Visualisation_examples1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3c356374",
   "metadata": {},
   "source": [
    "\n",
    "### This script contains the following points;\n",
    "### 1. Importing Data and Libraries\n",
    "### 2. Visualisations - Merged and Grouped \"Products - Orders\" Data Frame\n",
    "    2.1 Bar Charts\n",
    "    2.2 Histograms and Scatterplot\n",
    "    2.3 Sample Data and Line Chart\n",
    "### 3. Customers Data (New Data Frame) \n",
    "    3.1 Data Wrangling \n",
    "    3.2 Data Quality & Consistency Checks\n",
    "  \n",
    "### 4. Merging Data Frames (Product-Orders with Customers)\n",
    "\n",
    "### 5. Exporting Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92360e24",
   "metadata": {},
   "source": [
    "### 1. Importing Data and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52c52dc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing data and libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48150f20",
   "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": "d3403acc",
   "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": "ff91d7a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import grouped_merged pkl file\n",
    "\n",
    "ords_prods_merge = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared Data', 'grouped_merged.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e692d4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import new customer file\n",
    "\n",
    "df_customers = pd.read_csv(os.path.join(path, '02 Data', 'Original Data', 'customers.csv'), index_col = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83ee98da",
   "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>...</th>\n",
       "      <th>price_range_loc</th>\n",
       "      <th>Busiest day</th>\n",
       "      <th>Busiest days</th>\n",
       "      <th>busiest_period_of_day_range</th>\n",
       "      <th>max_order</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price</th>\n",
       "      <th>Spender_flag</th>\n",
       "      <th>median_order_frequency</th>\n",
       "      <th>Frequency_flag</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>...</td>\n",
       "      <td>Mid-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>32</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>6.935811</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Frequent customer</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>...</td>\n",
       "      <td>Mid-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>32</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>6.935811</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Frequent customer</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>...</td>\n",
       "      <td>Mid-range product</td>\n",
       "      <td>Busiest day</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>5</td>\n",
       "      <td>New customer</td>\n",
       "      <td>7.930208</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Frequent customer</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>...</td>\n",
       "      <td>Mid-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Slowest days</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>3</td>\n",
       "      <td>New customer</td>\n",
       "      <td>4.972414</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Frequent customer</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>...</td>\n",
       "      <td>Mid-range product</td>\n",
       "      <td>Least busy</td>\n",
       "      <td>Slowest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>3</td>\n",
       "      <td>New customer</td>\n",
       "      <td>4.972414</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\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",
       "   ...    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   \n",
       "\n",
       "  busiest_period_of_day_range max_order      loyalty_flag mean_price  \\\n",
       "0                 Most Orders        32  Regular customer   6.935811   \n",
       "1              Average Orders        32  Regular customer   6.935811   \n",
       "2              Average Orders         5      New customer   7.930208   \n",
       "3                 Most Orders         3      New customer   4.972414   \n",
       "4              Average Orders         3      New customer   4.972414   \n",
       "\n",
       "  Spender_flag  median_order_frequency     Frequency_flag  \n",
       "0  Low spender                     8.0  Frequent customer  \n",
       "1  Low spender                     8.0  Frequent customer  \n",
       "2  Low spender                     6.0  Frequent customer  \n",
       "3  Low spender                     9.0  Frequent customer  \n",
       "4  Low spender                     9.0  Frequent customer  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check merged data\n",
    "\n",
    "ords_prods_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb90564a",
   "metadata": {},
   "source": [
    "### 2. Visualisations - Merged and Grouped \"Products - Orders\" Data Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "777d771c",
   "metadata": {},
   "source": [
    "### 2.1 Bar Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3991e9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the bar chart for orders by weekdays from largest to smallest\n",
    "\n",
    "ords_prods_merge['orders_day_of_week'].value_counts().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c06f27e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create the bar chart for orders from Sun to Sat\n",
    "\n",
    "ords_prods_merge['orders_day_of_week'].value_counts().sort_index().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95db99c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ords_prods_merge['orders_day_of_week'].value_counts().plot.bar(color = ['purple','red','pink', 'orange','yellow', 'green','blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e48ed48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar = ords_prods_merge['orders_day_of_week'].value_counts().plot.bar(color =['purple', 'red', 'pink', 'orange', 'yellow', 'green', 'blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "79070a6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "bar.figure.savefig(os.path.join(path, '04 Analysis','Visualisations', 'bar_orders_dow.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73e46dd5",
   "metadata": {},
   "source": [
    "### 2.3 Histograms and Scatterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "21810a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a histogram for prices\n",
    "\n",
    "ords_prods_merge['prices'].plot.hist(bins = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f3bb8cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.243421e+07\n",
       "mean     1.198114e+01\n",
       "std      4.954311e+02\n",
       "min      1.000000e+00\n",
       "25%      4.200000e+00\n",
       "50%      7.400000e+00\n",
       "75%      1.130000e+01\n",
       "max      9.999900e+04\n",
       "Name: prices, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merge['prices'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2ab0798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11.981143922956724"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merge['prices'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0389dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merge['prices'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f372b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99999.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merge['prices'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c97ed45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='prices', ylabel='prices'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a price scatterplot to check outliners\n",
    "\n",
    "sns.scatterplot(x = 'prices', y = 'prices',data = ords_prods_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f16700d6",
   "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>...</th>\n",
       "      <th>price_range_loc</th>\n",
       "      <th>Busiest day</th>\n",
       "      <th>Busiest days</th>\n",
       "      <th>busiest_period_of_day_range</th>\n",
       "      <th>max_order</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price</th>\n",
       "      <th>Spender_flag</th>\n",
       "      <th>median_order_frequency</th>\n",
       "      <th>Frequency_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13128379</th>\n",
       "      <td>21553</td>\n",
       "      <td>Lowfat 2% Milkfat Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>912404</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>40</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>108.648299</td>\n",
       "      <td>High spender</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13128380</th>\n",
       "      <td>21553</td>\n",
       "      <td>Lowfat 2% Milkfat Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>603376</td>\n",
       "      <td>17</td>\n",
       "      <td>22</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>40</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>108.648299</td>\n",
       "      <td>High spender</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13128381</th>\n",
       "      <td>21553</td>\n",
       "      <td>Lowfat 2% Milkfat Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>3264360</td>\n",
       "      <td>135</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>4</td>\n",
       "      <td>New customer</td>\n",
       "      <td>1154.792308</td>\n",
       "      <td>High spender</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Regular customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13128382</th>\n",
       "      <td>21553</td>\n",
       "      <td>Lowfat 2% Milkfat Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>892534</td>\n",
       "      <td>135</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Busiest day</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>4</td>\n",
       "      <td>New customer</td>\n",
       "      <td>1154.792308</td>\n",
       "      <td>High spender</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Regular customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13128383</th>\n",
       "      <td>21553</td>\n",
       "      <td>Lowfat 2% Milkfat Cottage Cheese</td>\n",
       "      <td>108</td>\n",
       "      <td>16</td>\n",
       "      <td>14900.0</td>\n",
       "      <td>229704</td>\n",
       "      <td>342</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>16</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>114.426619</td>\n",
       "      <td>High spender</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Regular customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21815122</th>\n",
       "      <td>33664</td>\n",
       "      <td>2 % Reduced Fat  Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>2249946</td>\n",
       "      <td>204099</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Busiest day</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>39</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>1106.743956</td>\n",
       "      <td>High spender</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21815123</th>\n",
       "      <td>33664</td>\n",
       "      <td>2 % Reduced Fat  Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>2363282</td>\n",
       "      <td>204099</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Busiest day</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>39</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>1106.743956</td>\n",
       "      <td>High spender</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21815124</th>\n",
       "      <td>33664</td>\n",
       "      <td>2 % Reduced Fat  Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>3181945</td>\n",
       "      <td>204395</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Slowest days</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>15</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>451.153540</td>\n",
       "      <td>High spender</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21815125</th>\n",
       "      <td>33664</td>\n",
       "      <td>2 % Reduced Fat  Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>2486215</td>\n",
       "      <td>205227</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Regularly busy</td>\n",
       "      <td>Slowest days</td>\n",
       "      <td>Average Orders</td>\n",
       "      <td>12</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>1178.381871</td>\n",
       "      <td>High spender</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21815126</th>\n",
       "      <td>33664</td>\n",
       "      <td>2 % Reduced Fat  Milk</td>\n",
       "      <td>84</td>\n",
       "      <td>16</td>\n",
       "      <td>99999.0</td>\n",
       "      <td>2232716</td>\n",
       "      <td>205227</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>High-range product</td>\n",
       "      <td>Busiest day</td>\n",
       "      <td>Busiest days</td>\n",
       "      <td>Most Orders</td>\n",
       "      <td>12</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>1178.381871</td>\n",
       "      <td>High spender</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Frequent customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5127 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          product_id                      product_name  aisle_id  \\\n",
       "13128379       21553  Lowfat 2% Milkfat Cottage Cheese       108   \n",
       "13128380       21553  Lowfat 2% Milkfat Cottage Cheese       108   \n",
       "13128381       21553  Lowfat 2% Milkfat Cottage Cheese       108   \n",
       "13128382       21553  Lowfat 2% Milkfat Cottage Cheese       108   \n",
       "13128383       21553  Lowfat 2% Milkfat Cottage Cheese       108   \n",
       "...              ...                               ...       ...   \n",
       "21815122       33664             2 % Reduced Fat  Milk        84   \n",
       "21815123       33664             2 % Reduced Fat  Milk        84   \n",
       "21815124       33664             2 % Reduced Fat  Milk        84   \n",
       "21815125       33664             2 % Reduced Fat  Milk        84   \n",
       "21815126       33664             2 % Reduced Fat  Milk        84   \n",
       "\n",
       "          department_id   prices  order_id  user_id  order_number  \\\n",
       "13128379             16  14900.0    912404       17            12   \n",
       "13128380             16  14900.0    603376       17            22   \n",
       "13128381             16  14900.0   3264360      135             2   \n",
       "13128382             16  14900.0    892534      135             3   \n",
       "13128383             16  14900.0    229704      342             8   \n",
       "...                 ...      ...       ...      ...           ...   \n",
       "21815122             16  99999.0   2249946   204099            29   \n",
       "21815123             16  99999.0   2363282   204099            31   \n",
       "21815124             16  99999.0   3181945   204395            13   \n",
       "21815125             16  99999.0   2486215   205227             7   \n",
       "21815126             16  99999.0   2232716   205227            10   \n",
       "\n",
       "          orders_day_of_week  order_hour_of_day  ...     price_range_loc  \\\n",
       "13128379                   2                 14  ...  High-range product   \n",
       "13128380                   6                 16  ...  High-range product   \n",
       "13128381                   2                 21  ...  High-range product   \n",
       "13128382                   0                  8  ...  High-range product   \n",
       "13128383                   1                 19  ...  High-range product   \n",
       "...                      ...                ...  ...                 ...   \n",
       "21815122                   0                  8  ...  High-range product   \n",
       "21815123                   0                  9  ...  High-range product   \n",
       "21815124                   3                 15  ...  High-range product   \n",
       "21815125                   3                 20  ...  High-range product   \n",
       "21815126                   0                 11  ...  High-range product   \n",
       "\n",
       "             Busiest day    Busiest days busiest_period_of_day_range  \\\n",
       "13128379  Regularly busy  Regularly busy                 Most Orders   \n",
       "13128380  Regularly busy  Regularly busy                 Most Orders   \n",
       "13128381  Regularly busy  Regularly busy              Average Orders   \n",
       "13128382     Busiest day    Busiest days              Average Orders   \n",
       "13128383  Regularly busy    Busiest days              Average Orders   \n",
       "...                  ...             ...                         ...   \n",
       "21815122     Busiest day    Busiest days              Average Orders   \n",
       "21815123     Busiest day    Busiest days              Average Orders   \n",
       "21815124  Regularly busy    Slowest days                 Most Orders   \n",
       "21815125  Regularly busy    Slowest days              Average Orders   \n",
       "21815126     Busiest day    Busiest days                 Most Orders   \n",
       "\n",
       "         max_order      loyalty_flag   mean_price  Spender_flag  \\\n",
       "13128379        40  Regular customer   108.648299  High spender   \n",
       "13128380        40  Regular customer   108.648299  High spender   \n",
       "13128381         4      New customer  1154.792308  High spender   \n",
       "13128382         4      New customer  1154.792308  High spender   \n",
       "13128383        16  Regular customer   114.426619  High spender   \n",
       "...            ...               ...          ...           ...   \n",
       "21815122        39  Regular customer  1106.743956  High spender   \n",
       "21815123        39  Regular customer  1106.743956  High spender   \n",
       "21815124        15  Regular customer   451.153540  High spender   \n",
       "21815125        12  Regular customer  1178.381871  High spender   \n",
       "21815126        12  Regular customer  1178.381871  High spender   \n",
       "\n",
       "          median_order_frequency     Frequency_flag  \n",
       "13128379                     5.0  Frequent customer  \n",
       "13128380                     5.0  Frequent customer  \n",
       "13128381                    11.5   Regular customer  \n",
       "13128382                    11.5   Regular customer  \n",
       "13128383                    20.0   Regular customer  \n",
       "...                          ...                ...  \n",
       "21815122                     4.0  Frequent customer  \n",
       "21815123                     4.0  Frequent customer  \n",
       "21815124                     4.0  Frequent customer  \n",
       "21815125                     7.0  Frequent customer  \n",
       "21815126                     7.0  Frequent customer  \n",
       "\n",
       "[5127 rows x 24 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check prices above 100\n",
    "\n",
    "ords_prods_merge.loc[ords_prods_merge['prices'] > 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d237e64c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ad hoc data cleaning - mark prices above 100 as of missing \n",
    "\n",
    "ords_prods_merge.loc[ords_prods_merge['prices'] >100, 'prices'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e7fa707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the max\n",
    "\n",
    "ords_prods_merge['prices'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "db70aa81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a histogram \n",
    "\n",
    "hist = ords_prods_merge['prices'].plot.hist(bins = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "de1fac3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_2 = ords_prods_merge['prices'].plot.hist(bins = 70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d723eb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.figure.savefig(os.path.join(path, '04 Analysis','Visualisations', 'hist_prices.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcc5e34",
   "metadata": {},
   "source": [
    "### 2.3 Sample Data and Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03131e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3df27db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a list holding true/false values to test np.random.rand() <= 0.7\n",
    "\n",
    "dev = np.random.rand(len(ords_prods_merge)) <= 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f64111aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True, False, ..., False,  True,  True])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b19a1a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store 70% of the sample in the dataframe big\n",
    "\n",
    "big = ords_prods_merge[dev]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7c3e736a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# store 30% of the sample in the dataframe small\n",
    "\n",
    "small = ords_prods_merge[~dev]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cef44988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32434212"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ords_prods_merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "062ae915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32434212"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(big) + len(small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8b6f1845",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the subset from small data frame with only weekday and price data\n",
    "\n",
    "df_2 = small[['orders_day_of_week','prices']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06034f47",
   "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>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434188</th>\n",
       "      <td>1</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434191</th>\n",
       "      <td>4</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434199</th>\n",
       "      <td>6</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434204</th>\n",
       "      <td>2</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434209</th>\n",
       "      <td>3</td>\n",
       "      <td>13.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9729729 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          orders_day_of_week  prices\n",
       "0                          6     5.8\n",
       "2                          0     5.8\n",
       "3                          3     5.8\n",
       "6                          2     5.8\n",
       "10                         5     5.8\n",
       "...                      ...     ...\n",
       "32434188                   1    13.5\n",
       "32434191                   4    13.5\n",
       "32434199                   6    13.5\n",
       "32434204                   2    13.5\n",
       "32434209                   3    13.5\n",
       "\n",
       "[9729729 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c1b38390",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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a5bkKuZTHbLnOq5eKSxbH1YOAbV04+2E5ViBuw12Dacp1n3vGo0Gl5hbgFjMZY95KpR5QqgWkPJenJ2dQaB0tGqqCQF+6u7OXmqxA3IbBbBIljpkOjPD8+TlKccw0YzGTMeYmZkv16GB74PjkNDsG0uwdyQJR8RjJpVonyXXbxmhFj0knXPrSUcx09EAcM52NZjMJFjMZY1Z2uVgll/RYrPq8cHGew/tHWhF11Q/YPrAx4iWwAnHbdg6kKdV93ra9j9F8srVozmImY8xKqo2AhWqDdMLlmbMzBKG2Vk+rRpv59G+A2UtNViBu02A2CQqOCEcOjHLy/CzlusVMxpiVtW/Jc3xympFcknu25QGoNAKGsgmS3sb5WN44LekxmaRLPu1FMdPBURqB8uzZ2airqBYzGWPe7NJ8lWwi+tw4eW6Ow/tHcOJ4qdII2LHBdoW2AnEHdg5kKNV97tvex3D2esyUTbpcLtreTMaY62p+wFw52gH6uXOz1IOQw21Hi6pG2/lsJFYg7sBQrj1mGuG5c7NU6kG0aK5Ut5jJGNMyX260FscdmyjQl/Z4x84BIBqb6M8kSCe6c7ToSqxA3IFM0iWXcqn5UcxUD0JOnItmM6nFTMaYNpfnK2QSLo0g5NmzMzy+f6S1WrpU99m5gWYvNVmBuEM7BzOUaj737+hnMJtYEjNdsZjJGAPU/ZDZcrTz8wsX5qg0Ao60rZ5GaR0UtJFYgbhDQ7lo0ZzrRLOZTpybpdqIYqaZUp26H3a7icaYLpuvRGmCiHBsskAm4fLg7kEgGpvIpVwyyY0VL4EViDuWTXrkknHMdGCEmh/y3LnZ63szVSxmMmaru1qskvbc+IiAAu/aO9xaLV2uB+wY3Fizl5qsQKyBHXHM9I6dAwxkErYFuDGmpRGETC/WyCZdXrk0T7Hqt7b2BghDZWgDxktgBWJNDMVbgLuOcHj/CM+enaHmW8xkjIFiW7x0fKJA0nV49O4hICoe6aRLdgPGS2AFYk3kUh6ZpEvdD3nPwVGqjZCTFjMZY4jipZTnEqpyfLLAI3cPtqazNmcvNT8rNpqOFQgRuVdETrX9FEXk0zc8ZkBEviYiL4jIKyLy8fj23SLypyLyWnz7pzrVzrWyczBaNPfAXQP0pz2+cTo6JcpiJmO2Lj8ImV6sk026/OXVRQql+pKjRYNAo/VUG1THNh1X1deBhwBExAXeAL56w8N+BHhVVT8kImPA6yLyBcAH/hdVPSkifcBzIvJHqvpqp9p7p4ZzSU5fW2zFTH/xl9PU/XBJzLSR9lgxxnReseoTquKIcHxyGtcR3r13GIiKR8JzyKc2xtkPy1mvT6wngAlVPXfD7Qr0SdS/ygMzgK+ql1X1JICqLgCvAXetU1tvSzYZTVNrBCFHDo5SaQQ8f8FiJmO2smsLVVKui6pybKLAO+8aIB8fBlSqB+zYwPESrF+B+CjwxWVu/wxwP3AJeAn4lKouGdEVkb3Aw8DTHW7jHRGRaAvwms877xqgL+W1TppLey5XbdGcMVtKECpTCzUySZezhTKX56utrb0B/DBkJJ/qYgvfWscLhIgkgQ8DX1rm7u8FTgE7ieKoz4hIf9vv5oHfBD6tqsUVnv8TInJCRE5MTU2tcetvzXA+RaCK5zo8vn+EZ87M0AhCskmXwmLNZjMZs4UUKw2CUHEd4fjENAI8tj+Kl4JQ8RyhbwPHS7A+PYgPAidV9eoy930c+IpGTgNngPsARCRBVBy+oKpfWenJVfVzqnpIVQ+NjY11oPmrl0u6pL1mzDRCuR7w/Pm5aG8moFi1mMmYrWJqsUbCiT5ij08WePvO/tZ6h1LNZ/tAGsfZuPESrE+B+BjLx0sA54nGJxCRbcC9wGQ8JvFLwGuq+rPr0MY1ISLsiE+ae3DXILmU21o0l/ZcrsxbzGTMVhCGyrVilVzK49JchbOFMofb9l5qhCGjGzxegg4XCBHJAu8HvtJ22ydF5JPx1Z8GjojIS8AfAz+mqtPAUeCHgfe1TZP9K51s61oZzicJAiXhOjy2b4SnzxRaMdP0Yo1GYDGTMZvdQtXHj+OlYxPRlPdmgWjGTn0b6GjRlXQ0AFPVMjByw22fbbt8CfieZX7vG8DG7nutIJ/ySCUcGkG0aO5PvnmNFy7OcejuKHucrzR64puDMeb2TS1W8Vrx0jQHx/OM90fbeVfqAeN9qdZW3xvZqnoQInJURHLx5R8SkZ8Vkbs727TeFMVMGRZrPg/tHiSbdFtbgFvMZMzmF4bKlfka+ZTH1EKNb11dXLK1dy0IGOvbeGc/LGe1EdMvAmUReRD458A54Nc71qoeN5JPEoTNmGmYpyZn8C1mMmZLWKj5+GGI6whPTcbxUrw5X6iKCPSnN/bspabVFghfVRX4fuDnVfXngb7ONau3tcdMRw+OsljzefHivC2aM2YLKCzWcOP/149PFtg9nGXXUBaI4qWxfArP7Y1dFVbbygUR+QmigeP/Fm+dsfFHWLqkNZup5vPw7iEyiaWzmWzRnDGbk6pyeb5KPuUxX2nwyqX5JfFS1Q9aYxG9YLUF4geAGvB3VfUK0bYXP9OxVm0CI/GiuaTn8O59wxyfLFjMZMwmt1jzqfshnuvw1GSBUGmd/RCFMNDfA7OXmlZVIOKi8JtAc/rNNG/eeM+06Ut5JFwHP46ZFqo+L18qRovm9Poe8caYzaOwWG/NTjo+WWBbf4p9ozkAKo2A4VyypzbtXO0spn8AfBn4D/FNdwG/1aE2bQoiwvb+NKV6wCN7BqOYKZ7NlLKYyZhNJ4qXKuRTHqWazwsX5ji8f7Q19lhpBGzvoXgJVh8x/QjR4rUigKr+JTDeqUZtFqN9KfwwJOW5vGvvEMcnCwShkk26TFnMZMymUqoH1PyQhOvw7NkZ/FCXHC2qCv2Z3omXYPUFoqaq9eYVEfGItuo2N9GX8vCcKGY6cmC0NWjlWMxkzKYzc8PspaFsgnu3R5M9q42AwWyidZJcr1htgfhzEfkXQEZE3k+0M+vXOteszcFxmnszBTx69xApz2ltAW4xkzGby+X5KtmkR7UR8Ny5WR7fP4ITF4xS3WfHQG/FS7D6AvHjwBTRmQ3/I/C7wL/sVKM2kyhmCkgnXA7tHbaYyZhNqFTzqdQDkp7D8+dnqfkhR9vOfkBhILNxjxZdyWoLRAb4ZVX9m6r6EeCX49vMW2jGTEGoHD0wwly5wauXixYzGbOJzJbrNA+GOzZZoC/l8Y6d0dE2NT8gn/bIJHsrXoLVF4g/ZmlByAD/fe2bs/k4jrB9IM1izefQ3cMkPYdjFjMZs6lcnquQS3k0gpBnz8zw7n3DrdXSpVpvxkuw+gKRVtXF5pX4crYzTdp8xvJp/DAkk3R5dM8QxyYKhHo9ZvItZjKmZ1XqAaVaQMpzeeniPKV68KbZS4PZ3ouXYPUFoiQijzSviMijQKUzTdp8+tIeniMEofKeg6PMlOu8FsdMoartzWRMD5st1VuHExybmCaTcHlo9xAAdT/6Ypjb4EeLrmS1rf408CURuRRf30G0/YZZBccRtvWnuVascWjvEAlXePL0NO/YOUDa9bharG34w8uNMcu7VKyQS3oEofLUmRkO7R1qrZYu133uHundsGW1W208S3RW9P8E/M/A/ar6XCcbttmM9aVohCHZpMejd7fFTKlobyaLmYzpPdVGwGLVJ51wee1ykflKY8nRokGoDOV6M16CtygQIvK++M+/AXwIeBtwD/Ch+DazSn3pRCtmOnpglEKpzutXFloxU7Hqd7uJxphbNFeut46+PD5ZIOEKj94dxUuNICSVcMj3aLwEbx0xfQfwJ0TF4UZK21nT5uZcRxjvTzNVrEUzHJwoZrp/Rz8pN5rNNNzD3zSM2Youz1fJJDxUlWMTBR7ePUQ2GX2slmo+u4Yzrb2YetFNC4Sq/pSIOMDvqep/Wac2bVrjfSkuzVXoz6R4ZM8QT04U+Hvv2Uc25TK1UOOe8bBnDhIxZqurNgLmytEZ89+6usD0Yo0ffGxP6/5AleFcb48tvuWnkaqGwD9ch7Zsen3pBG4zZjo4yvRidF6txUzG9J5ipdFaHHd8ooAj8Ni+YSAae/Ach74ejpdg9dNc/0hEflREdovIcPOnoy3bhFxHGO9LUa7712Om+KS5ZsxkjOkNl+crZBJuHC9N885dg/TFhwEt1ny2D6RwnN6Nl2D1BeLvEs1e+nPgRNuPuUXjfWnqQUg+5fHQ7kGePD2NxrOZphZsNpMxvaDuh8yWG2QSLudnylyary6ZveSHIaObYOr6agvE24H/B3gBOAX838A7OtSmTa0v7bUipaMHR7m2UOP0NYuZjOklzcWtIsLxyQICPB4XiCheklZvopettkD8GnA/8H8RFYf749tWJCL3isiptp+iiHz6hscMiMjXROQFEXlFRD7edt8HROR1ETktIj9+S+9qA/Nch7G+FOVawGP7hnEtZjKm51wtVkl70eZ7xyYK3Le9rzULsVz3GetLtY4e7WWrLRD3qurfV9U/jX8+Adx7s19Q1ddV9SFVfQh4FCjz5nOsfwR4VVUfBL4T+PcikhQRl6jH8kGi3svHROTtq35XG9y2/jS1IKAvneDBXYM8ebpgMZMxPaIRhEwv1sgmXS7PVzgzXeJI29be9SBkrK83N+e70WoLxPMi8njziog8Bjx5C6/zBDChquduuF2BPokmCueBGcAH3g2cVtXJ+CS73wC+/xZeb0PrXxIzjXClWGViqoQj0Qwni5mM2biK7fHSRAGAx+PN+UJVHBH60709e6lptQXiMeCYiJwVkbPAceA7ROQlEXlxFb//UeCLy9z+GaK46hLRYUSfiqfV3gVcaHvcxfi2NxGRT4jICRE5MTU1tcq3013tMdPj+0ZwJNrkCyDlOVxbsJjJmI3qarFKKo6Xjk8W2D+WY3t/1GMo1wNG88lNs55pte/iA8A+opXV3xFf/ivA97H8KusWEUkCHyY6pvRG30s06L0TeAj4jIj009obcYllz8BW1c+p6iFVPTQ2Nraa97IhjPelqAUB/ZkE79w1yDfi2Uy5lMfUQo0gtCO/jdlo/CBkKo6XCos1vnllgSNts5dqfsB4/+aIl2D1m/Wdu9nPW/z6B4GTqnp1mfs+DnxFI6eBM0SbAl4Edrc9bhdRL2PTGMgkEIm6pO85OMrl+SpnC20xk20BbsyGU6z6qIIjwlOTUbx0OB5/UFUE6N8Es5ea1qMf9DGWj5cAzhONTyAi24gGvieBZ4F7RGRf3AP5KPDb69DWdeO5DmP5FOV6EB9uDt84Hf2DS7oWMxmzEV0tVkm58eylyQK7hjLsGY628640AoZyydZW35tBR9+JiGSB99O2qZ+IfFJEPhlf/WngiIi8RHSs6Y+p6rSq+kTbe/wB8BrwX1T1lU62tRu29aep+QEDmQTfdtfA9UVzSY9rFjMZs6EEoTK9WCOTdJmvNHj5jfkli+MqjaA1FrFZdHSoXVXLwMgNt3227fIl4HtW+N3fBX63k+3rtv5M1BXVeNHcL/zZBOdnytw9kmvFTL28l7wxm0mx0iAIFdcRnj0zQ6i0preqRl/mBrKbJ16C9YmYzAoSrsPom2KmaDaTxUzGbCxTizUSTvSReWxymrG+FAfGcgBUGyEDmURrdtNmYQWiy7YPpKn6AUPZJO/YOcCT8bxqi5mM2TjCULlWrJJLeZTrPs+fn+Pw/pHWWQ+Vhs+Ogc0VL4EViK4byCQQ4pjpwAgXZsqcnym3tgW32UzGdN9C1ceP46UTZ2fxQ+XIgevpuQKD2c0XB1uB6LKE6zASx0yHD4wiwJMWMxmzoUwtVvFa8VKBwWyC+7b3A9HBQfmURzqxueIlsAKxIWzrj2Km4VySt+/sbxWIbNIWzRnTbWGoXJmvkU951PyA587N8Pi+kdZmfOX65oyXwArEhjDQPpvpwCjnZspcmI1iJj9UFqoWMxnTLQs1Hz8McR3h1IU5qo2QwzfES5t1tqEViA0g6TkM55JUGkEr1zwW9yISjsO1hVo3m2fMlja9ULseL50ukEu5fNtdA0B0cFAm4ZJNbo7N+W5kBWKD2N6fplwPGMmnuH9Hf2s2Uy7lca1YtZjJmC5QVa4Uq+SSLn4Q8vTZAo/tHSERb8ZXrvvsHMx0uZWdYwVigxjIJkCuz2Y6M13i0lzFYiZjumih5tMIQjzX4aU35inVgiXxUqC6aeMlsAKxYaQ8l+FsIo6ZotWZT1rMZExXzSzWceK1DscnC6Q8h4f3DALRwUEpzyGX3Hyzl5qsQGwgOwYyVBoBY30p7t3WxzfiMyIsZjJm/akql+cr5FMeQagcnyxw6O6h1mrpUs1n+0C6tVhuM7ICsYE093HReAvwyakSl+ctZjKmG0r1gJofknAdvnmlyFy50draG6J4aSSf6mILO88KxAaS8lwGswmqjfD6bKZ4sNpiJmPW18xiDbcZL00U8BzhXXuHgOjgIM9xyG/S2UtNViA2mB39acoNn/H+NG/blm9t3teMmUKLmYxZF5fnq2STHqpRvPTQ7sHWdNZSPWD7QArH2bzxEliB2HAGskninYM5emCU09cWuVqstmKmosVMxnRcqeZTqQckPYeJqRLXFmpL9l7yw5Cx/OZcPd3OCsQGk05EMVOlHnDk4JtnM01ZzGRMx82W6zTHno9PFnAE3r0vKhBBqHiOkE9v7ngJrEBsSDsHophpe3+ag2P51jhELuVx1WImYzru8lyFXCoqAMcmpnlg50BrS5xy3WesL9Xai2kzswKxAQ20bRt85OAIr19d4NpCtW02k9/F1hmzuVXqAYs1n5TncmGmzMXZypJ4qR6EjPdt/ngJrEBsSOmES186QbURcDSeVtfsRXiOw9SibQFuTKfMluqttQ3HJqP/7x6Pz54OVXFE6NsC8RJYgdiwdg6kW/u87B/NtcYh8imPK/MWMxnTKZfmK+Ti2UrHJ6a5d1tfa71DuR4wmk/iuVvjo3NrvMseNJRL0iwBRw+O8s0rC0wv1ixmMqaDqo0oXkonXK4Wq0xMlZbESzU/YFv/1oiXwArEhpVOuORT3g0xU9SLsJjJmM6YK9dpDj0fn1gaL2k8/7w/HqzeCqxAbGA7BzKU6j53DWXYO5LlydPRP1iLmYzpjMvzVTKJePbSZIG9I9nWdt6VRnTqY2KLxEvQwQIhIveKyKm2n6KIfPqGx/yztvtfFpFARIbj+/6JiLwS3/5FEdk6/brYUC5JM2c6enCU1y4XKVjMZExHVBsBc+UGmaTLTKnONy8XWzsrQzT+sGNg8579sJyOFQhVfV1VH1LVh4BHgTLw1Rse8zNtj/kJ4M9VdUZE7gL+MXBIVR8AXOCjnWrrRpVJuuTTccx0cBQlWrQDFjMZs9aKlUZrcdzTZwootMYfVBUE+jNbY/ZS03r1lZ4AJlT13E0e8zHgi23XPSAjIh6QBS51sH0bVjNm2j2UZc9w1mYzGdMhl+crZBLRVt7HJgrsHEizZzgLQLURMphJtLb63irWq0B8lKUf/kuISBb4APCbAKr6BvDvgPPAZWBeVf9whd/9hIicEJETU1NTa97wbhvMJVqDY+85OMorl4rMlupxzBRazGTMGqj7IbPlBpmEy0K1wUtvzHP4wGhrPUS54bNzYMul3J0vECKSBD4MfOkmD/sQ8KSqzsS/MwR8P7AP2AnkROSHlvtFVf2cqh5S1UNjY2Nr2/gNIJv0yKc8an7AkQMjN8RMrsVMxqyB+Uq0CaaI8MyZGYJQl0xvhaU7HGwV69GD+CBwUlWv3uQxN/Ywvhs4o6pTqtoAvgIc6WAbN7QdgxlKNZ89w1l2DWVuiJlqFjMZc4euFquteOn4ZIHRfJJ7xvNANHidT3mkE1srXoL1KRA3ji0sISIDwHcA/7Xt5vPA4yKSlaiP9wTwWkdbuYENx4vmRISjB0d5+dI8c+W2mKlmMZMxt6sRhEwv1sgkXCr1gJPnZzm8f6QVL5XqPju32Oylpo4WiHhs4f1EPYDmbZ8UkU+2PeyvA3+oqqXmDar6NPBl4CTwUtzOz3WyrRtZNumRTbjU/ZCjB0YJdelspmnbAtyY21Zsi5eeOz9LI9AlR4tCNBa4FXW0QKhqWVVHVHW+7bbPqupn267/qqq+aQqrqv6Uqt6nqg+o6g+r6pb+FNwxGM1m2juS5a7BTGvzvnzK47LNZjLmtl0tVluzk45PTDOQSfD2Hf1ANHidTbitk+S2mq2zJLDHDeeShKEiIhw5MMKLF+eYrzQsZjLmDvhByNRijWwy6qE/e3aWx/YNt856KNV9dgxuzXgJrED0jFzKIxP/I37PwShmespiJmPuSLHqowqOCKcuzFFpBEtWT4eq0Y4GW5QViB6yczBDue6zbzTHjoF0azZTLulazGTMbbharJJym7OXpskmXd65awCIBq9TnkMuufVmLzVZgeghQ7kkgUYx09EDo7xwcY5ipYHnOhYzGXOLglCZXqyRTbkEofL05Azv3jvc2oxvseazYyDTms20FVmB6CG5pEs64dIIQo7GMdPTZyxmMuZ2zJXrBGF0QtzLl+ZZqPkcblscF6oynN+68RJYgegpIsLOgTSlms+BsRzb+lM8Gc9mspjJmLdWqvm8MVvmxNkZXrw4v2TvpaTn8MieISAavPYch77U1py91LS1330PGs6nmJhabMVMv/3CJRarPvm0hx82WKz79Ke35pxtY26kqizWfGZLdS7NV6nWA0SitUWj8TGioSpPTRR4dM9Qa7V0qR6wYyC9peMlsB5Ez7kxZvJDbcVMrojFTGbLC0Nlvtxg4toixyYKPHd2lnOFMknXYSSfYjiXWrJtxreuLDBTri/Ze8kPw1YB2cqsB9Fjopgpw/nZMveM5xnrS/HkxDRP3L+ttWhu32huy3/zMVuLH0Q7G08t1rhWrOKHiudEM5C8t+hRH5ss4DnCob3DQDR47TlCX9o+Hu1voAcN55OcmS7FMdMIv/PiZUo1n1zKo1FtMF9pMLgFd540W0vdDylWG1wrVplerBOqknQd8qlEa6HbW1FVjk8UeOeuQfLxeEOp5jPen8ZZ5XNsZlYgelA+5ZFKOK2Y6bdOXeKZszN8173jpDyXUxfmSHkOw7kkQ7kkuaRHJuHaP3jT86qNgGKlwZVilZlSHYCU5zKQSeDcRq/5zHSJK8UqH3l0V+u2RhgyZvESYAWiJ4kIOwYyXJgp87ZtfYzmkzx5eprvunecfCo6P6IRhMwsNrg8H50X4YowkE0wkkuSTyeirvcWOnzd9K5KPWC2VOdysdo6FjSTcBnOJu84Sj02WcAReGxfFC+FGk17tXgpYn8LPWo4n+RsoYQjwpEDo/zey5cp1/3WpmIJ1yHhOuTj/8ShKpV6wES5RBifUJdPeYzkkwxkkmTjwW9juk1VKcVF4cp8hVI9ACDXNvNorRyfKPD2Hf2tSLZcCxjrS9mXp5gViB7Vl/JIuA5+HDP99guXeObMDN957/iyj3dEom3D46EJVaUehLwxW+VcoQxgsZTpmjBUFus+hcUaV+ar1PwQR4Rc0mMk15mPqYuzZc7PlPkH793fuq0WBIz35Tvyer3ICkSPEhF2Dqa5OFPhvu19DOeSHJsorFgglvv9lOcuOYTdf4tYKpt0W9sQmFsThmrF9gZBqCxUG0wv1rgyX8MPQ1wRcimPfKrza3maZ6oc3h9Nb22e/d6fsXVETVYgethIPsXZ6XIcM43wh69cpVIPyNzm5mKe65BfIZZSVZQolhrOJRnMWizVLgyjHlk9CKn7IbVGQKkeUK77lGoBjSAk4TpkEi6ZpEMm4bUKbsJzSLhC0nU2/fTkRhBSrDS4tlBjerEWTyl1yKe8Vc88WivHJgqtqeIA5XrAaD5lX4LaWIHoYe0x03sOjvI7L17mxLkZ3nvP2Jo8/42xFEDND7g0V+X8TBkhKiojuSRD2QS5dILsJo2lVJWaH9KIC0DdD1ms+ZTjIlD3m1ucRIXUESHhOHhuFJO4jhCEih+GzJd9CmEDPwwRoPmbItGMnEzCbRXfdMIl6TokPGmNK/Wamh9QrPhcLVYplGqoQsp16buF6ahrKVTlpTfmOX1tkb99eG/r9qofcKDf4qV2ViB6WDSbKc3l+Sr3be9nKJvgG6en16xALGe5WGq21OBqsdpq00AmwXAuSX86QTbVG7GUqtIItNUDqPsBizWfSiOkVPOp+QFo24c5QsKNPrTTnks+9dbv0XUE13FZaXsfVSUIlbofUq4FNMKQUJXmR2hUeCCTiM4GSSccckmPpBf1QpJxAenGh+6Nqo2AuXKdy/NV5ioN0Gjm0VDmzmce3Y4gVF69NM+xiQLHJwsUSnWySZf33hOd/dDsIfdn7COxnf1t9LjRvhQXZsu4TjSb6Y9eu8pMqc7wOh1y0oylaIulao2Qs9PRbKmQqKczlE0ymE2QS3ldi6Wa3/6bf5brAaWaT6nmU/UD2vc5FKIdcr04+skm3I5/sIkInit4LrBCDB7GRaRU85kvK35YjfssUdFSosgmm3RJJx2y6xhllWo+s+U6V+arLFT9VjEbWYPpqLejEYS8eHGeYxPTPH1mhvlKg6Tr8PCeQf7WgVHevXeYfDydtdIIGM4mlnz5MVYgel5fysNzHIJQ+c63jfF7L1/mb//KMxwYy/Hw7iEe2TPIfTv61+1bvCNCJukuGQep+yGX56tcmC2DQiKeLTW8xrGUH48BNHylFgRU6wGLdZ9KLaTc8AnCpd/GPcdp9QIG16EArAVHBCdu80qaUVax7DOzTJQFkPYc0snorOXMbUZZzY3wZhajNQrLbYS33qqNgOfPz3JsssCzZ2Yo1QMyCZd37R3i8IFRHt0ztOwYXaURsHck24UWb2zSHLnfDA4dOqQnTpzodjPW3elrC1yZrzGQSXBmepFnzs7y/PlZvnllgSBU0gmHb7trgEf2DPHw7iF2DnZ3l0o/CKn6YSu2cRxhIOMxnEvdNJZqxi/NGKjaiHoA5UZAuebjL1MAPCf6wPNcua2VtptRM8ryQ8UP9E1RVgi4AmnPJZPyyNwQZWlINPOoWKURhLhONM7SrSixXPd59uwsxyamee7cLDU/JJ/yeGzfMEcOjPLQ7kGS3s3bVijVeHz/yJacdCEiz6nqoeXusx7EJjCWT3NxtgLAvtE8+0bz/MCh3ZTrPi9enOf5C3M8f36WZ8/OAjDel+LhPVHvon0PmvXSmi2VWhpLnZkqEcbfc3NJl6FcklBZMhNoaR4ff/g7ckv772x1txJllWs+xTdFWeCuciO8TilWGjxzZoYnJ6Y5dWEOP1SGsgned984Rw6M8sDO/lUvdqs2AvrSiS1ZHN6KFYhNoC/t4cWzZNo/JLNJj8f3j/B4PM/78nyF58/PcfL8LH/xrSn+4JUrOAL3buvj4T1DPLxnkHvG+9b9g3alWOrqfA0n/jBrzgQy62M1UdZ6mynVOT5Z4PjENC+9MU+o0Zedv/ptOzhycJR7t93ev91S3edt430daHHv61jEJCL3Av+57ab9wE+q6s+1PeafAT8YX/WA+4ExVZ0RkUHg88ADRF8Y/66qHr/Za27ViAmWxkyr4Qchr19daBWM09cWW+scHtw1EPcwhlpzxI3phqvFKscnChybmOabVxZQ4K7BDEcOjHDkwCgHxu58a/vpxSqP7x+97fVDve5mEdO6jEGIiAu8ATymqudWeMyHgH+iqu+Lr/8a8HVV/byIJIGsqs7d7HW2coGYLzc4eX72tgcH5ysNXrwYFYuT5+daO2XuGsrEYxeDPHDXgHXDTcddmC1H01EnppmYKgGwfzTH4bgo7B7KrNkYWs0PUFXetW/krR+8SW2EMYgngImVikPsY8AXAUSkH/h24O8AqGodqHe4jT2tL+2RcN8cM63WQCbBe+8Z4733jKGqnJ8p8/z5OZ6/MMvvv3yF337hEp4jvGNnf2v8Yu+IHUxk7pyqMjldavUULsTjafdu6+PjR/Zy+MAIOwYyHXntcj1g32iuI8+9GaxXD+KXgZOq+pkV7s8CF4GDcbz0EPA54FXgQeA54FOqWlrmdz8BfAJgz549j547d7MatLl96+oCU8Xamu8lU/MDXr1U5OT5OU5dmOVsvLnfYDbBw7sHo/GL3YN2SBHR4O5cOTrE5upCdLrZtYUaIrCtPx399KXY1p+mL+1t2QIbqvKtKws8OVHg+OQ0V4s1HIEHdg5w+MAIh/ePMLIOU2ULpRrv2jtMbp0namwkXY2Y4njoEvAOVb26wmN+APghVf1QfP0Q8BRwVFWfFpGfB4qq+q9u9lpbOWICmCvXOXVhjpFcZ//HKizW4plRUcEoVn0A9retvbh/HdderKdQldlSnWsLNa7GH/7X2grBtYUqjWDp/1P9aQ9VWKj5S27PJFy29aeuF47+NNv7U4z3RZc3WyYehMrLl+Y5Hq9mninV8Rzhwd2DHDkwwmP7RlY9hrYWmgsmH9s/vGULNXQ/YvogUe9h2eIQ+yhxvBS7CFxU1afj618GfrxD7ds0+tKJ1p4/nZzxM5JP8d33b+O7799GqMrkVImT56O1F7916g1+8+RF0gmHB3bGay/2DHLX4Nrlxp3ULADNb/9XF2pM3dAb8MOlBWAgk2C8L8Xe0RyP7RtmPO4ljPenGe9LtcZtSjWfawtVrhSj4tL8uTxf5dSFOWp+uOR5+9PekuLRLCbb+9OM9fXGpnKNIOSFC3Mcmyzw9GSBYtUn6Tk8umeIIwdGuvbt3Q9C5sp1Dozle+LfZbesRw/iN4A/UNVfWeH+AeAMsLs9QhKRrwN/X1VfF5F/DeRU9Z/d7LW2eg8C4PUrRa7M1xjM3t4RjHeqXPd56Y351uyo5tbh432pVhz14K7B1hYH6y0Ildly/fq3/2YRiHsEU8sUgMFMgvHWN/voz/H+FNv6og/qtRi4V1XmKw2uNovHQvX65WXaJcBIPhlHVnG72grJSC7VtWnB1UbAyfOzHJso8OzZGcr1gGzS5V17hzlyYIRH9gx1ZbJDEEYrv/0wxHMcdgyk2TWc2fLba3QtYorHFi4A+1V1Pr7tkwCq+tn4+t8BPqCqH73hdx8imuaaBCaBj6vq7M1ezwpE9AF9drrUOsS9W1spN12Zr/L8hVmePz/HCxfnKNcDHIG3bevj4d2DPLJniHtuc/76coJQmSnVuRZ/wF5bqHKt+WdcCN5UALIJtsUf+jcWgbH82hSAO9V8X+09j6vFWlxIqhQW60u20vAcYSwe62iOebT3RAYyiTX95lyq+Tx7doZjEwWeOz9L3Q/pS0frcI7sH+HB3YNd6fGEGu1bVQ9CPEfY1p9mvC8a/9mMuw7fjq5Pc10vViCu84NoO+r2w1iaJ3S91bYDnWzT61cXWiu7//JqtPYil3J5cNdgazrteH96xecIQqVQqrU+9K+2ffhfK9aYis8YaDeUTSz77X+8P8VYX2pTfINsBGGrF7S0FxJdn680ljw+5TlLYqtmL6RZRFYT+8xXGjx9psCxiQIvxKuZh3NJDu8f4ciBEd6xc6ArX0yaR5ZWfR9XnNZ/7/6MrbZfjhWILU5VWaj5zJWi7Zcrjetn/Hbz2/FCtcGpeLD7+QuzTC9GM5nvGszwyJ5B7h7Jtb41Ty1E35anF+tvKgDD2eRNI6BuFcSNpFIP4oK6dAykOdhejs99bsqnvDcNoG/rTzGcTfLa5SLHJgq8fClazbytP8WRA6Mc2T/C27b3dSXaVFUqjaD1b3s87jUNZBJ2vvRbsAJhlijXfeZKDa4uVJmvNNB4r/5M0u3ahnaqyoXZCs/HC/VevjRPPR60Hc4llwz6bmv7czRvBeBONXdlbR/zuNLWE1luZtbu4SxH4p7CvtHurYep1APKDR8FRnJJdgxkGMgk7N/ELbACYVbUftrXTCkat0g4DrkujltAtBfTTKnOSD7ZE7N1NrP2mV2FxRp7R3PsHure1tjVRkCpHk0ZHsgk2DmQZjCX3BRRYTd0e5qr2cBSnstYn8tYXwo/CFmo+kwtRrN7/FBxRMin1n8r56TnsH1g5bEIs34cEUbyqXVZuLaS6IjXRmu/sLeN9zGcT26ICQSbmRUI0+K5DkO5JEO5JAfH8izWo8NgrhSrFCsNkO6PW5itoxFEx736oZJNuuwfyzOST5JN2sfWerG/abMsxxH60wn60wn2juZa4xaXi1WmF6OtI9KeSzbZGyexmd7QnH3nxwdd3T2SZTifImf/zrrCCoRZlWzSI5v02DmUodoIKFYaXFuoUSjVUCU6t9nObDC34foCtgDPcblrKMNoX4q+1Nbdq2qjsAJhblk6PsN4vD+NH4QUq9F6i+a4hStCrgvjFhuBH4QrHuUJbz4L23NkS34INhew1YKQhCNsH0gzlrcFbBuNFQhzRzzXYTiXZDget1io+cyUalydr1GsNFqH2G+GcYtQow9+PwxpBEoQhq3Vy0L04Z/yHDJJl4FsgmwyKqTNQuAHSi0IKNeis7RLNZ+FWnQutwKq4Mr1M7QTrrOpemShKuV6QM0PcERaa1ZsAdvGZQXCrBnHEQYyCQYyCfaO5CjXA+bK0eK8QqkGxOstEhszTw5CpdHqAYQEbd/+ozOwo2KXT3lkktH4S9JzW72BpOus4ttvAtpOt1RV6vGuonU/jKdwBizWfBZrDfxwaRt6rfex/AK2vC1g6xFWIExHSBwz5VIedw1lW+MWV+L1FoqSct11G7dQVfxmAQiiyxp//xcERUm40bf/vrRHNumSSXokXYeEF30gdyIyExFSnrviHH4/CJcUkMWaT6ke9UCK1aj30awgjjTbGf3ZrUWP8OYFbAfGo6KwFWPHXmYFwqyL9nGLRnO9xcL1XUrvdNwiCKPop/nh74dhFPvEH6BOc9ZVyiOTcFp7UiW86Jv/Ro1zPNfBcx2aZzGNt90XhnHvIy4glbhwlOsBxWqDIFw6/hH1PqIC0olv7zcuYHv7aD8D2YQtYOthViDMuku0jVvcMx7tE1VYjLZ1KFYbOCJk4oIC17/9N/N/P1BCboxehEzCoz/rkktGEVDzg7/5objR45hb5ThC2nFXHN9ptPU8an4UW5XrQVxAmocXRf0oV673kjxXVt37aC5gC1XpSye4d1sfQzlbwLZZWIEwXdU+brFvNEcpHre40jZuIRKt+M4kXIaS0eBvKh78TXbwG3Gva37gL3fAYLP3UfObvQ+fUt2nXAtbvY8mQZaMeygsWcB2YDzPcM4WsG1G9l/UbBgSb+uRT3nsisctRKI1Fpvt23+3vbn3sbSK1P0oumq09T5KtYBy3UeQ1gK2/BY+y3krsP+6ZsOymKJ7kp4T7Yjave2XzAZg/XJjjDHLsgJhjDFmWVYgjDHGLMsKhDHGmGVZgTDGGLMsKxDGGGOWZQXCGGPMsqxAGGOMWZao6ls/qkeIyBRw7jZ/fRSYXsPmdNNmeS+b5X2AvZeNaLO8D7iz93K3qo4td8emKhB3QkROqOqhbrdjLWyW97JZ3gfYe9mINsv7gM69F4uYjDHGLMsKhDHGmGVZgbjuc91uwBraLO9ls7wPsPeyEW2W9wEdei82BmGMMWZZ1oMwxhizLCsQxhhjlrXlC4SIfEBEXheR0yLy491uz+0SkV8WkWsi8nK323KnRGS3iPypiLwmIq+IyKe63abbJSJpEXlGRF6I38v/1u023QkRcUXkeRH5nW635U6IyFkReUlETonIiW63506IyKCIfFlEvhn/P3N4zZ57K49BiIgLfAt4P3AReBb4mKq+2tWG3QYR+XZgEfh1VX2g2+25EyKyA9ihqidFpA94DvhrPfrfRYCcqi6KSAL4BvApVX2qy027LSLyT4FDQL+qfl+323O7ROQscEhVe36hnIj8GvB1Vf28iCSBrKrOrcVzb/UexLuB06o6qap14DeA7+9ym26Lqv4FMNPtdqwFVb2sqifjywvAa8Bd3W3V7dHIYnw1Ef/05LcyEdkF/FXg891ui4mISD/w7cAvAahqfa2KA1iBuAu40Hb9Ij36QbRZiche4GHg6S435bbFscwp4BrwR6raq+/l54B/DoRdbsdaUOAPReQ5EflEtxtzB/YDU8CvxNHf50Ukt1ZPvtULhCxzW09+u9uMRCQP/CbwaVUtdrs9t0tVA1V9CNgFvFtEei4CFJHvA66p6nPdbssaOaqqjwAfBH4kjmh7kQc8Avyiqj4MlIA1G0vd6gXiIrC77fou4FKX2mLaxHn9bwJfUNWvdLs9ayHu+v8Z8IHutuS2HAU+HGf3vwG8T0T+Y3ebdPtU9VL85zXgq0Rxcy+6CFxs65V+mahgrImtXiCeBe4RkX3x4M5Hgd/ucpu2vHhg95eA11T1Z7vdnjshImMiMhhfzgDfDXyzq426Dar6E6q6S1X3Ev1/8ieq+kNdbtZtEZFcPPmBOI75HqAnZ/+p6hXggojcG9/0BLBmkzm8tXqiXqSqvoj8Q+APABf4ZVV9pcvNui0i8kXgO4FREbkI/JSq/lJ3W3XbjgI/DLwUZ/cA/0JVf7d7TbptO4Bfi2fMOcB/UdWeniK6CWwDvhp9D8ED/pOq/n53m3RH/hHwhfhL7iTw8bV64i09zdUYY8zKtnrEZIwxZgVWIIwxxizLCoQxxphlWYEwxhizLCsQxhhjlmUFwhhjzLKsQJhNR0T+tYj86Dq91q+KyEfW4XXeG28XfipecNdx8ZbYo+vxWmZjsgJheppE7ujfsYj0woLRHwT+nao+pKqVbjfGbA1WIMyGJyL/VERejn8+LSJ744NRfgE4CewWkf81PvjpvwP3tv3uARH5/XjXzq+LyH3x7b8qIj8rIn8K/FsR+Y742/mpeFfMvhXaIiLyGRF5VUT+GzDedt9PisizcTs/Fz/2gIicbHvMPSKy4oZ3IvJE/PovSXQIVEpE/j7wPwA/KSJfWOH3fkFEPhxf/qqI/HJ8+e+JyP8eX/4hiQ4vOiUi/yFe3Y2IfI+IHBeRkyLypXiTxPbnzsR/h//gJv+ZzGakqvZjPxv2B3gUeAnIAXngFaLtv0Pg8RsekwX6gdPAj8b3/TFwT3z5MaI9hAB+FfgdwI2vf41oh0/i1/FWaM/fAP6IaGuWncAc8JH4vuG2x/1/wIfiy38KPBRf/jfAP1rhudNE28+/Lb7+60Q72Tbb+5Gb/D19FPiZ+PIzwFPx5V8Bvhe4P36Pifj2XwD+FjAK/AXRoUYAPwb8ZHz5LLAX+O/A3+r2vwX7Wf8f60GYje49wFdVtaTRwTtfAd4LnNPrp7K9N35MWaNtwX8bWtuFHwG+FO/p9B+I9kZq+pKqBvHlJ4GfFZF/DAyqqr9Ce74d+KJGW3hfAv6k7b7vEpGnReQl4H3AO+LbPw98PP7G/gPAf1rhue8Fzqjqt+Lrvxa/3mp8HXiviLydaLO2qxKdzHcYOEa0idujwLPx38UTRGcJPA68HXgyvv1vA3e3Pe9/BX5FVX99le0wm0gvZK9ma1vuzA6I9r1vt9ymYg4wp9FZDDd9DlX9P+LI6K8AT4nId6vqSruuvum1RCRN9K38kKpeEJF/TdQjgGjb8p8iKibPqWphhedd6b2+JVV9Q0SGiLYS/wtgmCiWWlTVhXiH3F9T1Z+4od0fIjrE6GMrPPWTwAdF5D+pqm3ctsVYD8JsdH8B/DURycZbM/91om/LNz7mr8dZeR/wIYC4N3FGRP4mtMYPHlzuRUTkgKq+pKr/FjgB3HeT9nxUolPidgDfFd/eLAbTcc+lNbNJVatEOwb/IlHks5JvAntF5GB8/YeBP7/J4290HPh03MavAz/K9b+rPwY+IiLjACIyLCJ3A08BR5uvGf89v63tOX8SKBAVP7PFWIEwG5pGZ1P/KlGu/jRRXDO7zGP+M3CK6Nt6ewH5QeDvicgLROMXK505/ul4cPkFoAL83gqP+yrwl0RjHr9I/AGu0WFA/298+28RnTXS7gvEx1ze5L1WibZq/lIcU4XAZ1d6/DK+TjR2cppo8H44vg1VfRX4l0THbL5INI6yQ1WngL8DfDG+/SneXBw/DaRF5P+8hbaYTcC2+zZmHUi0LmNAVf9Vt9tizGrZGIQxHSYiXwUOEA1cG9MzrAdhzDJE5NuIpqq2q6nqY2v0/F8F9t1w84+p6h90s13GtLMCYYwxZlk2SG2MMWZZViCMMcYsywqEMcaYZVmBMMYYs6z/H+Oj1jPM/IAJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create line chart for df_2 data\n",
    "\n",
    "line = sns.lineplot(data = df_2, x = 'orders_day_of_week',y = 'prices')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc99d172",
   "metadata": {},
   "source": [
    "### 3. Customers Data (New Data Frame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bd4c3a96",
   "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>user_id</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Surnam</th>\n",
       "      <th>Gender</th>\n",
       "      <th>STATE</th>\n",
       "      <th>Age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>n_dependants</th>\n",
       "      <th>fam_status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26711</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>Esquivel</td>\n",
       "      <td>Female</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>48</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>165665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33890</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Hart</td>\n",
       "      <td>Female</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>36</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>single</td>\n",
       "      <td>59285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65803</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>Farley</td>\n",
       "      <td>Male</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>35</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>99568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125935</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>Hicks</td>\n",
       "      <td>Female</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>40</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>single</td>\n",
       "      <td>42049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130797</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Gilmore</td>\n",
       "      <td>Female</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>26</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>40374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id First Name    Surnam  Gender       STATE  Age date_joined  \\\n",
       "0    26711    Deborah  Esquivel  Female    Missouri   48    1/1/2017   \n",
       "1    33890   Patricia      Hart  Female  New Mexico   36    1/1/2017   \n",
       "2    65803    Kenneth    Farley    Male       Idaho   35    1/1/2017   \n",
       "3   125935   Michelle     Hicks  Female        Iowa   40    1/1/2017   \n",
       "4   130797        Ann   Gilmore  Female    Maryland   26    1/1/2017   \n",
       "\n",
       "   n_dependants fam_status  income  \n",
       "0             3    married  165665  \n",
       "1             0     single   59285  \n",
       "2             2    married   99568  \n",
       "3             0     single   42049  \n",
       "4             1    married   40374  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check customer data\n",
    "\n",
    "df_customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fb3effbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(206209, 10)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67dc8851",
   "metadata": {},
   "source": [
    "### 3.1 Data Wranging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d93b98de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"First Name\"\n",
    "\n",
    "df_customers.rename(columns = {'First Name' : 'first_name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f188dde6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"Surnam\"\n",
    "\n",
    "df_customers.rename(columns = {'Surnam' : 'last_name'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "532d8abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"Gender\"\n",
    "\n",
    "df_customers.rename(columns = {'Gender' : 'sex'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a72e6880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"STATE\"\n",
    "\n",
    "df_customers.rename(columns = {'STATE' : 'us_state'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "999e3f99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"Age\"\n",
    "\n",
    "df_customers.rename(columns = {'Age' : 'age'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "605f8429",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"Age\"\n",
    "\n",
    "df_customers.rename(columns = {'n_dependants' : 'no_dependants'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "716f1387",
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming column \"Age\"\n",
    "\n",
    "df_customers.rename(columns = {'fam_status' : 'marital_status'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "564b76f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>us_state</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>no_dependants</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26711</td>\n",
       "      <td>Deborah</td>\n",
       "      <td>Esquivel</td>\n",
       "      <td>Female</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>48</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>165665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33890</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Hart</td>\n",
       "      <td>Female</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>36</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>single</td>\n",
       "      <td>59285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65803</td>\n",
       "      <td>Kenneth</td>\n",
       "      <td>Farley</td>\n",
       "      <td>Male</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>35</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>2</td>\n",
       "      <td>married</td>\n",
       "      <td>99568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125935</td>\n",
       "      <td>Michelle</td>\n",
       "      <td>Hicks</td>\n",
       "      <td>Female</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>40</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>single</td>\n",
       "      <td>42049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>130797</td>\n",
       "      <td>Ann</td>\n",
       "      <td>Gilmore</td>\n",
       "      <td>Female</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>26</td>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>40374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id first_name last_name     sex    us_state  age date_joined  \\\n",
       "0    26711    Deborah  Esquivel  Female    Missouri   48    1/1/2017   \n",
       "1    33890   Patricia      Hart  Female  New Mexico   36    1/1/2017   \n",
       "2    65803    Kenneth    Farley    Male       Idaho   35    1/1/2017   \n",
       "3   125935   Michelle     Hicks  Female        Iowa   40    1/1/2017   \n",
       "4   130797        Ann   Gilmore  Female    Maryland   26    1/1/2017   \n",
       "\n",
       "   no_dependants marital_status  income  \n",
       "0              3        married  165665  \n",
       "1              0         single   59285  \n",
       "2              2        married   99568  \n",
       "3              0         single   42049  \n",
       "4              1        married   40374  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4f6aab3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2049      1\n",
       "167163    1\n",
       "187633    1\n",
       "181490    1\n",
       "183539    1\n",
       "         ..\n",
       "150044    1\n",
       "147997    1\n",
       "154142    1\n",
       "152095    1\n",
       "2047      1\n",
       "Name: user_id, Length: 206209, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers['user_id'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ffefc843",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95891     10\n",
       "57192     10\n",
       "95710     10\n",
       "94809      9\n",
       "97532      9\n",
       "          ..\n",
       "139481     1\n",
       "152861     1\n",
       "464181     1\n",
       "228664     1\n",
       "28658      1\n",
       "Name: income, Length: 108012, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers['income'].value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76c5fb51",
   "metadata": {},
   "source": [
    "### 3.2 Data Quality & Consistency Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2c0a7619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>age</th>\n",
       "      <th>no_dependants</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>206209.000000</td>\n",
       "      <td>206209.000000</td>\n",
       "      <td>206209.000000</td>\n",
       "      <td>206209.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>103105.000000</td>\n",
       "      <td>49.501646</td>\n",
       "      <td>1.499823</td>\n",
       "      <td>94632.852548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>59527.555167</td>\n",
       "      <td>18.480962</td>\n",
       "      <td>1.118433</td>\n",
       "      <td>42473.786988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25903.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51553.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59874.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>103105.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>93547.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>154657.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>124244.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>206209.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>593901.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             user_id            age  no_dependants         income\n",
       "count  206209.000000  206209.000000  206209.000000  206209.000000\n",
       "mean   103105.000000      49.501646       1.499823   94632.852548\n",
       "std     59527.555167      18.480962       1.118433   42473.786988\n",
       "min         1.000000      18.000000       0.000000   25903.000000\n",
       "25%     51553.000000      33.000000       0.000000   59874.000000\n",
       "50%    103105.000000      49.000000       1.000000   93547.000000\n",
       "75%    154657.000000      66.000000       3.000000  124244.000000\n",
       "max    206209.000000      81.000000       3.000000  593901.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "06b9c870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first_name\n"
     ]
    }
   ],
   "source": [
    "# check the mix data\n",
    "\n",
    "for col in df_customers.columns.tolist():\n",
    "  weird = (df_customers[[col]].applymap(type) != df_customers[[col]].iloc[0].apply(type)).any(axis = 1)\n",
    "  if len (df_customers[weird]) > 0:\n",
    "    print (col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bf803a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert first_name column numberic data to string\n",
    "\n",
    "df_customers['first_name'] = df_customers['first_name'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fa6e48d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id           0\n",
       "first_name        0\n",
       "last_name         0\n",
       "sex               0\n",
       "us_state          0\n",
       "age               0\n",
       "date_joined       0\n",
       "no_dependants     0\n",
       "marital_status    0\n",
       "income            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the missing values \n",
    "\n",
    "df_customers.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7535b475",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check the duplicates\n",
    "\n",
    "df_dups = df_customers[df_customers.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c451e115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>us_state</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>no_dependants</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [user_id, first_name, last_name, sex, us_state, age, date_joined, no_dependants, marital_status, income]\n",
       "Index: []"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dups"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87f59845",
   "metadata": {},
   "source": [
    "### 4. Merging Data Frames (Product-Orders with Customers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8bcb7fca",
   "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', 'Busiest days',\n",
       "       'busiest_period_of_day_range', 'max_order', 'loyalty_flag',\n",
       "       'mean_price', 'Spender_flag', 'median_order_frequency',\n",
       "       'Frequency_flag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # check the column names for product-orders data frame\n",
    "    \n",
    "ords_prods_merge.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "05b152e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0              138\n",
       "1              138\n",
       "2              709\n",
       "3              764\n",
       "4              764\n",
       "             ...  \n",
       "32434207    200215\n",
       "32434208    200377\n",
       "32434209    200873\n",
       "32434210    200873\n",
       "32434211    205926\n",
       "Name: user_id, Length: 32434212, dtype: int64>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the key column user_id data type\n",
    "\n",
    "ords_prods_merge['user_id'].value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e55edcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method IndexOpsMixin.value_counts of 0          26711\n",
       "1          33890\n",
       "2          65803\n",
       "3         125935\n",
       "4         130797\n",
       "           ...  \n",
       "206204    168073\n",
       "206205     49635\n",
       "206206    135902\n",
       "206207     81095\n",
       "206208     80148\n",
       "Name: user_id, Length: 206209, dtype: int64>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers['user_id'].value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "789455d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32434212, 24)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ords_prods_merge.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a09cb846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(206209, 10)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f4e20226",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# create the new data frame \"cop\" including customer, orders, products data\n",
    "\n",
    "df_merged_cop = ords_prods_merge.merge(df_customers, on = ['user_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "defd57db",
   "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>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>...</th>\n",
       "      <th>Frequency_flag</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>sex</th>\n",
       "      <th>us_state</th>\n",
       "      <th>age</th>\n",
       "      <th>date_joined</th>\n",
       "      <th>no_dependants</th>\n",
       "      <th>marital_status</th>\n",
       "      <th>income</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>...</td>\n",
       "      <td>Frequent customer</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Male</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>81</td>\n",
       "      <td>8/1/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>49620</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>...</td>\n",
       "      <td>Frequent customer</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Male</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>81</td>\n",
       "      <td>8/1/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>49620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>907</td>\n",
       "      <td>Premium Sliced Bacon</td>\n",
       "      <td>106</td>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3160996</td>\n",
       "      <td>138</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>Frequent customer</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Male</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>81</td>\n",
       "      <td>8/1/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>49620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>907</td>\n",
       "      <td>Premium Sliced Bacon</td>\n",
       "      <td>106</td>\n",
       "      <td>12</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2254091</td>\n",
       "      <td>138</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>Frequent customer</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Male</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>81</td>\n",
       "      <td>8/1/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>49620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>Apricots</td>\n",
       "      <td>18</td>\n",
       "      <td>10</td>\n",
       "      <td>12.9</td>\n",
       "      <td>505689</td>\n",
       "      <td>138</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>Frequent customer</td>\n",
       "      <td>Charles</td>\n",
       "      <td>Cox</td>\n",
       "      <td>Male</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>81</td>\n",
       "      <td>8/1/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>49620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434207</th>\n",
       "      <td>49235</td>\n",
       "      <td>Organic Half &amp; Half</td>\n",
       "      <td>53</td>\n",
       "      <td>16</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2542346</td>\n",
       "      <td>80734</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>Frank</td>\n",
       "      <td>Bowers</td>\n",
       "      <td>Male</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>45</td>\n",
       "      <td>9/28/2019</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>41072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434208</th>\n",
       "      <td>49520</td>\n",
       "      <td>Orange Sparkling Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1363278</td>\n",
       "      <td>74506</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>...</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Coffey</td>\n",
       "      <td>Male</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>44</td>\n",
       "      <td>4/16/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>97209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434209</th>\n",
       "      <td>49520</td>\n",
       "      <td>Orange Sparkling Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2008002</td>\n",
       "      <td>74506</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>...</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Coffey</td>\n",
       "      <td>Male</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>44</td>\n",
       "      <td>4/16/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>97209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434210</th>\n",
       "      <td>49520</td>\n",
       "      <td>Orange Sparkling Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1595973</td>\n",
       "      <td>74506</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Coffey</td>\n",
       "      <td>Male</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>44</td>\n",
       "      <td>4/16/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>97209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32434211</th>\n",
       "      <td>49520</td>\n",
       "      <td>Orange Sparkling Water</td>\n",
       "      <td>115</td>\n",
       "      <td>7</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1680478</td>\n",
       "      <td>74506</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>...</td>\n",
       "      <td>Regular customer</td>\n",
       "      <td>Joe</td>\n",
       "      <td>Coffey</td>\n",
       "      <td>Male</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>44</td>\n",
       "      <td>4/16/2018</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>97209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32434212 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          product_id                product_name  aisle_id  department_id  \\\n",
       "0                  1  Chocolate Sandwich Cookies        61             19   \n",
       "1                  1  Chocolate Sandwich Cookies        61             19   \n",
       "2                907        Premium Sliced Bacon       106             12   \n",
       "3                907        Premium Sliced Bacon       106             12   \n",
       "4               1000                    Apricots        18             10   \n",
       "...              ...                         ...       ...            ...   \n",
       "32434207       49235         Organic Half & Half        53             16   \n",
       "32434208       49520      Orange Sparkling Water       115              7   \n",
       "32434209       49520      Orange Sparkling Water       115              7   \n",
       "32434210       49520      Orange Sparkling Water       115              7   \n",
       "32434211       49520      Orange Sparkling Water       115              7   \n",
       "\n",
       "          prices  order_id  user_id  order_number  orders_day_of_week  \\\n",
       "0            5.8   3139998      138            28                   6   \n",
       "1            5.8   1977647      138            30                   6   \n",
       "2           20.0   3160996      138             1                   5   \n",
       "3           20.0   2254091      138            10                   5   \n",
       "4           12.9    505689      138             9                   6   \n",
       "...          ...       ...      ...           ...                 ...   \n",
       "32434207     1.8   2542346    80734             3                   3   \n",
       "32434208     7.5   1363278    74506             1                   1   \n",
       "32434209     7.5   2008002    74506             2                   0   \n",
       "32434210     7.5   1595973    74506             3                   1   \n",
       "32434211     7.5   1680478    74506             4                   0   \n",
       "\n",
       "          order_hour_of_day  ...         Frequency_flag  first_name  \\\n",
       "0                        11  ...      Frequent customer     Charles   \n",
       "1                        17  ...      Frequent customer     Charles   \n",
       "2                        13  ...      Frequent customer     Charles   \n",
       "3                        14  ...      Frequent customer     Charles   \n",
       "4                        12  ...      Frequent customer     Charles   \n",
       "...                     ...  ...                    ...         ...   \n",
       "32434207                  8  ...  Non-frequent customer       Frank   \n",
       "32434208                 14  ...       Regular customer         Joe   \n",
       "32434209                 15  ...       Regular customer         Joe   \n",
       "32434210                 17  ...       Regular customer         Joe   \n",
       "32434211                 16  ...       Regular customer         Joe   \n",
       "\n",
       "          last_name   sex   us_state age date_joined no_dependants  \\\n",
       "0               Cox  Male  Minnesota  81    8/1/2019             1   \n",
       "1               Cox  Male  Minnesota  81    8/1/2019             1   \n",
       "2               Cox  Male  Minnesota  81    8/1/2019             1   \n",
       "3               Cox  Male  Minnesota  81    8/1/2019             1   \n",
       "4               Cox  Male  Minnesota  81    8/1/2019             1   \n",
       "...             ...   ...        ...  ..         ...           ...   \n",
       "32434207     Bowers  Male    Alabama  45   9/28/2019             1   \n",
       "32434208     Coffey  Male    Vermont  44   4/16/2018             3   \n",
       "32434209     Coffey  Male    Vermont  44   4/16/2018             3   \n",
       "32434210     Coffey  Male    Vermont  44   4/16/2018             3   \n",
       "32434211     Coffey  Male    Vermont  44   4/16/2018             3   \n",
       "\n",
       "          marital_status income  \n",
       "0                married  49620  \n",
       "1                married  49620  \n",
       "2                married  49620  \n",
       "3                married  49620  \n",
       "4                married  49620  \n",
       "...                  ...    ...  \n",
       "32434207         married  41072  \n",
       "32434208         married  97209  \n",
       "32434209         married  97209  \n",
       "32434210         married  97209  \n",
       "32434211         married  97209  \n",
       "\n",
       "[32434212 rows x 33 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_cop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "1feaa8cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32434212, 33)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_cop.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701f93a7",
   "metadata": {},
   "source": [
    "### 5. Exporting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f98f991b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_cop.to_pickle(os.path.join(path, '02 Data','Prepared Data', 'merged_cop.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd6de99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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