e+01  9.999900e+04  3.421083e+06   \n",
       "\n",
       "            user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "count  3.243421e+07  3.243421e+07        3.243421e+07       3.243421e+07   \n",
       "mean   1.029374e+05  1.714192e+01        2.738810e+00       1.342500e+01   \n",
       "std    5.946649e+04  1.753489e+01        2.090047e+00       4.246351e+00   \n",
       "min    1.000000e+00  1.000000e+00        0.000000e+00       0.000000e+00   \n",
       "25%    5.142000e+04  5.000000e+00        1.000000e+00       1.000000e+01   \n",
       "50%    1.026125e+05  1.100000e+01        3.000000e+00       1.300000e+01   \n",
       "75%    1.543910e+05  2.400000e+01        5.000000e+00       1.600000e+01   \n",
       "max    2.062090e+05  9.900000e+01        6.000000e+00       2.300000e+01   \n",
       "\n",
       "       days_since_prior_order  add_to_cart_order     reordered  \n",
       "count            3.243421e+07       3.243421e+07  3.243421e+07  \n",
       "mean             1.039271e+01       8.351176e+00  5.896888e-01  \n",
       "std              8.917726e+00       7.126688e+00  4.918902e-01  \n",
       "min              0.000000e+00       1.000000e+00  0.000000e+00  \n",
       "25%              4.000000e+00       3.000000e+00  0.000000e+00  \n",
       "50%              7.000000e+00       6.000000e+00  1.000000e+00  \n",
       "75%              1.400000e+01       1.100000e+01  1.000000e+00  \n",
       "max              3.000000e+01       1.450000e+02  1.000000e+00  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b23a0761",
   "metadata": {},
   "outputs": [],
   "source": [
    "def busiest_period_of_day_label(row):\n",
    "\n",
    "  if row['order_hour_of_day'] <= 6 or row['order_hour_of_day'] == 23:\n",
    "    return 'Fewest Orders'\n",
    "  elif (row['order_hour_of_day'] >= 10) and (row['order_hour_of_day'] <= 16):\n",
    "    return 'Most Orders'\n",
    "  else: return 'Average Orders'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e314b48",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-35-82253e0f39a9>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['busiest_period_of_day_range'] = df.apply(busiest_period_of_day_label, axis=1)\n"
     ]
    }
   ],
   "source": [
    "# apply the function into subset df\n",
    "\n",
    "df['busiest_period_of_day_range'] = df.apply(busiest_period_of_day_label, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e845baa",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Most Orders       575647\n",
       "Average Orders    384748\n",
       "Fewest Orders      39605\n",
       "Name: busiest_period_of_day_range, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['busiest_period_of_day_range'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f8d769b8",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 17)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d2b58c9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000000 entries, 0 to 999999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                       Non-Null Count    Dtype   \n",
      "---  ------                       --------------    -----   \n",
      " 0   product_id                   1000000 non-null  int64   \n",
      " 1   product_name                 975453 non-null   object  \n",
      " 2   aisle_id                     1000000 non-null  int64   \n",
      " 3   department_id                1000000 non-null  int64   \n",
      " 4   prices                       1000000 non-null  float64 \n",
      " 5   order_id                     1000000 non-null  int64   \n",
      " 6   user_id                      1000000 non-null  int64   \n",
      " 7   order_number                 1000000 non-null  int64   \n",
      " 8   orders_day_of_week           1000000 non-null  int64   \n",
      " 9   order_hour_of_day            1000000 non-null  int64   \n",
      " 10  days_since_prior_order       1000000 non-null  float64 \n",
      " 11  add_to_cart_order            1000000 non-null  int64   \n",
      " 12  reordered                    1000000 non-null  int64   \n",
      " 13  _merge                       1000000 non-null  category\n",
      " 14  price_range                  1000000 non-null  object  \n",
      " 15  price_range_loc              1000000 non-null  object  \n",
      " 16  busiest_period_of_day_range  1000000 non-null  object  \n",
      "dtypes: category(1), float64(2), int64(10), object(4)\n",
      "memory usage: 130.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# check the accuracy \n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ffacb72",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the function into the merged dataframe\n",
    "\n",
    "ords_prods_merged['busiest_period_of_day_range'] = ords_prods_merged.apply(busiest_period_of_day_label, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dd3fa3d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Most Orders       18680601\n",
       "Average Orders    12463413\n",
       "Fewest Orders      1290198\n",
       "Name: busiest_period_of_day_range, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged['busiest_period_of_day_range'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f673455e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32434212, 18)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merged.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6408e502",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 32434212 entries, 0 to 32434211\n",
      "Data columns (total 18 columns):\n",
      " #   Column                       Dtype   \n",
      "---  ------                       -----   \n",
      " 0   product_id                   int64   \n",
      " 1   product_name                 object  \n",
      " 2   aisle_id                     int64   \n",
      " 3   department_id                int64   \n",
      " 4   prices                       float64 \n",
      " 5   order_id                     int64   \n",
      " 6   user_id                      int64   \n",
      " 7   order_number                 int64   \n",
      " 8   orders_day_of_week           int64   \n",
      " 9   order_hour_of_day            int64   \n",
      " 10  days_since_prior_order       float64 \n",
      " 11  add_to_cart_order            int64   \n",
      " 12  reordered                    int64   \n",
      " 13  _merge                       category\n",
      " 14  price_range_loc              object  \n",
      " 15  Busiest day                  object  \n",
      " 16  Busiest days                 object  \n",
      " 17  busiest_period_of_day_range  object  \n",
      "dtypes: category(1), float64(2), int64(10), object(5)\n",
      "memory usage: 4.4+ GB\n"
     ]
    }
   ],
   "source": [
    "# check the accuracy \n",
    "\n",
    "ords_prods_merged.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b96e824",
   "metadata": {},
   "source": [
    "## 3. Exporting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "89861c44",
   "metadata": {},
   "outputs": [],
   "source": [
    "ords_prods_merged.to_pickle(os.path.join(path, '02 Data','Prepared Data', 'updated_merged.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581bbf2f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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