Pandas Black Friday Project - English Version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Project\n",
    "\n",
    "\n",
    "Assignment for students of the Data Science Degree module IV - Programming Techniques I at Let's Code.\n",
    "\n",
    "Professor: [Pedro Gengo](https://github.com/pedrogengo)\n",
    "\n",
    "**Peers of this assignment were**:\n",
    "\n",
    "* [Daniel Aquino](https://github.com/DanWolks)\n",
    "\n",
    "* [Jeremias Diefenthaler](https://github.com/JeremiasDief)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instructions\n",
    "\n",
    "As part of the Data Science and Analytics team at Popolishoshop, a database with information about the last Black Friday has been assigned to be worked with.\n",
    "\n",
    "The business team requested a report in which some requirements were specified as well as a study to be answered using the provided data.\n",
    "\n",
    "For this challenge, [Black Friday's](https://www.kaggle.com/sdolezel/black-friday) dataset will be used. It gathers purchase transactions information at a retail shop.\n",
    "This dataset was split into different files and it's the team's job to understand how they related to each other.\n",
    "\n",
    "In order to practice data analysis, Pandas library will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the following table it's possible to see Column Names as well as field description."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Coluna                 | Descrição                                                 |\n",
    "|------------------------|-----------------------------------------------------------|\n",
    "| User_ID                | ID do usuário                                             |\n",
    "| Product_ID             | ID do produto                                             |\n",
    "| Gender                 | Sexo do usuário                                           |\n",
    "| Age                    | Ano em intervalos                                         |\n",
    "| Occupation             | Ocupação (mascarada)                                      |\n",
    "| City_Category          | Categoria da cidade (A, B, C)                             |\n",
    "| StayInCurrentCityYears | Número de anos de permanência na cidade atual             |\n",
    "| Marital_Status         | Estado civil                                              |\n",
    "| ProductCategory1       | Categoria do produto (Mascarada)                          |\n",
    "| ProductCategory2       | Categoria que o produto pode pertencer também (Mascarada) |\n",
    "| ProductCategory3       | Categoria que o produto pode pertencer também (Mascarada) |\n",
    "| Purchase               | Valor da compra                                           | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code developed must be designed to be reused. The assessment will be performed running the entire notebook with another dataframe, with the same columns. Therefore, think about quality and code reproduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from pandas_profiling import ProfileReport"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis _Set up_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the three data bases provided and join them in a single DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Occupation</th>\n",
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       "      <td>A</td>\n",
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      "text/plain": [
       "   User_ID Gender    Age  Occupation City_Category Stay_In_Current_City_Years  \\\n",
       "0  1000001      F   0-17          10             A                          2   \n",
       "1  1000002      M    55+          16             C                         4+   \n",
       "2  1000003      M  26-35          15             A                          3   \n",
       "3  1000004      M  46-50           7             B                          2   \n",
       "4  1000005      M  26-35          20             A                          1   \n",
       "\n",
       "   Marital_Status  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               1  \n",
       "4               1  "
      ]
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     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "# This file had a different delimiter; therefore, it was necessary to specify it for readability.\n",
    "\n",
    "df_user_profile = pd.read_csv(\"user_profile.csv\", delimiter=\"|\")\n",
    "df_user_profile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   User_ID Product_ID  Purchase\n",
       "0  1000001  P00069042      8370\n",
       "1  1000001  P00248942     15200\n",
       "2  1000001  P00087842      1422\n",
       "3  1000001  P00085442      1057\n",
       "4  1000002  P00285442      7969"
      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "df_purchase = pd.read_csv(\"purchase.csv\")\n",
    "df_purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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    {
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       "  Product_ID  Product_Category_1  Product_Category_2  Product_Category_3\n",
       "0  P00069042                   3                 NaN                 NaN\n",
       "1  P00248942                   1                 6.0                14.0\n",
       "2  P00087842                  12                 NaN                 NaN\n",
       "3  P00085442                  12                14.0                 NaN\n",
       "4  P00285442                   8                 NaN                 NaN"
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   "source": [
    "# This file had a different delimiter; therefore, it was necessary to specify it for readability.\n",
    "\n",
    "df_product_info = pd.read_csv(\"product_info.csv\", delimiter=\";\")\n",
    "df_product_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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    {
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       "<p>537577 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase Gender    Age  Occupation City_Category  \\\n",
       "0       1000001  P00069042      8370      F   0-17          10             A   \n",
       "1       1000017  P00069042     13591      M  51-55           1             C   \n",
       "2       1000023  P00069042     11022      M  36-45           0             B   \n",
       "3       1000030  P00069042      8036      F  36-45           7             C   \n",
       "4       1000073  P00069042     10885      M  18-25           4             A   \n",
       "...         ...        ...       ...    ...    ...         ...           ...   \n",
       "537572  1005754  P00234942      7120      F  18-25           1             A   \n",
       "537573  1005948  P00126342      9875      M    55+          13             B   \n",
       "537574  1006001  P00013442      2277      F  26-35           7             A   \n",
       "537575  1002315  P00063442      1866      M    55+           7             C   \n",
       "537576  1003235  P00315242      7062      M  18-25           4             C   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               0               0                   3   \n",
       "2                               3               1                   3   \n",
       "3                               3               0                   3   \n",
       "4                              4+               0                   3   \n",
       "...                           ...             ...                 ...   \n",
       "537572                          2               0                   5   \n",
       "537573                          0               0                   8   \n",
       "537574                          0               1                   8   \n",
       "537575                         4+               1                   5   \n",
       "537576                         4+               0                   5   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  \n",
       "0                      NaN                 NaN  \n",
       "1                      NaN                 NaN  \n",
       "2                      NaN                 NaN  \n",
       "3                      NaN                 NaN  \n",
       "4                      NaN                 NaN  \n",
       "...                    ...                 ...  \n",
       "537572                 NaN                 NaN  \n",
       "537573                 NaN                 NaN  \n",
       "537574                 NaN                 NaN  \n",
       "537575                 NaN                 NaN  \n",
       "537576                 8.0                 NaN  \n",
       "\n",
       "[537577 rows x 12 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating one single dataset with datas of the previous 3.\n",
    "# As the purchase information is critical, it was set up as \"left\".\n",
    "\n",
    "df_blackfriday_sales = pd.merge(df_purchase, df_user_profile, on=\"User_ID\", how=\"outer\")\n",
    "df_blackfriday_sales = pd.merge(df_blackfriday_sales, df_product_info, on=\"Product_ID\", how=\"outer\")\n",
    "df_blackfriday_sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 537577 entries, 0 to 537576\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   User_ID                     537577 non-null  int64  \n",
      " 1   Product_ID                  537577 non-null  object \n",
      " 2   Purchase                    537577 non-null  int64  \n",
      " 3   Gender                      537577 non-null  object \n",
      " 4   Age                         537577 non-null  object \n",
      " 5   Occupation                  537577 non-null  int64  \n",
      " 6   City_Category               537577 non-null  object \n",
      " 7   Stay_In_Current_City_Years  537577 non-null  object \n",
      " 8   Marital_Status              537577 non-null  int64  \n",
      " 9   Product_Category_1          537577 non-null  int64  \n",
      " 10  Product_Category_2          370591 non-null  float64\n",
      " 11  Product_Category_3          164278 non-null  float64\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 53.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_blackfriday_sales.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating a report using Pandas Profile to have a better assessment of some of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Profiling Pandas to generate reports\n",
    "\n",
    "profile = ProfileReport(df_blackfriday_sales, title=\"Profiling Black Friday Sales\", \n",
    "                        html={\"style\": {\"full_width\": True}})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In order to visualize inside Jupyter the profile just created\n",
    "profile.to_notebook_iframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the report into a file\n",
    "\n",
    "profile.to_file(\"blackfriday_sales_report.html\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 1\n",
    "\n",
    "Please provide the number of observations and columns of the entire dataset (joining all databases). \n",
    "Answer with a tuple format `(n_observation, n_columns)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(537577, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the complete dataframe's \"shape\" to present the quantity of rows and columns\n",
    "\n",
    "df_blackfriday_sales.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 2\n",
    "\n",
    "Please provided the number of women amongst the ages of 26 and 35 years in the dataset.\n",
    "Answer as a single scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "531686\n",
      "5891\n"
     ]
    }
   ],
   "source": [
    "# Creating a copy of the original dataframe\n",
    "df_age_fem_26to35 = df_blackfriday_sales.copy()\n",
    "\n",
    "# Checking wether there're duplicate users, which can impact the quantity of women aged between 26 to 35\n",
    "print(df_age_fem_26to35[\"User_ID\"].duplicated().sum())\n",
    "\n",
    "# Checking how many unique users exist in the data, since there's duplicated data at the \"User_ID\" column\n",
    "print(df_age_fem_26to35[\"User_ID\"].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Product_ID</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8370</td>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000017</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>13591</td>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000023</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>11022</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000030</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8036</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000073</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>10885</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179643</th>\n",
       "      <td>1000821</td>\n",
       "      <td>P00195542</td>\n",
       "      <td>9644</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211208</th>\n",
       "      <td>1005043</td>\n",
       "      <td>P00230442</td>\n",
       "      <td>18811</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>6</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212738</th>\n",
       "      <td>1004331</td>\n",
       "      <td>P00128942</td>\n",
       "      <td>7959</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>6</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220977</th>\n",
       "      <td>1002908</td>\n",
       "      <td>P00250642</td>\n",
       "      <td>7630</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267924</th>\n",
       "      <td>1002982</td>\n",
       "      <td>P00052842</td>\n",
       "      <td>18622</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase Gender    Age  Occupation City_Category  \\\n",
       "0       1000001  P00069042      8370      F   0-17          10             A   \n",
       "1       1000017  P00069042     13591      M  51-55           1             C   \n",
       "2       1000023  P00069042     11022      M  36-45           0             B   \n",
       "3       1000030  P00069042      8036      F  36-45           7             C   \n",
       "4       1000073  P00069042     10885      M  18-25           4             A   \n",
       "...         ...        ...       ...    ...    ...         ...           ...   \n",
       "179643  1000821  P00195542      9644      M  18-25           2             C   \n",
       "211208  1005043  P00230442     18811      F  18-25           6             C   \n",
       "212738  1004331  P00128942      7959      M  36-45           6             B   \n",
       "220977  1002908  P00250642      7630      M  18-25           2             C   \n",
       "267924  1002982  P00052842     18622      M  26-35           4             C   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               0               0                   3   \n",
       "2                               3               1                   3   \n",
       "3                               3               0                   3   \n",
       "4                              4+               0                   3   \n",
       "...                           ...             ...                 ...   \n",
       "179643                          2               0                   2   \n",
       "211208                          2               0                  14   \n",
       "212738                          3               0                   1   \n",
       "220977                          3               0                  11   \n",
       "267924                          0               0                  10   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  \n",
       "0                      NaN                 NaN  \n",
       "1                      NaN                 NaN  \n",
       "2                      NaN                 NaN  \n",
       "3                      NaN                 NaN  \n",
       "4                      NaN                 NaN  \n",
       "...                    ...                 ...  \n",
       "179643                12.0                 NaN  \n",
       "211208                 NaN                 NaN  \n",
       "212738                11.0                 NaN  \n",
       "220977                 NaN                 NaN  \n",
       "267924                15.0                 NaN  \n",
       "\n",
       "[5891 rows x 12 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminating duplicate data at \"User_ID\" column in order to have the real quantity of women aged between 26 to 35 years\n",
    "\n",
    "df_age_fem_26to35.drop_duplicates(subset=[\"User_ID\"], inplace=True)\n",
    "df_age_fem_26to35"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  Age  \n",
       "F       26-35     545\n",
       "        36-45     333\n",
       "        18-25     287\n",
       "        46-50     182\n",
       "        51-55     142\n",
       "        55+        99\n",
       "        0-17       78\n",
       "M       26-35    1508\n",
       "        36-45     834\n",
       "        18-25     782\n",
       "        46-50     349\n",
       "        51-55     339\n",
       "        55+       273\n",
       "        0-17      140\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is the function used in order to group information while counting the values to check age groups and gender\n",
    "\n",
    "df_age_fem_26to35.groupby(['Gender'])[\"Age\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "545"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After finding in which position the age group wanted was, index was used to bring only that piece of info.\n",
    "\n",
    "df_age_fem_26to35.groupby(['Gender'])[\"Age\"].value_counts()[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 3\n",
    "\n",
    "Please provide the number of unique users of the dataset. Answer as a single scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5891"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This function returns the number of unique users by \"User_ID\" column.\n",
    "\n",
    "df_blackfriday_sales[\"User_ID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 4\n",
    "\n",
    "Please inform the record percentage (row percentage) with at least one null value (`None`, `ǸaN` etc). \n",
    "Answer as a single scalar between 0 and 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID                       0.000000\n",
       "Product_ID                    0.000000\n",
       "Purchase                      0.000000\n",
       "Gender                        0.000000\n",
       "Age                           0.000000\n",
       "Occupation                    0.000000\n",
       "City_Category                 0.000000\n",
       "Stay_In_Current_City_Years    0.000000\n",
       "Marital_Status                0.000000\n",
       "Product_Category_1            0.000000\n",
       "Product_Category_2            0.310627\n",
       "Product_Category_3            0.694410\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Presenting the percentage of null data by column\n",
    "\n",
    "(df_blackfriday_sales.isnull().sum() / (df_blackfriday_sales.isnull().sum() + df_blackfriday_sales.notnull().sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of DataFrame's complete rows (without any null data): 164278\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6944103"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a copy of the original dataframe\n",
    "df_na_drop = df_blackfriday_sales.copy()\n",
    "\n",
    "row_na_drop = df_na_drop.dropna().shape[0] # Removes rows that have at least 1 null value\n",
    "print(f\"Total of DataFrame's complete rows (without any null data): {row_na_drop}\")\n",
    "\n",
    "# Using the inverted percentage of complete rows, we have the percentage of rows with at least one null value\n",
    "percent_row_null = 1 - (row_na_drop / df_blackfriday_sales.shape[0])\n",
    "\n",
    "# Presenting a single scalar, rounding 7 after the comma, the row percentage with at least one null value\n",
    "round(percent_row_null, 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 5\n",
    "\n",
    "Inform the existing quantity of null values in the variable (column) with the highest number of nulls. Answer as a single scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "373299"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Presenting the data number for nulls at \"Product_Category_3\" column, which has the greatest number of nulls\n",
    "\n",
    "df_blackfriday_sales[\"Product_Category_3\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 6\n",
    "\n",
    "Inform the most frequent value (without nulls) at `Product_Category_3`. Answer as a single scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0    32148\n",
       "15.0    27611\n",
       "14.0    18121\n",
       "17.0    16449\n",
       "5.0     16380\n",
       "8.0     12384\n",
       "9.0     11414\n",
       "12.0     9094\n",
       "13.0     5385\n",
       "6.0      4818\n",
       "18.0     4563\n",
       "4.0      1840\n",
       "11.0     1773\n",
       "10.0     1698\n",
       "3.0       600\n",
       "Name: Product_Category_3, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here it was mapped which how many times each value appeared using \"value counts\"\n",
    "\n",
    "df_blackfriday_sales[\"Product_Category_3\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After finding how many times each number appeared, using \"idmax\" it was possible to bring the most frequent value\n",
    "\n",
    "df_blackfriday_sales[\"Product_Category_3\"].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 7\n",
    "\n",
    "Is it possible to assume that if an observation is null at `Product_Category_2` it is also null at `Product_Category_3`? \n",
    "Answer with a bool (`True`, `False`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "<p>166986 rows × 12 columns</p>\n",
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      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase Gender    Age  Occupation City_Category  \\\n",
       "0       1000001  P00069042      8370      F   0-17          10             A   \n",
       "1       1000017  P00069042     13591      M  51-55           1             C   \n",
       "2       1000023  P00069042     11022      M  36-45           0             B   \n",
       "3       1000030  P00069042      8036      F  36-45           7             C   \n",
       "4       1000073  P00069042     10885      M  18-25           4             A   \n",
       "...         ...        ...       ...    ...    ...         ...           ...   \n",
       "537571  1005717  P00206942     15224      M  26-35           0             B   \n",
       "537572  1005754  P00234942      7120      F  18-25           1             A   \n",
       "537573  1005948  P00126342      9875      M    55+          13             B   \n",
       "537574  1006001  P00013442      2277      F  26-35           7             A   \n",
       "537575  1002315  P00063442      1866      M    55+           7             C   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               0               0                   3   \n",
       "2                               3               1                   3   \n",
       "3                               3               0                   3   \n",
       "4                              4+               0                   3   \n",
       "...                           ...             ...                 ...   \n",
       "537571                          3               1                   1   \n",
       "537572                          2               0                   5   \n",
       "537573                          0               0                   8   \n",
       "537574                          0               1                   8   \n",
       "537575                         4+               1                   5   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  \n",
       "0                      NaN                 NaN  \n",
       "1                      NaN                 NaN  \n",
       "2                      NaN                 NaN  \n",
       "3                      NaN                 NaN  \n",
       "4                      NaN                 NaN  \n",
       "...                    ...                 ...  \n",
       "537571                 NaN                 NaN  \n",
       "537572                 NaN                 NaN  \n",
       "537573                 NaN                 NaN  \n",
       "537574                 NaN                 NaN  \n",
       "537575                 NaN                 NaN  \n",
       "\n",
       "[166986 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new data frame only with rows in which \"Product_Category_2\" has null value \n",
    "\n",
    "df_category2_isnull = df_blackfriday_sales[df_blackfriday_sales[\"Product_Category_2\"].isnull()]\n",
    "df_category2_isnull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase Gender    Age  Occupation City_Category  \\\n",
       "0       1000001  P00069042      8370      F   0-17          10             A   \n",
       "1       1000017  P00069042     13591      M  51-55           1             C   \n",
       "2       1000023  P00069042     11022      M  36-45           0             B   \n",
       "3       1000030  P00069042      8036      F  36-45           7             C   \n",
       "4       1000073  P00069042     10885      M  18-25           4             A   \n",
       "...         ...        ...       ...    ...    ...         ...           ...   \n",
       "537572  1005754  P00234942      7120      F  18-25           1             A   \n",
       "537573  1005948  P00126342      9875      M    55+          13             B   \n",
       "537574  1006001  P00013442      2277      F  26-35           7             A   \n",
       "537575  1002315  P00063442      1866      M    55+           7             C   \n",
       "537576  1003235  P00315242      7062      M  18-25           4             C   \n",
       "\n",
       "       Stay_In_Current_City_Years  Marital_Status  Product_Category_1  \\\n",
       "0                               2               0                   3   \n",
       "1                               0               0                   3   \n",
       "2                               3               1                   3   \n",
       "3                               3               0                   3   \n",
       "4                              4+               0                   3   \n",
       "...                           ...             ...                 ...   \n",
       "537572                          2               0                   5   \n",
       "537573                          0               0                   8   \n",
       "537574                          0               1                   8   \n",
       "537575                         4+               1                   5   \n",
       "537576                         4+               0                   5   \n",
       "\n",
       "        Product_Category_2  Product_Category_3  \n",
       "0                      NaN                 NaN  \n",
       "1                      NaN                 NaN  \n",
       "2                      NaN                 NaN  \n",
       "3                      NaN                 NaN  \n",
       "4                      NaN                 NaN  \n",
       "...                    ...                 ...  \n",
       "537572                 NaN                 NaN  \n",
       "537573                 NaN                 NaN  \n",
       "537574                 NaN                 NaN  \n",
       "537575                 NaN                 NaN  \n",
       "537576                 8.0                 NaN  \n",
       "\n",
       "[373299 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new data frame only with rows in which \"Product_Category_3\" has null value\n",
    "\n",
    "df_category3_isnull = df_blackfriday_sales[df_blackfriday_sales[\"Product_Category_3\"].isnull()]\n",
    "df_category3_isnull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    Comparing the quantity of null data at \"Product_Category_3\" columm against the dataframe \"df_category2_isnul\"\n",
    "    with total row numbers of this same data frame.\n",
    "    This way, it's possible to analyse that whenever the value in column \"Product_Category_2\" is null,\n",
    "    it's also going to be null at \"Product_Category_3 column.\" \n",
    "    \n",
    "    ''' \n",
    "\n",
    "\n",
    "df_category2_isnull[\"Product_Category_3\"].isnull().sum() == df_category2_isnull.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 8\n",
    "\n",
    "Inform the ID of the user who spent the most at Black Friday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID\n",
       "1004277    10536783\n",
       "1001680     8699232\n",
       "1002909     7577505\n",
       "1001941     6817493\n",
       "1000424     6573609\n",
       "             ...   \n",
       "1004464       46091\n",
       "1005944       46070\n",
       "1005012       45551\n",
       "1005117       44432\n",
       "1004991       44108\n",
       "Name: Purchase, Length: 5891, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summing up Purchase total to its \"User_ID\" to identify how much each user actually purchased\n",
    "\n",
    "df_blackfriday_sales.groupby([\"User_ID\"])[\"Purchase\"].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1004277"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# After finding purchase total by \"User_ID\", using \"idmax\" it was possible to bring the ID of the heaviest spender\n",
    "\n",
    "heavy_spender = df_blackfriday_sales.groupby([\"User_ID\"])[\"Purchase\"].sum().idxmax()\n",
    "heavy_spender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 9\n",
    "\n",
    "Inform which group (men or women) spent the most during Black Friday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    405380\n",
       "F    132197\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    Counting the amount of men and women.\n",
    "    Not excluding \"Uder_ID\" column repeated data since the same male or female user could have made more than \n",
    "    one purchase, impacting the total amount spent.\n",
    "'''\n",
    "\n",
    "df_blackfriday_sales[\"Gender\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>23959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>23961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Purchase\n",
       "Gender          \n",
       "F          23959\n",
       "M          23961"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum amount spent by gender\n",
    "\n",
    "df_blackfriday_sales.groupby(by=[\"Gender\"])[[\"Purchase\"]].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>8809.761349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>9504.771713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Purchase\n",
       "Gender             \n",
       "F       8809.761349\n",
       "M       9504.771713"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' \n",
    "    Grouping the \"Gender\" column it's possible to obtain the average amount spent by genre,\n",
    "    as well as to determine that \"Male\" was the genre that, on average, spent the most during Black Friday\n",
    "    \n",
    "    It's important to highlight that using the average is the most coherent statistical solution than simply \n",
    "    summing up the total amount spent by gender. The sum can mask that, for exemple, a single user is spending an \n",
    "    outstand amount which is way off, distorting the final conclusion of this answer. \n",
    "'''\n",
    "\n",
    "df_blackfriday_sales.groupby(by=[\"Gender\"])[[\"Purchase\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 10\n",
    "\n",
    "Create a new table with the most purchased category by each customer.\n",
    "\n",
    "Note: if one bought a product that has values in the three category columns, every category must then be considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a copy of the original dataframe\n",
    "\n",
    "df_by_category = df_blackfriday_sales.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>variable</th>\n",
       "      <th>Category_Options</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000017</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000023</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000030</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000073</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612726</th>\n",
       "      <td>1005754</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612727</th>\n",
       "      <td>1005948</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612728</th>\n",
       "      <td>1006001</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612729</th>\n",
       "      <td>1002315</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612730</th>\n",
       "      <td>1003235</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1612731 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         User_ID            variable  Category_Options\n",
       "0        1000001  Product_Category_1               3.0\n",
       "1        1000017  Product_Category_1               3.0\n",
       "2        1000023  Product_Category_1               3.0\n",
       "3        1000030  Product_Category_1               3.0\n",
       "4        1000073  Product_Category_1               3.0\n",
       "...          ...                 ...               ...\n",
       "1612726  1005754  Product_Category_3               NaN\n",
       "1612727  1005948  Product_Category_3               NaN\n",
       "1612728  1006001  Product_Category_3               NaN\n",
       "1612729  1002315  Product_Category_3               NaN\n",
       "1612730  1003235  Product_Category_3               NaN\n",
       "\n",
       "[1612731 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    '''\n",
    "     Applying \"melt\" into the complete dataframe having \"Users_ID\" column as index variable \n",
    "     while \"Product_Category_1\", \"Product_Category_2\" and \"Product_Category_3\" are set as values\n",
    "    '''\n",
    "\n",
    "    \n",
    "df_by_category = pd.melt(df_by_category[[\"User_ID\", \"Product_Category_1\", \"Product_Category_2\", \"Product_Category_3\"]], id_vars=[\"User_ID\"], value_name=\"Category_Options\")\n",
    "df_by_category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: At this exercise, from this point on, the solution was brought by the colleague [Daniel Watanabe](https://github.com/Daniel-Wata), who belongs to the Kung Fu Pandas group.\n",
    "\n",
    "Previously, we had used \"value_counts()\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Category_Options</th>\n",
       "      <th>variable</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000001</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000001</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000001</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000001</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82918</th>\n",
       "      <td>1006040</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82919</th>\n",
       "      <td>1006040</td>\n",
       "      <td>15.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82920</th>\n",
       "      <td>1006040</td>\n",
       "      <td>16.0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82921</th>\n",
       "      <td>1006040</td>\n",
       "      <td>17.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82922</th>\n",
       "      <td>1006040</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82923 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID  Category_Options  variable\n",
       "0      1000001               1.0         4\n",
       "1      1000001               2.0         3\n",
       "2      1000001               3.0        11\n",
       "3      1000001               4.0        13\n",
       "4      1000001               5.0         5\n",
       "...        ...               ...       ...\n",
       "82918  1006040              14.0        24\n",
       "82919  1006040              15.0        19\n",
       "82920  1006040              16.0        19\n",
       "82921  1006040              17.0         9\n",
       "82922  1006040              18.0         1\n",
       "\n",
       "[82923 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping the last dataframe by \"User_ID\" and \"Category_Options\" columns, couting each option.\n",
    "\n",
    "df_groupby_categ_opt = df_by_category.groupby([\"User_ID\", \"Category_Options\"]).count()\n",
    "df_groupby_categ_opt = df_groupby_categ_opt.reset_index()\n",
    "df_groupby_categ_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Category_Options</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
       "      <th>5.0</th>\n",
       "      <th>6.0</th>\n",
       "      <th>7.0</th>\n",
       "      <th>8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <th>10.0</th>\n",
       "      <th>11.0</th>\n",
       "      <th>12.0</th>\n",
       "      <th>13.0</th>\n",
       "      <th>14.0</th>\n",
       "      <th>15.0</th>\n",
       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000001</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000002</th>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000003</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000004</th>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000005</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006036</th>\n",
       "      <td>81.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006037</th>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006038</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006039</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006040</th>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5891 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Category_Options  1.0   2.0   3.0   4.0    5.0   6.0   7.0    8.0   9.0   \\\n",
       "User_ID                                                                    \n",
       "1000001            4.0   3.0  11.0  13.0    5.0   2.0   0.0   13.0   2.0   \n",
       "1000002           31.0   9.0   0.0   0.0   15.0   9.0   0.0   46.0   1.0   \n",
       "1000003           15.0  15.0   1.0   1.0   15.0   0.0   0.0    6.0   0.0   \n",
       "1000004           13.0   4.0   0.0   0.0    0.0   1.0   0.0    2.0   1.0   \n",
       "1000005           18.0   5.0   2.0   4.0   26.0   9.0   5.0   57.0   0.0   \n",
       "...                ...   ...   ...   ...    ...   ...   ...    ...   ...   \n",
       "1006036           81.0  41.0  14.0  26.0  140.0  34.0   7.0  209.0   8.0   \n",
       "1006037           14.0   5.0   0.0   2.0   25.0  12.0   0.0   61.0   4.0   \n",
       "1006038            0.0   0.0   2.0   0.0    6.0   0.0   0.0    6.0   0.0   \n",
       "1006039            7.0   6.0  11.0   9.0   36.0   1.0   0.0   13.0   3.0   \n",
       "1006040           23.0  10.0   1.0   3.0   49.0  15.0   5.0   87.0   4.0   \n",
       "\n",
       "Category_Options  10.0  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  \n",
       "User_ID                                                                 \n",
       "1000001            0.0   0.0   8.0   0.0   3.0   1.0   2.0   2.0   0.0  \n",
       "1000002            1.0   2.0   0.0   3.0  10.0   7.0  14.0   7.0   1.0  \n",
       "1000003            0.0   2.0   0.0   0.0   3.0   1.0   2.0   0.0   4.0  \n",
       "1000004            0.0   2.0   0.0   0.0   2.0   5.0   3.0   1.0   0.0  \n",
       "1000005            1.0   5.0   2.0   4.0  13.0   6.0  21.0   2.0   0.0  \n",
       "...                ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "1006036           11.0  39.0   9.0  16.0  73.0  79.0  65.0  15.0   6.0  \n",
       "1006037            4.0   4.0   1.0   8.0  16.0  14.0  38.0   9.0   1.0  \n",
       "1006038            0.0   1.0   0.0   0.0   4.0   0.0   0.0   3.0   0.0  \n",
       "1006039            3.0   1.0  20.0  12.0  11.0   2.0   7.0   2.0   1.0  \n",
       "1006040            6.0  12.0   3.0   6.0  24.0  19.0  19.0   9.0   1.0  \n",
       "\n",
       "[5891 rows x 18 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Pivot table to obtain quantity of products bought by cateogory option for each user\n",
    "\n",
    "df_pv_users_categ_opt = df_groupby_categ_opt.pivot_table(\"variable\", index=\"User_ID\", \n",
    "                                                                                    columns=\"Category_Options\", \n",
    "                                                                                    aggfunc=np.sum).fillna(0)\n",
    "df_pv_users_categ_opt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Category_Options</th>\n",
       "      <th>1.0</th>\n",
       "      <th>2.0</th>\n",
       "      <th>3.0</th>\n",
       "      <th>4.0</th>\n",
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       "      <th>13.0</th>\n",
       "      <th>14.0</th>\n",
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       "      <th>16.0</th>\n",
       "      <th>17.0</th>\n",
       "      <th>18.0</th>\n",
       "      <th>Max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000001</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000002</th>\n",
       "      <td>31.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000003</th>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000004</th>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000005</th>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
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       "      <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>1006036</th>\n",
       "      <td>81.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006037</th>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006038</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006039</th>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006040</th>\n",
       "      <td>23.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5891 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Category_Options   1.0   2.0   3.0   4.0    5.0   6.0  7.0    8.0  9.0  10.0  \\\n",
       "User_ID                                                                        \n",
       "1000001            4.0   3.0  11.0  13.0    5.0   2.0  0.0   13.0  2.0   0.0   \n",
       "1000002           31.0   9.0   0.0   0.0   15.0   9.0  0.0   46.0  1.0   1.0   \n",
       "1000003           15.0  15.0   1.0   1.0   15.0   0.0  0.0    6.0  0.0   0.0   \n",
       "1000004           13.0   4.0   0.0   0.0    0.0   1.0  0.0    2.0  1.0   0.0   \n",
       "1000005           18.0   5.0   2.0   4.0   26.0   9.0  5.0   57.0  0.0   1.0   \n",
       "...                ...   ...   ...   ...    ...   ...  ...    ...  ...   ...   \n",
       "1006036           81.0  41.0  14.0  26.0  140.0  34.0  7.0  209.0  8.0  11.0   \n",
       "1006037           14.0   5.0   0.0   2.0   25.0  12.0  0.0   61.0  4.0   4.0   \n",
       "1006038            0.0   0.0   2.0   0.0    6.0   0.0  0.0    6.0  0.0   0.0   \n",
       "1006039            7.0   6.0  11.0   9.0   36.0   1.0  0.0   13.0  3.0   3.0   \n",
       "1006040           23.0  10.0   1.0   3.0   49.0  15.0  5.0   87.0  4.0   6.0   \n",
       "\n",
       "Category_Options  11.0  12.0  13.0  14.0  15.0  16.0  17.0  18.0  Max  \n",
       "User_ID                                                                \n",
       "1000001            0.0   8.0   0.0   3.0   1.0   2.0   2.0   0.0  4.0  \n",
       "1000002            2.0   0.0   3.0  10.0   7.0  14.0   7.0   1.0  8.0  \n",
       "1000003            2.0   0.0   0.0   3.0   1.0   2.0   0.0   4.0  1.0  \n",
       "1000004            2.0   0.0   0.0   2.0   5.0   3.0   1.0   0.0  1.0  \n",
       "1000005            5.0   2.0   4.0  13.0   6.0  21.0   2.0   0.0  8.0  \n",
       "...                ...   ...   ...   ...   ...   ...   ...   ...  ...  \n",
       "1006036           39.0   9.0  16.0  73.0  79.0  65.0  15.0   6.0  8.0  \n",
       "1006037            4.0   1.0   8.0  16.0  14.0  38.0   9.0   1.0  8.0  \n",
       "1006038            1.0   0.0   0.0   4.0   0.0   0.0   3.0   0.0  5.0  \n",
       "1006039            1.0  20.0  12.0  11.0   2.0   7.0   2.0   1.0  5.0  \n",
       "1006040           12.0   3.0   6.0  24.0  19.0  19.0   9.0   1.0  8.0  \n",
       "\n",
       "[5891 rows x 19 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    '''\n",
    "    Addition of a new column named \"Max\" at dataframe's end \n",
    "    this will bring which products in the category option were purchased the most by the user\n",
    "    '''\n",
    "\n",
    "df_pv_users_categ_opt[\"Max\"] = df_pv_users_categ_opt.idxmax(axis=1)\n",
    "df_pv_users_categ_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 11\n",
    "\n",
    "Normalize the Purchase column. The normalization form is as follows:\n",
    "\n",
    "\n",
    "$$x_{norm} = \\frac{x - x_{min}}{x_{max} - x_{min}} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the original dataframe\n",
    "df_purchase_normalized = df_blackfriday_sales.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Purchase_Normalized</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000001</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8370</td>\n",
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       "      <td>1000017</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>13591</td>\n",
       "      <td>0.563846</td>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000023</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>11022</td>\n",
       "      <td>0.455796</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>0</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>1000030</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>8036</td>\n",
       "      <td>0.330207</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>1000073</td>\n",
       "      <td>P00069042</td>\n",
       "      <td>10885</td>\n",
       "      <td>0.450034</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>4</td>\n",
       "      <td>A</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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       "    <tr>\n",
       "      <th>537572</th>\n",
       "      <td>1005754</td>\n",
       "      <td>P00234942</td>\n",
       "      <td>7120</td>\n",
       "      <td>0.291681</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537573</th>\n",
       "      <td>1005948</td>\n",
       "      <td>P00126342</td>\n",
       "      <td>9875</td>\n",
       "      <td>0.407554</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>13</td>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>537574</th>\n",
       "      <td>1006001</td>\n",
       "      <td>P00013442</td>\n",
       "      <td>2277</td>\n",
       "      <td>0.087988</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>7</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>537575</th>\n",
       "      <td>1002315</td>\n",
       "      <td>P00063442</td>\n",
       "      <td>1866</td>\n",
       "      <td>0.070702</td>\n",
       "      <td>M</td>\n",
       "      <td>55+</td>\n",
       "      <td>7</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>537576</th>\n",
       "      <td>1003235</td>\n",
       "      <td>P00315242</td>\n",
       "      <td>7062</td>\n",
       "      <td>0.289241</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>4+</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>537577 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        User_ID Product_ID  Purchase  Purchase_Normalized Gender    Age  \\\n",
       "0       1000001  P00069042      8370             0.344255      F   0-17   \n",
       "1       1000017  P00069042     13591             0.563846      M  51-55   \n",
       "2       1000023  P00069042     11022             0.455796      M  36-45   \n",
       "3       1000030  P00069042      8036             0.330207      F  36-45   \n",
       "4       1000073  P00069042     10885             0.450034      M  18-25   \n",
       "...         ...        ...       ...                  ...    ...    ...   \n",
       "537572  1005754  P00234942      7120             0.291681      F  18-25   \n",
       "537573  1005948  P00126342      9875             0.407554      M    55+   \n",
       "537574  1006001  P00013442      2277             0.087988      F  26-35   \n",
       "537575  1002315  P00063442      1866             0.070702      M    55+   \n",
       "537576  1003235  P00315242      7062             0.289241      M  18-25   \n",
       "\n",
       "        Occupation City_Category Stay_In_Current_City_Years  Marital_Status  \\\n",
       "0               10             A                          2               0   \n",
       "1                1             C                          0               0   \n",
       "2                0             B                          3               1   \n",
       "3                7             C                          3               0   \n",
       "4                4             A                         4+               0   \n",
       "...            ...           ...                        ...             ...   \n",
       "537572           1             A                          2               0   \n",
       "537573          13             B                          0               0   \n",
       "537574           7             A                          0               1   \n",
       "537575           7             C                         4+               1   \n",
       "537576           4             C                         4+               0   \n",
       "\n",
       "        Product_Category_1  Product_Category_2  Product_Category_3  \n",
       "0                        3                 NaN                 NaN  \n",
       "1                        3                 NaN                 NaN  \n",
       "2                        3                 NaN                 NaN  \n",
       "3                        3                 NaN                 NaN  \n",
       "4                        3                 NaN                 NaN  \n",
       "...                    ...                 ...                 ...  \n",
       "537572                   5                 NaN                 NaN  \n",
       "537573                   8                 NaN                 NaN  \n",
       "537574                   8                 NaN                 NaN  \n",
       "537575                   5                 NaN                 NaN  \n",
       "537576                   5                 8.0                 NaN  \n",
       "\n",
       "[537577 rows x 13 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "    '''\n",
    "     \"purchase_normalized\" variable created using the data normalization formula \n",
    "     in order to normalize data at \"Purchase\" column\n",
    "    '''\n",
    "\n",
    "purchase_normalized = (df_purchase_normalized[\"Purchase\"] - df_purchase_normalized[\"Purchase\"].min()) / (df_purchase_normalized[\"Purchase\"].max() - df_purchase_normalized[\"Purchase\"].min())\n",
    "\n",
    "# Addition of a new column named \"Purchase_Normalized\" at a copy of the original dataframe\n",
    "\n",
    "df_purchase_normalized.insert(3, \"Purchase_Normalized\", purchase_normalized)\n",
    "df_purchase_normalized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 12\n",
    "\n",
    "Does the marital status has an influence over the amount spent and at the product category bought? Demonstrate it.\n",
    "\n",
    "In case the company wants to sell more products at the category 14, should it invest in advertisements to which marital status?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **For this analysis, it was assumed that \"0\" represents single customers while \"1\" stands for married customers*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the original dataframe\n",
    "\n",
    "df_marital_status = df_blackfriday_sales.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    317817\n",
       "1    219760\n",
       "Name: Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking \"Marital_Status\" values\n",
    "\n",
    "df_marital_status[\"Marital_Status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9333.325467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9334.632681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marital_Status     Purchase\n",
       "0               0  9333.325467\n",
       "1               1  9334.632681"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grouping by \"Marital_Status\", filtering by \"Purchase\" mean in order to determine which marital status spends most\n",
    "\n",
    "df_marital_purch = df_marital_status.groupby(by=[\"Marital_Status\"])[[\"Purchase\"]].mean()\n",
    "\n",
    "df_marital_purch = df_marital_purch.reset_index()\n",
    "\n",
    "df_marital_purch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": [
    "# Bringing data as a bar graph so it's easier to assess which marital status spent the most at Black Friday \n",
    "\n",
    "sns.barplot(x=(\"Marital_Status\"), y=(\"Purchase\"), data= df_marital_purch).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Marital_Status</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Category_Options</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612559</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612560</th>\n",
       "      <td>1</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612586</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612587</th>\n",
       "      <td>0</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1612717</th>\n",
       "      <td>1</td>\n",
       "      <td>Product_Category_3</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1072446 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Marital_Status          Categories  Category_Options\n",
       "0                     0  Product_Category_1               3.0\n",
       "1                     0  Product_Category_1               3.0\n",
       "2                     1  Product_Category_1               3.0\n",
       "3                     0  Product_Category_1               3.0\n",
       "4                     0  Product_Category_1               3.0\n",
       "...                 ...                 ...               ...\n",
       "1612559               0  Product_Category_3              16.0\n",
       "1612560               1  Product_Category_3              16.0\n",
       "1612586               0  Product_Category_3               6.0\n",
       "1612587               0  Product_Category_3               6.0\n",
       "1612717               1  Product_Category_3              16.0\n",
       "\n",
       "[1072446 rows x 3 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "     Applying \"melt\" function at the complete dataframe, having \"Marital_Status\" column as variable\n",
    "     and \"Product_Category_1\", \"Product_Category_2\" and \"Product_Category_3\" coluns were set to be value\n",
    "'''\n",
    "\n",
    "\n",
    "\n",
    "df_by_marital_status = pd.melt(df_marital_status, id_vars=[\"Marital_Status\"], \n",
    "                                value_vars=[\"Product_Category_1\", \"Product_Category_2\", \n",
    "                                            \"Product_Category_3\"], var_name=\"Categories\", \n",
    "                                value_name=\"Category_Options\").dropna()\n",
    "df_by_marital_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Category_Options</th>\n",
       "      <th>Categories</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>114206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>79005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Marital_Status  Category_Options  Categories\n",
       "0                0               1.0       83230\n",
       "1                0               2.0       43401\n",
       "2                0               3.0       14314\n",
       "3                0               4.0       23823\n",
       "4                0               5.0      114206\n",
       "5                0               6.0       24685\n",
       "6                0               7.0        2369\n",
       "7                0               8.0      108569\n",
       "8                0               9.0       10672\n",
       "9                0              10.0        5300\n",
       "10               0              11.0       24066\n",
       "11               0              12.0       10569\n",
       "12               0              13.0       12208\n",
       "13               0              14.0       43271\n",
       "14               0              15.0       42129\n",
       "15               0              16.0       50525\n",
       "16               0              17.0       17234\n",
       "17               0              18.0        5773\n",
       "18               1               1.0       55123\n",
       "19               1               2.0       28579\n",
       "20               1               3.0        8970\n",
       "21               1               4.0       14809\n",
       "22               1               5.0       76640\n",
       "23               1               6.0       16548\n",
       "24               1               7.0        1914\n",
       "25               1               8.0       79005\n",
       "26               1               9.0        6737\n",
       "27               1              10.0        4421\n",
       "28               1              11.0       15612\n",
       "29               1              12.0        7819\n",
       "30               1              13.0        8986\n",
       "31               1              14.0       30508\n",
       "32               1              15.0       29002\n",
       "33               1              16.0       33922\n",
       "34               1              17.0       12912\n",
       "35               1              18.0        4595"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a new dataframe with the quantity of purchased products by category at each marital status\n",
    "\n",
    "df_grouped_marital = df_by_marital_status.groupby([\"Marital_Status\", \"Category_Options\"]).count()\n",
    "df_grouped_marital = df_grouped_marital.reset_index()\n",
    "df_grouped_marital"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "636344"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total of products purchased by customers with single status\n",
    "\n",
    "total_single_categ = df_grouped_marital[df_grouped_marital[\"Marital_Status\"] == 0][\"Categories\"].sum()\n",
    "total_single_categ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "436102"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total of products purchased by customers with married status\n",
    "\n",
    "total_married_categ = df_grouped_marital[df_grouped_marital[\"Marital_Status\"] == 1][\"Categories\"].sum()\n",
    "total_married_categ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.130794\n",
       "1     0.068204\n",
       "2     0.022494\n",
       "3     0.037437\n",
       "4     0.179472\n",
       "5     0.038792\n",
       "6     0.003723\n",
       "7     0.170614\n",
       "8     0.016771\n",
       "9     0.008329\n",
       "10    0.037819\n",
       "11    0.016609\n",
       "12    0.019185\n",
       "13    0.067999\n",
       "14    0.066205\n",
       "15    0.079399\n",
       "16    0.027083\n",
       "17    0.009072\n",
       "18    0.126399\n",
       "19    0.065533\n",
       "20    0.020569\n",
       "21    0.033958\n",
       "22    0.175739\n",
       "23    0.037945\n",
       "24    0.004389\n",
       "25    0.181162\n",
       "26    0.015448\n",
       "27    0.010138\n",
       "28    0.035799\n",
       "29    0.017929\n",
       "30    0.020605\n",
       "31    0.069956\n",
       "32    0.066503\n",
       "33    0.077785\n",
       "34    0.029608\n",
       "35    0.010537\n",
       "Name: Categories, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    Creating a series of percentual of products purchased in each category \n",
    "    by the total of products purchased by each marital status\n",
    "'''\n",
    "\n",
    "percent_by_categ = pd.concat([df_grouped_marital[df_grouped_marital[\"Marital_Status\"] == 0][\"Categories\"] / total_single_categ,\n",
    "                                df_grouped_marital[df_grouped_marital[\"Marital_Status\"] == 1][\"Categories\"] / total_married_categ])\n",
    "percent_by_categ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Category_Options</th>\n",
       "      <th>Categories</th>\n",
       "      <th>Percent_by_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>83230</td>\n",
       "      <td>13.079404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43401</td>\n",
       "      <td>6.820368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14314</td>\n",
       "      <td>2.249412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23823</td>\n",
       "      <td>3.743730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>114206</td>\n",
       "      <td>17.947211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>24685</td>\n",
       "      <td>3.879191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2369</td>\n",
       "      <td>0.372283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>108569</td>\n",
       "      <td>17.061369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10672</td>\n",
       "      <td>1.677080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5300</td>\n",
       "      <td>0.832883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>24066</td>\n",
       "      <td>3.781917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10569</td>\n",
       "      <td>1.660894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12208</td>\n",
       "      <td>1.918459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>43271</td>\n",
       "      <td>6.799938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>42129</td>\n",
       "      <td>6.620476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>50525</td>\n",
       "      <td>7.939888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17234</td>\n",
       "      <td>2.708284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5773</td>\n",
       "      <td>0.907214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55123</td>\n",
       "      <td>12.639933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>28579</td>\n",
       "      <td>6.553283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8970</td>\n",
       "      <td>2.056858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14809</td>\n",
       "      <td>3.395765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>76640</td>\n",
       "      <td>17.573870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16548</td>\n",
       "      <td>3.794525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1914</td>\n",
       "      <td>0.438888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>79005</td>\n",
       "      <td>18.116175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6737</td>\n",
       "      <td>1.544822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4421</td>\n",
       "      <td>1.013754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15612</td>\n",
       "      <td>3.579896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7819</td>\n",
       "      <td>1.792929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8986</td>\n",
       "      <td>2.060527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>30508</td>\n",
       "      <td>6.995611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>29002</td>\n",
       "      <td>6.650279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>33922</td>\n",
       "      <td>7.778455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12912</td>\n",
       "      <td>2.960775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4595</td>\n",
       "      <td>1.053653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Marital_Status  Category_Options  Categories  Percent_by_Category\n",
       "0                0               1.0       83230            13.079404\n",
       "1                0               2.0       43401             6.820368\n",
       "2                0               3.0       14314             2.249412\n",
       "3                0               4.0       23823             3.743730\n",
       "4                0               5.0      114206            17.947211\n",
       "5                0               6.0       24685             3.879191\n",
       "6                0               7.0        2369             0.372283\n",
       "7                0               8.0      108569            17.061369\n",
       "8                0               9.0       10672             1.677080\n",
       "9                0              10.0        5300             0.832883\n",
       "10               0              11.0       24066             3.781917\n",
       "11               0              12.0       10569             1.660894\n",
       "12               0              13.0       12208             1.918459\n",
       "13               0              14.0       43271             6.799938\n",
       "14               0              15.0       42129             6.620476\n",
       "15               0              16.0       50525             7.939888\n",
       "16               0              17.0       17234             2.708284\n",
       "17               0              18.0        5773             0.907214\n",
       "18               1               1.0       55123            12.639933\n",
       "19               1               2.0       28579             6.553283\n",
       "20               1               3.0        8970             2.056858\n",
       "21               1               4.0       14809             3.395765\n",
       "22               1               5.0       76640            17.573870\n",
       "23               1               6.0       16548             3.794525\n",
       "24               1               7.0        1914             0.438888\n",
       "25               1               8.0       79005            18.116175\n",
       "26               1               9.0        6737             1.544822\n",
       "27               1              10.0        4421             1.013754\n",
       "28               1              11.0       15612             3.579896\n",
       "29               1              12.0        7819             1.792929\n",
       "30               1              13.0        8986             2.060527\n",
       "31               1              14.0       30508             6.995611\n",
       "32               1              15.0       29002             6.650279\n",
       "33               1              16.0       33922             7.778455\n",
       "34               1              17.0       12912             2.960775\n",
       "35               1              18.0        4595             1.053653"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inserting a column in the \"df_grouped_marital\" with the purchased percentual by category for each marital status\n",
    "\n",
    "df_grouped_marital[\"Percent_by_Category\"] = percent_by_categ * 100\n",
    "\n",
    "df_grouped_marital"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the data seen above, it's possible to identify that there're more single customers making purchases during Black Friday; however, the group that had higher expenditures were the married customers.\n",
    "\n",
    "This tends to happen due to the difference in lifestyle amongst single customers and married ones.\n",
    "\n",
    "Meaning that having a carefree lifestyle, i.e, with less financial responsibilities than a married person, single people can afford to purchase more products than a married person who needs to take into account household budgets, financial commitments, debts, etc. before actually purchasing stuff online.\n",
    "Nonetheless, even though the data exhibits that there are more single customers, the average of items purchased by married customers presents higher values.\n",
    "For example, while a single customer can purchase 10 products varying amongst stationary and office supplies, a married customer can purchase a single product, like a laptop, which would therefore surpass the total amount of the purchase made by the single customer.\n",
    "\n",
    "Further analyzing the purchase behaviour of each marital status by category option, while verifying the expenditure percentage, more specifically looking at the data at the column \"Percent_by_Category\", it's possible to identify that the marital statues does not really influence the category of product purchase, since, proportionally, the amount of products purchased by category in each marital status it's almost equivalent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Marital_Status             0.000000\n",
       " Category_Options          14.000000\n",
       " Categories             43271.000000\n",
       " Percent_by_Category        6.799938\n",
       " Name: 13, dtype: float64,\n",
       " Marital_Status             1.000000\n",
       " Category_Options          14.000000\n",
       " Categories             30508.000000\n",
       " Percent_by_Category        6.995611\n",
       " Name: 31, dtype: float64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bringing the quantity of category 14 products that each marital status bought\n",
    "\n",
    "df_maritalstatus_0 = df_grouped_marital.iloc[13]\n",
    "df_maritalstatus_1 = df_grouped_marital.iloc[31]\n",
    "\n",
    "df_maritalstatus_0, df_maritalstatus_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction of advertisement budget for Category 14 analysing customer behaviour by marital status.\n",
    "\n",
    "Defining to which Marital Status the advertisement budget needs to be invested in order to sell more Category 14 products will depend solely on the company's strategy.\n",
    "\n",
    "Single customers are organically buying more products in this category than married ones; therefore, if the company wants to play safe and ensure its sales will go through the roof, it would be logical to invest the advertisement budget into this group and further push sales.\n",
    "\n",
    "However, if the company wants to capture married customers in order to change their purchase behaviour in this product category, it would be a smart financial budget decision to bombard adds to this group to entice the customer to buy. \n",
    "Nonetheless, it's necessary to observe that investing all the advertisement budget into this strategy will not guarantee the married customers receiving the adds will actually purchase products in this category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question 13\n",
    "\n",
    "Which variables had the most impact over the purchase price? How did the team reach this conclusion?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variables: Gender | Age | Marital_Status | Occupation | City_Category | Stay_In_Current_City_Years"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Data preprocessing**\n",
    "\n",
    "Before assessing which variables have a great impact over purchase amount, first it's necessary to fix the ‘+’ sign both at \"Age\" and \"Stay_In_Current_City_Years\" columns.\n",
    "In order to do so, the *.replace()* command is going to be used:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_blackfriday_sales[\"Age\"] = df_blackfriday_sales[\"Age\"].apply(lambda x:str(x).replace('55+', '55'))\n",
    "df_blackfriday_sales[\"Stay_In_Current_City_Years\"] = df_blackfriday_sales[\"Stay_In_Current_City_Years\"].apply(lambda x : str(x).replace('4+', '4'))\n",
    "df_blackfriday_sales[\"Stay_In_Current_City_Years\"] = df_blackfriday_sales[\"Stay_In_Current_City_Years\"].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>User_ID</th>\n",
       "      <th>Purchase</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Product_Category_1</th>\n",
       "      <th>Product_Category_2</th>\n",
       "      <th>Product_Category_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005389</td>\n",
       "      <td>-0.023024</td>\n",
       "      <td>-0.030655</td>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>0.004045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purchase</th>\n",
       "      <td>0.005389</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>-0.314125</td>\n",
       "      <td>-0.209973</td>\n",
       "      <td>-0.022257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupation</th>\n",
       "      <td>-0.023024</td>\n",
       "      <td>0.021104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.031203</td>\n",
       "      <td>0.024691</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>0.013452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <td>-0.030655</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.031203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.012663</td>\n",
       "      <td>-0.004182</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>0.002039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marital_Status</th>\n",
       "      <td>0.018732</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>0.024691</td>\n",
       "      <td>-0.012663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020546</td>\n",
       "      <td>0.015116</td>\n",
       "      <td>0.019452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_1</th>\n",
       "      <td>0.003687</td>\n",
       "      <td>-0.314125</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>-0.004182</td>\n",
       "      <td>0.020546</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.540423</td>\n",
       "      <td>0.229490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_2</th>\n",
       "      <td>0.001471</td>\n",
       "      <td>-0.209973</td>\n",
       "      <td>-0.000031</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>0.015116</td>\n",
       "      <td>0.540423</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.543544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Category_3</th>\n",
       "      <td>0.004045</td>\n",
       "      <td>-0.022257</td>\n",
       "      <td>0.013452</td>\n",
       "      <td>0.002039</td>\n",
       "      <td>0.019452</td>\n",
       "      <td>0.229490</td>\n",
       "      <td>0.543544</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             User_ID  Purchase  Occupation  \\\n",
       "User_ID                     1.000000  0.005389   -0.023024   \n",
       "Purchase                    0.005389  1.000000    0.021104   \n",
       "Occupation                 -0.023024  0.021104    1.000000   \n",
       "Stay_In_Current_City_Years -0.030655  0.005470    0.031203   \n",
       "Marital_Status              0.018732  0.000129    0.024691   \n",
       "Product_Category_1          0.003687 -0.314125   -0.008114   \n",
       "Product_Category_2          0.001471 -0.209973   -0.000031   \n",
       "Product_Category_3          0.004045 -0.022257    0.013452   \n",
       "\n",
       "                            Stay_In_Current_City_Years  Marital_Status  \\\n",
       "User_ID                                      -0.030655        0.018732   \n",
       "Purchase                                      0.005470        0.000129   \n",
       "Occupation                                    0.031203        0.024691   \n",
       "Stay_In_Current_City_Years                    1.000000       -0.012663   \n",
       "Marital_Status                               -0.012663        1.000000   \n",
       "Product_Category_1                           -0.004182        0.020546   \n",
       "Product_Category_2                           -0.001781        0.015116   \n",
       "Product_Category_3                            0.002039        0.019452   \n",
       "\n",
       "                            Product_Category_1  Product_Category_2  \\\n",
       "User_ID                               0.003687            0.001471   \n",
       "Purchase                             -0.314125           -0.209973   \n",
       "Occupation                           -0.008114           -0.000031   \n",
       "Stay_In_Current_City_Years           -0.004182           -0.001781   \n",
       "Marital_Status                        0.020546            0.015116   \n",
       "Product_Category_1                    1.000000            0.540423   \n",
       "Product_Category_2                    0.540423            1.000000   \n",
       "Product_Category_3                    0.229490            0.543544   \n",
       "\n",
       "                            Product_Category_3  \n",
       "User_ID                               0.004045  \n",
       "Purchase                             -0.022257  \n",
       "Occupation                            0.013452  \n",
       "Stay_In_Current_City_Years            0.002039  \n",
       "Marital_Status                        0.019452  \n",
       "Product_Category_1                    0.229490  \n",
       "Product_Category_2                    0.543544  \n",
       "Product_Category_3                    1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking correlation amongst variables of the original dataframe using \"corr()\" function.\n",
    "df_blackfriday_sales.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As previously seen at Question 09:\n",
    "\n",
    "- Genre has a great influence over Purchase habits.\n",
    "- Meaning \"Male\" users bought way more than the \"Female\" ones.\n",
    "- Below there's again the data sampled to exhibit the importance of this variable to predict Customer Behaviour.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Gender</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>8809.761349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>9504.771713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Purchase\n",
       "0      F  8809.761349\n",
       "1      M  9504.771713"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_gender = df_blackfriday_sales.groupby(by=[\"Gender\"])[[\"Purchase\"]].mean()\n",
    "df_purchase_gender = df_purchase_gender.reset_index()\n",
    "df_purchase_gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVKklEQVR4nO3dedRddX3v8feHoWEoKEOkWAhTS1j0GioEyywIIgrIoEVkugxtimIWXKvXqVq1VwSk3OpdXiuIaC6Kel1VsIphUi8KGsIgUSuiUAbRGiYhQkoTvvePvYORkuwTknP2Cc/7tdZZzzlnn33291nrgU9+405VIUnS8qzRdwGSpPFnWEiSOhkWkqROhoUkqZNhIUnqZFhIkjqt1XcBw7DpppvW1ltv3XcZkrRaufHGG++vqsnPdOw5GRZbb701c+fO7bsMSVqtJLlrWcfshpIkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1ek6u4Jaey+5+/4v6LkFjaMp75g31+21ZSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE5uUb4Mu7x1Vt8laAzd+KET+i5B6oUtC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktRpLMMiyZpJ1uy7DklSY+zCIskRwCeBf0qyW5IN+q5Jkia6sQqLJDsC59CExTXAO4Gjk2wxwLkzksxNMnf+/PlDrlSSJpaxCgtgM+CnVfWtqvow8FFgN+CgJOslybJOrKrzq2p6VU2fPHnyqOqVpAlh3MLiO8CCJK8GqKrZwBeA1wA7VlX1WZwkTVS9h0WSPZMckOSAqnoCuBbYI8nu8FRgXA3MTNJ7vZI0EfX6P98kBwKXAAcCH0hyDjALKOCwJH/efvRB4LH2fUnSiK3V14XbqbHHAu+rqguTrA9cBbwVOLM99vokM4AtgaPthpKkfow8LJKkGouTzAE2TrJOVf0myctpZkGtVVVvS3IBsAtwd1X9ctS1SpIafXRDvWCp5/OAA4CtAKpqQft6ryS7V9XiqppjUEhSv0YaFkkOAb6c5IIk7wduAb4EzEqyQ5J1q+oR4Eejrk2StGwj64ZKsh3wEeBkYDGwH/A14FCagevzgO8leRLYHzhrVLVJkpZvlGMWDwCXV9U328V13wYWAZcChwC3AjsA04GDq+pnI6xNkrQcQw+LJLsBfwzcT7N+4s1VdV577CxgEnBaVX0QuD7Jp5z1JEnjZajjAu1K7POBVwAHA+8FTk7yRoCqehK4Dpiy5ByDQpLGz9DCIskmwGnAMVV1HLAh8DgwE3h7kje23VGbA9u7u6wkja9hdkMtAtYFdkhyD7A3sAnNTKfvAqcA04CXAkdV1aNDrEWStBKG1rKoql/TzH56BzAbuKiqDgauBP4VOB04A9inquYNqw5J0sob6phFVX2RZpHdtcBN7XtX0Ax4b1JVC6vKm09I0pgb+myoqnooyTXAUUmeANahWbH9/WFfW5K0aoxqncX1NK2JdwELgZOq6l9HdG1J0koaSVhU1cPAR5JcBKTd0kOStJoY6a6zzniSpNWTm/VJkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSepkWEiSOhkWkqROhoUkqZNhIUnqZFhIkjoZFpKkToaFJKmTYSFJ6mRYSJI6GRaSpE6GhSSpk2EhSeo0lmGRZL0kk/quQ5LUGLuwSHIkcDFweZJDkmzbd02SNNGt1XcBS0uyDXAWcCwwFXglsEuSy6rq5l6Lk6QJbNxaFhsC91bVDVV1MXARTaAdmmSr5Z2YZEaSuUnmzp8/fxS1StKEMVZhUVXfBx5OMrN9PRe4DNgS2KHj3POranpVTZ88efLwi5WkCWSgsEiyfZKrk/ygfT0tyd+sigKS7JvkqCTHt2/NArZKcjRAVd0AXA+8Mcnaq+KakqQVM2jL4gLgHcB/AFTVrcDRK3vxJPsBlwBTgDOSnAfcBtwJ7Jrkr9uPPg4sALKy15QkrbhBw2K9qprztPcWrcyFk4RmAPucqjoX2BPYHDgBuKp97J3kauDdwLlV9cTKXFOS9OwMGhb3J9kOKIAkrwV+sTIXrqoCbgKmJtmsqhYCpwDbAadW1eVVdThwKrC3s6EkqT+DhsVpwMeBHZL8HDgDeMOzuWCSLZNMSrIuzVjEBsC0JOtW1WPAicA+SY4AqKrbq+qBZ3MtSdKqMdA6i6q6AzggyfrAGlX16LO5WJKDgbOB62hC4s00YxanN4czr6p+0XY92eUkSWNi0NlQpyfZEHgM+J9Jbkpy4IpcKMkf0gTFm2jGIG4G5rQ/PwYcB5yd5IPAMcDtK/L9kqThGXQF98lV9eEkrwA2AY4H/g9wxSAnt11O9wPXAj8BflVV5yRZRNPK2I0mNHYFdgL2r6qfrNBvIkkamkHHLJZMWX0VMKuqfsiA01iTHAacC7wQ2Bg4qR3cpqrOA/4R+ADwQFVdWlXvr6rbVuB3kCQN2aBhcWOSK2jCYnaSDYAnu05K8lKarqfLqupO4K3AjCRvXupjl9BMw3WMQpLG1KDdUKcAfwrcUVWPJdkEOGmA83YBPlFVs5NMAX4f+BvgfydZCFwN7A7sDDwfeGjFypckjcKgs6GeTHInsH2SdVbg+xcBv9c+/xxwH/AzYB5wIM3OsnvQdE0ZFJI0pgYKiyR/QTO9dQvgFpoB6euBl3Wc+g3gi0mmAxdU1UVJtqfpwvpuVV2aZCODQpLG26BjFqfTzFS6q6r2A14MPNx1UlXNA94C/BmwTfveT4AXAM9rP9b5PZKkfg06ZrGwqhYmIcmkqvpxkqkDnns58LfAe5Pc1b63E3AmPLXthyRpjA0aFvcmeT7wZeDKJA8Bdy33jFZVLQJmtdubvxaYRDNG8bMVL1eS1IdBB7iPaJ++N8k3aLqQvr4iF6qqm2g2DpQkrWYGvgd3kjWBzWjuNQHwB8DdwyhKkjReBp0NNZNm3OHf+O1ivAKmDakuSdIYGbRlcTow1a3CJWliGnTq7D3Ar4dZiCRpfC23ZbHUHk53AN9M8lXg35ccbzcClCQ9x3V1Q23Q/ry7ffwev92+Q5I0QSw3LKrqfaMqRJI0vga9U96V7aK8Ja83SjJ7aFVJksbKoAPck6vq4SUv2o3/XjCUiiRJY2fQsFjc3o8CgCRb0ayzkCRNAIOus3gn8O0k36K5nerewIyhVSVJGiudYZFkDZq9oHamuY8FwBlVdf8wC5MkjY/OsGjvkvffq+oLwD+PoCZJ0pgZdMziqiRvSbJlko2XPIZamSRpbAw6ZvG69udpS71XwLarthxJ0jga9H4W2wy7EEnS+Bp0i/ITnun9qpq1asuRJI2jQbuhdl3q+TrA/jR3vTMsJGkCGLQbaubSr9utPz43jIIkSeNn0NlQT/cbwHEMSZogBh2z+Aq/3d5jDWBH4AvDKkqSNF4GHbM4d6nni4C7qureIdQjSRpDXXfKWwc4FfgjYB5wYVUtGmZBSdYEqKrFw7yOJGlwXWMWnwam0wTFK4G/H2YxSY4APgn8U5LdkmzQdY4kafi6uqF2rKoXASS5EJgzrEKS7AicA/wF8Kc0O91+Jcnlg3R5JZlBuxPulClTOj4tSVoRXS2L/1jyZNjdT8BmwE+r6ltV9WHgozS73B6UZL0kWd7JVXV+VU2vqumTJ08ecqmSNLF0hcVOSR5pH48C05Y8T/LIKq7lO8CCJK8GqKrZNDOuXkPTwvFmS5LUk+V2Q1XVmsO8eJI9gXXba12V5FpgjyTzq+r6qpqd5EXAzCQnVdWTw6xHkvTMnu2ivJWW5EDgEuBA4ANJzqHZPqSAw5L8efvRB4HH8DauktSbQddZrFLt9NhjgfdV1YVJ1geuAt4KnNkee307aL0lcLTdUJLUn5GGRZJUY3GSOcDGSdapqt8keTlwDbBWVb0tyQXALsDdVfXLUdYpSfpdo+6GesFSz+cBBwBbAVTVgvb1Xkl2r6rFVTXHoJCk/o0sLJIcAnw5yQVJ3g/cAnwJmJVkhyTrVtUjwI9GWZckqdtIuqGSbAd8BDgZWAzsB3wNOJRm4Po84HtJnqS5V8ZZo6hLkjSYUY1Z3A98o6q+2S6u+zbNhoSXAocAtwI70GwtcnBV/WxEdUmSBjDU7p4keyU5DngE2D7J29sB7qJpPXwDOK1dU3ER8Kaq+pdh1iRJWnFDaVkkWQNYD/h4e40FwFHAFUker6oPV9WTSa4DDl9yntNjJWk8DSUs2pXWC5J8mmaM4ihgI+BlwHVJFlXVR4HNaVocG1TVo8OoRZK08oY9ZrEImAJcCPwlsAXNlNnXJXkJ8BLgKINCksbbsKeoXgr8sqquBm4A3gN8v6r2oQmPvatq3pBrkCStpGGHxePA1CR/SXPHvf8BvCTJqVX1RFXdP+TrS5JWgaF2Q1XVfUnuAd5NM+vpK0n2A346zOtKklatUayzuAC4tKpubF9/y63GJWn1MvSwqKp7gHuW2kTQoJCk1czI9mByDYUkrb7csE+S1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUqexDIsk6yWZ1HcdkqTG2IVFkiOBi4HLkxySZNu+a5KkiW6tvgtYWpJtgLOAY4GpwCuBXZJcVlU391qcJE1g49ay2BC4t6puqKqLgYtoAu3QJFst78QkM5LMTTJ3/vz5o6hVkiaMsQqLqvo+8HCSme3rucBlwJbADh3nnl9V06tq+uTJk4dfrCRNIL2HRZJ9kxyV5Pj2rVnAVkmOBqiqG4DrgTcmWbuvOiVpIus1LJLsB1wCTAHOSHIecBtwJ7Brkr9uP/o4sABIL4VK0gTXW1gkCc0A9jlVdS6wJ7A5cAJwVfvYO8nVwLuBc6vqib7qlaSJrLewqKoCbgKmJtmsqhYCpwDbAadW1eVVdThwKrC3s6EkqT8jD4skWyaZlGRdmrGIDYBpSdatqseAE4F9khwBUFW3V9UDo65TkvRbI11nkeRg4GzgOpqQeDPNmMXpzeHMq6pftF1PdjlJ0pgYSVi04xNb0Cy4exPwL8B/BeYAuwEfA45rP/tz4BjgE6OoTZLUbSRhUVWV5D6abqfbgV9V1TlJFtG0MnYDbgZ2BXYC9q+qn4yiNklSt6GHRZI/AjYC7gCeBxxbVecAVNV57YaBHwDeUFWXApcOuyZJ0ooZalgkOQQ4E3gImAd8BvhIkjWr6oPtxy4B3oljFJI0toYWFkn2AD4EHFNVNyc5H3gJsAfw3SRrAp8D9gJ2Bp5PEyqSpDEz7KmzZy+1PuJdwIur6j5gX2BbmtlQM4GTqsqgkKQxNcxuqO8BPwBoWxGTgBcm2byq7kjyPuDnwPpV9esh1iFJWklDa1lU1eKqeqR9GeBh4MF2HcVxNOMUaxsUkjT+RjV1dhGwIMk9ST4IHAicWFWPj+L6kqSVM8pFeWsDe7c/96+q20dxbUnSyhvZojzgiSR/B9xgUEjS6mXU9+D+dBsckqTVyEh3nTUoJGn11PttVSVJ48+wkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ0MC0lSJ8NCktTJsJAkdTIsJEmdDAtJUqexDIskU5PsnmTtJGv2XY8kTXRr9V3A0yU5EjgT+Hn7mJvkU1X1SL+VSdLENVYtiyRrA68DTqmq/YFLgS2BtyXZsNfiJGkCS1X1XcNT2rC4DPh8VX0qyRrA3sDBwB3Ax2sZBSeZAcxoX04FbhtByRPFpsD9fRchPQP/Nletrapq8jMdGKuwAEjycmAm8KGqurYds3gd8Crg+GWFhYYnydyqmt53HdLT+bc5OmPVDdW6FrgCOD7JPlW1uKo+C7wQ2Knf0iRpYhq7Ae6qWpjkM0AB70iyA/DvwGbAL3otTpImqLELC4CqeijJBcCPgL8CFgLHVdW/9VvZhHV+3wVIy+Df5oiM3ZjF07VjFlVVT/ZdiyRNVGMfFpKk/o3jALfGQJI/6LsGaVmSbNR3DRONYaH/JMnBwGVJnnG+tdSnJAcCV7Y/NSKGhX5HkoOAtwPvqar57UJJaZxMBf4L8JYkh/dcy4QxlrOh1I8kGwNfA46sqq8n2Q54T5L/BjzkgkiNiUuAbYG7gROSrF1V/7fnmp7zbFnoKVX1IHAoTUBMo5mWeHNVPWhQqE9JprV/kwAPAk8AfwJ8DDguyWt6K26CMCz0O6rqq8A7gVuAK6vqH5KskST9VqaJKskmNH+PX03yWmAX4F00i3XXAD5L08J4fW9FTgCGhf6Tqvo68ArgxCTPa9e4eF8R9aKqHgAOAP4QmAYcBMwCHgMmV9XngS8BhyXZoLdCn+NcZ6FlSvJK4B+A3dsuKqk3SfYHPgnsDLwWOAa4BzgZmARQVY/2VuBznGGh5UpyGPC3wHSalfT+wag3SV4FnE3zD5gFSbapqjv7rmsiMCzUKcnvV9WCvuuQ4KnA+HtgzyUt3iTxHzLD5dRZdTIoNE6q6mvt+p+rktjiHRFbFpJWS7Z4R8uwkCR1cuqsJKmTYSFJ6mRYSJI6GRaSpE6GhTSgJJsl+WySO5LcmOT6JEesgu/dN8k/r4oapWExLKQBtBspfhn4f1W1bVXtAhwNbNFDLa6P0sgZFtJgXgY8UVX/uOSNqrqrqv5XkjWTfCjJDUluTfJX8FSL4ZtJvpjkx0k+s2T33iQHte/dBBy55DuTrJ/kk0nmJLm53W6FJCcmuSzJNcDVI/3NJVzBLQ3qT4CblnHsFODXVbVrkknAd5Jc0R57cXvufcB3gD2TzAUuoAmgnwKfX+q73gVcU1UnJ3k+MCfJVe2xnYFpbuqoPhgW0rOQ5KPAXjQ34bkLmNbeawHgecAft8fmVNW97Tm3AFsDC4A7q+r29v2LgRntuQcCr07ylvb1OsCU9vmVBoX6YlhIg/kh8NTd2KrqtCSbAnNpbu85s6pmL31Ckn1pbtCzxGK6/5sL8Jqquu1p3/VnwG+ebfHSynLMQhrMNcA6Sd6w1HvrtT9nA29oN7cjyfZJ1l/Od/0Y2Lq9xznA0nd4mw3MXGps48WrpHppJRkW0gDaXU0PB16a5M4kc4BPA28DPgH8CLgpyQ+Aj7OcFkRVLaTpdvpqO8D9q6UO/x2wNnBrkh+2r6XeuZGgJKmTLQtJUifDQpLUybCQJHUyLCRJnQwLSVInw0KS1MmwkCR1MiwkSZ3+P63PPQphT1SdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bringing data as a bar graph so it's easier to assess gender vs. purchase total\n",
    "\n",
    "sns.barplot(x=(\"Gender\"), y=(\"Purchase\"), data=df_purchase_gender).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As Genre has an impact over Purchase amount, it's necessary to determine weather Age has an impact as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>9020.126878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-25</td>\n",
       "      <td>9235.197575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-35</td>\n",
       "      <td>9314.588970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36-45</td>\n",
       "      <td>9401.478758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46-50</td>\n",
       "      <td>9284.872277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51-55</td>\n",
       "      <td>9620.616620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55</td>\n",
       "      <td>9453.898579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age     Purchase\n",
       "0   0-17  9020.126878\n",
       "1  18-25  9235.197575\n",
       "2  26-35  9314.588970\n",
       "3  36-45  9401.478758\n",
       "4  46-50  9284.872277\n",
       "5  51-55  9620.616620\n",
       "6     55  9453.898579"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_age = df_blackfriday_sales.groupby(by=[\"Age\"])[[\"Purchase\"]].mean()\n",
    "df_purchase_age = df_purchase_age.reset_index()\n",
    "df_purchase_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gfGAz4GhJJ1AWXzSwjaQP1Kc+DDzNy7PeIyKih4Y7w3zE1eG4uwBH2T5L0rzAT4CPAsfUx3aqHeFLATum2Soioh2tFw9JcvGCpEnAwpLmsv2UpE2BnwKz2f64pDOA1YC7bN/favCIiFGsC81Wiw66fjOwCbAMgO0n6+11Ja1l+wXbk1I4IiLa1WrxkLQV8H1JZ0j6LHAj8D3gPEkrSZrb9uPALXSj0EVEBC02W0laHjgF2At4AdgIuBjYmtIRfjLwK0kvUs5ceFxLUSMiYipt9nk8CFxu+2eSBFxFWe79AmAr4CZgJcrCjFvavr21pBER8Qo9bwqStK6kXYHHgRUlfaJ2mJtydHE5cJDtX9g+BzjY9u96nTMiIqatZ0cedULfPMBX6vs+SVlq5EeSnrH9RdsvSroG2HbgdRmOGxHRPT0rHrZfpCw18lVKH8f2wELAu4BrJE2xfSqwBOWIZD7bT/QqX0REDF8bfR5TgKWBs4B9gddThujuIOmdwDuB7VM4IiK6q43hrxcA99u+DLgW+Dfg17bXpxST9Wzf3EKuiIgYpjaKxzPAeEn7Us6P/jngnZIOsP287QdbyBQREQ30vNnK9n2S7gaOpIyqukjSRsBtvc4SEREzp615HmcAF9i+rt6+onaoR0REH2ileNi+G7h70KKIKRwREX2k1fWiMocjIqI/ZbHBiIhoLMUjIiIaS/GIiIjGUjwiIqKxFI+IiGgsxSMiIhpL8YiIiMZSPCIiorEUj4iIaCzFIyIiGkvxiIiIxlI8IiKisRSPiIhoLMUjIiIaS/GIiIjGUjwiIqKxFI+IiGgsxSMiIhpL8YiIiMZSPCIiorG+KB6S5pE0Z9s5IiKi6HzxkPQ+4OvAJZK2krRc25kiIka72doOMD2S3gAcB+wCjAc2B1aTdKHtG2bw2v2A/QCWXnrpkY4aETGqdP3IY37gHtvX2v46cA6l4G0taZnpvdD2RNsTbE8YN25cL7JGRIwanS4etn8NPCrpkHp7MnAhsBSwUpvZIiJGs84VD0kbStpe0m71rvOAZSTtCGD7WuAXwIGSZm8rZ0TEaNap4iFpI+B8YGngMEknA38A7gRWl/Th+tRngCcBtRI0ImKU60zxkCRKh/gJtk8C1gGWAHYHflIv60m6DDgSOMn2823ljYgYzTpTPGwbuB4YL2kx288CewPLAwfYvsT2tsABwHozGm0VEREjp/XiIWkpSXNKmpvSlzEf8DZJc9t+GtgDWF/SewFs/9H2Q+0ljoiIVud5SNoSOB64hlI0Dqf0eRxaHtbNtv9Sm6rSRBUR0RGtFI/av/F6ygTAg4HfAR8EJgFrAqcBu9bn3gvsDJzZRtaIiPhHrRQP25Z0H6WZ6o/A32yfIGkK5ShkTeAGYHVgFWBj27e2kTUiIv5Rz4uHpBWAhYA7gAWAXWyfAGD75LoA4tHAh2xfAFzQ64wRETF9PS0ekrYCjgEeAW4GvgGcImms7WPr084HPkX6OCIiOqtnxUPS2sCJwM62b5A0EXgnsDbwS0ljgW8B6wLvABakFJmIiOiYXg/VPX7Q/IwjgFVt3wdsCCxHGW11CLCn7RSOiIiO6mWz1a+A3wDUo4w5gddJWsL2HZKOAu4F5rX9WA9zRUREQz078rD9gu3H600BjwIP13kcu1L6OWZP4YiI6L62hupOAZ6UdLekY4HNgD1sP9NGnoiIaKbNSYKzA+vVnxvb/mMbWSIiornWJgkCz0v6D+DaFI6IiP7S9jnMv1oLSURE9JFWV9VN4YiI6E+tL8keERH9J8UjIiIaS/GIiIjGUjwiIqKxFI+IiGgsxSMiIhpL8YiIiMZSPCIiorEUj4iIaCzFIyIiGkvxiIiIxlI8IiKisRSPiIhoLMUjIiIaS/GIiIjGUjwiIqKxFI+IiGgsxSMiIhpL8YiIiMb6onhIGi9pLUmzSxrbdp6IiNFutrYDzIik9wHHAPfWy2RJ59p+vN1kERGjl2y3nWGaJM0OfB04xfbVkt4PrAk8Dxw/vQIiaT9gv3pzPPCHEYy6CPDgCP77Iy3525X87ern/COdfRnb44Z6oB+Kx4XAf9s+V9IYYD1gS+AO4CvuwC8gabLtCW3nmFnJ367kb1c/528ze6f7PGz/HTgZeJ+k9Wy/CFwF3Ais22a2iIjRrNPFo7oS+BGwm6T1bb9g+5vA64BV2o0WETE6db7D3Pazkr4BGPikpJWA54DFgL+0Gu5lE9sO8E9K/nYlf7v6OX9r2Tvd5zGYpDmAdYD9gWeBL9q+od1UERGjU98UjwF1nodr/0dERLSg74pH10lSF0aARUSMpH7oMO8387QdIF4mSW1niP7UD5+dNjOmeMxCkjYGvibpdW1nGe0kLQ6lfbPtLDND0kJtZ5gV+mEDPDVJy0D3PzuS1gDWbuv9UzxmEUnvBs4EFqXM+qROauw7kt4y8AXqR5L+BThR0pAzY7uu5j9S0vxtZ2lK0qaS9pd0CHR/Azy1+j3+iqQl284yPTXnVymDh1rRlxu3rpG0FfA54APAD4DTAPqxU79uuE4H5mo7y8yQtCXwH8CZth+Y6rHO7wVL2oKyltuFUy+/0/WdEUnrAedQtitbS/qOpDf2w98dXvoeHwUca/vetvNMi6R1gbOBD9m+TtJr6v1z1589+Zx0+sPYD+oXY2vgE7YnA58H7qsLOvbFBmtA3XB9ATjU9h/6KTuApMWAU4ErbF8habG6J7wLlL3gLv9Okpal5P+O7Z9JGidpTUmbQNkZ6XgBWR84y/ZptjejzMM6AngDdPu7IGkByt/+lvrZWULSDpIOGdg4d8ibgauBB2sLwURJpwPnSXpj/ZyM+N86o61mgYERVpJmoxTkTwNz2f5Yy9GGTdJcwInAGrbfWb8wRwDzAVfa/u9WAw5D3fN6L7AFcB3wHuDXlPlB99retr10M1Y3BDsAC1AW8twTuBN4E/Cg7a1bjDdNgz7/mwI7AZ+xfVd97FRgMdvbtRpyOiSNqRvcdYGjKatarA38grIQ61+Bg20/3GJMJL2HMjn6G8C+wFuAzYETgF8CGwMbAtvZfmLEA9nOZSYuwBuBOer12erPgWK8HGWv631t52z4Oy0FfAa4BLiJUgT3BW4Hdms733RyjweWoGykoGyA7wEOH/ScqyhHVK3nHSL/aoOurwD8K/AnygZr4P6rgR3azjpE9rWAtw76f/gasB0w36Dn/AT4YNtZp5F/XeDdwJh6e23gfkpLwsBzLqQUxDZzbkZZ0+9f6u3XAIcB+wx6zuspzVlz9CJT55cn6aK6B3Aa8ENJB7ksoTKw9zLG9h2SPgWsIemHtp9uOfI0SVqFsqf7V5emqtOBxYHLbZ9Qn/M3YFdJ33DH+nFqH81xlI3r6yQdBFwE/MH2jZLG2n6Bsj7aIy1GHVI92rhS0iTbG9q+TdL3gOtdmk/G1L/5JKBTn6PaaXsqsCNA/fxcAOwFjJF0re07KXvwL7SXdGg1/38Cew58rm1fI2kN238e9Le/HGjtcy9pbUpR3tr2JEkLU1o4vsYrO8w3oOy4zkM5bcXIarvy99MFEPBaypfhCOBY4CuUJiqAsYOeuyWl83zetnNP5/fZGrgB+B6lo3OHev8iUz3vAMrIjjFtZ54q13rAb+rPxSgd/Uvyj0eCuwDXA+PbzjzE77BA/dvfAFw31WdtIP/2lGa4FdrOO9Xf/nZgk3p78JHGhvXz8i1KcbkLeFPbmafKvwaldWBgT35+ylp/C0712flg/du3lp9yRHcPsE3d/lwO/BD4b0rT5hzAPpQjkzf3LFfb/4n9eKG0QY+rP0+tBWSeIZ63cNtZp/M7rA78duDDBhxEOenW1M/bvW54e/ahbPA77AFsUK+/jtI2fSZw7qD7NweuAd7Sdt4h8g9soD4JLFg3uFdR+mgm1Me2oxx1dCo/8Cngu5Sh6ctTTtp2LvBlSgf5ApQmoUO7VPQG5d8VOL9mHA/8b81/8aDPzrrAz7vwt6esIH5HLSL7Uo489gK+Wf/ex/a6wKXDvIGhlh6R9CbgYMre7v6S3gY8bvtPXV6qRNKqwIa2v1Bvvw74NqUJ4j6XJriVgY8BJ9n+TXtpp692lJ8E3ErZoG1EKYabUzZiz9i+v72E0yfpE8ATtk+VdBWl3X1b2xdKegfwgO272035SioLlX6a0k+2FqVo3ErpxF2F0l8z8p22M6kObtmd0iG+JWV49GX19t7AtpQN9Iu2H2op5ivU7+O7bH950H2XUobs3tHrPOnzaGAaheBWytHHrpJupEwQfOd0nt8Jtm+QdBu8dMbGpylL3T9bC8cytm+pfTpPtRp2Bmw/I+lY2/cASPo+ZeTJiy5t7p00aOdiErCqpDdQmt+uooz6udD29W1mHErtC3he0ucozbe/sn16fex+4B3AM21mnJ76d58i6bx619W2v1ofux94FzDF9mOthRyC7VuAWwZuq5yWexwt9YV1ecx4J0gaL2ktSbOrrOg7sNfCQGds/U+dQmmP3Nz2fS1GHrZBe4ZTgMeBp4AnJO0MnCBpgS4VjoG/+1T3jQGwfc/A/w/liGMFYPYexpuhQfmAV+xcXEfp17gFOMz2+sANtZh0wuDsdedirO3nKZPqzhz0+JsoAy66NjfiJbZdC+AUSlPVNwflfzelGa5Tn53BVOxFmQy7W1tH1Wm2mg6ViX7HAPfWy3XAebYfkbQapXP2Ukq773eBvW3f1Fbe4RooeoOb1epG+BuUAvIOyofyt23mHEzSRpQmhZNtP1fvGxjhtmxtJpyd0rm/F7Brx/KvR2mb/lbd6A7OvwSlD+oB279oM+dQZpD9dQM7SypLkuwJ7N6lZs5pNDcPzE1Zxvaf6337AgfSsc/O1OoEwA2A+23/vq0cOfKYhroh2oFSEDamjPVeEjhMZQHE71L6Np6r7dEbd7VwSHqXpH3rl4NaOMbUL89bJa3qMiRxSWATyqirznx5JG0OnEUZjfTcwP1147U2cEHtezKl2XDnjuXfglKY76Uc5Q1svF6UtA7ld7t1oHDUjUMnDCP7VyUtX/fcV6DsdHSpcGwJ/KsGzRIfVDjWAi6UtHLNvwiwU5c+O0Nx8bM2C8dAkFyGHt0wO2Wy3B719hjK8gvHUEY2bDpw/8DOe9uZp/F7bE4ZzvoR4GeUL8fAYxtS2tvXqbe3BVZpO/OgfKIMQ/wSsEW9b0HKkd6i9fbFwJZtZ51O/vkoo3o2q/fNB8wLLFQfvxnYqu2sM5n9pi5mH/Q7rE45kr4N2A94zaDHFqUMpd+i7Zz9ekmH+TTY/rukk4FDJN1u+0pJVwPLUkYp/XjgqfX5nWv/kzQvZRbqx23/QNKz9f532p5EWb7jONtX1yOR77eX9h/Vv+nzkp4HFpH0esqclFuAjSRtDWxT/6806DWdULM8oTLJ8glJS1E2xr+nbNi2ogzJfW6oppU2Dcr+MPDYNLKvPpB90Gu6ZGC5mr9R1mybXdK5tp+y/TdJH7J9V4fzd1qKx/RdSRkDvlv9cv+csvjYHpLebvvGPvjA/QVA0tspRx+/ApaWdIvtvetjohbBrpC0ArAwZe/2N5Q2912As22fJukAypHhBMrQ4q7lfzNl5NTvKXu/m1AGJXzT9n9J+ghlstfawN+6lF9ljaflXUYgPUBZK+xh+iA7QN2pWIqyCsRCth+W9HHKSgSS9DWXkVQvDcHt2u/QD1I8psNl2ZFvUDasn5S0EmU462LUjXIXSVrR9q22n1IZPrwHsAzwbdfFGiVdK2kX29/o2hdHZWnsYygbrNspM2l3orRJHwlg+3SVk+HMD3RqdFvtozmesqjhU5Sh3GdRlpLYCcD2SbWfpjP9jnXQxDyUSa9j6xHfFylDh1+kjAjrZPYBkjajjEL6WP1cPwzgsqzHpymnTnhQ0iKU5YP2dBl1FQ2leMyAy8iqMyhNJftTNgC72v5ru8mGVje835Z0oe0dbf+npImUmcqDN7KX04v1bxqqHeAnUjq9b5D0Fcpe+/aUWcDrS3qc0ny4FtCpsfiSNqRscHetG6yLKBnfTTmS3VbSY5S5BKvT4ppJU3MZNPGkpK9S1qLaut7/Zkl3Us7R8ShlDk2nssOQa0AtQOkjexh4zmXdqj0pK9BOofSVpXDMpAzVbaCOyLA7tjjggNrH8b+UkWBrU1bX3Lk+9kHKHtl2wNuBD1FGVd3aTtqh1Q3AirbPrbfHAefY3krl7G67Uzpt3wF81B0bGVP3yBe3fbnKqXBvoCzvci1lT305yhIT6wAHdC0/gKTDgaUpC0weQBlUsRpl4t+jlKbCzmWXNJ4yS/wgytHSdyiZnwR+aPvsOkLsImC9ruXvNykerzIqy4w8TjkT4OmUPa5d6mNHUiZxzU9ZcrozQyoH1AI9r+3H6/UlKF/2zW3frzqvQNK87tAExqFIOoLyHfucpH0oG+DjXeakLGS7c6v8AkhaHviA7eMkfZjSV3Cc7SPr4wvafrTNjNOiskr09yij9I6iNBfuAWxKWep+OUo/zW1tZXy1SPF4FZP0WmAi8HfbO0pajlI4bnGd7NVlKjPK5wIusL2xpF0pq7keZruzy19Mi6RLgCNtT+7a6KrB6g7I0ZQFJT9GWfRwdeAHdbBCZ7PDNNeA+iHwkS7uMPWr9Hm8itl+SNL+wImSbqWMzd+oHwoHQG2PflLS3ZKOpZwQZ49+KBxTb2BV1iFalNJk1enRPfXI7m7K4ISDbF+kMsP/tvp4Z7PDdNeAerC1UK9COfIYBST9K/BxysTGm9vOM1x1CPHswO/qz41t/7HdVM1ImpOy/PfhlD6mvtjzrfM6FrV9Xb09cGKkvlE/P3tShqh/IH0cs1aKx6ucpIUoS61/2B1dPmVGJO0BXNuPX36VZW42BW63/Ye28zTV9Saq6anFo/U1oF6tUjxGAUlz2X52xs/spn7egEW8WqV4REREY52bIRoREd2X4hEREY2leERERGMpHhEjTNK2klwX1ox4VUjxiBh5O1HWWtqp7SARs0qKR8QIUjn96brA3sCO9b4xkv5L0u8l/VjSxZK2q4+tJukKSddJulTl/OYRnZPiETGytqGs6Hor8JCk1YD3UZaUXxnYjbK0/MCEwi8B29leDTibssZUROdkbauIkbUT5fweAN+qt2cD/qcu93G/pMvr4+OBtwA/LpOjGUuHTzoWo1uKR8QIkbQw5aRPb5VkSjEwZcnwIV8C/Nb2Wj2KGDHT0mwVMXK2A75mexnby9peinJq2oeB99e+j8WADevz/wCMk/RSM5bKudAjOifFI2Lk7MQ/HmX8L7A4ZWn2WyjnyrgeeKwulb8dcLykXwM3Us4IGdE5WdsqogWSXmP7yXrCrknAOrbvbztXxHClzyOiHf8naUHK6VL/I4Uj+k2OPCIiorH0eURERGMpHhER0ViKR0RENJbiERERjaV4REREYykeERHR2P8HIrMrDomh5lYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bringing data as a bar graph so it's easier to assess age groups vs. purchase total \n",
    "\n",
    "sns.barplot(x=(\"Age\"), y=(\"Purchase\"), data=df_purchase_age).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now that it was seen that both Genre and Age do impact how much a customer will spend while separately, grouping both data will present a much clearer view of the purchase habits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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></th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">F</th>\n",
       "      <th>0-17</th>\n",
       "      <td>8444.703210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-25</th>\n",
       "      <td>8405.430852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>8791.798654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>9046.573808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-50</th>\n",
       "      <td>8929.448740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51-55</th>\n",
       "      <td>9131.451837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>9119.577196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">M</th>\n",
       "      <th>0-17</th>\n",
       "      <td>9312.322227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-25</th>\n",
       "      <td>9506.501081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26-35</th>\n",
       "      <td>9470.621052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36-45</th>\n",
       "      <td>9517.126321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46-50</th>\n",
       "      <td>9429.151563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51-55</th>\n",
       "      <td>9789.020476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>9557.058094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Purchase\n",
       "Gender Age               \n",
       "F      0-17   8444.703210\n",
       "       18-25  8405.430852\n",
       "       26-35  8791.798654\n",
       "       36-45  9046.573808\n",
       "       46-50  8929.448740\n",
       "       51-55  9131.451837\n",
       "       55     9119.577196\n",
       "M      0-17   9312.322227\n",
       "       18-25  9506.501081\n",
       "       26-35  9470.621052\n",
       "       36-45  9517.126321\n",
       "       46-50  9429.151563\n",
       "       51-55  9789.020476\n",
       "       55     9557.058094"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purch_gender_age = df_blackfriday_sales.loc[:,[\"Gender\", \"Age\",\"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_purch_gender_age = purch_gender_age.pivot_table(purch_gender_age,\n",
    "                                                   index = [\"Gender\", \"Age\"],aggfunc = \"mean\")\n",
    "\n",
    "pv_purch_gender_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bringing data as a bar graph so it's easier to assess age groups vs. genre vs. purchase total \n",
    "pv_purch_gender_age.plot(kind='bar', figsize=(10, 5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the previous graph, it can be seen that the average number of purchases made by \"Women\" remains very close regardless of age.\n",
    "\n",
    "Furthermore, it's clear that not only \"Male\" users tend to spend most, but they also are the heaviest spenders when assessing Age Group.\n",
    "\n",
    "Male customers amongst the ages of 51 to 55 represent the heaviest spenders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### After understanding consumer habits that are influenced by one's genre and age, it's necessary to determine wether the marital status can also play a big role into one's purchase habits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9333.325467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9334.632681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Marital_Status     Purchase\n",
       "0               0  9333.325467\n",
       "1               1  9334.632681"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_maritalstatus = df_blackfriday_sales.groupby(by=[\"Marital_Status\"])[[\"Purchase\"]].mean()\n",
    "df_purchase_maritalstatus = df_purchase_maritalstatus.reset_index()\n",
    "df_purchase_maritalstatus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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": [
    "# Bringing data as a bar graph so it's easier to assess marital status vs. purchase total\n",
    "\n",
    "sns.barplot(x=(\"Marital_Status\"), y=(\"Purchase\"), data=df_purchase_maritalstatus).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As previously seen, it's possible to identify that there's more single customers making purchases during Black Friday; however, the group that had higher expenses was the one of married customers.\n",
    "\n",
    "The average spending between both marital status is quite similar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not only one's genre, age and marital status can influence spending habits, one's occupation can also play a big role in it.\n",
    "Looking at the data, a clearer understanding can be acquired:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>Occupation</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9186.946726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9017.703095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9025.938982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9238.077277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9279.026742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>9388.848978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>9336.378620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>9502.175276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>9576.508530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>8714.335934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>9052.836410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>9299.467190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>9883.052460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>9424.449391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>9568.536426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>9866.239925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>9457.133118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>9906.378997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>9233.671418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>8754.249162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>8881.099514</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Occupation     Purchase\n",
       "0            0  9186.946726\n",
       "1            1  9017.703095\n",
       "2            2  9025.938982\n",
       "3            3  9238.077277\n",
       "4            4  9279.026742\n",
       "5            5  9388.848978\n",
       "6            6  9336.378620\n",
       "7            7  9502.175276\n",
       "8            8  9576.508530\n",
       "9            9  8714.335934\n",
       "10          10  9052.836410\n",
       "11          11  9299.467190\n",
       "12          12  9883.052460\n",
       "13          13  9424.449391\n",
       "14          14  9568.536426\n",
       "15          15  9866.239925\n",
       "16          16  9457.133118\n",
       "17          17  9906.378997\n",
       "18          18  9233.671418\n",
       "19          19  8754.249162\n",
       "20          20  8881.099514"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_occupation = df_blackfriday_sales.groupby(by=[\"Occupation\"])[[\"Purchase\"]].mean()\n",
    "df_purchase_occupation = df_purchase_occupation.reset_index()\n",
    "df_purchase_occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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": [
    "# Bringing data as a bar graph so it's easier to assess occupation vs. purchase total\n",
    "sns.barplot(x=(\"Occupation\"), y=(\"Purchase\"), data=df_purchase_occupation).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When assessing the Purchase behaviour according to one's \"Occupation\", it's possible to see that this data has also an impact, meaning that those whose professional occupation belongs to 12, 15 and 17 are the heaviest spenders during blackfriday.\n",
    "\n",
    "- In the data presented below, there's an assessment of purchase behaviour taking into account one's Genre, Age and Professional Occupation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>0</td>\n",
       "      <td>9189.356944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>1</td>\n",
       "      <td>9982.919075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>2</td>\n",
       "      <td>7892.977778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>7</td>\n",
       "      <td>9121.638889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>10</td>\n",
       "      <td>8316.528105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>9304.741978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>17</td>\n",
       "      <td>8715.551837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>18</td>\n",
       "      <td>8617.709091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>19</td>\n",
       "      <td>8224.539535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>20</td>\n",
       "      <td>10962.219839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age  Occupation      Purchase\n",
       "0        F  0-17           0   9189.356944\n",
       "1        F  0-17           1   9982.919075\n",
       "2        F  0-17           2   7892.977778\n",
       "3        F  0-17           7   9121.638889\n",
       "4        F  0-17          10   8316.528105\n",
       "..     ...   ...         ...           ...\n",
       "236      M    55          16   9304.741978\n",
       "237      M    55          17   8715.551837\n",
       "238      M    55          18   8617.709091\n",
       "239      M    55          19   8224.539535\n",
       "240      M    55          20  10962.219839\n",
       "\n",
       "[241 rows x 4 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purch_habits = df_blackfriday_sales.loc[:,[\"Gender\", \"Age\", \"Occupation\", \"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_purch_habits = purch_habits.pivot_table(purch_habits,\n",
    "                                           index=([\"Gender\", \"Age\", \"Occupation\"]),\n",
    "                                           aggfunc = \"mean\")\n",
    "\n",
    "pv_purch_habits = pv_purch_habits.reset_index()\n",
    "\n",
    "pv_purch_habits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The city where one lives can also determine the purchase behaviour when it comes to shopping during Black Friday.\n",
    "\n",
    "As the data is masked, it's not possible to determine each city nor to understand if it represents a countryside city or a capital.\n",
    "Nonetheless, it's quite clear that those living at city with category C tend to spend the most during BF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>8958.011014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>9198.657848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>9844.441855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  City_Category     Purchase\n",
       "0             A  8958.011014\n",
       "1             B  9198.657848\n",
       "2             C  9844.441855"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_citycategory = df_blackfriday_sales.groupby(by=[\"City_Category\"])[[\"Purchase\"]].mean()\n",
    "df_purchase_citycategory = df_purchase_citycategory.reset_index()\n",
    "df_purchase_citycategory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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": [
    "# Bringing data as a bar graph so it's easier to assess city category vs. purchase total\n",
    "sns.barplot(x=(\"City_Category\"), y=(\"Purchase\"), data=df_purchase_citycategory).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### City category somewhat has an impact into one's purchase behaviour; therefore, it's necessary to assess whether how many years one's been living in that specific city also portrays an impact at the purchase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>Stay_In_Current_City_Years</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9247.238625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9319.865095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>9397.607316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>9350.685121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9346.370158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Stay_In_Current_City_Years     Purchase\n",
       "0                           0  9247.238625\n",
       "1                           1  9319.865095\n",
       "2                           2  9397.607316\n",
       "3                           3  9350.685121\n",
       "4                           4  9346.370158"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purch_yearslivedcity = df_blackfriday_sales.groupby(by=[\"Stay_In_Current_City_Years\"])[[\"Purchase\"]].mean()\n",
    "purch_yearslivedcity = purch_yearslivedcity.reset_index()\n",
    "purch_yearslivedcity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the data above, it's possible to understand that those who have been living in a city for 2 years are likely to spend more at Black Friday purchases.\n",
    "\n",
    "It's possible to make an inference that this is linked to the fact that a person living for 2 years in a certain city:\n",
    "- Is in a comfortable and stable situation financially, and/or\n",
    "- Has defined that particular city as a long-term home;\n",
    "\n",
    "Therefore, it's possible to make decoration adjustments and/or furnish one's house; thus, Black Friday deals are a huge plus.\n",
    "\n",
    "Based on the data, the average spending for the period the customer lives in the city is equivalent amongst the years 2 to 4+."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": [
    "# Bringing data as a bar graph so it's easier to assess years lived in a city vs. purchase total\n",
    "sns.barplot(x=(\"Stay_In_Current_City_Years\"), y=(\"Purchase\"), data=purch_yearslivedcity).tick_params(labelrotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assessing City Category and Years lived in it can also determine one's purchase behaviour, as not necessarily a person who's been living for 2 years in city C will have the same spending habits as a person living for 2 years in city A & B.\n",
    "\n",
    "\n",
    "Further observing the data, through the information it's possible to verify that the city in category C remains as the city with the customers who spend the most and, besides that, they represent the group that spends the most when assessing how long one's been living in that particular city category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>City_Category</th>\n",
       "      <th>Stay_In_Current_City_Years</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>9028.710591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>1</td>\n",
       "      <td>8927.870743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>2</td>\n",
       "      <td>9014.848463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>8956.718849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>8880.110251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B</td>\n",
       "      <td>0</td>\n",
       "      <td>8946.812564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B</td>\n",
       "      <td>1</td>\n",
       "      <td>9225.205104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>9250.978676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>9231.433705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B</td>\n",
       "      <td>4</td>\n",
       "      <td>9240.194353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>C</td>\n",
       "      <td>0</td>\n",
       "      <td>9900.145676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>9767.681720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>9900.361216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>9892.719685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>C</td>\n",
       "      <td>4</td>\n",
       "      <td>9855.517169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   City_Category  Stay_In_Current_City_Years     Purchase\n",
       "0              A                           0  9028.710591\n",
       "1              A                           1  8927.870743\n",
       "2              A                           2  9014.848463\n",
       "3              A                           3  8956.718849\n",
       "4              A                           4  8880.110251\n",
       "5              B                           0  8946.812564\n",
       "6              B                           1  9225.205104\n",
       "7              B                           2  9250.978676\n",
       "8              B                           3  9231.433705\n",
       "9              B                           4  9240.194353\n",
       "10             C                           0  9900.145676\n",
       "11             C                           1  9767.681720\n",
       "12             C                           2  9900.361216\n",
       "13             C                           3  9892.719685\n",
       "14             C                           4  9855.517169"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purch_city_stay = df_blackfriday_sales.groupby(by=[\"City_Category\", \"Stay_In_Current_City_Years\"])[[\"Purchase\"]].mean()\n",
    "purch_city_stay = purch_city_stay.reset_index()\n",
    "purch_city_stay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>City_Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
       "      <th>A</th>\n",
       "      <td>8630.771856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>8590.518480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>9264.964642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
       "      <th>A</th>\n",
       "      <td>9061.717739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>9400.754481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>10033.197730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Purchase\n",
       "Gender City_Category              \n",
       "F      A               8630.771856\n",
       "       B               8590.518480\n",
       "       C               9264.964642\n",
       "M      A               9061.717739\n",
       "       B               9400.754481\n",
       "       C              10033.197730"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_gender_city = df_blackfriday_sales.loc[:,[\"Gender\", \"City_Category\",\"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_purch_gender_city = purchase_gender_city.pivot_table(purchase_gender_city, \n",
    "                                                                           index = [\"Gender\", \"City_Category\"],\n",
    "                                                                           aggfunc = \"mean\")\n",
    "\n",
    "pv_purch_gender_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>City_Category</th>\n",
       "      <th>Purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>7886.698179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>B</td>\n",
       "      <td>8939.330729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0-17</td>\n",
       "      <td>C</td>\n",
       "      <td>8463.347411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>8213.228492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>B</td>\n",
       "      <td>8055.719317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>C</td>\n",
       "      <td>9207.009581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>8738.211626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>B</td>\n",
       "      <td>8616.923493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>9190.515763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>A</td>\n",
       "      <td>8788.829380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>B</td>\n",
       "      <td>8815.413924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>9543.087429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>A</td>\n",
       "      <td>8781.355263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>B</td>\n",
       "      <td>8683.648175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F</td>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>9250.296255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>F</td>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>8765.080849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>F</td>\n",
       "      <td>51-55</td>\n",
       "      <td>B</td>\n",
       "      <td>8926.599713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F</td>\n",
       "      <td>51-55</td>\n",
       "      <td>C</td>\n",
       "      <td>9534.807443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>A</td>\n",
       "      <td>9406.535613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>B</td>\n",
       "      <td>9066.117110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>F</td>\n",
       "      <td>55</td>\n",
       "      <td>C</td>\n",
       "      <td>9110.253754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>A</td>\n",
       "      <td>9724.054256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>B</td>\n",
       "      <td>9003.989872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>C</td>\n",
       "      <td>9457.613420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>A</td>\n",
       "      <td>9083.941897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>B</td>\n",
       "      <td>9444.732531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>C</td>\n",
       "      <td>10002.663005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>A</td>\n",
       "      <td>9068.051243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>B</td>\n",
       "      <td>9375.844004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>C</td>\n",
       "      <td>10163.187717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>A</td>\n",
       "      <td>9133.318165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>B</td>\n",
       "      <td>9251.536701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>C</td>\n",
       "      <td>10163.306991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>A</td>\n",
       "      <td>8309.599584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>B</td>\n",
       "      <td>9579.478902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>M</td>\n",
       "      <td>46-50</td>\n",
       "      <td>C</td>\n",
       "      <td>9848.911465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>A</td>\n",
       "      <td>9909.960710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>B</td>\n",
       "      <td>9541.451520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>M</td>\n",
       "      <td>51-55</td>\n",
       "      <td>C</td>\n",
       "      <td>10052.684942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>A</td>\n",
       "      <td>8495.479133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>B</td>\n",
       "      <td>10176.719634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>C</td>\n",
       "      <td>9670.135606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender    Age City_Category      Purchase\n",
       "0       F   0-17             A   7886.698179\n",
       "1       F   0-17             B   8939.330729\n",
       "2       F   0-17             C   8463.347411\n",
       "3       F  18-25             A   8213.228492\n",
       "4       F  18-25             B   8055.719317\n",
       "5       F  18-25             C   9207.009581\n",
       "6       F  26-35             A   8738.211626\n",
       "7       F  26-35             B   8616.923493\n",
       "8       F  26-35             C   9190.515763\n",
       "9       F  36-45             A   8788.829380\n",
       "10      F  36-45             B   8815.413924\n",
       "11      F  36-45             C   9543.087429\n",
       "12      F  46-50             A   8781.355263\n",
       "13      F  46-50             B   8683.648175\n",
       "14      F  46-50             C   9250.296255\n",
       "15      F  51-55             A   8765.080849\n",
       "16      F  51-55             B   8926.599713\n",
       "17      F  51-55             C   9534.807443\n",
       "18      F     55             A   9406.535613\n",
       "19      F     55             B   9066.117110\n",
       "20      F     55             C   9110.253754\n",
       "21      M   0-17             A   9724.054256\n",
       "22      M   0-17             B   9003.989872\n",
       "23      M   0-17             C   9457.613420\n",
       "24      M  18-25             A   9083.941897\n",
       "25      M  18-25             B   9444.732531\n",
       "26      M  18-25             C  10002.663005\n",
       "27      M  26-35             A   9068.051243\n",
       "28      M  26-35             B   9375.844004\n",
       "29      M  26-35             C  10163.187717\n",
       "30      M  36-45             A   9133.318165\n",
       "31      M  36-45             B   9251.536701\n",
       "32      M  36-45             C  10163.306991\n",
       "33      M  46-50             A   8309.599584\n",
       "34      M  46-50             B   9579.478902\n",
       "35      M  46-50             C   9848.911465\n",
       "36      M  51-55             A   9909.960710\n",
       "37      M  51-55             B   9541.451520\n",
       "38      M  51-55             C  10052.684942\n",
       "39      M     55             A   8495.479133\n",
       "40      M     55             B  10176.719634\n",
       "41      M     55             C   9670.135606"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pur_gen_age_city = df_blackfriday_sales.loc[:,[\"Gender\", \"Age\", \"City_Category\",\"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_pur_gen_age_city = pur_gen_age_city.pivot_table(pur_gen_age_city,\n",
    "                                                index = [\"Gender\", \"Age\", \"City_Category\"],aggfunc = \"mean\")\n",
    "\n",
    "pv_pur_gen_age_city = pv_pur_gen_age_city.reset_index()\n",
    "\n",
    "pv_pur_gen_age_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
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       "    Gender  Occupation City_Category     Purchase\n",
       "0        F           0             A  8951.309242\n",
       "1        F           0             B  8639.637516\n",
       "2        F           0             C  9171.550958\n",
       "3        F           1             A  8775.600244\n",
       "4        F           1             B  8235.173601\n",
       "..     ...         ...           ...          ...\n",
       "119      M          19             B  9121.524590\n",
       "120      M          19             C  8959.955528\n",
       "121      M          20             A  8586.632863\n",
       "122      M          20             B  9316.301677\n",
       "123      M          20             C  9658.378034\n",
       "\n",
       "[124 rows x 4 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pur_gen_occ_city = df_blackfriday_sales.loc[:,[\"Gender\", \"Occupation\", \"City_Category\",\"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_pur_gen_occ_city = pur_gen_occ_city.pivot_table(pur_gen_occ_city,\n",
    "                                                   index = [\"Gender\", \"Occupation\", \"City_Category\"],\n",
    "                                                   aggfunc = \"mean\")\n",
    "\n",
    "pv_pur_gen_occ_city = pv_pur_gen_occ_city.reset_index()\n",
    "\n",
    "pv_pur_gen_occ_city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>588 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Gender   Age  Occupation City_Category      Purchase\n",
       "0        F  0-17           0             A   7601.700000\n",
       "1        F  0-17           0             B  10202.267139\n",
       "2        F  0-17           0             C   7757.198556\n",
       "3        F  0-17           1             B   9982.919075\n",
       "4        F  0-17           2             A   7458.394737\n",
       "..     ...   ...         ...           ...           ...\n",
       "583      M    55          18             C   8617.709091\n",
       "584      M    55          19             B   9787.062500\n",
       "585      M    55          19             C   7775.431138\n",
       "586      M    55          20             B  10906.804781\n",
       "587      M    55          20             C  11076.229508\n",
       "\n",
       "[588 rows x 5 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pur_gen_ag_occ_city = df_blackfriday_sales.loc[:,[\"Gender\", \"Age\", \"Occupation\", \"City_Category\",\"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_pur_gen_ag_occ_city = pur_gen_ag_occ_city .pivot_table(pur_gen_ag_occ_city , \n",
    "                                                                 index = [\"Gender\", \"Age\", \"Occupation\", \n",
    "                                                                          \"City_Category\"], aggfunc = \"mean\")\n",
    "pv_pur_gen_ag_occ_city = pv_pur_gen_ag_occ_city.reset_index()\n",
    "\n",
    "pv_pur_gen_ag_occ_city "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2427</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>9843.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>11993.142857</td>\n",
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       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>M</td>\n",
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       "      <td>12611.921053</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2430 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender   Age  Marital_Status  Occupation City_Category  \\\n",
       "0         F  0-17               0           0             A   \n",
       "1         F  0-17               0           0             B   \n",
       "2         F  0-17               0           0             B   \n",
       "3         F  0-17               0           0             C   \n",
       "4         F  0-17               0           0             C   \n",
       "...     ...   ...             ...         ...           ...   \n",
       "2425      M    55               1          20             B   \n",
       "2426      M    55               1          20             C   \n",
       "2427      M    55               1          20             C   \n",
       "2428      M    55               1          20             C   \n",
       "2429      M    55               1          20             C   \n",
       "\n",
       "      Stay_In_Current_City_Years      Purchase  \n",
       "0                              2   7601.700000  \n",
       "1                              1   9878.324538  \n",
       "2                              3  12992.590909  \n",
       "3                              0   7797.660714  \n",
       "4                              1   7300.016304  \n",
       "...                          ...           ...  \n",
       "2425                           3   9165.892216  \n",
       "2426                           0  11886.916667  \n",
       "2427                           1   9843.285714  \n",
       "2428                           2  11993.142857  \n",
       "2429                           3  12611.921053  \n",
       "\n",
       "[2430 rows x 7 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purch_analysis = df_blackfriday_sales.loc[:,[\"Gender\", \"Age\", \"Marital_Status\", \"Occupation\", \n",
    "                                             \"City_Category\", \"Stay_In_Current_City_Years\", \"Purchase\"]]\n",
    "\n",
    "# Pivot Table with Purchase Average\n",
    "pv_purch_analysis = purch_analysis.pivot_table(purch_analysis,index = [\"Gender\", \"Age\", \"Marital_Status\", \n",
    "                                                                     \"Occupation\", \"City_Category\", \n",
    "                                                                     \"Stay_In_Current_City_Years\"], aggfunc = \"mean\")\n",
    "\n",
    "pv_purch_analysis = pv_purch_analysis.reset_index()\n",
    "\n",
    "pv_purch_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A special thanks to [Gabriel Idalino](https://www.kaggle.com/gabrieloliveirasan) for helping Ana Gabriela Almeida in order to understand how to generate all those graphs into a subgraph plot system!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Lived years in city vs Purchase')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 3600x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### Generating all graphs to assess the complete customer behaviour at Black Friday Purchases\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(1, 6, figsize=(50, 5), sharey=True)\n",
    "fig.suptitle(\"Purchase Behaviour Analysis\")\n",
    "\n",
    "\n",
    "# Gender\n",
    "sns.barplot(ax=axes[0], x=(\"Gender\"), y=(\"Purchase\"), data= df_purchase_gender).tick_params(labelrotation=45)\n",
    "axes[0].set_title(\"Gender vs Purchase\")\n",
    "\n",
    "\n",
    "# Age\n",
    "sns.barplot(ax=axes[1], x=(\"Age\"), y=(\"Purchase\"), data= df_purchase_age).tick_params(labelrotation=45)\n",
    "axes[1].set_title(\"Age vs Purchase\")\n",
    "\n",
    "# Marital Status\n",
    "sns.barplot(ax=axes[2], x=(\"Marital_Status\"), y=(\"Purchase\"), data= df_purchase_maritalstatus).tick_params(labelrotation=45)\n",
    "axes[2].set_title(\"Marital Status vs Purchase\")\n",
    "\n",
    "# Occupation\n",
    "sns.barplot(ax=axes[3], x=(\"Occupation\"), y=(\"Purchase\"), data= df_purchase_occupation).tick_params(labelrotation=45)\n",
    "axes[3].set_title(\"Occupation vs Purchase\")\n",
    "\n",
    "# City Category\n",
    "sns.barplot(ax=axes[4], x=(\"City_Category\"), y=(\"Purchase\"), data= df_purchase_citycategory).tick_params(labelrotation=45)\n",
    "axes[4].set_title(\"City Category vs Purchase\")\n",
    "\n",
    "\n",
    "\n",
    "# Years Lived in the City\n",
    "sns.barplot(ax=axes[5], x=(\"Stay_In_Current_City_Years\"), y=(\"Purchase\"), data= purch_yearslivedcity).tick_params(labelrotation=45)\n",
    "axes[5].set_title(\"Lived years in city vs Purchase\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}