XYZ Campaign Correlation Matrix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "0. Notebook Setup\n",
    "1. Data Cleaning\n",
    "2. Visualizing Dataframes\n",
    "3. Exporting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0. Notebook Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notebook Setup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import regex as re\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading our .csv we pulled from https://www.kaggle.com/jackdaoud/marketing-data\n",
    "shopdf = pd.read_csv(Path('Datasets') / 'mnt_marketing_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All in all, these are the dataframes we're creating\n",
    "\n",
    "| Dataframe | Description |\n",
    "| :----------- | :----------- |\n",
    "| productsdf | Sum of numerical values, intended for simple Tableau bar chart |\n",
    "| shopdf | Not formatted for Tableau, is used to create shopmatrixdf |\n",
    "| shopmatrixdf | shopdf with .melt applied, is properly formatted for our use in Tableau |\n",
    "\n",
    "*In order to create a many-to-many correlation matrix with our cleaned data (**'shopdf'**), we would need to use multiple pivots in Tableau to restructure our columns. Tableau Public only allows for a single pivot, so instead, I've opted to use the .melt function while we're still in Python to create a new, Tableau-friendly dataframe: **'shopmatrixdf'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Year_Birth</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Income</th>\n",
       "      <th>Kidhome</th>\n",
       "      <th>Teenhome</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Recency</th>\n",
       "      <th>MntWines</th>\n",
       "      <th>...</th>\n",
       "      <th>NumStorePurchases</th>\n",
       "      <th>NumWebVisitsMonth</th>\n",
       "      <th>AcceptedCmp3</th>\n",
       "      <th>AcceptedCmp4</th>\n",
       "      <th>AcceptedCmp5</th>\n",
       "      <th>AcceptedCmp1</th>\n",
       "      <th>AcceptedCmp2</th>\n",
       "      <th>Response</th>\n",
       "      <th>Complain</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1826</td>\n",
       "      <td>1970</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$84,835.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/16/14</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1961</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>$57,091.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6/15/14</td>\n",
       "      <td>0</td>\n",
       "      <td>464</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10476</td>\n",
       "      <td>1958</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>$67,267.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5/13/14</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1386</td>\n",
       "      <td>1967</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Together</td>\n",
       "      <td>$32,474.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5/11/14</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AUS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5371</td>\n",
       "      <td>1989</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>$21,474.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4/8/14</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2235</th>\n",
       "      <td>10142</td>\n",
       "      <td>1976</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$66,476.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3/7/13</td>\n",
       "      <td>99</td>\n",
       "      <td>372</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>5263</td>\n",
       "      <td>1977</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Married</td>\n",
       "      <td>$31,056.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1/22/13</td>\n",
       "      <td>99</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2237</th>\n",
       "      <td>22</td>\n",
       "      <td>1976</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>$46,310.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12/3/12</td>\n",
       "      <td>99</td>\n",
       "      <td>185</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2238</th>\n",
       "      <td>528</td>\n",
       "      <td>1978</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>$65,819.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11/29/12</td>\n",
       "      <td>99</td>\n",
       "      <td>267</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>IND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239</th>\n",
       "      <td>4070</td>\n",
       "      <td>1969</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>$94,871.00</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9/1/12</td>\n",
       "      <td>99</td>\n",
       "      <td>169</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2240 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         ID  Year_Birth   Education Marital_Status      Income   Kidhome  \\\n",
       "0      1826        1970  Graduation       Divorced  $84,835.00         0   \n",
       "1         1        1961  Graduation         Single  $57,091.00         0   \n",
       "2     10476        1958  Graduation        Married  $67,267.00         0   \n",
       "3      1386        1967  Graduation       Together  $32,474.00         1   \n",
       "4      5371        1989  Graduation         Single  $21,474.00         1   \n",
       "...     ...         ...         ...            ...          ...      ...   \n",
       "2235  10142        1976         PhD       Divorced  $66,476.00         0   \n",
       "2236   5263        1977    2n Cycle        Married  $31,056.00         1   \n",
       "2237     22        1976  Graduation       Divorced  $46,310.00         1   \n",
       "2238    528        1978  Graduation        Married  $65,819.00         0   \n",
       "2239   4070        1969         PhD        Married  $94,871.00         0   \n",
       "\n",
       "      Teenhome Dt_Customer  Recency  MntWines  ...  NumStorePurchases  \\\n",
       "0            0     6/16/14        0       189  ...                  6   \n",
       "1            0     6/15/14        0       464  ...                  7   \n",
       "2            1     5/13/14        0       134  ...                  5   \n",
       "3            1     5/11/14        0        10  ...                  2   \n",
       "4            0      4/8/14        0         6  ...                  2   \n",
       "...        ...         ...      ...       ...  ...                ...   \n",
       "2235         1      3/7/13       99       372  ...                 11   \n",
       "2236         0     1/22/13       99         5  ...                  3   \n",
       "2237         0     12/3/12       99       185  ...                  5   \n",
       "2238         0    11/29/12       99       267  ...                 10   \n",
       "2239         2      9/1/12       99       169  ...                  4   \n",
       "\n",
       "      NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  \\\n",
       "0                     1             0             0             0   \n",
       "1                     5             0             0             0   \n",
       "2                     2             0             0             0   \n",
       "3                     7             0             0             0   \n",
       "4                     7             1             0             0   \n",
       "...                 ...           ...           ...           ...   \n",
       "2235                  4             0             0             0   \n",
       "2236                  8             0             0             0   \n",
       "2237                  8             0             0             0   \n",
       "2238                  3             0             0             0   \n",
       "2239                  7             0             1             1   \n",
       "\n",
       "      AcceptedCmp1  AcceptedCmp2  Response  Complain  Country  \n",
       "0                0             0         1         0       SP  \n",
       "1                0             1         1         0       CA  \n",
       "2                0             0         0         0       US  \n",
       "3                0             0         0         0      AUS  \n",
       "4                0             0         1         0       SP  \n",
       "...            ...           ...       ...       ...      ...  \n",
       "2235             0             0         0         0       US  \n",
       "2236             0             0         0         0       SP  \n",
       "2237             0             0         0         0       SP  \n",
       "2238             0             0         0         0      IND  \n",
       "2239             0             0         1         0       CA  \n",
       "\n",
       "[2240 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's take a look at our data\n",
    "display(shopdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Married     864\n",
       "Together    580\n",
       "Single      480\n",
       "Divorced    232\n",
       "Widow        77\n",
       "Alone         3\n",
       "Absurd        2\n",
       "YOLO          2\n",
       "Name: Marital_Status, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shopdf.Marital_Status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2240 entries, 0 to 2239\n",
      "Data columns (total 28 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   ID                   2240 non-null   int64 \n",
      " 1   Year_Birth           2240 non-null   int64 \n",
      " 2   Education            2240 non-null   object\n",
      " 3   Marital_Status       2240 non-null   object\n",
      " 4    Income              2216 non-null   object\n",
      " 5   Kidhome              2240 non-null   int64 \n",
      " 6   Teenhome             2240 non-null   int64 \n",
      " 7   Dt_Customer          2240 non-null   object\n",
      " 8   Recency              2240 non-null   int64 \n",
      " 9   MntWines             2240 non-null   int64 \n",
      " 10  MntFruits            2240 non-null   int64 \n",
      " 11  MntMeatProducts      2240 non-null   int64 \n",
      " 12  MntFishProducts      2240 non-null   int64 \n",
      " 13  MntSweetProducts     2240 non-null   int64 \n",
      " 14  MntGoldProds         2240 non-null   int64 \n",
      " 15  NumDealsPurchases    2240 non-null   int64 \n",
      " 16  NumWebPurchases      2240 non-null   int64 \n",
      " 17  NumCatalogPurchases  2240 non-null   int64 \n",
      " 18  NumStorePurchases    2240 non-null   int64 \n",
      " 19  NumWebVisitsMonth    2240 non-null   int64 \n",
      " 20  AcceptedCmp3         2240 non-null   int64 \n",
      " 21  AcceptedCmp4         2240 non-null   int64 \n",
      " 22  AcceptedCmp5         2240 non-null   int64 \n",
      " 23  AcceptedCmp1         2240 non-null   int64 \n",
      " 24  AcceptedCmp2         2240 non-null   int64 \n",
      " 25  Response             2240 non-null   int64 \n",
      " 26  Complain             2240 non-null   int64 \n",
      " 27  Country              2240 non-null   object\n",
      "dtypes: int64(23), object(5)\n",
      "memory usage: 490.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Before we do anything, let's take an inventory of our datatypes\n",
    "# Income has extra white space and should be a float \n",
    "# Dt_Customer should be a datetime\n",
    "# Year_Birth is great as an int - Tableau can interpret four-character ints as years\n",
    "shopdf.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stripping extra whitespace from column values\n",
    "shopdf.columns = shopdf.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Income values to float\n",
    "shopdf['Income'] = shopdf['Income'].replace('^[$]',\"\",regex=True)\n",
    "shopdf['Income'] = shopdf['Income'].replace(',',\"\",regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Dt_Customer values to datetime\n",
    "shopdf['Dt_Customer'] = pd.to_datetime(shopdf['Dt_Customer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting Year_Birth to a new Age column\n",
    "# Then dropping Year_Birth\n",
    "# We don't need their exact age to the date - year is just fine for our intentions\n",
    "shopdf['Age'] = 2021 - shopdf.Year_Birth\n",
    "shopdf.drop(columns = 'Year_Birth', inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Then we'll rename a few columns for readability\n",
    "# # Kidhome, TeenHome, And Dt_customer will effectively receive titlecasing\n",
    "# All AcceptedCmp# columns will be shortened to Cmp#\n",
    "# Response is analagous to the AcceptedCmp# columns (for the last campaign). It will be renamed Cmp6\n",
    "shopdf = shopdf.rename(columns={\n",
    "       'Kidhome' : 'KidHome', \n",
    "       'Teenhome' : 'TeenHome',\n",
    "       'Dt_customer' : 'Dt_Customer', \n",
    "       'Recency' : 'Recency', \n",
    "       'MntWines' : 'WineSales', \n",
    "       'MntFruits' :  'FruitSales', \n",
    "       'MntMeatProducts' : 'MeatSales',\n",
    "       'MntFishProducts' : 'FishSales', \n",
    "       'MntSweetProducts' : 'SweetSales', \n",
    "       'MntGoldProds' : 'GoldSales',\n",
    "       'NumDealsPurchases' : 'DealPurchases', \n",
    "       'NumWebPurchases' : 'WebPurchases', \n",
    "       'NumCatalogPurchases' : 'CatalogPurchases',\n",
    "       'NumStorePurchases' : 'StorePurchases', \n",
    "       'NumWebVisitsMonth' : 'WebVisitsMonth', \n",
    "       'AcceptedCmp3' : 'Cmp3',\n",
    "       'AcceptedCmp4' : 'Cmp4', \n",
    "       'AcceptedCmp5' : 'Cmp5', \n",
    "       'AcceptedCmp1' : 'Cmp1', \n",
    "       'AcceptedCmp2' : 'Cmp2',\n",
    "       'Response' : 'Cmp6', \n",
    "       'Complain' : 'Complaints', \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# And now rearranging the Cmp# columns in numerical order\n",
    "shopdf = shopdf[['ID', 'Age', 'Recency', 'Income', 'KidHome', 'TeenHome', 'Complaints', \n",
    "        'Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5', 'Cmp6', 'Education', 'Marital_Status', \n",
    "        'Dt_Customer', 'WineSales', 'FruitSales', 'MeatSales', 'FishSales', 'SweetSales', \n",
    "        'GoldSales', 'CatalogPurchases', 'DealPurchases', 'StorePurchases', 'WebPurchases', 'WebVisitsMonth', 'Country']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cmp2</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cmp1</th>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cmp3</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cmp5</th>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cmp4</th>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cmp6</th>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CatalogPurchases</th>\n",
       "      <td>5963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WebPurchases</th>\n",
       "      <td>9150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StorePurchases</th>\n",
       "      <td>12970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FruitSales</th>\n",
       "      <td>58917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SweetSales</th>\n",
       "      <td>60621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FishSales</th>\n",
       "      <td>84057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GoldSales</th>\n",
       "      <td>98609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MeatSales</th>\n",
       "      <td>373968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WineSales</th>\n",
       "      <td>680816</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   total\n",
       "Cmp2                  30\n",
       "Cmp1                 144\n",
       "Cmp3                 163\n",
       "Cmp5                 163\n",
       "Cmp4                 167\n",
       "Cmp6                 334\n",
       "CatalogPurchases    5963\n",
       "WebPurchases        9150\n",
       "StorePurchases     12970\n",
       "FruitSales         58917\n",
       "SweetSales         60621\n",
       "FishSales          84057\n",
       "GoldSales          98609\n",
       "MeatSales         373968\n",
       "WineSales         680816"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In Tableau, we'll be making a simple bar chart with sales totals of each product category\n",
    "# Creating another quick dataframe here will save us a bit of time later on\n",
    "productsdf = shopdf[['WineSales', 'GoldSales', 'FishSales', 'SweetSales', 'FruitSales', 'MeatSales', \\\n",
    "            'StorePurchases', 'WebPurchases', 'CatalogPurchases', 'Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5', 'Cmp6']] \\\n",
    "            .sum().sort_values().to_frame(name = 'total')\n",
    "display(productsdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>52.194196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>52247.251354</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Mean\n",
       "Age        52.194196\n",
       "Income  52247.251354"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean will be calculated here to double-check our future Tableau calculations\n",
    "meandf = shopdf[['Age', 'Income']] \\\n",
    "            .mean().sort_values().to_frame(name = 'Mean')\n",
    "display(meandf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The importance of marital status as a metric is its pertinence to detailed targeting in FB/IG ads. \n",
    "We can target a few different types of relationship statuses, however our dataset includes some undesirable/ambiguous values.\n",
    "\n",
    "In addition to Married and Single, it contains:\n",
    "\n",
    "- Together\n",
    "- Divorced\n",
    "- Widowed\n",
    "- Alone\n",
    "- Absurd\n",
    "- Yolo\n",
    "\n",
    "There is no documentation to elaborate on the meaning of these values. \n",
    "While some inferences can be made, they were grouped into a collective Other category for the sake of consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using a lambda function to recategorize Marital Status values\n",
    "shopdf['Marital_Status'] = shopdf.Marital_Status.apply(lambda x: x if x in ['Married', 'Single'] else 'Other')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Visualizing Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can use Seaborn to imagine how our Correlation Matrix might look in Tableau\n",
    "plt.figure(figsize=(12,8))\n",
    "heatmap = sns.heatmap(shopdf.corr(), vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here it is again, with a more appropriate/polarizing cmap\n",
    "plt.figure(figsize=(12,8))\n",
    "cmap = sns.diverging_palette(20, 220, as_cmap=True)\n",
    "heatmap = sns.heatmap(shopdf.corr(), vmin=-1, vmax=1, cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can use shopdf with Seaborn and many other cases in Tableau \n",
    "# But in order to create a corr matrix in Tableau, we'll have to restructure our data\n",
    "# The .melt function will make all of this possible\n",
    "# We'll apply .melt to 'shopdf' and create a brand new dataframe: 'shopmatrixdf'\n",
    "shopmatrixdf = pd.melt(shopdf, id_vars=['ID', 'Education', 'Marital_Status', 'Dt_Customer', 'Country'], \n",
    "       value_vars=['Age', 'Income', 'KidHome','TeenHome', 'Recency', 'WineSales', 'FruitSales',\n",
    "       'MeatSales', 'FishSales', 'SweetSales', 'GoldSales', 'DealPurchases', 'WebPurchases',\n",
    "       'CatalogPurchases', 'StorePurchases', 'WebVisitsMonth', 'Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5',  'Cmp6', 'Complaints'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Dt_Customer</th>\n",
       "      <th>Country</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1826</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Other</td>\n",
       "      <td>2014-06-16</td>\n",
       "      <td>SP</td>\n",
       "      <td>Age</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>2014-06-15</td>\n",
       "      <td>CA</td>\n",
       "      <td>Age</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10476</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>2014-05-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Age</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1386</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Other</td>\n",
       "      <td>2014-05-11</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Age</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5371</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Single</td>\n",
       "      <td>2014-04-08</td>\n",
       "      <td>SP</td>\n",
       "      <td>Age</td>\n",
       "      <td>32.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51515</th>\n",
       "      <td>10142</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Other</td>\n",
       "      <td>2013-03-07</td>\n",
       "      <td>US</td>\n",
       "      <td>Complaints</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51516</th>\n",
       "      <td>5263</td>\n",
       "      <td>2n Cycle</td>\n",
       "      <td>Married</td>\n",
       "      <td>2013-01-22</td>\n",
       "      <td>SP</td>\n",
       "      <td>Complaints</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51517</th>\n",
       "      <td>22</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Other</td>\n",
       "      <td>2012-12-03</td>\n",
       "      <td>SP</td>\n",
       "      <td>Complaints</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51518</th>\n",
       "      <td>528</td>\n",
       "      <td>Graduation</td>\n",
       "      <td>Married</td>\n",
       "      <td>2012-11-29</td>\n",
       "      <td>IND</td>\n",
       "      <td>Complaints</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51519</th>\n",
       "      <td>4070</td>\n",
       "      <td>PhD</td>\n",
       "      <td>Married</td>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>CA</td>\n",
       "      <td>Complaints</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51520 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID   Education Marital_Status Dt_Customer Country    variable  value\n",
       "0       1826  Graduation          Other  2014-06-16      SP         Age   51.0\n",
       "1          1  Graduation         Single  2014-06-15      CA         Age   60.0\n",
       "2      10476  Graduation        Married  2014-05-13      US         Age   63.0\n",
       "3       1386  Graduation          Other  2014-05-11     AUS         Age   54.0\n",
       "4       5371  Graduation         Single  2014-04-08      SP         Age   32.0\n",
       "...      ...         ...            ...         ...     ...         ...    ...\n",
       "51515  10142         PhD          Other  2013-03-07      US  Complaints    0.0\n",
       "51516   5263    2n Cycle        Married  2013-01-22      SP  Complaints    0.0\n",
       "51517     22  Graduation          Other  2012-12-03      SP  Complaints    0.0\n",
       "51518    528  Graduation        Married  2012-11-29     IND  Complaints    0.0\n",
       "51519   4070         PhD        Married  2012-09-01      CA  Complaints    0.0\n",
       "\n",
       "[51520 rows x 7 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Our new dataframe looks great\n",
    "display(shopmatrixdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2.0 - Our dataframes are now ready for export\n",
    "shopmatrixdf.to_csv(Path('Tableau Inputs') / 'mnt_shopmatrixdf.csv')\n",
    "shopdf.to_csv(Path('Tableau Inputs') / 'mnt_shopdf.csv')\n",
    "productsdf.to_csv(Path('Tableau Inputs') / 'mnt_productsdf.csv')"
   ]
  }
 ],
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