2. Purchase Analytics Descriptive Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ${\\textbf{Libraries}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# We import the sk learn modules we'll need to segment our new data. We'll need scaler, pca and k-means.\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "# We import pickle in order to be able to load our pickled objects.\n",
    "import pickle\n",
    "\n",
    "#We import the necessary libraries for visualization. We set seaborn do be our default.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ${\\textbf{Data Import}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data. We read the purchase data csv file.\n",
    "df_purchase = pd.read_csv('data/purchase data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ${\\textbf{Data Exploration}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Day</th>\n",
       "      <th>Incidence</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Last_Inc_Brand</th>\n",
       "      <th>Last_Inc_Quantity</th>\n",
       "      <th>Price_1</th>\n",
       "      <th>Price_2</th>\n",
       "      <th>Price_3</th>\n",
       "      <th>...</th>\n",
       "      <th>Promotion_3</th>\n",
       "      <th>Promotion_4</th>\n",
       "      <th>Promotion_5</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Marital status</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Income</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Settlement size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "<p>5 rows × 24 columns</p>\n",
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      "text/plain": [
       "          ID  Day  Incidence  Brand  Quantity  Last_Inc_Brand  \\\n",
       "0  200000001    1          0      0         0               0   \n",
       "1  200000001   11          0      0         0               0   \n",
       "2  200000001   12          0      0         0               0   \n",
       "3  200000001   16          0      0         0               0   \n",
       "4  200000001   18          0      0         0               0   \n",
       "\n",
       "   Last_Inc_Quantity  Price_1  Price_2  Price_3       ...         Promotion_3  \\\n",
       "0                  0     1.59     1.87     2.01       ...                   0   \n",
       "1                  0     1.51     1.89     1.99       ...                   0   \n",
       "2                  0     1.51     1.89     1.99       ...                   0   \n",
       "3                  0     1.52     1.89     1.98       ...                   0   \n",
       "4                  0     1.52     1.89     1.99       ...                   0   \n",
       "\n",
       "   Promotion_4  Promotion_5  Sex  Marital status  Age  Education  Income  \\\n",
       "0            0            0    0               0   47          1  110866   \n",
       "1            0            0    0               0   47          1  110866   \n",
       "2            0            0    0               0   47          1  110866   \n",
       "3            0            0    0               0   47          1  110866   \n",
       "4            0            0    0               0   47          1  110866   \n",
       "\n",
       "   Occupation  Settlement size  \n",
       "0           1                0  \n",
       "1           1                0  \n",
       "2           1                0  \n",
       "3           1                0  \n",
       "4           1                0  \n",
       "\n",
       "[5 rows x 24 columns]"
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     "execution_count": 3,
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   ],
   "source": [
    "# We use the head method on our data and check for missing values.\n",
    "df_purchase.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                   0\n",
       "Day                  0\n",
       "Incidence            0\n",
       "Brand                0\n",
       "Quantity             0\n",
       "Last_Inc_Brand       0\n",
       "Last_Inc_Quantity    0\n",
       "Price_1              0\n",
       "Price_2              0\n",
       "Price_3              0\n",
       "Price_4              0\n",
       "Price_5              0\n",
       "Promotion_1          0\n",
       "Promotion_2          0\n",
       "Promotion_3          0\n",
       "Promotion_4          0\n",
       "Promotion_5          0\n",
       "Sex                  0\n",
       "Marital status       0\n",
       "Age                  0\n",
       "Education            0\n",
       "Income               0\n",
       "Occupation           0\n",
       "Settlement size      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_purchase.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ${\\textbf{Data Segmentation}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Import Segmentation Model}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We load our pickled objects in order to segment the purchase data set.\n",
    "scaler = pickle.load(open('scaler.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = pickle.load(open('pca.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans_pca = pickle.load(open('kmeans_pca.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Standardization}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nikol\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: Data with input dtype int64 were all converted to float64 by StandardScaler.\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# We standardize the purchase data in the same way we did the segmentation data, using the standard scaler.\n",
    "features = df_purchase[['Sex', 'Marital status', 'Age', 'Education', 'Income', 'Occupation', 'Settlement size']]\n",
    "df_purchase_segm_std = scaler.transform(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{PCA}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We apply pca on the purchase data and obtain 3 principal components for each row in the table. \n",
    "df_purchase_segm_pca = pca.transform(df_purchase_segm_std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{K-means PCA}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the principal components, we use the predict method from pca to segment the purchase data into the four segments.\n",
    "purchase_segm_kmeans_pca = kmeans_pca.predict(df_purchase_segm_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy resulting data frame as a purchase predictors data frame. \n",
    "# We'll be changing the predictors data frame, by adding and changing columns.\n",
    "# Therefore, we want to keep an original copy.\n",
    "df_purchase_predictors = df_purchase.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add segment information as a new column in our predictors data frame.\n",
    "df_purchase_predictors['Segment'] = purchase_segm_kmeans_pca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ${\\textbf{Descriptive Analysis by Segments}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Data Analysis by Customer}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "          ID  Day  Incidence  Brand  Quantity  Last_Inc_Brand  \\\n",
       "0  200000001    1          0      0         0               0   \n",
       "1  200000001   11          0      0         0               0   \n",
       "2  200000001   12          0      0         0               0   \n",
       "3  200000001   16          0      0         0               0   \n",
       "4  200000001   18          0      0         0               0   \n",
       "\n",
       "   Last_Inc_Quantity  Price_1  Price_2  Price_3   ...     Promotion_4  \\\n",
       "0                  0     1.59     1.87     2.01   ...               0   \n",
       "1                  0     1.51     1.89     1.99   ...               0   \n",
       "2                  0     1.51     1.89     1.99   ...               0   \n",
       "3                  0     1.52     1.89     1.98   ...               0   \n",
       "4                  0     1.52     1.89     1.99   ...               0   \n",
       "\n",
       "   Promotion_5  Sex  Marital status  Age  Education  Income  Occupation  \\\n",
       "0            0    0               0   47          1  110866           1   \n",
       "1            0    0               0   47          1  110866           1   \n",
       "2            0    0               0   47          1  110866           1   \n",
       "3            0    0               0   47          1  110866           1   \n",
       "4            0    0               0   47          1  110866           1   \n",
       "\n",
       "   Settlement size  Segment  \n",
       "0                0        2  \n",
       "1                0        2  \n",
       "2                0        2  \n",
       "3                0        2  \n",
       "4                0        2  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_predictors.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_Visits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>87</td>\n",
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       "      <th>200000003</th>\n",
       "      <td>97</td>\n",
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       "      <th>200000004</th>\n",
       "      <td>85</td>\n",
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       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>111</td>\n",
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      ],
      "text/plain": [
       "           N_Visits\n",
       "ID                 \n",
       "200000001       101\n",
       "200000002        87\n",
       "200000003        97\n",
       "200000004        85\n",
       "200000005       111"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Purchase occasions by customer ID\n",
    "temp1 = df_purchase_predictors[['ID', 'Incidence']].groupby(['ID'], as_index = False).count()\n",
    "temp1 = temp1.set_index('ID')\n",
    "temp1 = temp1.rename(columns = {'Incidence': 'N_Visits'})\n",
    "temp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
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       "      <th>200000002</th>\n",
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       "      <td>11</td>\n",
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       "      <th>200000003</th>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
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       "      <td>85</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>200000005</th>\n",
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       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Visits  N_Purchases\n",
       "ID                              \n",
       "200000001       101            9\n",
       "200000002        87           11\n",
       "200000003        97           10\n",
       "200000004        85           11\n",
       "200000005       111           13"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of purchases per customer ID\n",
    "temp2 = df_purchase_predictors[['ID', 'Incidence']].groupby(['ID'], as_index = False).sum()\n",
    "temp2 = temp2.set_index('ID')\n",
    "temp2 = temp2.rename(columns = {'Incidence': 'N_Purchases'})\n",
    "temp3 = temp1.join(temp2)\n",
    "temp3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "      <th>Average_N_Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>101</td>\n",
       "      <td>9</td>\n",
       "      <td>0.089109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
       "      <td>0.126437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000003</th>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
       "      <td>0.103093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000004</th>\n",
       "      <td>85</td>\n",
       "      <td>11</td>\n",
       "      <td>0.129412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>0.117117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Visits  N_Purchases  Average_N_Purchases\n",
       "ID                                                   \n",
       "200000001       101            9             0.089109\n",
       "200000002        87           11             0.126437\n",
       "200000003        97           10             0.103093\n",
       "200000004        85           11             0.129412\n",
       "200000005       111           13             0.117117"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average number of purchases by customer ID\n",
    "temp3['Average_N_Purchases'] = temp3['N_Purchases'] / temp3['N_Visits']\n",
    "temp3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We obtain the segment for each customer in the data set.\n",
    "temp4 = df_purchase_predictors[['ID', 'Segment']].groupby(['ID'], as_index = False).mean()\n",
    "temp4 = temp4.set_index('ID')\n",
    "df_purchase_descr = temp3.join(temp4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "      <th>Average_N_Purchases</th>\n",
       "      <th>Segment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200000001</th>\n",
       "      <td>101</td>\n",
       "      <td>9</td>\n",
       "      <td>0.089109</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>200000002</th>\n",
       "      <td>87</td>\n",
       "      <td>11</td>\n",
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       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>200000003</th>\n",
       "      <td>97</td>\n",
       "      <td>10</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>200000004</th>\n",
       "      <td>85</td>\n",
       "      <td>11</td>\n",
       "      <td>0.129412</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200000005</th>\n",
       "      <td>111</td>\n",
       "      <td>13</td>\n",
       "      <td>0.117117</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Visits  N_Purchases  Average_N_Purchases  Segment\n",
       "ID                                                            \n",
       "200000001       101            9             0.089109        2\n",
       "200000002        87           11             0.126437        3\n",
       "200000003        97           10             0.103093        2\n",
       "200000004        85           11             0.129412        2\n",
       "200000005       111           13             0.117117        1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_purchase_descr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Segment Proportions}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
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       "\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>Segment Proportions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Segment Proportions\n",
       "Segment                     \n",
       "0                      0.206\n",
       "1                      0.220\n",
       "2                      0.378\n",
       "3                      0.196"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We calculate the proportions of each segment and set the appropriate column name. \n",
    "segm_prop = df_purchase_descr[['N_Purchases', 'Segment']].groupby(['Segment']).count() / df_purchase_descr.shape[0]\n",
    "segm_prop = segm_prop.rename(columns = {'N_Purchases': 'Segment Proportions'})\n",
    "segm_prop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Segment Proportions')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the segment proportions as a pie chart. \n",
    "# We can easily see which is the largest segment and the relative sizes of each segment.\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.pie(segm_prop['Segment Proportions'],\n",
    "        labels = ['Standard', 'Career-Focused', 'Fewer-Opportunities', 'Well-Off'],\n",
    "        autopct = '%1.1f%%', \n",
    "        colors = ('b', 'g', 'r', 'orange'))\n",
    "plt.title('Segment Proportions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Purchase Occasion and Purchase Incidence}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>N_Visits</th>\n",
       "      <th>N_Purchases</th>\n",
       "      <th>Average_N_Purchases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>117.699029</td>\n",
       "      <td>24.902913</td>\n",
       "      <td>0.212773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123.454545</td>\n",
       "      <td>39.827273</td>\n",
       "      <td>0.283102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113.730159</td>\n",
       "      <td>22.756614</td>\n",
       "      <td>0.201588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>117.295918</td>\n",
       "      <td>34.602041</td>\n",
       "      <td>0.284478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           N_Visits  N_Purchases  Average_N_Purchases\n",
       "Segment                                              \n",
       "0        117.699029    24.902913             0.212773\n",
       "1        123.454545    39.827273             0.283102\n",
       "2        113.730159    22.756614             0.201588\n",
       "3        117.295918    34.602041             0.284478"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We calculate the mean by the four segments. It will help us determine the average customer behaivour in each segment.\n",
    "segments_mean = df_purchase_descr.groupby(['Segment']).mean()\n",
    "segments_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We calculate the standard deviation by segments. It will help us determine how homogoneus each of the segments is.\n",
    "segments_std = df_purchase_descr.groupby(['Segment']).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Number of Store Visits by Segment')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5+7p9Jze37+TnNh4b4xZogDcAG0fEdcBMYI2IOAvYE3hcREzNzEeANWnppmrXggUP0NvbPxdp9uyVmT///m6XoQ5YtGgx06dPc/tOYr5/Jz+38WNNmdIzqkaKcQs0mfnmvsu1heaIzNytXv85sBvwTeCNwPnjVZckSWq+iXIcmrcCB0TELcCLgMO7XI8kSWqQjrfQZOb6A0y7FNim5fpdrdclSZrs9t13L6ZPn8bxx5/S7VImhYnSQiNJkjRqBppJbN9992KXXXbpdhmSJHWcgUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJDWegUaSJiDPxSaNjIFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ1noFGkiQ13rRuFyB108qPX54Zy03vdhmjMn16efvOnr1ylysZvYUPL+L+ex7qdhmSJgEDjZZpM5abzuvPOqjbZYzKzX+9DaCx9QN8e7fjuR8DjaSlZ5eTJElqvI630ETEKsAVwM6ZeWdEHAC8HVgCXAO8JTMXRcQc4ERgFeAy4MDMXNzp+iRJUvN1tIUmIrYEfgFsWK9vCPwP8ALgWXX9B9fZTwcOycwNgR5g/07WJkmSJo9OdzntTwksc+v1h4C3ZuZ9mbkEuBFYNyLWA1bIzKvqfKcCu3a4NkmSNEkM2+UUEU8EtszMH0bEUcBzgXdn5vXD3Tcz96vL6Lt+F3BXnTYbOATYG1gLmNdy13nAOiN5IJIkadnVzhiaU4ELIuKlwA7AMcBxwNajXWlErA2cD5yUmZdGxFaUMTV9eoDekSxz1qyZoy1n0poMu/Vq8vP1OTDfv5Of23hstRNoZmXmMRHxGeCbmXlqRBw87L0GEREbAT8BjsvMo+vkPwJrtsy2Bo92U7VlwYIH6O1dMvyMy5BFixYzffo05s+/v9ulTFh+kHSfr8+B+f6d/NzGA5sypWdUjRTtjKGZHhHLATsCF0bEisComkMiYmXgAuDwljDT1xW1sLbUAOxFacGRJEkaVjuB5gfAfOBvmXkt8Evgm6Nc337AE4FDI+K6+ndkvW0P4JiIuJUSmI4b5TokSdIypp0upyOArwF/qtd3B+4eyUoyc/168Zj6N9A81wNbjGS542HlVVZgxvLNPKDyZOifXfjQYu6/71/dLkOSNMG18019bWZu1nclM2+IiJuAZ3aurIljxvLTeOWhP+h2GaNy2//9DaCx9QP86OhXY++ypIHMevxUpiy3YrfLGLXJ8KOz9+EHWXDPI90uAxgi0ETERcDmwIoRcV/LTVOBX3W6MEmShjJluRXhmz3dLmP0/jKr/G/wY5iy+xKYID87h2qheS2wGnAysE/L9MX85zFjJEmSumrIQcGZeSewCyV+9f39C3h8xyuTJElq01AtNJcCmwF/oxz0rrVNbAml60mSJKnrBg00fQOBM7PT53uSJElaKiM9l9OnKAOF35WZN3S8OklaCquuPJ1pM5bvdhmjMhn2gFm88CH+cf+ibpehZcRIz+W0I+U4Ml9gKc7lJEnjYdqM5bn81a/rdhmjcu+tNwM0tn6ArX5wNhhoNE7a6U6alZnHUMLMNzPzVKC5O/5LkqRJZ1zP5SRJktQJ430uJ0mSpDE3bKDJzA9TTnPwkjpp98z8aEerkiRJGoFBA01E7Fn/vxt4PfCuennb+l+SJGlCGGovp6fV/5sMcNuSDtQiSZI0KkMFml9HRE9m7jPEPJIkSV031BiajwJ3RMT7ImLWeBUkSZI0UoMGmsx8FvAGIIDbIuK0iNhi3CqTJElq03Bn274yM98MPJmyu/YJEfGriHjjuFQnSZLUhrZOPJmZ92Xml4AXAz8HTu5oVZIkSSPQzrmciIgXA/sCOwE/Ap7XyaIkSZJGYtBAExFrAnsDb66TTqCcZfvv41CXJElS24ZqobkTuAB4O/DjzPTYM5IkaUIaKtBEZt45XoVIkiSN1lC7bd85jnVIkiSNWlt7OUmSJE1kBhpJktR4w+62HRFTgEOBZwKH1L9PZ+YjHa5NkiSpLe0ch+YzwGxgc6AH2AFYk7L3kyRJUte1E2heBmwGXJuZ90XEy4Hr2l1BRKwCXAHsnJl3RsS2wOeAFYCzMvPwOt8c4ERgFeAy4MDMXDyiRyNJkpZJ7YyheTgze/uuZOZDQFtBIyK2BH4BbFivr0A5bcKrgacDm0fEjnX204FDMnNDSkvQ/u0+CEmStGxrJ9DcFBEHA1OjOIH2W2j2Bw4G5tbrWwC/y8w7auvL6cCuEbEesEJmXlXnOxXYtd0HIUmSlm3tBJp3ULqcnkhpbVkJeGc7C8/M/TLz5y2T1gLmtVyfB6wzxHRJkqRhtTOGZq/M3HeM1jcFaD2FQg/QO8T0ts2aNXOpi9PENHv2yt0uQR3k9p3c3L6T30TZxu0EmoOAL43R+v5I2UOqzxqU7qjBprdtwYIH6O0d+9NNTZQNtSybP//+ji3b7dt9bt/Jze07+Y31Np4ypWdUjRTtBJqMiK8BPwce+PfEzO+OeG1wNRARsQFwB7A7cHJm3hURCyNiq8y8HNgLOH8Uy1eLDV/2rm6XIEnSuGgn0KxW/zZombYEGHGgycyFEbE3cDYwAzgP+E69eQ/ga3U3718Dx410+ZIkadk0bKDJzJcARMQ0oCczHx7pSjJz/ZbLFwHPHmCe6yl7QUmSJI1IO6c+eAJwGvBSYFpE/AzYMzNHNMZFktS+IzbauNslSI3STpfTF4GrgDcAUymnPDiecnA8SZI0Ct85aEG3S5hU2gk0G2bm61uufzgibu5UQZIkSSPVzoH1louIGX1XImJF/vOYMZIkSV3VTgvNmcCFEXEKJci8mbKXkiRJ0oTQzl5OH42IPwI7UMbQnJqZJ3a8MkmSpDa1s5fTRzPzg8ApLdM+n5nv6GhlkiRJbRo00ETER4BVgd0i4nEtNy0HbE85aaUkSVLXDdVCczWwOeUkka37li2mHNVXkiRpQhg00GTmecB5EXF+Zv5yHGuSJEkakSHH0ETEfsAV9fKBwFuBa4FDMvOfnS9PkiRpeIMehyYiPgAcCPRGxBzgc8BRwJ+BT49PeZIkScMb6sB6uwEvycxbgV2AczPzDOB/Ked1kiRJmhCGPFJwZt5fL24FXFynLaEMDJYkSZoQhhpD0xMRPcAKwBbAQQARsQIwY4j7SZIkjauhAs2PgbMox525JTNvjYinAUcCPxqP4iRJktoxVJfTYcAvgd8Dr6nT9gMWUsbRSJIkTQhDHYdmMfDZftPe1/GKJEmSRmjIQcGSJElNYKCRJEmNN9SB9V5T/y8/fuVIkiSN3FAtNB+t/68cj0IkSZJGa6jdtu+LiNuAtSPihv43ZuazOleWJElS+4YKNDsAmwInAW8bn3IkSZJGbqjdtu8HLouIVwBzgedQDrJ3dcspESRJkrqunb2cHgfcBhxLOeP2XRHxgo5WJUmSNALtBJqjgT0yc9M6bmYXSrCRJEmaENoJNCtn5iV9VzLzYmDFzpUkSZI0MkMNCu6zJCLWy8y7ACJifeCRpVlpROwJfKBePT8z3xMRc4ATgVWAy4AD6+kXJEmShtROC82RwFUR8Y2I+DpwNfCx0a4wIlYEjgO2Bp4NvCgitgVOBw7JzA2BHmD/0a5DkiQtW4YNNJn5fWAb4ArK2be3ycyzl2KdU+t6V6LsNbUc8DCwQmZeVec5Fdh1KdYhSZKWIe10OZGZCeRYrDAz74+IDwK3Ag8CPwMWAfNaZpsHrDMW65MkSZNfW4FmLEXEs4A3A+sB91K6ml4OLGmZrQfoHclyZ82aOVYlaoKZPXvlbpegDnL7Tm5u38lvomzjcQ80wPbARZn5V4CIOBV4D7BmyzxrUA7m17YFCx6gt3fJ8DOO0ETZUMuy+fM7dxxHt2/3uX0nN7fv5DfW23jKlJ5RNVIMO4amDgQeS9cD20bEShHRA7yS0u20MCK2qvPsBZw/xuuVJpVNDt6KTQ7eavgZJWkZ0M5eTnNq8BgTmXkB8C3gWuAGyqDgTwF7AMdExK3ATMqeUJIkScNqp8tpLnBzRFwFPNA3MTPfPtqVZuZRwFH9Jl8PbDHaZUqSpGVXO4HmyvonSZI0IQ0baDLzIxGxArABcDMwIzMf7HhlkiRJbWpnUPCWwO3AucBawN2ebVuSJE0k7QwK/iywLbAgM/9I2QPp8x2tSpIkaQTaCTQrZuYtfVcy8zy6c/waSZKkAbUTaB6OiFWpR/KNiOhsSZIkSSPTTkvLxygHvlszIr5FOU3BAR2tSpIkaQTa2cvpnHqwu+0oZ8o+MjN/2/HKJEmS2tROlxOUo/lOBR6uf5IkSRNGO7tt7wNcAmwOvAj4eUS8rtOFSZIktaudMTTvBjbNzHkAEbEucA5wdicLkyRJalc7XU6L+sIMQGb+AbudJEnSBDJoC01EbFYvXh8RXwROAB4B9gYu73xpkiRJ7Rmqy6l/l9IrWi4vAUZ9tm1JkqSxNGigycwnj2chkiRJozXsoOCIWIPSzbRa6/TMfG+HapIkSRqRdgYF/xDYAujp9ydJkjQhtLPb9vTM/K+OVyJJkjRK7bTQXBsRz+x4JZIkSaPUTgvN5cB1ETGPluPPZOZTOlaVJEnSCLQTaP4H2B24vcO1SJIkjUo7geaezPx2xyuRJEkapXYCzcUR8VnKgfYe6puYmb/uWFWSJEkj0E6g2b3+bz3D9hLAMTSSJGlCGDbQeMRgSZI00bVzpOB3DzQ9Mz839uVIkiSNXDtdTpu0XJ4ObA1c1JlyJEmSRq6dLqd9Wq9HxFrASR2rSJIkaYTaaaH5D5k5NyLWX5qVRsQrgQ8DKwEXZOY7ImJb4HPACsBZmXn40qxDkiQtO0Y6hqYHeC7w19GuMCKeAnwF2BL4C2W38B2BEyjdWXcD50bEjpl5/mjXI0mSlh0jHUOzBPgD5ejBo/VaSgvMHwEiYjfgacDvMvOOOu10YFfAQCNJkoY14jE0Y2ADYFFE/BBYFzgHuBmY1zLPPGCdkSx01qyZY1agJpbZs1fudgnqILfv5Ob2nfwmyjYeNNBExCmUFpmBLMnMfZdinS8GtgEeAH4I/KvfunqA3pEsdMGCB+jtHazc0ZsoG2pZNn/+/R1bttu3+9y+k5vbd/Ib6208ZUrPqBophmqhuWmAaasD7wTuHPGaHvVn4MLMnA8QEd+jdC890jLPGsDcpViHJElahgwaaDLz6NbrdS+k04AzgLcvxTrPAU6LiMcD9wM7At8B3h8RGwB3UE63cPJSrEOSJC1D2tnLaRrwSWBv4MDMPHtpVpiZV0fEp4FfAMsBPwWOB26lnABzBnAeJeRIkiQNa8hAExFPA75FGeuyad+eSUsrM0/msS0wFwHPHovlS5KkZcuUwW6IiH2Aq4HvZeY2YxVmJEmSxtpQLTQnUfY0en9EvK9leg9lL6dVOlqZJElSm4YKNE8etyokSZKWwlB7Od01noVIkiSN1qBjaCRJkprCQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhrPQCNJkhpvWjdXHhGfBVbPzL0jYg5wIrAKcBlwYGYu7mZ9kiSpGbrWQhMRLwPe1DLpdOCQzNwQ6AH270phkiSpcboSaCJiNeDjwCfq9fWAFTLzqjrLqcCu3ahNkiQ1T7e6nE4ADgOeVK+vBcxruX0esM5IFjhr1syxqUwTzuzZK3e7BHWQ23dyc/tOfhNlG497oImI/YC7M/OiiNi7Tp4CLGmZrQfoHclyFyx4gN7eJcPPOEITZUMty+bPv79jy3b7dp/bd3Jz+05+Y72Np0zpGVUjRTdaaHYD1oyI64DVgJmUMLNmyzxrAHO7UJskSWqgcR9Dk5nbZeYzM3MO8CHgh5m5D7AwIraqs+0FnD/etUmSpGaaSMeh2QM4JiJupbTaHNfleiRJUkN09Tg0mXmu1B4WAAARt0lEQVQqZY8mMvN6YItu1iNJkpppIrXQSJIkjYqBRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNZ6BRpIkNd60bqw0Ij4MvL5ePTcz3xsR2wKfA1YAzsrMw7tRmyRJap5xb6GpweXlwKbAHOA5EfEG4GTg1cDTgc0jYsfxrk2SJDVTN7qc5gGHZuaizHwY+C2wIfC7zLwjMxcDpwO7dqE2SZLUQOPe5ZSZN/ddjoinUbqevkAJOn3mAeuMc2mSJKmhujKGBiAiNgbOBf4HWExppenTA/SOZHmzZs0cu+I0ocyevXK3S1AHuX0nN7fv5DdRtnG3BgVvBZwNvDMzz4yIrYE1W2ZZA5g7kmUuWPAAvb1LxrDKYqJsqGXZ/Pn3d2zZbt/uc/tObm7fyW+st/GUKT2jaqQY90ATEU8Cvg/slpkX18lXl5tiA+AOYHfKIGFJkqRhdaOF5j3ADOBzEdE37SvA3pRWmxnAecB3ulCbJElqoG4MCn4H8I5Bbn72eNYiSZImB48ULEmSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGs9AI0mSGm9atwtoFRG7A4cDywHHZuaXulySJElqgAnTQhMRawMfB14IzAEOiIhndLcqSZLUBBMm0ADbAhdn5t8z85/Ad4BdulyTJElqgInU5bQWMK/l+jxgizbuNxVgypSeTtQEwBNWXaFjy9bwOrltAWavuFpHl6+hdXr7Lv+E2R1dvobW6e3LSut1dvka1lhv45blTR3J/XqWLFkypoWMVkQcBszIzA/W6/sDz8nMA4e56wuBn3e6PkmSNK5eBPyi3ZknUgvNHynF91kDmNvG/X5V7zcPeKQDdUmSpPEzFViT8v3etonUQrM2JYltAfwTuAI4IDN/2dXCJEnShDdhBgVn5p+Aw4BLgOuAbxpmJElSOyZMC40kSdJoTZgWGkmSpNEy0EiSpMYz0EiSpMYz0EiSpMYz0EiSpMabSAfWW+ZFxC7AByjbZQrw9cz8TER8BLgwM5f6iMgRcSewTWbe2Y37N1VErAJ8EtgaWAz8Azg0M3/dpXqOAA4E/twy+TeZuU836mkVEesDl2bm+hOgjtuAW/rd9MrMvHuca9kDeC/lvd0LfBv4ZGYu7uA6HwecmpmvHeX9zwP2A5YHDs/MfSPiucCBmbnfGJbaKBHxU+BLmfn9ev2zlPfiapm5qE6bC7xgoM/JiLgUOKJePSIztxlkPe8E3kI5YOxi4KuZ+eV62yrAxcAM4HXAccD6wFsy89Klf5TNZKCZIOqBBY8GNsvMBRExE/hZRCTlS/SSrha4DIuIKcB5lG0wJzMXR8RLgPMj4hmZuaBLpX0lM4/o0rqbYm5mzulmARGxN/Bu4LWZeXtErAycBpwA7NvBVa8KbDraO2fmTgARsQ3w1DrtGkrIWZZdDGwFfL9e3xa4knIanosjYgPgn0vzo6/+YHkx5cfjXyJiNvD9iJiVmR8F5gCLMvO5EbEusElmrjXqRzRJGGgmjtWB5YAVgQWZ+UBEvAn4L+C5wIkR8VpgNeDjdb7HA+/KzB9ExKnAvcBzgLWBIzPzlIhYDTgdeBLll+oM+HfCPwlYh3Ji0AspH1RbA5+mHHr6JuBdA91/GfMSYF3gw5nZC5CZl0TEPsDUiPga8EzgicANwBvq5R8DfwP+BewAfAbYhvLcnpqZxwBExPuB19fpPwHeB6zXev/M3K7dYiNiZ+BjlFa+31N+tf0lIralhOYpwF3A7pTX1zaZuXe976WUX4//B5wBrERpUXh7Zl4VEZsDx1Bef3+ry74jIjalvJ4Arm+31m6IiCdSwsSTKI/tA5Sar8vMtes8fwLenZlnRcQHKL+Qjwe+RNnWU4GjMvNbNbC8ifIe/lFm/m+/VR4B7J2ZtwNk5v0RsS8wNyKOpLy+dgaeQDnc+4+AQynvxQ8CDwNPBn4J7JeZD9XX3qHAEuBa4JD6mTEfuKYuZx6wVkR8j/I+/nerWf3CJDOPiIh5wHcoX8iLgdfXbXon5fV6HPCUiPgS8P+orQr1i/t4YBbwIPC2zPxNROxOaY16BLgD2DMzF45oI01sFwHHAkTEWsBDlOdve0rYeRFwwWDvleEWHhErAv8DbJyZfwHIzPn1/IZXR8RpwMnAGhHxQ0rLzOoRcU1mPndMH2nDOIZmgsjM64EfAL+PiF9GxFHA1Mw8kvIBtV9m3gi8rV7ejBJAPtaymCdR3kyvAj5bpx0J/DozN6F8GD+xTn8F5QP8+cDTKB+em9XbNgRemplvGuL+y5JNKc9Vb+vEzDwP2IjyS+n5wAaUkLlTnSUoH+bbAfvX+2xGOb3HqyPiRRGxAyWEbl7XszawxwD3H8iBEXFdy19ExBMoX9avycxnAZcDX4yI5SkB5U11W95I+RIezL7AOfUD8kPACyNiOnAisHt9HEcDX6vzfx14X53++yGWO97W6vcc/Q/weeDkzHwO5b1yArAQuDsinhkRG1F+7G1dl7EDcA5wOHBtvd+LgcMi4il1nnWATfuHmfrLej1KGPm3zPwHcDNl20MJE7sCGwPPA/q6iV4AvIPyOpsBHBwRm1COqr513Zb/BD5c51+dErTmAAdRWqiG63JaA7goMzcFLgMO6Xf724FrMvPgftNPA95bt/kBwJl1+seAl9fn6Y5a+2RyLfDUiJhBCTEX1L/t6+0vBn7G4O+V4WzMAC08mXkLJTytTvnsvyYzX0V5Dc9d1sMM2EIzoWTmQRHxMeDllDfHVbXvvdWewM4RsSvlg29my20XZOaSiLiJ0pID5RfWG+ryL4uI39fL34qILWo/7dMpv7L6lpWZee9Q91/G9FK+8B6jPicLIuJgygf303j0efxry4fStsCciHhpvT4T2AR4CrAl5UMSYAXgD5Tzmv21/4daP4/pcqqtM79sud9XKS0QmwB/yszrat0fqPPvPciyLwS+W1tezgW+SAm6TwV+GBF9860SEasDa2XmT+u0U+lsV8pIPKbLKSL+BmxUW0egtIw+ldKt+DJKi8jngTfUcShPzMzf1hauFSPizfV+K1G+fKCE/oHGw/Qdin2gz9rpLbf/oO/XeEScCbyU8qv/sszMOv0blOCwiNIS1NfV+VXglJblXj3oszG4H9f/N1G+kIdUu8Q3B05peS3MjIhZlBamy2vL0Nl9r7nJIjMfiYirKC3n21PG09wREStGxKrA8yktM495r7S5iiUM/t28HI++ZtSPgWaCiIhXADMz8yzKh9MptYmx/xfDzyljOS6lNH1+s+W2hQA11PRNWwL0tMyzuK7vbcAulA/DCynN6H3z/atl/gHvv4y5BnhrRPRk5r8/TCLiE5Qvj49QvgBPofx6Guh5nEr5Nfvdet/VgQcoA42PzczP1emPpzzHq7fePyJOpHyAwtBjGPq3uvZQ3ucP0/JBWL+oV+ax23c5gMy8PCKeQekK2Q3YG3gP8Pu+gBARUyktdk17jUyltED+HSAi1gT+WqcfQXkffZDSDbg7pRuw73579g0Er11Xf6e0qLVuq39/gWfmnIi4nfIl95OWeVanfOH9mtLl1PqcTWm5PtD0wbZx3zr/xWMNtJ0fbrnPwkHmG8xUYGFrWIyIdYC/Z+Y7IuIkSivw6RFxRGae3sYym6RvHM0WlB+ZUD5HX03pXuph4PfKY9Ruq/Pq1bmUz+XlIiL6wmydb2PKts+6XvVjl9PE8SDwySh7ZhARPZSBX7+hfIhNq+NhNqR0AZxPefNMHWa5FwJ71WVuTukWAdgOOCEzz6A0Zc8ZZFmD3X9Z8nPKF96H6wcTEbE9sA+lO+LbmXkKcA/ly2mg5/FiYP+IWK7+uv0FpYXtYmCviJgZEdMoAw136X/nzNwvM+fUv2uGqPVq4Hl9ryPKL/pLKB+CT6ghBcoYhwMpH75Pj4ieiHgy8Kz6+D5N+fI+jdIFsRlwK7BaRLyoLuPNlJPILgDuqqEcSgiYyC4G3gpQn4+bKOMcfk15f22YmbdSnrfDKd1Nffc7qN5vTcp4qXX7L7xlO/V92R8OHNvXPVW3/4nAmZl5V51nh4h4XO3GeAPl/Q2lq2/tKAPT31inXwq8qn4eQOnOHGingcU8GnTuoWy72bX7cYe2nqnHLqfvMd4L/C4i9qyPaTtKd9W0iPgd8LfM/CSlK3LUA5MnsIso2+PGlpa5n1LGNf2UQd4rAy0oM+e2vGZ2yswHKeMkT6pdyNT/XwM+XW/XAAw0E0RmXkL5pX9OlD2bbqUMqvsopTn4K5QujZMofe+/pfzCXjEiVhpi0R+m9PfeDLyfR8c3HEv5gr6xXr6CMvCw3fsvM2qrzKsov6hviogbKAN3dwK+TOmauJEyYPJyBn4evwL8jhJQrwFOycxLM/NHwNmUIHIT5Uzzpy1FrX+hhJjv1W22DWU324WUX5Jfr/U/A/gUJbDeTQk8n6cELYAvALvU1obvAW/MzIco4zyOrst4E4+2IO5JeT39pj5PE9nbKKHvBuAsSnC7v27nX1DeW1ACzCqU8RBQ3p8r1C7diyktbrcPt7LMPJPS4vPtet9fUcLTW1pmm0/5lX49ZexSX2vOXEoouAX4E3BiZt5Aadn7WUTcShm3dfgAq/4L8IeIuKQGkE/XdV9IvzE9w/gt8Pja5dVqD2C/+jx+EtgtMx+m/OD6aURcQwntR41gXY2QmTdRuukvaJl8MeUz+qfDvFfaWf6nKDtjXFhfMxcBZ2TZw0mD8GzbktRFdRzTv/c0a5m+DUMcp0TSf7KFRpIkNZ4tNJIkqfFsoZEkSY1noJEkSY1noJEkSY3ngfUkjbmIeB5lV95ZlB9OdwPvycybu1rYAOrxdz6bma/rdi2SRs8WGkljqh647Rzg0Mx8VmY+k3IeqfP7Dkw4waxHOW+WpAazhUbSWOs7E3zrecbOAO6jnJ18J8qB4KZTjpD9nsy8MspZhr9CORjbPZSDyZGZe0c58/M3Kec4WpVykLitKCd3fBh4VWbOjYi1KeedWpdyeP8zM/MT9cjJF1EOXrdlXcZ7gR9Sjtq7dkT8JDP7TjAoqWFsoZE0puqZpN8L/Dgifl+PMLsP5Qi16wGfAHaqZ3c+gHISzJUoR9OdRjna6rY89pD5MzLzeZQj0X4V+HxmPpvSnbV3necbPHom7S2AbSPi9fW2pwA/ycwtKEe9PjYzH6GcG+t2w4zUbAYaSWOunmzzicDbgXmUU0X8hnK6iDWBi+ppFc6gnM18g3rbSZnZm5n38dhTQJxd/98O/Dkzr2+5vloNRVsDH63LvorSUtN3TqWHefQkgL/m0TPSS5oE7HKSNKYiYivgBZn5GcpYmnMi4n8p56paBbgoM3drmf9JlHMWLeY/z/T8SL9FP9Ry+WEea2q9/wv6TuBXz2q9kHL28kWZ2Vvnbfes0pIawhYaSWNtPnB4RLywZdqawOMoY1ZeHhEbAdTxNDcAKwDnAvtExJQ6nmZ3SvBoS23VuQp4d1324yknC331MHddTBlvI6nBDDSSxlRm3ga8BvhEHUNzC/BtYJ/aTXQAcGZEXE85m/yrMvMBym7eC4EbKeNt/koZNDwSu1POpH0j5Qzm38rMM4a5zy3Awoj4ZUTYaiM1lOdykjQhRMR/A/dl5nkRMYUyZuaCzDy+y6VJagBbaCRNFDcBh9UBvTdRxtWc2N2SJDWFLTSSJKnxbKGRJEmNZ6CRJEmNZ6CRJEmNZ6CRJEmNZ6CRJEmNZ6CRJEmN9/8B4rNKf7ggOlcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We plot the average number of store visits for each of the four segments using a bar chart.\n",
    "# We display the standard deviation as a straight line. The bigger the length, the higher the standard deviation is.\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.bar(x = (0, 1, 2, 3),\n",
    "        tick_label = ('Standard', 'Career-Focused', 'Fewer-Opportunities', 'Well-Off'), \n",
    "        height = segments_mean['N_Visits'],\n",
    "        yerr = segments_std['N_Visits'],\n",
    "        color = ('b', 'g', 'r', 'orange'))\n",
    "plt.xlabel('Segment')\n",
    "plt.ylabel('Number of Store Visits')\n",
    "plt.title('Average Number of Store Visits by Segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Purchases by Segment')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We display the average number of purchases by segments. \n",
    "# They will help us understand how often each group buys chocholate candy bars.\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.bar(x = (0, 1, 2, 3),\n",
    "        tick_label = ('Standard', 'Career-Focused', 'Fewer-Opportunities', 'Well-Off'), \n",
    "        height = segments_mean['N_Purchases'],\n",
    "        yerr = segments_std['N_Purchases'],\n",
    "        color = ('b', 'g', 'r', 'orange'))\n",
    "plt.xlabel('Segment')\n",
    "plt.ylabel('Purchase Incidences')\n",
    "plt.title('Number of Purchases by Segment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Number of Purchases by Segment')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We display the average number of purchases.\n",
    "# We observe, that the ‘career focused’ segment buys products more often. However, once again, \n",
    "# we see that its standard deviation is the highest. \n",
    "# It might be that a part of the segment buys products very frequently, and another part less so. \n",
    "# Although consumers in this segment have a somewhat similar income, the way they might want to spend their money might differ. \n",
    "# The most homogenous segment appears to be that of the ‘Fewer-Opportunities’. \n",
    "# This is signified by the segment having the lowest standard deviation or shortest vertical line. \n",
    "# The first segment seems consistent as well, with about 25 average purchases and a standard deviation of 30.\n",
    "# What’s worth mentioning is that the ‘career focused’ cluster is very different in both graphs. \n",
    "# The other 3 segments had a very similar ‘store visits’ information,\n",
    "# yet it is evident that the well-off cluster actually buys goods more often.\n",
    "plt.figure(figsize = (9, 6))\n",
    "plt.bar(x = (0, 1, 2, 3), \n",
    "        tick_label = ('Standard','Career Focused','Fewer Opportunities','Well-off'),\n",
    "        height = segments_mean['Average_N_Purchases'], \n",
    "        yerr = segments_std['Average_N_Purchases'], \n",
    "        color = ('b', 'g', 'r', 'orange'))\n",
    "plt.xlabel('Segment')\n",
    "plt.ylabel('Purchase Incidences')\n",
    "plt.title('Average Number of Purchases by Segment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Brand Choice}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select only rows where incidence is one. In other words, we are only interested in the times a purchase was made.\n",
    "df_purchase_incidence = df_purchase_predictors[df_purchase_predictors['Incidence'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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       "      <th>58615</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58617</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58621</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58648</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58674</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58687</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58691</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200000500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14638 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Brand_1  Brand_2  Brand_3  Brand_4  Brand_5  Segment         ID\n",
       "6            0        1        0        0        0        2  200000001\n",
       "11           0        0        0        0        1        2  200000001\n",
       "19           1        0        0        0        0        2  200000001\n",
       "24           0        0        0        1        0        2  200000001\n",
       "29           0        1        0        0        0        2  200000001\n",
       "52           0        0        0        1        0        2  200000001\n",
       "79           0        1        0        0        0        2  200000001\n",
       "80           0        1        0        0        0        2  200000001\n",
       "97           0        0        0        0        1        2  200000001\n",
       "101          0        0        0        1        0        3  200000002\n",
       "115          0        0        0        1        0        3  200000002\n",
       "138          0        0        0        1        0        3  200000002\n",
       "141          0        0        0        1        0        3  200000002\n",
       "153          0        0        0        1        0        3  200000002\n",
       "159          0        0        0        1        0        3  200000002\n",
       "160          0        0        0        1        0        3  200000002\n",
       "161          0        0        0        1        0        3  200000002\n",
       "163          0        1        0        0        0        3  200000002\n",
       "164          0        0        0        1        0        3  200000002\n",
       "166          0        0        0        1        0        3  200000002\n",
       "188          0        1        0        0        0        2  200000003\n",
       "209          0        0        0        0        1        2  200000003\n",
       "212          0        0        0        0        1        2  200000003\n",
       "224          0        1        0        0        0        2  200000003\n",
       "278          0        1        0        0        0        2  200000003\n",
       "279          0        1        0        0        0        2  200000003\n",
       "280          0        1        0        0        0        2  200000003\n",
       "281          0        1        0        0        0        2  200000003\n",
       "282          0        1        0        0        0        2  200000003\n",
       "283          0        1        0        0        0        2  200000003\n",
       "...        ...      ...      ...      ...      ...      ...        ...\n",
       "58527        1        0        0        0        0        2  200000499\n",
       "58530        0        1        0        0        0        2  200000499\n",
       "58533        0        1        0        0        0        2  200000499\n",
       "58536        0        1        0        0        0        2  200000499\n",
       "58539        0        1        0        0        0        2  200000499\n",
       "58540        0        1        0        0        0        2  200000499\n",
       "58542        0        1        0        0        0        2  200000499\n",
       "58545        0        1        0        0        0        2  200000499\n",
       "58548        0        1        0        0        0        2  200000499\n",
       "58549        0        1        0        0        0        2  200000499\n",
       "58554        0        1        0        0        0        2  200000499\n",
       "58559        0        1        0        0        0        2  200000499\n",
       "58560        0        0        0        1        0        2  200000499\n",
       "58565        0        1        0        0        0        2  200000499\n",
       "58568        0        1        0        0        0        2  200000499\n",
       "58590        0        1        0        0        0        2  200000500\n",
       "58592        0        1        0        0        0        2  200000500\n",
       "58596        0        1        0        0        0        2  200000500\n",
       "58600        0        1        0        0        0        2  200000500\n",
       "58605        0        1        0        0        0        2  200000500\n",
       "58606        0        1        0        0        0        2  200000500\n",
       "58608        0        1        0        0        0        2  200000500\n",
       "58612        0        1        0        0        0        2  200000500\n",
       "58615        0        1        0        0        0        2  200000500\n",
       "58617        1        0        0        0        0        2  200000500\n",
       "58621        0        1        0        0        0        2  200000500\n",
       "58648        1        0        0        0        0        2  200000500\n",
       "58674        0        1        0        0        0        2  200000500\n",
       "58687        0        1        0        0        0        2  200000500\n",
       "58691        0        1        0        0        0        2  200000500\n",
       "\n",
       "[14638 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here we make dummies for each of the five brands.\n",
    "brand_dummies = pd.get_dummies(df_purchase_incidence['Brand'], prefix = 'Brand', prefix_sep = '_')\n",
    "brand_dummies['Segment'], brand_dummies['ID'] = df_purchase_incidence['Segment'], df_purchase_incidence['ID']\n",
    "brand_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = brand_dummies.groupby(['ID'], as_index = True).mean()\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_brand_choice = temp.groupby(['Segment'], as_index = True).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mean_brand_choice,\n",
    "            vmin = 0, \n",
    "            vmax = 1,\n",
    "            cmap = 'PuBu',\n",
    "            annot = True)\n",
    "plt.yticks([0, 1, 2, 3], ['Standard', 'Career-Focused', 'Fewer-Opportunities', 'Well-Off'], rotation = 45, fontsize = 9)\n",
    "plt.title('Average Brand Choice by Segment')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ${\\textbf{Revenue}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Segment</th>\n",
       "      <th>Revenue Brand 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2611.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>736.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2258.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>699.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Segment  Revenue Brand 1\n",
       "0        0          2611.19\n",
       "1        1           736.09\n",
       "2        2          2258.90\n",
       "3        3           699.47"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the revenue for brand 1.\n",
    "# For each entry where Brand 1 was purchased, \n",
    "# we multiply the price of the brand for that particular day by the quantity of the product purchased.\n",
    "temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 1]\n",
    "temp.loc[:, 'Revenue Brand 1'] = temp['Price_1'] * temp['Quantity']\n",
    "segments_brand_revenue = pd.DataFrame()\n",
    "segments_brand_revenue[['Segment', 'Revenue Brand 1']] = temp[['Segment', 'Revenue Brand 1']].groupby(['Segment'], as_index = False).sum()\n",
    "segments_brand_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute revenue for brand 2. We use the same formula, but here we filter our data to contain only purchases of brand 2.\n",
    "temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 2]\n",
    "temp.loc[:, 'Revenue Brand 2'] = temp['Price_2'] * temp['Quantity']\n",
    "segments_brand_revenue[['Segment', 'Revenue Brand 2']] = temp[['Segment', 'Revenue Brand 2']].groupby(['Segment'], as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute revenue for brand 3\n",
    "temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 3]\n",
    "temp.loc[:,'Revenue Brand 3'] = temp['Price_3']*temp['Quantity']\n",
    "segments_brand_revenue[['Segment','Revenue Brand 3']] = temp[['Revenue Brand 3','Segment']].groupby(['Segment'], as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute revenue for brand 4\n",
    "temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 4]\n",
    "temp.loc[:,'Revenue Brand 4'] = temp['Price_4']*temp['Quantity']\n",
    "segments_brand_revenue[['Segment','Revenue Brand 4']] = temp[['Revenue Brand 4','Segment']].groupby(['Segment'], as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute revenue for brand 5\n",
    "temp = df_purchase_predictors[df_purchase_predictors['Brand'] == 5]\n",
    "temp.loc[:,'Revenue Brand 5'] = temp['Price_5']*temp['Quantity']\n",
    "segments_brand_revenue[['Segment','Revenue Brand 5']] = temp[['Revenue Brand 5','Segment']].groupby(['Segment'], as_index = False).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>Segment</th>\n",
       "      <th>Revenue Brand 1</th>\n",
       "      <th>Revenue Brand 2</th>\n",
       "      <th>Revenue Brand 3</th>\n",
       "      <th>Revenue Brand 4</th>\n",
       "      <th>Revenue Brand 5</th>\n",
       "      <th>Total Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2611.19</td>\n",
       "      <td>4768.52</td>\n",
       "      <td>3909.17</td>\n",
       "      <td>861.38</td>\n",
       "      <td>2439.75</td>\n",
       "      <td>14590.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>736.09</td>\n",
       "      <td>1746.42</td>\n",
       "      <td>664.75</td>\n",
       "      <td>2363.84</td>\n",
       "      <td>19441.06</td>\n",
       "      <td>24952.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2258.90</td>\n",
       "      <td>13955.14</td>\n",
       "      <td>716.25</td>\n",
       "      <td>1629.31</td>\n",
       "      <td>2230.50</td>\n",
       "      <td>20790.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>699.47</td>\n",
       "      <td>1298.23</td>\n",
       "      <td>731.35</td>\n",
       "      <td>14185.57</td>\n",
       "      <td>5509.69</td>\n",
       "      <td>22424.31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Segment  Revenue Brand 1  Revenue Brand 2  Revenue Brand 3  \\\n",
       "0        0          2611.19          4768.52          3909.17   \n",
       "1        1           736.09          1746.42           664.75   \n",
       "2        2          2258.90         13955.14           716.25   \n",
       "3        3           699.47          1298.23           731.35   \n",
       "\n",
       "   Revenue Brand 4  Revenue Brand 5  Total Revenue  \n",
       "0           861.38          2439.75       14590.01  \n",
       "1          2363.84         19441.06       24952.16  \n",
       "2          1629.31          2230.50       20790.10  \n",
       "3         14185.57          5509.69       22424.31  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We compute the total revenue for each of the segments. We simply sum the revenue for each of the five brands.\n",
    "segments_brand_revenue['Total Revenue'] = (segments_brand_revenue['Revenue Brand 1'] +\n",
    "                                           segments_brand_revenue['Revenue Brand 2'] +\n",
    "                                           segments_brand_revenue['Revenue Brand 3'] +\n",
    "                                           segments_brand_revenue['Revenue Brand 4'] +\n",
    "                                           segments_brand_revenue['Revenue Brand 5'] )\n",
    "segments_brand_revenue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Revenue Brand 1</th>\n",
       "      <th>Revenue Brand 2</th>\n",
       "      <th>Revenue Brand 3</th>\n",
       "      <th>Revenue Brand 4</th>\n",
       "      <th>Revenue Brand 5</th>\n",
       "      <th>Total Revenue</th>\n",
       "      <th>Segment Proportions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</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>Standard</th>\n",
       "      <td>2611.19</td>\n",
       "      <td>4768.52</td>\n",
       "      <td>3909.17</td>\n",
       "      <td>861.38</td>\n",
       "      <td>2439.75</td>\n",
       "      <td>14590.01</td>\n",
       "      <td>0.206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Career-Focused</th>\n",
       "      <td>736.09</td>\n",
       "      <td>1746.42</td>\n",
       "      <td>664.75</td>\n",
       "      <td>2363.84</td>\n",
       "      <td>19441.06</td>\n",
       "      <td>24952.16</td>\n",
       "      <td>0.220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fewer-Opportunities</th>\n",
       "      <td>2258.90</td>\n",
       "      <td>13955.14</td>\n",
       "      <td>716.25</td>\n",
       "      <td>1629.31</td>\n",
       "      <td>2230.50</td>\n",
       "      <td>20790.10</td>\n",
       "      <td>0.378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well-Off</th>\n",
       "      <td>699.47</td>\n",
       "      <td>1298.23</td>\n",
       "      <td>731.35</td>\n",
       "      <td>14185.57</td>\n",
       "      <td>5509.69</td>\n",
       "      <td>22424.31</td>\n",
       "      <td>0.196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Revenue Brand 1  Revenue Brand 2  Revenue Brand 3  \\\n",
       "Segment                                                                  \n",
       "Standard                     2611.19          4768.52          3909.17   \n",
       "Career-Focused                736.09          1746.42           664.75   \n",
       "Fewer-Opportunities          2258.90         13955.14           716.25   \n",
       "Well-Off                      699.47          1298.23           731.35   \n",
       "\n",
       "                     Revenue Brand 4  Revenue Brand 5  Total Revenue  \\\n",
       "Segment                                                                \n",
       "Standard                      861.38          2439.75       14590.01   \n",
       "Career-Focused               2363.84         19441.06       24952.16   \n",
       "Fewer-Opportunities          1629.31          2230.50       20790.10   \n",
       "Well-Off                    14185.57          5509.69       22424.31   \n",
       "\n",
       "                     Segment Proportions  \n",
       "Segment                                   \n",
       "Standard                           0.206  \n",
       "Career-Focused                     0.220  \n",
       "Fewer-Opportunities                0.378  \n",
       "Well-Off                           0.196  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We further modify our table to include the segment proportions. \n",
    "# It is interesting to see the size of the segment compared to the revenue they bring.\n",
    "# We also add the labels for the segments.s\n",
    "segments_brand_revenue['Segment Proportions'] = segm_prop['Segment Proportions']\n",
    "segments_brand_revenue['Segment'] = segments_brand_revenue['Segment'].map({0:'Standard',\n",
    "                                                                           1:'Career-Focused',\n",
    "                                                                           2:'Fewer-Opportunities',\n",
    "                                                                           3:'Well-Off'})\n",
    "segments_brand_revenue = segments_brand_revenue.set_index(['Segment'])\n",
    "segments_brand_revenue"
   ]
  }
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