explore_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploration of Online Retail Data\n",
    "The block below reads the raw data file and drops the records pertaining to:\n",
    "1. Records having missing values\n",
    "2. Invoice line items pertaining to bank charges\n",
    "3. Cancelled transactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "os.chdir(\"/home/admin123/Starspace/\")\n",
    "fp = \"/home/admin123/Starspace/\"+ \"data/Online_Retail.csv\"\n",
    "df = pd.read_csv(fp)\n",
    "df = df[-df['InvoiceNo'].str.startswith(\"C\")]\n",
    "df = df[-df['StockCode'].str.startswith(\"BANK\")]\n",
    "#req_cols = [\"CustomerID\", \"InvoiceNo\", \"StockCode\"]\n",
    "df = df.dropna(how ='any')\n",
    "df[\"CustomerID\"] = df[\"CustomerID\"].astype(int)\n",
    "#df = df.sort_values(by=['CustomerID'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verify that no records with missing values exist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[pd.isnull(df).any(axis=1)].shape\n",
    "#df.query(\"CustomerID == 18198\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of the Data\n",
    "Each line of the data file represents a line item in an invoice. Some attributes are added for analysis. Here is a snapshot of the data in the raw format (as obtained). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0  12/1/2010 8:26       2.55       17850  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39       17850  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75       17850  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39       17850  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39       17850  United Kingdom  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attribute Addition:\n",
    "The total cost associated with each line item is $ quantity \\times unit\\ price$. This is missing in the raw data. We create an attribute for this. It helps with the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"ItemTotal\"] = df[\"UnitPrice\"] * df[\"Quantity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>InvoiceDate</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>ItemTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536365</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.55</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536365</td>\n",
       "      <td>71053</td>\n",
       "      <td>WHITE METAL LANTERN</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536365</td>\n",
       "      <td>84406B</td>\n",
       "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029G</td>\n",
       "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536365</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>6</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>3.39</td>\n",
       "      <td>17850</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>20.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo StockCode                          Description  Quantity  \\\n",
       "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
       "1    536365     71053                  WHITE METAL LANTERN         6   \n",
       "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
       "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
       "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
       "\n",
       "      InvoiceDate  UnitPrice  CustomerID         Country  ItemTotal  \n",
       "0  12/1/2010 8:26       2.55       17850  United Kingdom      15.30  \n",
       "1  12/1/2010 8:26       3.39       17850  United Kingdom      20.34  \n",
       "2  12/1/2010 8:26       2.75       17850  United Kingdom      22.00  \n",
       "3  12/1/2010 8:26       3.39       17850  United Kingdom      20.34  \n",
       "4  12/1/2010 8:26       3.39       17850  United Kingdom      20.34  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate Customer Characteristics\n",
    "We want to charactrize the shopping habbits of the customers. We want to know:\n",
    "1. How much do they spend when they make a purchase?\n",
    "2. How often do they visit the online store?\n",
    "3. What kind of things do they purchase and how many at a time of each?\n",
    "4. How much has the customer spent at the store over the period captured by the data?\n",
    "5. What is a typical spend at the store?\n",
    "6. Who are the valuable customers and what do they buy?\n",
    "\n",
    "\n",
    "To answer questions like this, we will restructure the raw data to answer these questions. We will group the data by customer and the invoice, i.e., for each customer we will capture their invoices. For each invoice, we will calculate the total spend and the total number of line items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agrregate Invoice Line Items per Customer and Invoice\n",
    "We will use the group by construct with pandas to group invoice line items by customer and invoice. The result would order records by customer. For each customer the data is ordered by the invoices associated with him/her. For each invoice, we will determine the number of items purchased, the total spend and the number of line items in the invoice. The number of line items in the invoice gives us a sense of the variety of things the customer purchased in that visit. \n",
    "\n",
    "Let's start with aggregating the invoices for each customer. This is shown below. In the aggregation below:\n",
    "\n",
    "\n",
    "- StockCode count: represents the number of distinct items in the invoices. For example, if the invoice lists item A and item B only. Then the StockCode count is 2\n",
    "\n",
    "\n",
    "- ItemTotal sum: This is the amount the customer spent on that invoice. It is sum of the itemTotals.\n",
    "\n",
    "\n",
    "- Quantity sum: This is the total number of items associated with that invoice. For example if the invoice has 3 of item A and 2 of item B, the Quantity sum is 5.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_inv_summary = df.groupby([\"CustomerID\",\"InvoiceNo\"]).\\\n",
    "agg({'ItemTotal': ['sum'], 'Quantity':['sum'], 'StockCode':['count']}).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_inv_summary.columns = [''.join(col).strip() for col in df_inv_summary.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample of Invoice Aggregation\n",
    "See a sample of the invoice aggregation below. Interpretation: On invoice 537626, Customer 12347 spent 711.79. The purchase involved 31 different types of items. 319 items (total quantity) were purchased as part of that invoice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>CustomerID</th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>StockCodecount</th>\n",
       "      <th>ItemTotalsum</th>\n",
       "      <th>Quantitysum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>541431</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "      <td>74215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>537626</td>\n",
       "      <td>31</td>\n",
       "      <td>711.79</td>\n",
       "      <td>319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12347</td>\n",
       "      <td>542237</td>\n",
       "      <td>29</td>\n",
       "      <td>475.39</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12347</td>\n",
       "      <td>549222</td>\n",
       "      <td>24</td>\n",
       "      <td>636.25</td>\n",
       "      <td>483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12347</td>\n",
       "      <td>556201</td>\n",
       "      <td>18</td>\n",
       "      <td>382.52</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID InvoiceNo  StockCodecount  ItemTotalsum  Quantitysum\n",
       "0       12346    541431               1      77183.60        74215\n",
       "1       12347    537626              31        711.79          319\n",
       "2       12347    542237              29        475.39          315\n",
       "3       12347    549222              24        636.25          483\n",
       "4       12347    556201              18        382.52          196"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inv_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarization of the Invoices\n",
    "A summary view of customer invoices is shown below. The mean associated with the invoice summary gives us the average values for the attributes. As is evident from reviewing the summary below, the data has some outliers. The mean values for the StockCode count, invoice total and the item quantity count is very different from the maximum values for these attributes. A typical purchase is fairly small, but we have some outliers. Histograms for the Stockcode count, invoice total and the item quantity confirm this skewed behavior. Most of the invoices lie in the left region of these histogram plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>CustomerID</th>\n",
       "      <th>StockCodecount</th>\n",
       "      <th>ItemTotalsum</th>\n",
       "      <th>Quantitysum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18525.000000</td>\n",
       "      <td>18525.000000</td>\n",
       "      <td>18525.000000</td>\n",
       "      <td>18525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15266.329123</td>\n",
       "      <td>21.479730</td>\n",
       "      <td>481.038753</td>\n",
       "      <td>279.713036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1734.417835</td>\n",
       "      <td>24.921691</td>\n",
       "      <td>1678.489109</td>\n",
       "      <td>977.062927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13774.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>158.230000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15178.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>303.080000</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16779.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>471.880000</td>\n",
       "      <td>291.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18287.000000</td>\n",
       "      <td>542.000000</td>\n",
       "      <td>168469.600000</td>\n",
       "      <td>80995.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID  StockCodecount   ItemTotalsum   Quantitysum\n",
       "count  18525.000000    18525.000000   18525.000000  18525.000000\n",
       "mean   15266.329123       21.479730     481.038753    279.713036\n",
       "std     1734.417835       24.921691    1678.489109    977.062927\n",
       "min    12346.000000        1.000000       0.000000      1.000000\n",
       "25%    13774.000000        6.000000     158.230000     74.000000\n",
       "50%    15178.000000       15.000000     303.080000    155.000000\n",
       "75%    16779.000000       28.000000     471.880000    291.000000\n",
       "max    18287.000000      542.000000  168469.600000  80995.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inv_summary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be6795090>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_inv_summary[\"StockCodecount\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be61b7750>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_inv_summary[\"ItemTotalsum\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate Customers\n",
    "Let us analyze customers next. To do so we need to aggregate by Customer ID and count invoices and sum the invoice totals. This will tell us the total amount spent by the customer and the number of store visits "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df_freq_cust = df_inv_summary.groupby('CustomerID').\\\n",
    "agg({'ItemTotalsum': ['sum'], 'InvoiceNo':['count']}).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample of Customer Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>CustomerID</th>\n",
       "      <th>Store_Visits</th>\n",
       "      <th>Amount_Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12347</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12348</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12349</td>\n",
       "      <td>1</td>\n",
       "      <td>1757.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12350</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerID  Store_Visits  Amount_Spent\n",
       "0       12346             1      77183.60\n",
       "1       12347             7       4310.00\n",
       "2       12348             4       1797.24\n",
       "3       12349             1       1757.55\n",
       "4       12350             1        334.40"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq_cust.columns = [''.join(col).strip() for col in df_freq_cust.columns]\n",
    "df_freq_cust.columns = [\"CustomerID\", \"Store_Visits\", \"Amount_Spent\"]\n",
    "df_freq_cust.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize Customer View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Store_Visits</th>\n",
       "      <th>Amount_Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4338.000000</td>\n",
       "      <td>4338.000000</td>\n",
       "      <td>4338.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>15299.816275</td>\n",
       "      <td>4.270401</td>\n",
       "      <td>2054.228424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1722.069938</td>\n",
       "      <td>7.701992</td>\n",
       "      <td>8989.225381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12346.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13812.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>307.415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15298.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>674.485000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16778.750000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1661.740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18287.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>280206.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CustomerID  Store_Visits   Amount_Spent\n",
       "count   4338.000000   4338.000000    4338.000000\n",
       "mean   15299.816275      4.270401    2054.228424\n",
       "std     1722.069938      7.701992    8989.225381\n",
       "min    12346.000000      1.000000       0.000000\n",
       "25%    13812.250000      1.000000     307.415000\n",
       "50%    15298.500000      2.000000     674.485000\n",
       "75%    16778.750000      5.000000    1661.740000\n",
       "max    18287.000000    210.000000  280206.020000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_freq_cust.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations About Customers\n",
    "- The store enjoys customer loyalty. On average, a customer generates about 5 invoices at the store.\n",
    "- There are some customer outliers - some very atypical customers. This can be identified by inspecting the maximum values for the store visits and the amount spent\n",
    "\n",
    "We will confirm these with histogram views for these attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be60b3090>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_freq_cust[\"Store_Visits\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be601b4d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_freq_cust[\"Amount_Spent\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A View of Atypical Customers\n",
    "Let's take a look at customers who are atypical. We will define an atypical customer to be one that spends over 3 standard deviations from the mean customer spend at the store. We will first define this threshold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "atypical_threshold = df_freq_cust[\"Amount_Spent\"].mean() + 3 * df_freq_cust[\"Amount_Spent\"].std()\n",
    "df_ob = df_freq_cust[df_freq_cust.Amount_Spent > atypical_threshold]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample of Atypical Customers"
   ]
  },
  {
   "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>CustomerID</th>\n",
       "      <th>Store_Visits</th>\n",
       "      <th>Amount_Spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12346</td>\n",
       "      <td>1</td>\n",
       "      <td>77183.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>12415</td>\n",
       "      <td>21</td>\n",
       "      <td>124914.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>12748</td>\n",
       "      <td>210</td>\n",
       "      <td>33719.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>12931</td>\n",
       "      <td>15</td>\n",
       "      <td>42055.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>13089</td>\n",
       "      <td>97</td>\n",
       "      <td>58825.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     CustomerID  Store_Visits  Amount_Spent\n",
       "0         12346             1      77183.60\n",
       "55        12415            21     124914.53\n",
       "326       12748           210      33719.73\n",
       "453       12931            15      42055.96\n",
       "562       13089            97      58825.83"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ob.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram View of Atypical Customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be6073790>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ob[\"Amount_Spent\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1be5f62ad0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mfDMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_ob[\"Store_Visits\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations\n",
    "- Both histograms are skewed and captures the atypical customers. For example there is a customer who spends over 250 K at the store and there is a customer who has generated over 200 invoices at the store."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(\"CustomerID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}