Ecommerce.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis Testing for Decision Making in an Ecommerce Business"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision making is an indispensable part of every business, and every shrewd businessman knows that there are strategic processes to this - and that there are really no cut and dried rules. E-commerce businesses are a big partaker in the data economy, given their mode of operation, and online shops happen to be one of the most popular.\n",
    "\n",
    "Hypothesis testing is basically checking how plausible an assumption is, and given the volume of data at the disposal of some of these businesses, it is likely that managers would need come up with good and timely decisions. This is where hypothesis testing becomes an invaluable tool."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scenarios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Let's imagine the manager of the retail store decides to give some gift to the highest paying customer in every country, and to do this he assumes that customer who has the highest number of order count per country are the highest paying customers. In other words, he believes that the average revenue generated from these customers is more than the revenue generated from the whole population of customers. Is there enough evidence to accept the manager's hypothesis?\n",
    "\n",
    "* The assistant manager thinks the manager's method was not comprehensive enough and opines that a customer may actually have high order count but not necessarily high number of units per order. So he recommends that customers with the highest average total units purchased should be given preference instead. The manager acknowledged his point but was not sure if there will be any major difference between them. So they decided to enlist the help of the data analyst for a quick hypothesis testing on their claims.\n",
    "\n",
    "**Data Source:** The data \"is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.\" It is available [here](https://www.kaggle.com/carrie1/ecommerce-data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding Data Structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import researchpy as rpy\n",
    "sns.set_context('talk')\n",
    "sns.set_style(\"darkgrid\")\n",
    "import math\n",
    "import datapane as dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#read the data\n",
    "dfm = pd.read_csv('ecommerce.csv', encoding = 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(541909, 8)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the shape\n",
    "dfm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<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>17850.0</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>17850.0</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>17850.0</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>17850.0</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>17850.0</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     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the first five rows\n",
    "dfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#check the information\n",
    "dfm.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "* The data contains two floats, one integer, and five object data types\n",
    "* There are missing values in the *Description* and *CustomerID* columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the number of null values in each column\n",
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      25900\n",
       "StockCode       4070\n",
       "Description     4223\n",
       "Quantity         722\n",
       "InvoiceDate    23260\n",
       "UnitPrice       1630\n",
       "CustomerID      4372\n",
       "Country           38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the unique values in each column\n",
    "dfm.apply(lambda x : x.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "#check if there are negative values in Quantity and UnitPrice\n",
    "print(any(dfm['Quantity'] <= 0))\n",
    "print(any(dfm['UnitPrice'] <= 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 541909 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    541909 non-null  object \n",
      " 1   StockCode    541909 non-null  object \n",
      " 2   Description  540455 non-null  object \n",
      " 3   Quantity     541909 non-null  int64  \n",
      " 4   InvoiceDate  541909 non-null  object \n",
      " 5   UnitPrice    541909 non-null  float64\n",
      " 6   CustomerID   406829 non-null  float64\n",
      " 7   Country      541909 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 33.1+ MB\n"
     ]
    }
   ],
   "source": [
    "dfm.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CustomerID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check if there's a pattern to filling the missing CustomerID cells\n",
    "dfm['CustomerID'] = dfm.groupby(['InvoiceNo', 'InvoiceDate'])['CustomerID'].apply(lambda x : x.ffill().bfill())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo           0\n",
       "StockCode           0\n",
       "Description      1454\n",
       "Quantity            0\n",
       "InvoiceDate         0\n",
       "UnitPrice           0\n",
       "CustomerID     135080\n",
       "Country             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is any difference\n",
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since there is no difference, and there is no plausible way to fill them up correctly, the rows are dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop orders with missing CustomerID\n",
    "dfm = dfm.dropna(subset = ['CustomerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo      0\n",
       "StockCode      0\n",
       "Description    0\n",
       "Quantity       0\n",
       "InvoiceDate    0\n",
       "UnitPrice      0\n",
       "CustomerID     0\n",
       "Country        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values again\n",
    "dfm.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Negative and Zero Values for UnitPrice and Quantity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>406829.000000</td>\n",
       "      <td>406829.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.061303</td>\n",
       "      <td>3.460471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>248.693370</td>\n",
       "      <td>69.315162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-80995.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80995.000000</td>\n",
       "      <td>38970.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice\n",
       "count  406829.000000  406829.000000\n",
       "mean       12.061303       3.460471\n",
       "std       248.693370      69.315162\n",
       "min    -80995.000000       0.000000\n",
       "25%         2.000000       1.250000\n",
       "50%         5.000000       1.950000\n",
       "75%        12.000000       3.750000\n",
       "max     80995.000000   38970.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check summary statistics for Quantity and UnitPrice\n",
    "dfm[['Quantity', 'UnitPrice']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above table shows most of the values in Quantity and UnitPrice are single-digit values. However, there also seems to be heavy outliers highlighted by the maximum values.\n",
    "*  Also the match between the minimum and maximum values for Quantity can be explore further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manual                            99\n",
       "POSTAGE                           12\n",
       "CRUK Commission                   12\n",
       "DOTCOM POSTAGE                    12\n",
       "VINTAGE RED KITCHEN CABINET        8\n",
       "Discount                           5\n",
       "VINTAGE BLUE KITCHEN CABINET       3\n",
       "PICNIC BASKET WICKER 60 PIECES     2\n",
       "Name: Description, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if there is a pattern to items with high UnitPrice \n",
    "high_unit = dfm[dfm['UnitPrice'] >= 200]\n",
    "high_unit['Description'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The above shows that items with high unit prices are generally not items of sales. Manual, postage, and discount are explicit enough, while a quick check on CRUK Commission online shows it means Cancer Research UK. It may be some charitable activity. So all the highlighted value labels will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove unit prices for manual, dotcom postage, etc\n",
    "remove_unit = ['Manual', 'DOTCOM POSTAGE', 'CRUK Commission', 'POSTAGE', 'Discount']\n",
    "dfm = dfm[~dfm['Description'].isin(remove_unit)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "540422   -80995\n",
       "61624    -74215\n",
       "4287      -9360\n",
       "160145    -3114\n",
       "160144    -2000\n",
       "160143    -1930\n",
       "224419    -1515\n",
       "160142    -1300\n",
       "390544    -1296\n",
       "390548    -1296\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the highest negative values for Quantity\n",
    "dfm['Quantity'].nsmallest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "    <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>540421</th>\n",
       "      <td>581483</td>\n",
       "      <td>23843</td>\n",
       "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
       "      <td>80995</td>\n",
       "      <td>12/9/2011 9:15</td>\n",
       "      <td>2.08</td>\n",
       "      <td>16446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540422</th>\n",
       "      <td>C581484</td>\n",
       "      <td>23843</td>\n",
       "      <td>PAPER CRAFT , LITTLE BIRDIE</td>\n",
       "      <td>-80995</td>\n",
       "      <td>12/9/2011 9:27</td>\n",
       "      <td>2.08</td>\n",
       "      <td>16446.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                  Description  Quantity  \\\n",
       "540421    581483     23843  PAPER CRAFT , LITTLE BIRDIE     80995   \n",
       "540422   C581484     23843  PAPER CRAFT , LITTLE BIRDIE    -80995   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "540421  12/9/2011 9:15       2.08     16446.0  United Kingdom  \n",
       "540422  12/9/2011 9:27       2.08     16446.0  United Kingdom  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm[(dfm['Quantity'] == 80995) | (dfm['Quantity'] == -80995)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>61619</th>\n",
       "      <td>541431</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>74215</td>\n",
       "      <td>1/18/2011 10:01</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61624</th>\n",
       "      <td>C541433</td>\n",
       "      <td>23166</td>\n",
       "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
       "      <td>-74215</td>\n",
       "      <td>1/18/2011 10:17</td>\n",
       "      <td>1.04</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      InvoiceNo StockCode                     Description  Quantity  \\\n",
       "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
       "61624   C541433     23166  MEDIUM CERAMIC TOP STORAGE JAR    -74215   \n",
       "\n",
       "           InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "61619  1/18/2011 10:01       1.04     12346.0  United Kingdom  \n",
       "61624  1/18/2011 10:17       1.04     12346.0  United Kingdom  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm[(dfm['Quantity'] == 74215) | (dfm['Quantity'] == -74215)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Observation shows that some of the big negative values actually have corresponding positive values\n",
    "* These pair seems not too far from each other in entry time (tentatively)\n",
    "* The rows are also quite similar except for the InvoiceNo column for the negative quantity, which is prefixed with C (tentatively)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "540421    80995\n",
       "61619     74215\n",
       "502122    12540\n",
       "421632     4800\n",
       "206121     4300\n",
       "97432      3906\n",
       "270885     3186\n",
       "52711      3114\n",
       "160546     3114\n",
       "433788     3000\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the highest positive values for Quantity\n",
    "dfm['Quantity'].nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52711</th>\n",
       "      <td>540815</td>\n",
       "      <td>21108</td>\n",
       "      <td>FAIRY CAKE FLANNEL ASSORTED COLOUR</td>\n",
       "      <td>3114</td>\n",
       "      <td>1/11/2011 12:55</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160145</th>\n",
       "      <td>C550456</td>\n",
       "      <td>21108</td>\n",
       "      <td>FAIRY CAKE FLANNEL ASSORTED COLOUR</td>\n",
       "      <td>-3114</td>\n",
       "      <td>4/18/2011 13:08</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160546</th>\n",
       "      <td>550461</td>\n",
       "      <td>21108</td>\n",
       "      <td>FAIRY CAKE FLANNEL ASSORTED COLOUR</td>\n",
       "      <td>3114</td>\n",
       "      <td>4/18/2011 13:20</td>\n",
       "      <td>2.1</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description  Quantity  \\\n",
       "52711     540815     21108  FAIRY CAKE FLANNEL ASSORTED COLOUR      3114   \n",
       "160145   C550456     21108  FAIRY CAKE FLANNEL ASSORTED COLOUR     -3114   \n",
       "160546    550461     21108  FAIRY CAKE FLANNEL ASSORTED COLOUR      3114   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "52711   1/11/2011 12:55        2.1     15749.0  United Kingdom  \n",
       "160145  4/18/2011 13:08        2.1     15749.0  United Kingdom  \n",
       "160546  4/18/2011 13:20        2.1     15749.0  United Kingdom  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm[(dfm['Quantity'] == 3114) | (dfm['Quantity'] == -3114)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52709</th>\n",
       "      <td>540815</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>1930</td>\n",
       "      <td>1/11/2011 12:55</td>\n",
       "      <td>2.55</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160143</th>\n",
       "      <td>C550456</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>-1930</td>\n",
       "      <td>4/18/2011 13:08</td>\n",
       "      <td>2.55</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160542</th>\n",
       "      <td>550461</td>\n",
       "      <td>85123A</td>\n",
       "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
       "      <td>1930</td>\n",
       "      <td>4/18/2011 13:20</td>\n",
       "      <td>2.40</td>\n",
       "      <td>15749.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description  Quantity  \\\n",
       "52709     540815    85123A  WHITE HANGING HEART T-LIGHT HOLDER      1930   \n",
       "160143   C550456    85123A  WHITE HANGING HEART T-LIGHT HOLDER     -1930   \n",
       "160542    550461    85123A  WHITE HANGING HEART T-LIGHT HOLDER      1930   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "52709   1/11/2011 12:55       2.55     15749.0  United Kingdom  \n",
       "160143  4/18/2011 13:08       2.55     15749.0  United Kingdom  \n",
       "160542  4/18/2011 13:20       2.40     15749.0  United Kingdom  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfm[(dfm['Quantity'] == 1930) | (dfm['Quantity'] == -1930)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A different pattern is observed here again\n",
    "* The same customer carried out the six transactions above with two set of quantities\n",
    "* Each of the negative quantities shows more InvoiceDate similarity with one of the positive quantities\n",
    "* Meaning, if these more matching pair of negative and positive are to cancel out each other, there will still be a heavy postive left.\n",
    "* Since further information that could aid the understanding of the data is not given, the variables will not be removed yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9302</th>\n",
       "      <td>537197</td>\n",
       "      <td>22841</td>\n",
       "      <td>ROUND CAKE TIN VINTAGE GREEN</td>\n",
       "      <td>1</td>\n",
       "      <td>12/5/2010 14:02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12647.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33576</th>\n",
       "      <td>539263</td>\n",
       "      <td>22580</td>\n",
       "      <td>ADVENT CALENDAR GINGHAM SACK</td>\n",
       "      <td>4</td>\n",
       "      <td>12/16/2010 14:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16560.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40089</th>\n",
       "      <td>539722</td>\n",
       "      <td>22423</td>\n",
       "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
       "      <td>10</td>\n",
       "      <td>12/21/2010 13:45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>EIRE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47068</th>\n",
       "      <td>540372</td>\n",
       "      <td>22090</td>\n",
       "      <td>PAPER BUNTING RETROSPOT</td>\n",
       "      <td>24</td>\n",
       "      <td>1/6/2011 16:41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47070</th>\n",
       "      <td>540372</td>\n",
       "      <td>22553</td>\n",
       "      <td>PLASTERS IN TIN SKULLS</td>\n",
       "      <td>24</td>\n",
       "      <td>1/6/2011 16:41</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56674</th>\n",
       "      <td>541109</td>\n",
       "      <td>22168</td>\n",
       "      <td>ORGANISER WOOD ANTIQUE WHITE</td>\n",
       "      <td>1</td>\n",
       "      <td>1/13/2011 15:10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15107.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86789</th>\n",
       "      <td>543599</td>\n",
       "      <td>84535B</td>\n",
       "      <td>FAIRY CAKES NOTEBOOK A6 SIZE</td>\n",
       "      <td>16</td>\n",
       "      <td>2/10/2011 13:08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17560.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130188</th>\n",
       "      <td>547417</td>\n",
       "      <td>22062</td>\n",
       "      <td>CERAMIC BOWL WITH LOVE HEART DESIGN</td>\n",
       "      <td>36</td>\n",
       "      <td>3/23/2011 10:25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13239.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139453</th>\n",
       "      <td>548318</td>\n",
       "      <td>22055</td>\n",
       "      <td>MINI CAKE STAND  HANGING STRAWBERY</td>\n",
       "      <td>5</td>\n",
       "      <td>3/30/2011 12:45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13113.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145208</th>\n",
       "      <td>548871</td>\n",
       "      <td>22162</td>\n",
       "      <td>HEART GARLAND RUSTIC PADDED</td>\n",
       "      <td>2</td>\n",
       "      <td>4/4/2011 14:42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14410.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157042</th>\n",
       "      <td>550188</td>\n",
       "      <td>22636</td>\n",
       "      <td>CHILDS BREAKFAST SET CIRCUS PARADE</td>\n",
       "      <td>1</td>\n",
       "      <td>4/14/2011 18:57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12457.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187613</th>\n",
       "      <td>553000</td>\n",
       "      <td>47566</td>\n",
       "      <td>PARTY BUNTING</td>\n",
       "      <td>4</td>\n",
       "      <td>5/12/2011 15:21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17667.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198383</th>\n",
       "      <td>554037</td>\n",
       "      <td>22619</td>\n",
       "      <td>SET OF 6 SOLDIER SKITTLES</td>\n",
       "      <td>80</td>\n",
       "      <td>5/20/2011 14:13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12415.0</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279324</th>\n",
       "      <td>561284</td>\n",
       "      <td>22167</td>\n",
       "      <td>OVAL WALL MIRROR DIAMANTE</td>\n",
       "      <td>1</td>\n",
       "      <td>7/26/2011 12:24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16818.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282912</th>\n",
       "      <td>561669</td>\n",
       "      <td>22960</td>\n",
       "      <td>JAM MAKING SET WITH JARS</td>\n",
       "      <td>11</td>\n",
       "      <td>7/28/2011 17:09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12507.0</td>\n",
       "      <td>Spain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298054</th>\n",
       "      <td>562973</td>\n",
       "      <td>23157</td>\n",
       "      <td>SET OF 6 NATIVITY MAGNETS</td>\n",
       "      <td>240</td>\n",
       "      <td>8/11/2011 11:42</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14911.0</td>\n",
       "      <td>EIRE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314745</th>\n",
       "      <td>564651</td>\n",
       "      <td>23270</td>\n",
       "      <td>SET OF 2 CERAMIC PAINTED HEARTS</td>\n",
       "      <td>96</td>\n",
       "      <td>8/26/2011 14:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314746</th>\n",
       "      <td>564651</td>\n",
       "      <td>23268</td>\n",
       "      <td>SET OF 2 CERAMIC CHRISTMAS REINDEER</td>\n",
       "      <td>192</td>\n",
       "      <td>8/26/2011 14:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314747</th>\n",
       "      <td>564651</td>\n",
       "      <td>22955</td>\n",
       "      <td>36 FOIL STAR CAKE CASES</td>\n",
       "      <td>144</td>\n",
       "      <td>8/26/2011 14:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314748</th>\n",
       "      <td>564651</td>\n",
       "      <td>21786</td>\n",
       "      <td>POLKADOT RAIN HAT</td>\n",
       "      <td>144</td>\n",
       "      <td>8/26/2011 14:19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14646.0</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358655</th>\n",
       "      <td>568158</td>\n",
       "      <td>PADS</td>\n",
       "      <td>PADS TO MATCH ALL CUSHIONS</td>\n",
       "      <td>1</td>\n",
       "      <td>9/25/2011 12:22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16133.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379913</th>\n",
       "      <td>569716</td>\n",
       "      <td>22778</td>\n",
       "      <td>GLASS CLOCHE SMALL</td>\n",
       "      <td>2</td>\n",
       "      <td>10/6/2011 8:17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15804.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420404</th>\n",
       "      <td>572893</td>\n",
       "      <td>21208</td>\n",
       "      <td>PASTEL COLOUR HONEYCOMB FAN</td>\n",
       "      <td>5</td>\n",
       "      <td>10/26/2011 14:36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18059.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436428</th>\n",
       "      <td>574138</td>\n",
       "      <td>23234</td>\n",
       "      <td>BISCUIT TIN VINTAGE CHRISTMAS</td>\n",
       "      <td>216</td>\n",
       "      <td>11/3/2011 11:26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12415.0</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436597</th>\n",
       "      <td>574175</td>\n",
       "      <td>22065</td>\n",
       "      <td>CHRISTMAS PUDDING TRINKET POT</td>\n",
       "      <td>12</td>\n",
       "      <td>11/3/2011 11:47</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14110.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439361</th>\n",
       "      <td>574469</td>\n",
       "      <td>22385</td>\n",
       "      <td>JUMBO BAG SPACEBOY DESIGN</td>\n",
       "      <td>12</td>\n",
       "      <td>11/4/2011 11:55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12431.0</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446125</th>\n",
       "      <td>574879</td>\n",
       "      <td>22625</td>\n",
       "      <td>RED KITCHEN SCALES</td>\n",
       "      <td>2</td>\n",
       "      <td>11/7/2011 13:22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13014.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446793</th>\n",
       "      <td>574920</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>1</td>\n",
       "      <td>11/7/2011 16:34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13985.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446794</th>\n",
       "      <td>574920</td>\n",
       "      <td>23480</td>\n",
       "      <td>MINI LIGHTS WOODLAND MUSHROOMS</td>\n",
       "      <td>1</td>\n",
       "      <td>11/7/2011 16:34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13985.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454463</th>\n",
       "      <td>575579</td>\n",
       "      <td>22437</td>\n",
       "      <td>SET OF 9 BLACK SKULL BALLOONS</td>\n",
       "      <td>20</td>\n",
       "      <td>11/10/2011 11:49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454464</th>\n",
       "      <td>575579</td>\n",
       "      <td>22089</td>\n",
       "      <td>PAPER BUNTING VINTAGE PAISLEY</td>\n",
       "      <td>24</td>\n",
       "      <td>11/10/2011 11:49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479079</th>\n",
       "      <td>577129</td>\n",
       "      <td>22464</td>\n",
       "      <td>HANGING METAL HEART LANTERN</td>\n",
       "      <td>4</td>\n",
       "      <td>11/17/2011 19:52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15602.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480649</th>\n",
       "      <td>577314</td>\n",
       "      <td>23407</td>\n",
       "      <td>SET OF 2 TRAYS HOME SWEET HOME</td>\n",
       "      <td>2</td>\n",
       "      <td>11/18/2011 13:23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12444.0</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502122</th>\n",
       "      <td>578841</td>\n",
       "      <td>84826</td>\n",
       "      <td>ASSTD DESIGN 3D PAPER STICKERS</td>\n",
       "      <td>12540</td>\n",
       "      <td>11/25/2011 15:57</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13256.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                          Description  Quantity  \\\n",
       "9302      537197     22841         ROUND CAKE TIN VINTAGE GREEN         1   \n",
       "33576     539263     22580         ADVENT CALENDAR GINGHAM SACK         4   \n",
       "40089     539722     22423             REGENCY CAKESTAND 3 TIER        10   \n",
       "47068     540372     22090              PAPER BUNTING RETROSPOT        24   \n",
       "47070     540372     22553               PLASTERS IN TIN SKULLS        24   \n",
       "56674     541109     22168        ORGANISER WOOD ANTIQUE WHITE          1   \n",
       "86789     543599    84535B         FAIRY CAKES NOTEBOOK A6 SIZE        16   \n",
       "130188    547417     22062  CERAMIC BOWL WITH LOVE HEART DESIGN        36   \n",
       "139453    548318     22055   MINI CAKE STAND  HANGING STRAWBERY         5   \n",
       "145208    548871     22162          HEART GARLAND RUSTIC PADDED         2   \n",
       "157042    550188     22636   CHILDS BREAKFAST SET CIRCUS PARADE         1   \n",
       "187613    553000     47566                        PARTY BUNTING         4   \n",
       "198383    554037     22619            SET OF 6 SOLDIER SKITTLES        80   \n",
       "279324    561284     22167           OVAL WALL MIRROR DIAMANTE          1   \n",
       "282912    561669     22960             JAM MAKING SET WITH JARS        11   \n",
       "298054    562973     23157           SET OF 6 NATIVITY MAGNETS        240   \n",
       "314745    564651     23270     SET OF 2 CERAMIC PAINTED HEARTS         96   \n",
       "314746    564651     23268  SET OF 2 CERAMIC CHRISTMAS REINDEER       192   \n",
       "314747    564651     22955             36 FOIL STAR CAKE CASES        144   \n",
       "314748    564651     21786                   POLKADOT RAIN HAT        144   \n",
       "358655    568158      PADS           PADS TO MATCH ALL CUSHIONS         1   \n",
       "379913    569716     22778                   GLASS CLOCHE SMALL         2   \n",
       "420404    572893     21208          PASTEL COLOUR HONEYCOMB FAN         5   \n",
       "436428    574138     23234        BISCUIT TIN VINTAGE CHRISTMAS       216   \n",
       "436597    574175     22065       CHRISTMAS PUDDING TRINKET POT         12   \n",
       "439361    574469     22385            JUMBO BAG SPACEBOY DESIGN        12   \n",
       "446125    574879     22625                   RED KITCHEN SCALES         2   \n",
       "446793    574920     22899         CHILDREN'S APRON DOLLY GIRL          1   \n",
       "446794    574920     23480       MINI LIGHTS WOODLAND MUSHROOMS         1   \n",
       "454463    575579     22437        SET OF 9 BLACK SKULL BALLOONS        20   \n",
       "454464    575579     22089        PAPER BUNTING VINTAGE PAISLEY        24   \n",
       "479079    577129     22464          HANGING METAL HEART LANTERN         4   \n",
       "480649    577314     23407       SET OF 2 TRAYS HOME SWEET HOME         2   \n",
       "502122    578841     84826       ASSTD DESIGN 3D PAPER STICKERS     12540   \n",
       "\n",
       "             InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "9302     12/5/2010 14:02        0.0     12647.0         Germany  \n",
       "33576   12/16/2010 14:36        0.0     16560.0  United Kingdom  \n",
       "40089   12/21/2010 13:45        0.0     14911.0            EIRE  \n",
       "47068     1/6/2011 16:41        0.0     13081.0  United Kingdom  \n",
       "47070     1/6/2011 16:41        0.0     13081.0  United Kingdom  \n",
       "56674    1/13/2011 15:10        0.0     15107.0  United Kingdom  \n",
       "86789    2/10/2011 13:08        0.0     17560.0  United Kingdom  \n",
       "130188   3/23/2011 10:25        0.0     13239.0  United Kingdom  \n",
       "139453   3/30/2011 12:45        0.0     13113.0  United Kingdom  \n",
       "145208    4/4/2011 14:42        0.0     14410.0  United Kingdom  \n",
       "157042   4/14/2011 18:57        0.0     12457.0     Switzerland  \n",
       "187613   5/12/2011 15:21        0.0     17667.0  United Kingdom  \n",
       "198383   5/20/2011 14:13        0.0     12415.0       Australia  \n",
       "279324   7/26/2011 12:24        0.0     16818.0  United Kingdom  \n",
       "282912   7/28/2011 17:09        0.0     12507.0           Spain  \n",
       "298054   8/11/2011 11:42        0.0     14911.0            EIRE  \n",
       "314745   8/26/2011 14:19        0.0     14646.0     Netherlands  \n",
       "314746   8/26/2011 14:19        0.0     14646.0     Netherlands  \n",
       "314747   8/26/2011 14:19        0.0     14646.0     Netherlands  \n",
       "314748   8/26/2011 14:19        0.0     14646.0     Netherlands  \n",
       "358655   9/25/2011 12:22        0.0     16133.0  United Kingdom  \n",
       "379913    10/6/2011 8:17        0.0     15804.0  United Kingdom  \n",
       "420404  10/26/2011 14:36        0.0     18059.0  United Kingdom  \n",
       "436428   11/3/2011 11:26        0.0     12415.0       Australia  \n",
       "436597   11/3/2011 11:47        0.0     14110.0  United Kingdom  \n",
       "439361   11/4/2011 11:55        0.0     12431.0       Australia  \n",
       "446125   11/7/2011 13:22        0.0     13014.0  United Kingdom  \n",
       "446793   11/7/2011 16:34        0.0     13985.0  United Kingdom  \n",
       "446794   11/7/2011 16:34        0.0     13985.0  United Kingdom  \n",
       "454463  11/10/2011 11:49        0.0     13081.0  United Kingdom  \n",
       "454464  11/10/2011 11:49        0.0     13081.0  United Kingdom  \n",
       "479079  11/17/2011 19:52        0.0     15602.0  United Kingdom  \n",
       "480649  11/18/2011 13:23        0.0     12444.0          Norway  \n",
       "502122  11/25/2011 15:57        0.0     13256.0  United Kingdom  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check the rows with zero UnitPrice\n",
    "dfm[dfm['UnitPrice'] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#select only rows with UnitPrice greater than zero\n",
    "dfm = dfm[dfm['UnitPrice'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check the distribution of UnitPrice\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = dfm, x='UnitPrice');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check the distribution of Quantity\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = dfm, x='Quantity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_outlier(df_in, col_name):\n",
    "    \"\"\" Function to remove outliers using interquartile range.\n",
    "    \n",
    "    Args:\n",
    "        df_in (Dataframe) - Pandas Dataframe containing the income buckets.\n",
    "        \n",
    "        col_name (String) - Name of the column for which the outliers are to be removed\n",
    "        \n",
    "    Returns:\n",
    "        df_out (Dataframe) - Pandas Dataframe without the outliers\n",
    "    \"\"\"\n",
    "    \n",
    "    Q1 = df_in[col_name].quantile(0.25)\n",
    "    Q3 = df_in[col_name].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    df_out = df_in[~((df_in[col_name] < (Q1 - 1.5 * IQR)) | (df_in[col_name] > (Q3 + 1.5 * IQR)))]\n",
    "    return df_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove outliers from UnitPrice\n",
    "new_unit = remove_outlier(dfm, 'UnitPrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#UnitPrice distribution without outliers\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = new_unit, x='UnitPrice');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    370409.000000\n",
       "mean          2.143236\n",
       "std           1.537971\n",
       "min           0.001000\n",
       "25%           0.850000\n",
       "50%           1.650000\n",
       "75%           2.950000\n",
       "max           7.500000\n",
       "Name: UnitPrice, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#UnitPrice summary without outliers\n",
    "new_unit['UnitPrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#remove outliers from Quantity using new_unit dataframe ad check its distribution\n",
    "new_unit = remove_outlier(new_unit, 'Quantity')\n",
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = new_unit, x='Quantity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    344087.000000\n",
       "mean          7.283989\n",
       "std           6.880329\n",
       "min         -13.000000\n",
       "25%           2.000000\n",
       "50%           5.000000\n",
       "75%          12.000000\n",
       "max          27.000000\n",
       "Name: Quantity, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_unit['Quantity'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 344087 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    344087 non-null  object \n",
      " 1   StockCode    344087 non-null  object \n",
      " 2   Description  344087 non-null  object \n",
      " 3   Quantity     344087 non-null  int64  \n",
      " 4   InvoiceDate  344087 non-null  object \n",
      " 5   UnitPrice    344087 non-null  float64\n",
      " 6   CustomerID   344087 non-null  float64\n",
      " 7   Country      344087 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 23.6+ MB\n"
     ]
    }
   ],
   "source": [
    "new_unit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove outliers from quantity\n",
    "new_quant = remove_outlier(dfm, 'Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x277a6799808>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = new_quant, x='Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    378406.000000\n",
       "mean          6.841305\n",
       "std           6.769372\n",
       "min         -13.000000\n",
       "25%           2.000000\n",
       "50%           4.000000\n",
       "75%          12.000000\n",
       "max          27.000000\n",
       "Name: Quantity, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_quant['Quantity'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_quant = remove_outlier(new_quant, 'UnitPrice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = new_quant, x='UnitPrice');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    344087.000000\n",
       "mean          2.201480\n",
       "std           1.548116\n",
       "min           0.001000\n",
       "25%           1.250000\n",
       "50%           1.650000\n",
       "75%           2.950000\n",
       "max           7.500000\n",
       "Name: UnitPrice, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_quant['UnitPrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 344087 entries, 0 to 541908\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count   Dtype  \n",
      "---  ------       --------------   -----  \n",
      " 0   InvoiceNo    344087 non-null  object \n",
      " 1   StockCode    344087 non-null  object \n",
      " 2   Description  344087 non-null  object \n",
      " 3   Quantity     344087 non-null  int64  \n",
      " 4   InvoiceDate  344087 non-null  object \n",
      " 5   UnitPrice    344087 non-null  float64\n",
      " 6   CustomerID   344087 non-null  float64\n",
      " 7   Country      344087 non-null  object \n",
      "dtypes: float64(2), int64(1), object(5)\n",
      "memory usage: 23.6+ MB\n"
     ]
    }
   ],
   "source": [
    "new_quant.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "newu = remove_outlier(dfm, 'UnitPrice')\n",
    "newq = remove_outlier(dfm, 'Quantity')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge the two dataframes together to achieve independence\n",
    "cols = newq.columns.difference(newu.columns)\n",
    "df = pd.merge(newu, newq[cols], how = 'inner', left_index = True, right_index = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>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>17850.0</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>17850.0</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>17850.0</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>17850.0</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>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541904</th>\n",
       "      <td>581587</td>\n",
       "      <td>22613</td>\n",
       "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
       "      <td>12</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541905</th>\n",
       "      <td>581587</td>\n",
       "      <td>22899</td>\n",
       "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
       "      <td>6</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541906</th>\n",
       "      <td>581587</td>\n",
       "      <td>23254</td>\n",
       "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541907</th>\n",
       "      <td>581587</td>\n",
       "      <td>23255</td>\n",
       "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
       "      <td>4</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.15</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541908</th>\n",
       "      <td>581587</td>\n",
       "      <td>22138</td>\n",
       "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
       "      <td>3</td>\n",
       "      <td>12/9/2011 12:50</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12680.0</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>344087 rows × 8 columns</p>\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",
       "541904    581587     22613          PACK OF 20 SPACEBOY NAPKINS        12   \n",
       "541905    581587     22899         CHILDREN'S APRON DOLLY GIRL          6   \n",
       "541906    581587     23254        CHILDRENS CUTLERY DOLLY GIRL          4   \n",
       "541907    581587     23255      CHILDRENS CUTLERY CIRCUS PARADE         4   \n",
       "541908    581587     22138        BAKING SET 9 PIECE RETROSPOT          3   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "0        12/1/2010 8:26       2.55     17850.0  United Kingdom  \n",
       "1        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2        12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4        12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "...                 ...        ...         ...             ...  \n",
       "541904  12/9/2011 12:50       0.85     12680.0          France  \n",
       "541905  12/9/2011 12:50       2.10     12680.0          France  \n",
       "541906  12/9/2011 12:50       4.15     12680.0          France  \n",
       "541907  12/9/2011 12:50       4.15     12680.0          France  \n",
       "541908  12/9/2011 12:50       4.95     12680.0          France  \n",
       "\n",
       "[344087 rows x 8 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = df, x='UnitPrice');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10,6))\n",
    "sns.boxplot(data = df, x='Quantity');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Quantity</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>344087.000000</td>\n",
       "      <td>344087.000000</td>\n",
       "      <td>344087.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.283989</td>\n",
       "      <td>2.201480</td>\n",
       "      <td>15319.478989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.880329</td>\n",
       "      <td>1.548116</td>\n",
       "      <td>1711.348761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-13.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>12347.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>13988.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>15235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>2.950000</td>\n",
       "      <td>16814.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>18287.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Quantity      UnitPrice     CustomerID\n",
       "count  344087.000000  344087.000000  344087.000000\n",
       "mean        7.283989       2.201480   15319.478989\n",
       "std         6.880329       1.548116    1711.348761\n",
       "min       -13.000000       0.001000   12347.000000\n",
       "25%         2.000000       1.250000   13988.000000\n",
       "50%         5.000000       1.650000   15235.000000\n",
       "75%        12.000000       2.950000   16814.000000\n",
       "max        27.000000       7.500000   18287.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>C536383</td>\n",
       "      <td>35004C</td>\n",
       "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
       "      <td>-1</td>\n",
       "      <td>12/1/2010 9:49</td>\n",
       "      <td>4.65</td>\n",
       "      <td>15311.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22556</td>\n",
       "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>C536391</td>\n",
       "      <td>21484</td>\n",
       "      <td>CHICK GREY HOT WATER BOTTLE</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>3.45</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>C536391</td>\n",
       "      <td>22557</td>\n",
       "      <td>PLASTERS IN TIN VINTAGE PAISLEY</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/1/2010 10:24</td>\n",
       "      <td>1.65</td>\n",
       "      <td>17548.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>C536506</td>\n",
       "      <td>22960</td>\n",
       "      <td>JAM MAKING SET WITH JARS</td>\n",
       "      <td>-6</td>\n",
       "      <td>12/1/2010 12:38</td>\n",
       "      <td>4.25</td>\n",
       "      <td>17897.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540176</th>\n",
       "      <td>C581470</td>\n",
       "      <td>23084</td>\n",
       "      <td>RABBIT NIGHT LIGHT</td>\n",
       "      <td>-4</td>\n",
       "      <td>12/8/2011 19:28</td>\n",
       "      <td>2.08</td>\n",
       "      <td>17924.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540448</th>\n",
       "      <td>C581490</td>\n",
       "      <td>22178</td>\n",
       "      <td>VICTORIAN GLASS HANGING T-LIGHT</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/9/2011 9:57</td>\n",
       "      <td>1.95</td>\n",
       "      <td>14397.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540449</th>\n",
       "      <td>C581490</td>\n",
       "      <td>23144</td>\n",
       "      <td>ZINC T-LIGHT HOLDER STARS SMALL</td>\n",
       "      <td>-11</td>\n",
       "      <td>12/9/2011 9:57</td>\n",
       "      <td>0.83</td>\n",
       "      <td>14397.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541716</th>\n",
       "      <td>C581569</td>\n",
       "      <td>84978</td>\n",
       "      <td>HANGING HEART JAR T-LIGHT HOLDER</td>\n",
       "      <td>-1</td>\n",
       "      <td>12/9/2011 11:58</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541717</th>\n",
       "      <td>C581569</td>\n",
       "      <td>20979</td>\n",
       "      <td>36 PENCILS TUBE RED RETROSPOT</td>\n",
       "      <td>-5</td>\n",
       "      <td>12/9/2011 11:58</td>\n",
       "      <td>1.25</td>\n",
       "      <td>17315.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6121 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                       Description  Quantity  \\\n",
       "154      C536383    35004C   SET OF 3 COLOURED  FLYING DUCKS        -1   \n",
       "235      C536391     22556    PLASTERS IN TIN CIRCUS PARADE        -12   \n",
       "239      C536391     21484       CHICK GREY HOT WATER BOTTLE       -12   \n",
       "240      C536391     22557  PLASTERS IN TIN VINTAGE PAISLEY        -12   \n",
       "939      C536506     22960          JAM MAKING SET WITH JARS        -6   \n",
       "...          ...       ...                               ...       ...   \n",
       "540176   C581470     23084                RABBIT NIGHT LIGHT        -4   \n",
       "540448   C581490     22178   VICTORIAN GLASS HANGING T-LIGHT       -12   \n",
       "540449   C581490     23144   ZINC T-LIGHT HOLDER STARS SMALL       -11   \n",
       "541716   C581569     84978  HANGING HEART JAR T-LIGHT HOLDER        -1   \n",
       "541717   C581569     20979     36 PENCILS TUBE RED RETROSPOT        -5   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "154      12/1/2010 9:49       4.65     15311.0  United Kingdom  \n",
       "235     12/1/2010 10:24       1.65     17548.0  United Kingdom  \n",
       "239     12/1/2010 10:24       3.45     17548.0  United Kingdom  \n",
       "240     12/1/2010 10:24       1.65     17548.0  United Kingdom  \n",
       "939     12/1/2010 12:38       4.25     17897.0  United Kingdom  \n",
       "...                 ...        ...         ...             ...  \n",
       "540176  12/8/2011 19:28       2.08     17924.0  United Kingdom  \n",
       "540448   12/9/2011 9:57       1.95     14397.0  United Kingdom  \n",
       "540449   12/9/2011 9:57       0.83     14397.0  United Kingdom  \n",
       "541716  12/9/2011 11:58       1.25     17315.0  United Kingdom  \n",
       "541717  12/9/2011 11:58       1.25     17315.0  United Kingdom  \n",
       "\n",
       "[6121 rows x 8 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check remaining rows with negative quantity \n",
    "df[df['Quantity'] <= 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove matching postive and negative rows\n",
    "pos_neg = df[df.groupby(['Description', 'CustomerID', 'StockCode', 'Country', 'UnitPrice'])['Quantity'].transform('sum').eq(0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
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       "      <td>4</td>\n",
       "      <td>12/5/2010 15:40</td>\n",
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       "      <th>9782</th>\n",
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       "      <td>4</td>\n",
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       "      <td>5.95</td>\n",
       "      <td>15502.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>10828</th>\n",
       "      <td>537238</td>\n",
       "      <td>22470</td>\n",
       "      <td>HEART OF WICKER LARGE</td>\n",
       "      <td>3</td>\n",
       "      <td>12/6/2010 10:03</td>\n",
       "      <td>2.95</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "      <th>536926</th>\n",
       "      <td>C581229</td>\n",
       "      <td>21507</td>\n",
       "      <td>ELEPHANT BIRTHDAY CARD</td>\n",
       "      <td>-12</td>\n",
       "      <td>12/8/2011 10:14</td>\n",
       "      <td>0.42</td>\n",
       "      <td>12558.0</td>\n",
       "      <td>USA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538067</th>\n",
       "      <td>C581390</td>\n",
       "      <td>23374</td>\n",
       "      <td>RED SPOT PAPER GIFT BAG</td>\n",
       "      <td>-10</td>\n",
       "      <td>12/8/2011 13:13</td>\n",
       "      <td>0.82</td>\n",
       "      <td>13081.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538567</th>\n",
       "      <td>C581409</td>\n",
       "      <td>85127</td>\n",
       "      <td>SMALL SQUARE CUT GLASS CANDLESTICK</td>\n",
       "      <td>-5</td>\n",
       "      <td>12/8/2011 14:08</td>\n",
       "      <td>4.95</td>\n",
       "      <td>12476.0</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540170</th>\n",
       "      <td>581469</td>\n",
       "      <td>84029E</td>\n",
       "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
       "      <td>1</td>\n",
       "      <td>12/8/2011 19:28</td>\n",
       "      <td>4.25</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>United Kingdom</td>\n",
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       "    <tr>\n",
       "      <th>540171</th>\n",
       "      <td>581469</td>\n",
       "      <td>21485</td>\n",
       "      <td>RETROSPOT HEART HOT WATER BOTTLE</td>\n",
       "      <td>1</td>\n",
       "      <td>12/8/2011 19:28</td>\n",
       "      <td>4.95</td>\n",
       "      <td>14606.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1892 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       InvoiceNo StockCode                         Description  Quantity  \\\n",
       "9293      537197     22199            FRYING PAN RED RETROSPOT         4   \n",
       "9769      537216     47580               TEA TIME DES TEA COSY         6   \n",
       "9781      537217     22927      GREEN GIANT GARDEN THERMOMETER         4   \n",
       "9782      537217     22926      IVORY GIANT GARDEN THERMOMETER         4   \n",
       "10828     537238     22470               HEART OF WICKER LARGE         3   \n",
       "...          ...       ...                                 ...       ...   \n",
       "536926   C581229     21507             ELEPHANT BIRTHDAY CARD        -12   \n",
       "538067   C581390     23374             RED SPOT PAPER GIFT BAG       -10   \n",
       "538567   C581409     85127  SMALL SQUARE CUT GLASS CANDLESTICK        -5   \n",
       "540170    581469    84029E      RED WOOLLY HOTTIE WHITE HEART.         1   \n",
       "540171    581469     21485    RETROSPOT HEART HOT WATER BOTTLE         1   \n",
       "\n",
       "            InvoiceDate  UnitPrice  CustomerID         Country  \n",
       "9293    12/5/2010 14:02       4.25     12647.0         Germany  \n",
       "9769    12/5/2010 15:40       2.55     15502.0  United Kingdom  \n",
       "9781    12/5/2010 15:40       5.95     15502.0  United Kingdom  \n",
       "9782    12/5/2010 15:40       5.95     15502.0  United Kingdom  \n",
       "10828   12/6/2010 10:03       2.95     16161.0  United Kingdom  \n",
       "...                 ...        ...         ...             ...  \n",
       "536926  12/8/2011 10:14       0.42     12558.0             USA  \n",
       "538067  12/8/2011 13:13       0.82     13081.0  United Kingdom  \n",
       "538567  12/8/2011 14:08       4.95     12476.0         Germany  \n",
       "540170  12/8/2011 19:28       4.25     14606.0  United Kingdom  \n",
       "540171  12/8/2011 19:28       4.95     14606.0  United Kingdom  \n",
       "\n",
       "[1892 rows x 8 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_neg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  9293,   9769,   9781,   9782,  10828,  11761,  11799,  11920,\n",
       "             12426,  12455,\n",
       "            ...\n",
       "            536916, 536920, 536923, 536924, 536925, 536926, 538067, 538567,\n",
       "            540170, 540171],\n",
       "           dtype='int64', length=1892)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos_neg.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
       "      <th>Quantity</th>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>3.39</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>8</td>\n",
       "      <td>12/1/2010 8:26</td>\n",
       "      <td>2.75</td>\n",
       "      <td>17850.0</td>\n",
       "      <td>United Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536365</td>\n",
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       "      <td>6</td>\n",
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       "      <td>3.39</td>\n",
       "      <td>17850.0</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>17850.0</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     17850.0  United Kingdom  \n",
       "1  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "2  12/1/2010 8:26       2.75     17850.0  United Kingdom  \n",
       "3  12/1/2010 8:26       3.39     17850.0  United Kingdom  \n",
       "4  12/1/2010 8:26       3.39     17850.0  United Kingdom  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(pos_neg.index, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove remaining negative quantity rows\n",
    "df = df[df['Quantity'] > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Variables to Correct Dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['CustomerID'] = df['CustomerID'].astype(int)\n",
    "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Revenue Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>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>Revenue</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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  Revenue  \n",
       "0 2010-12-01 08:26:00       2.55       17850  United Kingdom    15.30  \n",
       "1 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  \n",
       "2 2010-12-01 08:26:00       2.75       17850  United Kingdom    22.00  \n",
       "3 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  \n",
       "4 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Revenue'] = df['UnitPrice'] * df['Quantity']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [InvoiceNo, StockCode, Description, Quantity, InvoiceDate, UnitPrice, CustomerID, Country, Revenue]\n",
       "Index: []"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['UnitPrice'] >= 500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answering Number 1 Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To reiterate, we want to check if the average revenue generated from these customers is more than the hypothesized revenue generated from the whole population of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>InvoiceNo</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Description</th>\n",
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       "      <th>InvoiceDate</th>\n",
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       "      <td>2.55</td>\n",
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       "      <td>United Kingdom</td>\n",
       "      <td>15.30</td>\n",
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       "    <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>2010-12-01 08:26:00</td>\n",
       "      <td>3.39</td>\n",
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       "      <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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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>2010-12-01 08:26:00</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  Revenue  \n",
       "0 2010-12-01 08:26:00       2.55       17850  United Kingdom    15.30  \n",
       "1 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  \n",
       "2 2010-12-01 08:26:00       2.75       17850  United Kingdom    22.00  \n",
       "3 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  \n",
       "4 2010-12-01 08:26:00       3.39       17850  United Kingdom    20.34  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Australia</th>\n",
       "      <th>12386</th>\n",
       "      <th>537676</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <th rowspan=\"4\" valign=\"top\">12388</th>\n",
       "      <th>541271</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543357</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>546135</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560033</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Unspecified</th>\n",
       "      <th>12743</th>\n",
       "      <th>561661</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">14265</th>\n",
       "      <th>559929</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564051</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">16320</th>\n",
       "      <th>552695</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557499</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16789 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  StockCode  Description  Quantity  \\\n",
       "Country     CustomerID InvoiceNo                                     \n",
       "Australia   12386      537676             7            7         7   \n",
       "            12388      541271            19           19        19   \n",
       "                       543357             6            6         6   \n",
       "                       546135            10           10        10   \n",
       "                       560033            34           34        34   \n",
       "...                                     ...          ...       ...   \n",
       "Unspecified 12743      561661            49           49        49   \n",
       "            14265      559929            12           12        12   \n",
       "                       564051             8            8         8   \n",
       "            16320      552695            45           45        45   \n",
       "                       557499             6            6         6   \n",
       "\n",
       "                                  InvoiceDate  UnitPrice  Revenue  \n",
       "Country     CustomerID InvoiceNo                                   \n",
       "Australia   12386      537676               7          7        7  \n",
       "            12388      541271              19         19       19  \n",
       "                       543357               6          6        6  \n",
       "                       546135              10         10       10  \n",
       "                       560033              34         34       34  \n",
       "...                                       ...        ...      ...  \n",
       "Unspecified 12743      561661              49         49       49  \n",
       "            14265      559929              12         12       12  \n",
       "                       564051               8          8        8  \n",
       "            16320      552695              45         45       45  \n",
       "                       557499               6          6        6  \n",
       "\n",
       "[16789 rows x 6 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Country', 'CustomerID', 'InvoiceNo']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Selecting customers with the highest number of orders per country \n",
    "k = (df.groupby(['Country'],as_index=False).agg({'CustomerID': pd.Series.mode}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australia</td>\n",
       "      <td>12431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>12360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>12355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>12362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>12769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Canada</td>\n",
       "      <td>17444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Channel Islands</td>\n",
       "      <td>14936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>12359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>12781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>12406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>EIRE</td>\n",
       "      <td>14911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>European Community</td>\n",
       "      <td>15108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Finland</td>\n",
       "      <td>12428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>12681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Germany</td>\n",
       "      <td>12621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Greece</td>\n",
       "      <td>12717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>12347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Israel</td>\n",
       "      <td>12688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Italy</td>\n",
       "      <td>12578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Japan</td>\n",
       "      <td>12754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>12764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lithuania</td>\n",
       "      <td>15332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Malta</td>\n",
       "      <td>17828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>14646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Norway</td>\n",
       "      <td>12433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Poland</td>\n",
       "      <td>12779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>12757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>RSA</td>\n",
       "      <td>12446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>12565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>12744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Spain</td>\n",
       "      <td>12540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>12483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>12451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>USA</td>\n",
       "      <td>12646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>12739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>17841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Unspecified</td>\n",
       "      <td>12743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country  CustomerID\n",
       "0              Australia       12431\n",
       "1                Austria       12360\n",
       "2                Bahrain       12355\n",
       "3                Belgium       12362\n",
       "4                 Brazil       12769\n",
       "5                 Canada       17444\n",
       "6        Channel Islands       14936\n",
       "7                 Cyprus       12359\n",
       "8         Czech Republic       12781\n",
       "9                Denmark       12406\n",
       "10                  EIRE       14911\n",
       "11    European Community       15108\n",
       "12               Finland       12428\n",
       "13                France       12681\n",
       "14               Germany       12621\n",
       "15                Greece       12717\n",
       "16               Iceland       12347\n",
       "17                Israel       12688\n",
       "18                 Italy       12578\n",
       "19                 Japan       12754\n",
       "20               Lebanon       12764\n",
       "21             Lithuania       15332\n",
       "22                 Malta       17828\n",
       "23           Netherlands       14646\n",
       "24                Norway       12433\n",
       "25                Poland       12779\n",
       "26              Portugal       12757\n",
       "27                   RSA       12446\n",
       "28          Saudi Arabia       12565\n",
       "29             Singapore       12744\n",
       "30                 Spain       12540\n",
       "31                Sweden       12483\n",
       "32           Switzerland       12451\n",
       "33                   USA       12646\n",
       "34  United Arab Emirates       12739\n",
       "35        United Kingdom       17841\n",
       "36           Unspecified       12743"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_ord = pd.merge(df, k, how = 'inner', left_on = ['Country', 'CustomerID'], right_on = ['Country', 'CustomerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "or1 = highest_ord.groupby(['Country', 'CustomerID'], as_index=False).agg(Order_count = ('InvoiceNo', np.size), Total_revenue = ('Revenue', np.sum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#r = dp.Report(dp.Table(or1))\n",
    "#r.publish(name = 'Highestorderingcustomers', visibility = 'PUBLIC')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.773880310840772"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.820533683871123"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df.groupby('CustomerID')['Revenue'].mean()).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13.150567284636306"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_ord['Revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.737200275243854"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(highest_ord.groupby('CustomerID')['Revenue'].mean()).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Mean Revenue by Customer Types')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#generate a bar chart for the means\n",
    "bar1 = pd.DataFrame({'Customer': {0: 'Highest-ordering Customers', 1: 'All Customers'}, \n",
    "                   'Mean': {0: highest_ord['Revenue'].mean(), 1: df['Revenue'].mean()}}) \n",
    "\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "clrs = ['rebeccapurple' if (x == min(bar1)) else 'steelblue' for x in bar1 ]\n",
    "sns.barplot(x=\"Customer\", y = 'Mean', data = bar1, palette = clrs)\n",
    "plt.title(\"Mean Revenue by Customer Types\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### Sample Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a basic understanding of the sample, its distribution is displayed below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = df.groupby('CustomerID')['Revenue']\n",
    "n = highest_ord['Revenue']\n",
    "s = df['Revenue'].mean()\n",
    "t = highest_ord['Revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.kdeplot(n, label='Sample distribution', color='rebeccapurple', shade = True)\n",
    "plt.axvline(x=t, color='rebeccapurple', linestyle='--', label='Sample mean: highest-ordering customers')\n",
    "plt.axvline(x=s, color='steelblue', linestyle='--', label='Hypothesized population mean')\n",
    "plt.xlabel(\"Revenue\", labelpad=14)\n",
    "plt.ylabel(\"Frequency\", labelpad=14)\n",
    "plt.title(\"Distribution of Revenue by Highest-ordering Customers\", y=1.015)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The kde shows the sample is somewhat bimodal and also skewed to the right. However to validate whether or not the sample is normally distributed, tests of normality will be carried out."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assumptions for One-sample T-test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we do not have access to all the data and thus cannot obtain the population standard deviation, the T-test will be employed. And the test will be one-sample because the sample mean is being compared to an hypothesized value. The necessary assumptions needed to validate the test goes thus:\n",
    "\n",
    "* Each unit of the sample is an independent observation. (This assumption is simply implied)\n",
    "* The population from which the sample is drawn is normally distributed. (This is checked using both Shapiro Wilk's test and the QQ-Plot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Shapiro Wilk's Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null hypothesis: The sample is normally distributed</p>\n",
    "\n",
    "<p style=\"text-align: center;\">Alternative hypothesis: The sample is not normally distributed</p>\n",
    "\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Conducting Shapiro Wilk's test: a sub-sample is taken off the sample to conduct the test since Shapiro Wilks is sensitive to large sample size \n",
    "alpha = 0.05\n",
    "statistic, pvalue = stats.shapiro(scale(np.array(highest_ord['Revenue'].sample(100))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.05 \n",
      "T-statistic: 0.8669066429138184 \n",
      "P-value: 5.270255343248209e-08\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample is not normally distributed\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha: {alpha} \\nT-statistic: {statistic} \\nP-value: {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample is normally distributed')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample is not normally distributed')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To further validate the claim, a QQ-plot is plotted to see the sample distribution relative to the normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### QQ-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Using QQ-plot to check normality\n",
    "plt.figure(figsize=(10,6))\n",
    "stats.probplot(highest_ord['Revenue'], plot=plt, dist='norm') ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset doesn't correspond to the normal distribution, and thus a parametric test will be preferred. However since the sample size is very large, the CLT assumes the one-sample T-test can stil be carried out. So in conclusion both tests will be employed and their findings discussed. However, the result of the non-parametric test will be accepted (in the case of divergent results)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-tailed One-sample T-test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null Hypothesis: There is no difference between the sample mean and the hypothesized mean</p>\n",
    "$$\n",
    "H_{0}: \\mu = \\mu_{0}\n",
    "$$\n",
    "<p style=\"text-align: center;\">Alternative Hypothesis: The sample mean is greater than the hypothesized mean  </p>\n",
    "$$\n",
    "H_{1}: \\mu > \\mu_{0}\n",
    "$$\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic, pvalue = stats.ttest_1samp(highest_ord['Revenue'], df['Revenue'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.05 \n",
      "T-statistic: 3.452338837620878 \n",
      "P-value: 0.000557093650399353\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha. Hence the sample mean is greater than the hypothesized mean\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha: {alpha} \\nT-statistic: {statistic} \\nP-value: {pvalue}')\n",
    "if pvalue/2 > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample mean is same as the hypothesized mean')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha. Hence the sample mean is greater than the hypothesized mean')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cohen's D Effect Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cohend(d1, d2):\n",
    "    # calculate the size of samples\n",
    "    n1, n2 = len(d1), len(d2)\n",
    "    # calculate the variance of the samples\n",
    "    s1, s2 = np.var(d1, ddof=1), np.var(d2, ddof=1)\n",
    "    # calculate the pooled standard deviation\n",
    "    s = math.sqrt(((n1 - 1) * s1 + (n2 - 1) * s2) / (n1 + n2 - 2))\n",
    "    # calculate the means of the samples\n",
    "    u1, u2 = np.mean(d1), np.mean(d2)\n",
    "    # calculate the effect size\n",
    "    return (u1 - u2) / s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Cohen's D test:** Mathematically, this shows how many sample standard deviations are between the sample mean and hypothesized mean. Practically it is the measure of how small or large the effect of the hypothesis result. Below is a common comparison scale:\n",
    "* Small effect size: $d = 0.20$\n",
    "* Medium effect size: $d = 0.50$\n",
    "* Large effect size: $d = 0.80$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.028447291575541617"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohend(highest_ord['Revenue'], df['Revenue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cohen's D result shows that there's roughly 0.028 difference in means in terms of sample standard deviation units. The negative means the sample mean is greater than the population mean. Which means that, even though we reject the null hypothesis of equal means, the difference between the average revenue generated from all customer as a whole and that generated from customers with the highest order rate per country is trivial.\n",
    "\n",
    "PS. It is noteworthy that with a large sample size, any difference in means, no matter how small, can be statistically significant, hence a likely reason the result is significant. However, p-values are designed to tell if our result is a fluke, not and not actually the magnitude of its effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### One-sample Wilcoxon Signed Rank Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the parametric assumption of normality is failed by the sample, then the non-parametric Wilcoxon test will be employed instead. Here are the assumptions:\n",
    "\n",
    "* Differences between the data value and the hypothesized median are continuous (Implied)\n",
    "* Data follows symmetric distribution (Met)\n",
    "* Observations are mutually independent to each other (Implied)\n",
    "* Measurement scale is at least interval (This is met)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null Hypothesis: There is no difference between the sample median and the hypothesized central value</p>\n",
    "$$\n",
    "H_{0}: \\mu = \\mu_{0}\n",
    "$$\n",
    "<p style=\"text-align: center;\">Alternative Hypothesis: The sample median is greater than the hypothesized central value  </p>\n",
    "$$\n",
    "H_{1}: \\mu > \\mu_{0}\n",
    "$$\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using Wilcoxon sign rank test\n",
    "statistic, pvalue = stats.wilcoxon(np.array((highest_ord['Revenue']))-(df['Revenue'].mean()), alternative = 'greater')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha: 0.05 \n",
      "T-statistic: 58673428.0 \n",
      "P-value: 1.0\n",
      "Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample median is same as the hypothesized central value\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha: {alpha} \\nT-statistic: {statistic} \\nP-value: {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample median is same as the hypothesized central value')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha. Hence the sample median is greater than the hypothesized central value')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align: center;\">The result above shows there is no significant difference between the sample mean and the hypothesized mean. Hence we accept the null hypothesis. So we can say the managers assumption is wrong, so long we are willing to take a 5 percent risk of saying the manager was correct</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answering Number 2 Question"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#displaying the average quantity bought by each customer\n",
    "h =(df.groupby(['Country', 'CustomerID']).agg(mean_quantity = ('Quantity', np.mean)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean_quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Australia</th>\n",
       "      <th>12386</th>\n",
       "      <td>13.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12388</th>\n",
       "      <td>11.763441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12393</th>\n",
       "      <td>10.655172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12415</th>\n",
       "      <td>17.790323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12422</th>\n",
       "      <td>9.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>18287</th>\n",
       "      <td>15.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Unspecified</th>\n",
       "      <th>12363</th>\n",
       "      <td>19.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12743</th>\n",
       "      <td>2.495935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14265</th>\n",
       "      <td>15.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16320</th>\n",
       "      <td>13.450980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4194 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean_quantity\n",
       "Country        CustomerID               \n",
       "Australia      12386           13.428571\n",
       "               12388           11.763441\n",
       "               12393           10.655172\n",
       "               12415           17.790323\n",
       "               12422            9.600000\n",
       "...                                  ...\n",
       "United Kingdom 18287           15.560000\n",
       "Unspecified    12363           19.263158\n",
       "               12743            2.495935\n",
       "               14265           15.650000\n",
       "               16320           13.450980\n",
       "\n",
       "[4194 rows x 1 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean_quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <th>12415</th>\n",
       "      <td>17.790323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <th>12817</th>\n",
       "      <td>17.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <th>12355</th>\n",
       "      <td>8.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <th>12364</th>\n",
       "      <td>16.423077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <th>12769</th>\n",
       "      <td>13.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canada</th>\n",
       "      <th>17444</th>\n",
       "      <td>12.594595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel Islands</th>\n",
       "      <th>14937</th>\n",
       "      <td>12.516129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <th>12455</th>\n",
       "      <td>10.636364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Czech Republic</th>\n",
       "      <th>12781</th>\n",
       "      <td>20.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <th>12435</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EIRE</th>\n",
       "      <th>14016</th>\n",
       "      <td>11.378788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>European Community</th>\n",
       "      <th>15108</th>\n",
       "      <td>10.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finland</th>\n",
       "      <th>12348</th>\n",
       "      <td>23.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <th>12643</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <th>12497</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <th>14439</th>\n",
       "      <td>15.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iceland</th>\n",
       "      <th>12347</th>\n",
       "      <td>11.403614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Israel</th>\n",
       "      <th>12653</th>\n",
       "      <td>16.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <th>14912</th>\n",
       "      <td>13.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <th>12756</th>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lebanon</th>\n",
       "      <th>12764</th>\n",
       "      <td>9.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lithuania</th>\n",
       "      <th>15332</th>\n",
       "      <td>14.827586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Malta</th>\n",
       "      <th>17828</th>\n",
       "      <td>10.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <th>12778</th>\n",
       "      <td>15.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <th>12752</th>\n",
       "      <td>22.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Poland</th>\n",
       "      <th>12779</th>\n",
       "      <td>12.752381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <th>12356</th>\n",
       "      <td>16.606061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RSA</th>\n",
       "      <th>12446</th>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saudi Arabia</th>\n",
       "      <th>12565</th>\n",
       "      <td>8.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <th>12744</th>\n",
       "      <td>13.370861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <th>12547</th>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <th>12676</th>\n",
       "      <td>16.210526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <th>12409</th>\n",
       "      <td>19.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USA</th>\n",
       "      <th>12607</th>\n",
       "      <td>23.521739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <th>17829</th>\n",
       "      <td>12.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>15313</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unspecified</th>\n",
       "      <th>12363</th>\n",
       "      <td>19.263158</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 mean_quantity\n",
       "Country              CustomerID               \n",
       "Australia            12415           17.790323\n",
       "Austria              12817           17.333333\n",
       "Bahrain              12355            8.888889\n",
       "Belgium              12364           16.423077\n",
       "Brazil               12769           13.120000\n",
       "Canada               17444           12.594595\n",
       "Channel Islands      14937           12.516129\n",
       "Cyprus               12455           10.636364\n",
       "Czech Republic       12781           20.111111\n",
       "Denmark              12435           20.000000\n",
       "EIRE                 14016           11.378788\n",
       "European Community   15108           10.063830\n",
       "Finland              12348           23.333333\n",
       "France               12643           24.000000\n",
       "Germany              12497           24.000000\n",
       "Greece               14439           15.900000\n",
       "Iceland              12347           11.403614\n",
       "Israel               12653           16.233333\n",
       "Italy                14912           13.437500\n",
       "Japan                12756           24.000000\n",
       "Lebanon              12764            9.757576\n",
       "Lithuania            15332           14.827586\n",
       "Malta                17828           10.074074\n",
       "Netherlands          12778           15.285714\n",
       "Norway               12752           22.444444\n",
       "Poland               12779           12.752381\n",
       "Portugal             12356           16.606061\n",
       "RSA                  12446            7.244444\n",
       "Saudi Arabia         12565            8.888889\n",
       "Singapore            12744           13.370861\n",
       "Spain                12547           22.000000\n",
       "Sweden               12676           16.210526\n",
       "Switzerland          12409           19.666667\n",
       "USA                  12607           23.521739\n",
       "United Arab Emirates 17829           12.962963\n",
       "United Kingdom       15313           25.000000\n",
       "Unspecified          12363           19.263158"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select customers with the highest average mean count per country\n",
    "h = pd.DataFrame(h.groupby(level=0)['mean_quantity'].nlargest(1))\n",
    "h.index = h.index.droplevel()\n",
    "h.reset_index()\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_quant = pd.merge(df, h, how = 'inner', left_on = ['Country', 'CustomerID'], right_on = ['Country', 'CustomerID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>536971</td>\n",
       "      <td>21232</td>\n",
       "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-03 13:40:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12779</td>\n",
       "      <td>Poland</td>\n",
       "      <td>30.00</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>536971</td>\n",
       "      <td>37446</td>\n",
       "      <td>MINI CAKE STAND WITH HANGING CAKES</td>\n",
       "      <td>8</td>\n",
       "      <td>2010-12-03 13:40:00</td>\n",
       "      <td>1.45</td>\n",
       "      <td>12779</td>\n",
       "      <td>Poland</td>\n",
       "      <td>11.60</td>\n",
       "      <td>12.752381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>536971</td>\n",
       "      <td>37448</td>\n",
       "      <td>CERAMIC CAKE DESIGN SPOTTED MUG</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-03 13:40:00</td>\n",
       "      <td>1.49</td>\n",
       "      <td>12779</td>\n",
       "      <td>Poland</td>\n",
       "      <td>35.76</td>\n",
       "      <td>12.752381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>536971</td>\n",
       "      <td>21700</td>\n",
       "      <td>BIG DOUGHNUT FRIDGE MAGNETS</td>\n",
       "      <td>24</td>\n",
       "      <td>2010-12-03 13:40:00</td>\n",
       "      <td>0.85</td>\n",
       "      <td>12779</td>\n",
       "      <td>Poland</td>\n",
       "      <td>20.40</td>\n",
       "      <td>12.752381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>536971</td>\n",
       "      <td>22961</td>\n",
       "      <td>JAM MAKING SET PRINTED</td>\n",
       "      <td>12</td>\n",
       "      <td>2010-12-03 13:40:00</td>\n",
       "      <td>1.45</td>\n",
       "      <td>12779</td>\n",
       "      <td>Poland</td>\n",
       "      <td>17.40</td>\n",
       "      <td>12.752381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>571035</td>\n",
       "      <td>22381</td>\n",
       "      <td>TOY TIDY PINK POLKADOT</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-10-13 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12446</td>\n",
       "      <td>RSA</td>\n",
       "      <td>10.50</td>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>571035</td>\n",
       "      <td>22380</td>\n",
       "      <td>TOY TIDY SPACEBOY</td>\n",
       "      <td>5</td>\n",
       "      <td>2011-10-13 12:50:00</td>\n",
       "      <td>2.10</td>\n",
       "      <td>12446</td>\n",
       "      <td>RSA</td>\n",
       "      <td>10.50</td>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>571035</td>\n",
       "      <td>22585</td>\n",
       "      <td>PACK OF 6 BIRDY GIFT TAGS</td>\n",
       "      <td>12</td>\n",
       "      <td>2011-10-13 12:50:00</td>\n",
       "      <td>1.25</td>\n",
       "      <td>12446</td>\n",
       "      <td>RSA</td>\n",
       "      <td>15.00</td>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432</th>\n",
       "      <td>571035</td>\n",
       "      <td>85099B</td>\n",
       "      <td>JUMBO BAG RED RETROSPOT</td>\n",
       "      <td>10</td>\n",
       "      <td>2011-10-13 12:50:00</td>\n",
       "      <td>2.08</td>\n",
       "      <td>12446</td>\n",
       "      <td>RSA</td>\n",
       "      <td>20.80</td>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1433</th>\n",
       "      <td>571035</td>\n",
       "      <td>47591D</td>\n",
       "      <td>PINK FAIRY CAKE CHILDRENS APRON</td>\n",
       "      <td>8</td>\n",
       "      <td>2011-10-13 12:50:00</td>\n",
       "      <td>1.95</td>\n",
       "      <td>12446</td>\n",
       "      <td>RSA</td>\n",
       "      <td>15.60</td>\n",
       "      <td>7.244444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1434 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     InvoiceNo StockCode                         Description  Quantity  \\\n",
       "0       536971     21232      STRAWBERRY CERAMIC TRINKET BOX        24   \n",
       "1       536971     37446  MINI CAKE STAND WITH HANGING CAKES         8   \n",
       "2       536971     37448     CERAMIC CAKE DESIGN SPOTTED MUG        24   \n",
       "3       536971     21700         BIG DOUGHNUT FRIDGE MAGNETS        24   \n",
       "4       536971     22961              JAM MAKING SET PRINTED        12   \n",
       "...        ...       ...                                 ...       ...   \n",
       "1429    571035     22381              TOY TIDY PINK POLKADOT         5   \n",
       "1430    571035     22380                 TOY TIDY SPACEBOY           5   \n",
       "1431    571035     22585           PACK OF 6 BIRDY GIFT TAGS        12   \n",
       "1432    571035    85099B             JUMBO BAG RED RETROSPOT        10   \n",
       "1433    571035    47591D     PINK FAIRY CAKE CHILDRENS APRON         8   \n",
       "\n",
       "             InvoiceDate  UnitPrice  CustomerID Country  Revenue  \\\n",
       "0    2010-12-03 13:40:00       1.25       12779  Poland    30.00   \n",
       "1    2010-12-03 13:40:00       1.45       12779  Poland    11.60   \n",
       "2    2010-12-03 13:40:00       1.49       12779  Poland    35.76   \n",
       "3    2010-12-03 13:40:00       0.85       12779  Poland    20.40   \n",
       "4    2010-12-03 13:40:00       1.45       12779  Poland    17.40   \n",
       "...                  ...        ...         ...     ...      ...   \n",
       "1429 2011-10-13 12:50:00       2.10       12446     RSA    10.50   \n",
       "1430 2011-10-13 12:50:00       2.10       12446     RSA    10.50   \n",
       "1431 2011-10-13 12:50:00       1.25       12446     RSA    15.00   \n",
       "1432 2011-10-13 12:50:00       2.08       12446     RSA    20.80   \n",
       "1433 2011-10-13 12:50:00       1.95       12446     RSA    15.60   \n",
       "\n",
       "      mean_quantity  \n",
       "0         12.752381  \n",
       "1         12.752381  \n",
       "2         12.752381  \n",
       "3         12.752381  \n",
       "4         12.752381  \n",
       "...             ...  \n",
       "1429       7.244444  \n",
       "1430       7.244444  \n",
       "1431       7.244444  \n",
       "1432       7.244444  \n",
       "1433       7.244444  \n",
       "\n",
       "[1434 rows x 10 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_quant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "or2 = highest_quant.groupby(['Country', 'CustomerID'], as_index=False).agg(Mean_quantity = ('Quantity', np.mean), Total_revenue = ('Revenue', np.sum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.671645746164586"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_quant['Revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar2 = pd.DataFrame({'Customer': {0: 'Highest-ordering Customers', 1: 'Highest-quantity Customers'}, \n",
    "                   'Mean': {0: highest_ord['Revenue'].mean(), 1: highest_quant['Revenue'].mean()}})\n",
    "\n",
    "fig = plt.figure(figsize = (10, 6))\n",
    "clrs1 = ['rebeccapurple' if (x == min(bar2)) else 'steelblue' for x in bar2 ]\n",
    "sns.barplot(x=\"Customer\", y = 'Mean', data = bar2, palette = clrs1)\n",
    "plt.title(\"Mean Revenue by Customer Types\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sample Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = highest_quant['Revenue']\n",
    "q = highest_ord['Revenue']\n",
    "y = highest_quant['Revenue'].mean()\n",
    "z = highest_ord['Revenue'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.kdeplot(q, color = 'rebeccapurple', shade=True)\n",
    "sns.kdeplot(p, shade=True)\n",
    "plt.legend(['Sample distribution: highest-ordering customers','Sample distribution: highest-quantity customers'], fontsize=14)\n",
    "plt.axvline(x = z, color = 'rebeccapurple', linestyle = '--', label='Sample mean: highest-ordering customers')\n",
    "plt.axvline(x=y, color = 'steelblue', linestyle = '--', label='Sample mean: highest-quantity customers') \n",
    "plt.title('Sample Distribution by Highest Order and Highest Quantity')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Revenues')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hypothesis Testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though one of the samples (the sample used in question 1) has failed the normality test, it is still a good practice to check for the other sample. Thus, based on the most basic characteristics of the data, the two-sample t-test will be the assumed test to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assumptions for Independent Samples T-test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The variances of the 2 samples are equal. (Levene's test will be used to check this assumption)\n",
    "2. The two samples should follow the normal distribution. (Histogram will be used to check whether or not the residual of the samples and see whether the distribution follow the normal distribution. In addition, Shapiro Wilk's Test and QQ-plot will be used to check normality)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Levene's Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null hypothesis: The samples have equal variances</p>\n",
    "\n",
    "<p style=\"text-align: center;\">Alternative hypothesis: The samples have unequal variances</p>\n",
    "\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic, pvalue = stats.levene(highest_ord['Revenue'], highest_quant['Revenue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha : 0.05 \n",
      "T-statistic : 135.55894755618988 \n",
      "P-value : 3.200365644188014e-31\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha. Hence the sample variances are not the same\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha : {alpha} \\nT-statistic : {statistic} \\nP-value : {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample variances are the same')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha. Hence the sample variances are not the same')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Normality Check with Histogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is done by either plotting both samples separately or by plotting the scaled residuals of both samples. The latter is used in the histogram check as well as in the ensuing Shapiro Wilk's test and QQ-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17099, 9) (1434, 10)\n"
     ]
    }
   ],
   "source": [
    "#checking sample sizes\n",
    "print(highest_ord.shape, highest_quant.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#obtaining the residual of both samples\n",
    "diff = scale((np.array(highest_ord['Revenue'].sample(1434)) - np.array(highest_quant['Revenue'])))\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(diff, bins = 20)\n",
    "plt.title('Sample Distribution of Residuals')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Scaled Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution, though looking somewhat normal, is skewed to the left. A proper validation will be done with Shapiro Wilk's Test and QQ-plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Shapiro Wilk's Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null hypothesis: The sample is normally distributed</p>\n",
    "\n",
    "<p style=\"text-align: center;\">Alternative hypothesis: The sample is not normally distributed</p>\n",
    "\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "statistic, pvalue = stats.shapiro(pd.Series(diff).sample(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha : 0.05 \n",
      "Test-statistic : 0.789955735206604 \n",
      "P-value : 1.2312124930691226e-10\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the samples are not normally distributed\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha : {alpha} \\nTest-statistic : {statistic} \\nP-value : {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the samples are normally distributed')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the samples are not normally distributed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### QQ-plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "stats.probplot(diff, plot=plt, dist='norm')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The heavy divergence away from the line indicates non-normality. Hence the parametric test is not the best method. However, like in the first case, both the t-test (due to large sample size) and the corresponding non-parametric alternative will be used. Again preference will be given to the non-parametric test. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two-tailed Independent Samples T-test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the question is to check whether there is any difference in high-inv customers and high-quant customer, the test will be two tailed. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null Hypothesis: There is no difference between the sample medians</p>\n",
    "$$\n",
    "H_{0}: \\mu_{1} = \\mu_{2}\n",
    "$$\n",
    "<p style=\"text-align: center;\">Alternative Hypothesis: The sample medians are different</p>\n",
    "$$\n",
    "H_{1}: \\mu_{} \\neq \\mu_{2}\n",
    "$$\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Independent sample t-test\n",
    "statistic, pvalue = stats.ttest_ind(highest_ord['Revenue'], highest_quant['Revenue'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha : 0.05 \n",
      "T-statistic : -32.85039533300202 \n",
      "P-value : 4.290651939698936e-230\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample means are not the same\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha : {alpha} \\nT-statistic : {statistic} \\nP-value : {pvalue}')\n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample means are the same')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample means are not the same')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.9031367847060263"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohend(highest_ord['Revenue'], highest_quant['Revenue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This gives a more compact view of the results\n",
    "des, res = rpy.ttest(highest_ord['Revenue'], highest_quant['Revenue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variable</th>\n",
       "      <th>N</th>\n",
       "      <th>Mean</th>\n",
       "      <th>SD</th>\n",
       "      <th>SE</th>\n",
       "      <th>95% Conf.</th>\n",
       "      <th>Interval</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>17099.0</td>\n",
       "      <td>13.150567</td>\n",
       "      <td>14.267653</td>\n",
       "      <td>0.109111</td>\n",
       "      <td>12.936699</td>\n",
       "      <td>13.364435</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Revenue</td>\n",
       "      <td>1434.0</td>\n",
       "      <td>26.671646</td>\n",
       "      <td>21.670181</td>\n",
       "      <td>0.572253</td>\n",
       "      <td>25.549103</td>\n",
       "      <td>27.794189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>combined</td>\n",
       "      <td>18533.0</td>\n",
       "      <td>14.196767</td>\n",
       "      <td>15.400581</td>\n",
       "      <td>0.113126</td>\n",
       "      <td>13.975029</td>\n",
       "      <td>14.418506</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Variable        N       Mean         SD        SE  95% Conf.   Interval\n",
       "0   Revenue  17099.0  13.150567  14.267653  0.109111  12.936699  13.364435\n",
       "1   Revenue   1434.0  26.671646  21.670181  0.572253  25.549103  27.794189\n",
       "2  combined  18533.0  14.196767  15.400581  0.113126  13.975029  14.418506"
      ]
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    "des"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Independent t-test</th>\n",
       "      <th>results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Difference (Revenue - Revenue) =</td>\n",
       "      <td>-13.5211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Degrees of freedom =</td>\n",
       "      <td>18531.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>t =</td>\n",
       "      <td>-32.8504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Two side test p value =</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Difference &lt; 0 p value =</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Difference &gt; 0 p value =</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Cohen's d =</td>\n",
       "      <td>-0.9031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hedge's g =</td>\n",
       "      <td>-0.9031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Glass's delta =</td>\n",
       "      <td>-0.9477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>r =</td>\n",
       "      <td>0.2346</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Independent t-test     results\n",
       "0  Difference (Revenue - Revenue) =     -13.5211\n",
       "1              Degrees of freedom =   18531.0000\n",
       "2                               t =     -32.8504\n",
       "3           Two side test p value =       0.0000\n",
       "4          Difference < 0 p value =       0.0000\n",
       "5          Difference > 0 p value =       1.0000\n",
       "6                       Cohen's d =      -0.9031\n",
       "7                       Hedge's g =      -0.9031\n",
       "8                   Glass's delta =      -0.9477\n",
       "9                               r =       0.2346"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Cohen's D result shows that there's roughly -0.90 difference in means in terms of sample standard deviation units. The negative means the sample mean for high-quant is greater than the sample mean for high_inv. And the magnitude of the value attests to the fact the difference in two samples is actually great."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Two-sample Wilcoxon MannWhitney Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1: Hypothesis**\n",
    "<p style=\"text-align: center;\">Null Hypothesis: There is no difference between the sample medians</p>\n",
    "$$\n",
    "H_{0}: \\mu_{1} = \\mu_{2}\n",
    "$$\n",
    "<p style=\"text-align: center;\">Alternative Hypothesis: The sample medians are different</p>\n",
    "$$\n",
    "H_{1}: \\mu_{1} \\neq \\mu_{2}\n",
    "$$\n",
    "\n",
    "**Step 2: Criteria for Decision**\n",
    "$$\\alpha = 0.5\\%$$\n",
    "<p style=\"text-align: center;\">Accept null hypothesis if p-value is greater than $\\alpha$</p>\n",
    "<p style=\"text-align: center;\">Reject null hypothesis if p-value is less than $\\alpha$</p>\n",
    "\n",
    "**Step 3: Calculating Test Statistic and P-value ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Carry out Wilcoxon MannWithney test\n",
    "statistic, pvalue = stats.mannwhitneyu(highest_ord['Revenue'], highest_quant['Revenue'], alternative = 'two-sided')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 4: Making Decision ...**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alpha : 0.05 \n",
      "T-statistic : 5914558.5 \n",
      "P-value : 2.6134877543510213e-233\n",
      "Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample medians are not the same\n"
     ]
    }
   ],
   "source": [
    "print(f'Alpha : {alpha} \\nT-statistic : {statistic} \\nP-value : {pvalue}')    \n",
    "if pvalue > alpha:\n",
    "    print('Decision: Accept Null Hypothesis as p-value is greater than alpha. Hence the sample medians are the same')\n",
    "else: \n",
    "    print('Decision: Reject Null Hypothesis as p-value is less than alpha.  Hence the sample medians are not the same')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a detailed description of the findings in the project, check out this [Medium article](https://medium.com/@babagee500/hypothesis-testing-in-an-e-commerce-business-1025bdab1f1d)"
   ]
  }
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