forage-anz-task-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>status</th>\n",
       "      <th>card_present_flag</th>\n",
       "      <th>bpay_biller_code</th>\n",
       "      <th>account</th>\n",
       "      <th>currency</th>\n",
       "      <th>long_lat</th>\n",
       "      <th>txn_description</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>merchant_code</th>\n",
       "      <th>first_name</th>\n",
       "      <th>...</th>\n",
       "      <th>age</th>\n",
       "      <th>merchant_suburb</th>\n",
       "      <th>merchant_state</th>\n",
       "      <th>extraction</th>\n",
       "      <th>amount</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>country</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>merchant_long_lat</th>\n",
       "      <th>movement</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>POS</td>\n",
       "      <td>81c48296-73be-44a7-befa-d053f48ce7cd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Ashmore</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:01:15.000+0000</td>\n",
       "      <td>16.25</td>\n",
       "      <td>a623070bfead4541a6b0fff8a09e706c</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.38 -27.99</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>authorized</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>830a451c-316e-4a6a-bf25-e37caedca49e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2018-08-01T01:13:45.000+0000</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13270a2a902145da9db4c951e04b51b9</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1222300524</td>\n",
       "      <td>AUD</td>\n",
       "      <td>151.23 -33.94</td>\n",
       "      <td>POS</td>\n",
       "      <td>835c231d-8cdf-4e96-859d-e9d571760cf0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Michael</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>NSW</td>\n",
       "      <td>2018-08-01T01:26:15.000+0000</td>\n",
       "      <td>6.42</td>\n",
       "      <td>feb79e7ecd7048a5a36ec889d1a94270</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2142601169</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1037050564</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.10 -27.66</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>48514682-c78a-4a88-b0da-2d6302e64673</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rhonda</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>Buderim</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:38:45.000+0000</td>\n",
       "      <td>40.90</td>\n",
       "      <td>2698170da3704fd981b15e64a006079e</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-1614226872</td>\n",
       "      <td>153.05 -26.68</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>AUD</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>b4e02c10-0852-4273-b8fd-7b3395e32eb0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diana</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>Mermaid Beach</td>\n",
       "      <td>QLD</td>\n",
       "      <td>2018-08-01T01:51:15.000+0000</td>\n",
       "      <td>3.25</td>\n",
       "      <td>329adf79878c4cf0aeb4188b4691c266</td>\n",
       "      <td>Australia</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.44 -28.06</td>\n",
       "      <td>debit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       status  card_present_flag bpay_biller_code         account currency  \\\n",
       "0  authorized                1.0              NaN  ACC-1598451071      AUD   \n",
       "1  authorized                0.0              NaN  ACC-1598451071      AUD   \n",
       "2  authorized                1.0              NaN  ACC-1222300524      AUD   \n",
       "3  authorized                1.0              NaN  ACC-1037050564      AUD   \n",
       "4  authorized                1.0              NaN  ACC-1598451071      AUD   \n",
       "\n",
       "        long_lat txn_description                           merchant_id  \\\n",
       "0  153.41 -27.95             POS  81c48296-73be-44a7-befa-d053f48ce7cd   \n",
       "1  153.41 -27.95       SALES-POS  830a451c-316e-4a6a-bf25-e37caedca49e   \n",
       "2  151.23 -33.94             POS  835c231d-8cdf-4e96-859d-e9d571760cf0   \n",
       "3  153.10 -27.66       SALES-POS  48514682-c78a-4a88-b0da-2d6302e64673   \n",
       "4  153.41 -27.95       SALES-POS  b4e02c10-0852-4273-b8fd-7b3395e32eb0   \n",
       "\n",
       "   merchant_code first_name  ...  age merchant_suburb merchant_state  \\\n",
       "0            NaN      Diana  ...   26         Ashmore            QLD   \n",
       "1            NaN      Diana  ...   26          Sydney            NSW   \n",
       "2            NaN    Michael  ...   38          Sydney            NSW   \n",
       "3            NaN     Rhonda  ...   40         Buderim            QLD   \n",
       "4            NaN      Diana  ...   26   Mermaid Beach            QLD   \n",
       "\n",
       "                     extraction amount                    transaction_id  \\\n",
       "0  2018-08-01T01:01:15.000+0000  16.25  a623070bfead4541a6b0fff8a09e706c   \n",
       "1  2018-08-01T01:13:45.000+0000  14.19  13270a2a902145da9db4c951e04b51b9   \n",
       "2  2018-08-01T01:26:15.000+0000   6.42  feb79e7ecd7048a5a36ec889d1a94270   \n",
       "3  2018-08-01T01:38:45.000+0000  40.90  2698170da3704fd981b15e64a006079e   \n",
       "4  2018-08-01T01:51:15.000+0000   3.25  329adf79878c4cf0aeb4188b4691c266   \n",
       "\n",
       "     country     customer_id merchant_long_lat movement  \n",
       "0  Australia  CUS-2487424745     153.38 -27.99    debit  \n",
       "1  Australia  CUS-2487424745     151.21 -33.87    debit  \n",
       "2  Australia  CUS-2142601169     151.21 -33.87    debit  \n",
       "3  Australia  CUS-1614226872     153.05 -26.68    debit  \n",
       "4  Australia  CUS-2487424745     153.44 -28.06    debit  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_excel(\"C:/Users/Jason Chong/Documents/Forage ANZ/Data/transaction.xlsx\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset contains 3 months' worth of transactions for 100 customers which includes purchases, recurring transactions and salary transactions.\n",
    "\n",
    "Let's see what all the columns are. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>status</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>card_present_flag</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bpay_biller_code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>account</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>currency</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>long_lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>txn_description</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>merchant_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>merchant_code</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>first_name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>date</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gender</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>merchant_suburb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>merchant_state</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>extraction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>amount</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>transaction_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>country</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>customer_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>merchant_long_lat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>movement</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Columns\n",
       "0              status\n",
       "1   card_present_flag\n",
       "2    bpay_biller_code\n",
       "3             account\n",
       "4            currency\n",
       "5            long_lat\n",
       "6     txn_description\n",
       "7         merchant_id\n",
       "8       merchant_code\n",
       "9          first_name\n",
       "10            balance\n",
       "11               date\n",
       "12             gender\n",
       "13                age\n",
       "14    merchant_suburb\n",
       "15     merchant_state\n",
       "16         extraction\n",
       "17             amount\n",
       "18     transaction_id\n",
       "19            country\n",
       "20        customer_id\n",
       "21  merchant_long_lat\n",
       "22           movement"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Columns\": data.columns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape:  (12043, 23)\n"
     ]
    }
   ],
   "source": [
    "print(\"Data shape: \", data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Basic checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Unique customers, unique transactions and 3 months of data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we explore explore each column individually, let's check for the following:\n",
    "\n",
    "- 100 unique customers (customer_id)\n",
    "- Each row corresponds to a unique transaction ID (transaction_id)\n",
    "- 3 months worth of data (date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique customer ID's:  100\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of unique customer ID's: \", data.customer_id.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do indeed have 100 unique customers in this dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in dataset:  12043\n",
      "Number of unique transaction ID's:  12043\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows in dataset: \", len(data))\n",
    "print(\"Number of unique transaction ID's: \", data.transaction_id.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of unique transaction ID's correspond to the number of rows in the dataset which suggests that each row represent a single, unique transaction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   12043\n",
       "unique                     91\n",
       "top       2018-09-28 00:00:00\n",
       "freq                      174\n",
       "first     2018-08-01 00:00:00\n",
       "last      2018-10-31 00:00:00\n",
       "Name: date, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.date.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do indeed have 3 months worth of data, starting from 1/8/2018 to 31/10/2018. However, there are only 91 unique days which means that there is one missing day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-08-16'], dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start = \"2018-08-01\", end = \"2018-10-31\").difference(data.date)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have one missing date on 16/8/2018."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Non-null count and data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12043 entries, 0 to 12042\n",
      "Data columns (total 23 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   status             12043 non-null  object        \n",
      " 1   card_present_flag  7717 non-null   float64       \n",
      " 2   bpay_biller_code   885 non-null    object        \n",
      " 3   account            12043 non-null  object        \n",
      " 4   currency           12043 non-null  object        \n",
      " 5   long_lat           12043 non-null  object        \n",
      " 6   txn_description    12043 non-null  object        \n",
      " 7   merchant_id        7717 non-null   object        \n",
      " 8   merchant_code      883 non-null    float64       \n",
      " 9   first_name         12043 non-null  object        \n",
      " 10  balance            12043 non-null  float64       \n",
      " 11  date               12043 non-null  datetime64[ns]\n",
      " 12  gender             12043 non-null  object        \n",
      " 13  age                12043 non-null  int64         \n",
      " 14  merchant_suburb    7717 non-null   object        \n",
      " 15  merchant_state     7717 non-null   object        \n",
      " 16  extraction         12043 non-null  object        \n",
      " 17  amount             12043 non-null  float64       \n",
      " 18  transaction_id     12043 non-null  object        \n",
      " 19  country            12043 non-null  object        \n",
      " 20  customer_id        12043 non-null  object        \n",
      " 21  merchant_long_lat  7717 non-null   object        \n",
      " 22  movement           12043 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(4), int64(1), object(17)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The info function provides information about the data types and presence of missing values in each column of the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3 Missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of missing values</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bpay_biller_code</th>\n",
       "      <td>11158</td>\n",
       "      <td>92.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_code</th>\n",
       "      <td>11160</td>\n",
       "      <td>92.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_present_flag</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_suburb</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_state</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_long_lat</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Number of missing values  Percentage\n",
       "bpay_biller_code                      11158        92.7\n",
       "merchant_code                         11160        92.7\n",
       "card_present_flag                      4326        35.9\n",
       "merchant_id                            4326        35.9\n",
       "merchant_suburb                        4326        35.9\n",
       "merchant_state                         4326        35.9\n",
       "merchant_long_lat                      4326        35.9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = data.isnull().sum()\n",
    "missing = missing[missing > 0]\n",
    "missing_percentage = round(missing / len(data), 3) * 100\n",
    "pd.DataFrame({\"Number of missing values\": missing, \"Percentage\": missing_percentage}).sort_values(by = \"Percentage\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both bpay_biller_code and merchant_code columns contain a significant number of missing values. We will deal with those missing values later.\n",
    "\n",
    "Let's examine the summary statistics of the numerical columns in the dataset. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4 Descriptive statistics of numerical variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>card_present_flag</th>\n",
       "      <th>merchant_code</th>\n",
       "      <th>balance</th>\n",
       "      <th>age</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7717.000000</td>\n",
       "      <td>883.0</td>\n",
       "      <td>12043.000000</td>\n",
       "      <td>12043.000000</td>\n",
       "      <td>12043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.802644</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14704.195553</td>\n",
       "      <td>30.582330</td>\n",
       "      <td>187.933588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.398029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31503.722652</td>\n",
       "      <td>10.046343</td>\n",
       "      <td>592.599934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.240000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3158.585000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6432.010000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12465.945000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>53.655000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>267128.520000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>8835.980000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       card_present_flag  merchant_code        balance           age  \\\n",
       "count        7717.000000          883.0   12043.000000  12043.000000   \n",
       "mean            0.802644            0.0   14704.195553     30.582330   \n",
       "std             0.398029            0.0   31503.722652     10.046343   \n",
       "min             0.000000            0.0       0.240000     18.000000   \n",
       "25%             1.000000            0.0    3158.585000     22.000000   \n",
       "50%             1.000000            0.0    6432.010000     28.000000   \n",
       "75%             1.000000            0.0   12465.945000     38.000000   \n",
       "max             1.000000            0.0  267128.520000     78.000000   \n",
       "\n",
       "             amount  \n",
       "count  12043.000000  \n",
       "mean     187.933588  \n",
       "std      592.599934  \n",
       "min        0.100000  \n",
       "25%       16.000000  \n",
       "50%       29.000000  \n",
       "75%       53.655000  \n",
       "max     8835.980000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The describe function is a good way to observe any anomalies in the numerical columns for example, we can check if the age and amount columns contain any negative values, or if a customer has an age of 150 etc. Here, I do not see any anomalies.\n",
    "\n",
    "It also provides an overview of some basic statistics for the amount column. However, we do not know the breakdown of these transaction amounts for example, what sort of transactions are they, who made them etc. We will explore this as we proceed with our further analysis.\n",
    "\n",
    "We can see see a huge spread between the maximum and minimum amount in the balance and amount columns. We will look into this further. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Exploratory data analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Categorical variables\n",
    "\n",
    "Here, we will skip the following columns as they are merely identifiers and won't provide us with much information:\n",
    "- bpay_biller_code\n",
    "- account\n",
    "- merchant_id\n",
    "- merchant_code\n",
    "- first_name\n",
    "- transaction_id\n",
    "- customer_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.1 Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "authorized    7717\n",
       "posted        4326\n",
       "Name: status, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.status.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Status of of the individual transactions: authorised means transaction has already been approved, posted means still in the process. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.2 Card present flag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    6194\n",
       "NaN    4326\n",
       "0.0    1523\n",
       "Name: card_present_flag, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.card_present_flag.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Card-not-present (CNP)](https://squareup.com/au/en/townsquare/what-is-a-card-not-present-transaction) transaction occurs when neither the cardholder nor the credit card is physically present at the time of the transaction. It's most common for orders that happen remotely over the phone, internet or mail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.3 Currency of transaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AUD    12043\n",
       "Name: currency, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.currency.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All transactions are made in AUD. Can potentially drop this column because unnecessary since it does not provide us with any additional information. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.4 Longitude and latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    153.41 -27.95\n",
       "1    153.41 -27.95\n",
       "2    151.23 -33.94\n",
       "3    153.10 -27.66\n",
       "4    153.41 -27.95\n",
       "Name: long_lat, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.long_lat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the coordinates where the transactions were made. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.5 Transaction description (types of transactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SALES-POS     3934\n",
       "POS           3783\n",
       "PAYMENT       2600\n",
       "PAY/SALARY     883\n",
       "INTER BANK     742\n",
       "PHONE BANK     101\n",
       "Name: txn_description, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.txn_description.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transactions by category')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "sns.countplot(data.txn_description)\n",
    "plt.title(\"Number of transactions by category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the descriptions for each transaction. Transactions mostly consist of sales payment. \n",
    "\n",
    "Also, this might explain the missing values in the merchant columns as not all transactions are purchases of goods and services from merchants. We can validate this later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>txn_description</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PAY/SALARY</td>\n",
       "      <td>1898.728029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PHONE BANK</td>\n",
       "      <td>106.099010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INTER BANK</td>\n",
       "      <td>86.699461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>77.613077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>POS</td>\n",
       "      <td>40.407412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>39.909789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  txn_description       amount\n",
       "1      PAY/SALARY  1898.728029\n",
       "3      PHONE BANK   106.099010\n",
       "0      INTER BANK    86.699461\n",
       "2         PAYMENT    77.613077\n",
       "4             POS    40.407412\n",
       "5       SALES-POS    39.909789"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"txn_description\", \"amount\"]].groupby(\"txn_description\", as_index = False).mean().sort_values(by = \"amount\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average transaction volume by category')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rOSd7MzOzknOyNzMzKzknezMzs5JzsjczMys5J3szM7OSc7I3MzMrOSd7MzOzknOyNzMzKzknezMzs5JzsjczMys5J3szM7OSc7I3MzMrOSd7MzOzknOyNzMzKzknezMzs5JzsjczMys5J3szM7OSc7I3MzMruaYle0lTJD0l6d5C2WRJcyVNz699C/1OkjRL0kOS9iqU753LZkk6sVnxmpmZlVUzj+zPA/auUf69iBiTX1cDSNoaOATYJg/zY0n9JPUDfgTsA2wNjM91zczMrEH9mzXiiLhZ0qgGq+8PXBQRLwOPSpoF7Jj7zYqIRwAkXZTr3t/H4ZqZmZVWO87ZHy1pRm7mH5TLhgFzCnW6clm9cjMzM2tQq5P92cAWwBhgPnBGLleNutFNeU2SJkqaJmnawoUL32isZmZmpdDSZB8RT0bEKxHxKvBTXmuq7wJGFKoOB+Z1U15v/OdExNiIGDtkyJC+Dd7MzKxDtTTZSxpa6DwQqFypfyVwiKQ1JG0OjAbuAO4ERkvaXNIA0kV8V7YyZjMzs07XtAv0JE0FxgGDJXUBpwDjJI0hNcU/BnwOICLuk3QJ6cK7pcBREfFKHs/RwO+BfsCUiLivWTGbmZmVUTOvxh9fo/jcbuqfBpxWo/xq4Oo+DM3MzGyV4jvomZmZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTUt2UuaIukpSfcWyr4j6UFJMyRdLmmDXD5K0hJJ0/PrJ4VhdpA0U9IsST+UpGbFbGZmVkbNPLI/D9i7quw6YNuIeAfwN+CkQr/ZETEmvz5fKD8bmAiMzq/qcZqZmVk3mpbsI+JmYFFV2bURsTR33g4M724ckoYC60XEbRERwAXAAc2I18zMrKzaec7+M8A1he7NJd0j6SZJu+ayYUBXoU5XLjMzM7MG9W/HRCV9FVgKXJiL5gMjI+IZSTsAv5G0DVDr/Hx0M96JpCZ/Ro4c2bdBm5mZdaiWH9lLmgB8CPhkbponIl6OiGfy57uA2cCWpCP5YlP/cGBevXFHxDkRMTYixg4ZMqRZs2BmZtZRWprsJe0NnAB8OCJeLJQPkdQvf34z6UK8RyJiPrBY0s75KvzDgCtaGbOZmVmna1ozvqSpwDhgsKQu4BTS1fdrANflf9Ddnq+83w34uqSlwCvA5yOicnHfkaQr+9cineMvnuc3MzOzHjQt2UfE+BrF59apeylwaZ1+04Bt+zA0MzOzVYrvoGdmZlZyTvZmZmYl52RvZmZWck72ZmZmJedkb2ZmVnINJXtJmzdSZmZmZiufRo/sa/0t7td9GYiZmZk1R7f/s5f0VmAbYH1JHyn0Wg9Ys5mBmZmZWd/o6aY6W5HuY78BsF+hfDHwb80KyszMzPpOt8k+Iq4ArpD07oi4rUUxmZmZWR9q9Ha5sySdDIwqDhMRn2lGUGZmZtZ3Gk32VwC3AH8gPajGzMzMOkSjyX7tiDihqZGYmZlZUzT617vfStq3qZGYmZlZUzSa7L9MSvhLJL0gabGkF5oZmJmZmfWNhprxI2JgswMxMzOz5mgo2UvarVZ5RNzct+GYmZlZX2v0Ar2vFD6vCewI3AW8v88jMjMzsz7VaDN+8e55SBoBfLspEZmZmVmfWtFH3HYB2/ZlIGZmZtYcjZ6z/39A5M7VgDHAX5sVlJmZmfWdRs/ZTyt8XgpMjYg/NSEeMzMz62ONnrM/X9IAYMtc9FDzQjIzM7O+1Ggz/jjgfOAxQMAISRP81zszM7OVX6MX6J0B7BkR74uI3YC9gO/1NJCkKZKeknRvoWxDSddJeji/D8rlkvRDSbMkzZC0fWGYCbn+w5Im9G4WzczMVm2NJvvVI2JZ031E/A1YvYHhzgP2rio7Ebg+IkYD1+dugH2A0fk1ETgb0s4BcAqwE+n//adUdhDMzMysZ40m+2mSzpU0Lr9+SrqpTrdyM/+iquL9SacEyO8HFMoviOR2YANJQ0mtCNdFxKKIeBa4juV3IMzMzKyORq/GPxI4CvgS6Zz9zcCPV3CaG0fEfICImC/pTbl8GDCnUK8rl9UrNzMzswY0ejX+y8CZ+dUsqjXpbsqXH4E0kXQKgJEjR/ZdZGZmZh2soWZ8SR+SdI+kRX3wiNsnc/M8+f2pXN4FjCjUGw7M66Z8ORFxTkSMjYixQ4YMWcHwzMzMyqXRc/bfByYAG0XEehExMCLWW8FpXpnHRX6/olB+WL4qf2fg+dzc/3tgT0mD8oV5e+YyMzMza0Cj5+znAPdGRM3m83okTQXGAYMldZGuqj8duETSEcATwEG5+tXAvsAs4EXgcICIWCTpG8Cdud7XI6L6oj8zMzOro9FkPwm4WtJNwMuVwojo9hx+RIyv02uPGnWDdBFgrfFMAaY0GKuZmZkVNJrsTwP+TnqW/YDmhWNmZmZ9rdFkv2FE7NnUSMzMzKwpGr1A7w+SnOzNzMw6UKPJ/ijgd5KW9MFf78zMzKyFGr2pzsB8j/rRpPP2ZmZm1iEafcTtZ4Evk25oMx3YGfgzNa6qNzMzs5VLo834XwbeBTweEbsD7wSeblpUZmZm1mcaTfYvRcRLAJLWiIgHga2aF5aZmZn1lUb/etclaQPgN8B1kp6lzv3pzczMbOXS6AV6B+aPkyX9EVgf+F3TojIzM7M+0+iR/TIRcVMzAjEzM7PmaPScvZmZmXUoJ3szM7OSc7I3MzMrOSd7MzOzknOyNzMzKzknezMzs5JzsjczMys5J3szM7OSc7I3MzMrOSd7MzOzknOyNzMzKzknezMzs5JzsjczMyu5lid7SVtJml54vSDpGEmTJc0tlO9bGOYkSbMkPSRpr1bHbGZm1sl6/YjbNyoiHgLGAEjqB8wFLgcOB74XEd8t1pe0NXAIsA2wKfAHSVtGxCstDdzMzKxDtbsZfw9gdkQ83k2d/YGLIuLliHgUmAXs2JLozMzMSqDdyf4QYGqh+2hJMyRNkTQolw0D5hTqdOWy5UiaKGmapGkLFy5sTsRmZmYdpm3JXtIA4MPAr3LR2cAWpCb++cAZlao1Bo9a44yIcyJibESMHTJkSB9HbGZm1pnaeWS/D3B3RDwJEBFPRsQrEfEq8FNea6rvAkYUhhsOzGtppGZmZh2sncl+PIUmfElDC/0OBO7Nn68EDpG0hqTNgdHAHS2L0szMrMO1/Gp8AElrAx8EPlco/rakMaQm+scq/SLiPkmXAPcDS4GjfCW+mZlZ49qS7CPiRWCjqrJDu6l/GnBas+MyMzMro3ZfjW9mZmZN5mRvZmZWck72ZmZmJedkb2ZmVnJO9mZmZiXnZG9mZlZyTvZmZmYl52RvZmZWck72ZmZmJedkb2ZmVnJO9mZmZiXnZG9mZlZyTvZmZmYl52RvZmZWck72ZmZmJedkb2ZmVnJO9mZmZiXnZG9mZlZyTvZmZmYl52RvZmZWck72ZmZmJedkb2ZmVnJO9mZmZiXXtmQv6TFJMyVNlzQtl20o6TpJD+f3Qblckn4oaZakGZK2b1fcZmZmnabdR/a7R8SYiBibu08Ero+I0cD1uRtgH2B0fk0Ezm55pGZmZh2q3cm+2v7A+fnz+cABhfILIrkd2EDS0HYEaGZm1mnamewDuFbSXZIm5rKNI2I+QH5/Uy4fBswpDNuVy8zMzKwH/ds47V0iYp6kNwHXSXqwm7qqURbLVUo7DRMBRo4c2TdRmpmZdbi2HdlHxLz8/hRwObAj8GSleT6/P5WrdwEjCoMPB+bVGOc5ETE2IsYOGTKkmeGbmZl1jLYke0nrSBpY+QzsCdwLXAlMyNUmAFfkz1cCh+Wr8ncGnq8095uZmVn32tWMvzFwuaRKDL+MiN9JuhO4RNIRwBPAQbn+1cC+wCzgReDw1odsZmbWmdqS7CPiEWC7GuXPAHvUKA/gqBaEZmZmVjor21/vzMzMrI852ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWck52ZuZmZWck72ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlVzLk72kEZL+KOkBSfdJ+nIunyxprqTp+bVvYZiTJM2S9JCkvVods5mZWSfr34ZpLgWOi4i7JQ0E7pJ0Xe73vYj4brGypK2BQ4BtgE2BP0jaMiJeaWnUZmZmHarlR/YRMT8i7s6fFwMPAMO6GWR/4KKIeDkiHgVmATs2P1IzM7NyaOs5e0mjgHcCf8lFR0uaIWmKpEG5bBgwpzBYF93vHJiZmVlB25K9pHWBS4FjIuIF4GxgC2AMMB84o1K1xuBRZ5wTJU2TNG3hwoVNiNrMzKzztCXZS1qdlOgvjIjLACLiyYh4JSJeBX7Ka031XcCIwuDDgXm1xhsR50TE2IgYO2TIkObNgJmZWQdpx9X4As4FHoiIMwvlQwvVDgTuzZ+vBA6RtIakzYHRwB2titfMzKzTteNq/F2AQ4GZkqbnspOB8ZLGkJroHwM+BxAR90m6BLifdCX/Ub4S38zMrHEtT/YRcSu1z8Nf3c0wpwGnNS0oMzOzEvMd9MzMzErOyd7MzKzknOzNzMxKzsnezMys5JzszczMSs7J3szMrOTa8T97MzOzjjVp0iQWLFjAJptswre//e12h9MQJ3szM7NeWLBgAXPnzm13GL3iZnwzM7OSc7I3MzMrOSd7MzOzknOyNzMzKzknezMzs5JzsjczMys5//XOzEqtE/8TbY077VMfa/k0Fz31fHpfML/l0//qL369QsM52ZtZqXXif6LN+pqTvVmH8hGrmTXKyd6sQ3XiEetNu72v5dNc0r8fSCzp6mr59N93800tnZ5ZPU72Zn1gl/+3S8unOeC5AazGasx5bk5Lp/+nL/6pZdMys77hZG/WoWLt4FVeJdaOdoeyUtsg4nXvZqsiJ3uzDvWvXf7V7hA6wqdeebXdIVjJrNlvtde9dwIn+xbxxVSN8XIys5XdOzca2O4Qes3JvkU68WKqdvByMjPre6tkst/hKxe0fJoDn15MP+CJpxe3fPp3feewFRruia+/vY8j6dnSRRsC/Vm66PGWT3/k12a2dHpmZq3SMSccJO0t6SFJsySd2O54euvVAevwyhrr8eqAddodykpt8JqvsvFaSxm8ps+zmpn1lY44spfUD/gR8EGgC7hT0pURcX97I2vcP0bv2e4QOsLx73iu3SGYmZVOpxzZ7wjMiohHIuKfwEXA/m2OyczMrCN0SrIfBswpdHflMjMzM+uBogNuNCHpIGCviPhs7j4U2DEivlhVbyIwMXduBTzU0kB7Nhh4ut1BdAAvp8Z5WTXGy6kxXk6NWxmX1WYRMaRWj444Z086kh9R6B4OzKuuFBHnAOe0KqjekjQtIsa2O46VnZdT47ysGuPl1Bgvp8Z12rLqlGb8O4HRkjaXNAA4BLiyzTGZmZl1hI44so+IpZKOBn4P9AOmRMR9bQ7LzMysI3REsgeIiKuBq9sdxxu00p5iWMl4OTXOy6oxXk6N8XJqXEctq464QM/MzMxWXKecszczM7MV5GTfRyS9Imm6pHsl/UrS2rl8uKQrJD0sabakH+SLDJG0tqQLJc3Mw90qad32zkl9kr4q6T5JM/K87pTLh0j6l6TPVdV/TNLgqrJPS1qYh6+8tpa0mqQf5uUwU9KdkjavEcM4Sc9LukfSA5JOKfR7r6Q7JD2YXxML/baSdGOe3gOSWtYEV2/dyP0OlBSS3pq718yxv71QZ5Kkn0galet+o9BvcF72Z+XuyZLmVi3fDfJyC0n7FYb9bS6/PNeblZdtZbj3NGl5/D2/V+bni4V+Z+V15Ec5hvslLSnE9DFJ50l6tFD25zxscd16UNKxdaZfrHefpF8Xv5Nc56+SplaVnZeX7Rq5e7CkxwrzciiREtkAAAz6SURBVG+h7r9JulvSoDewnPpkvSl0/07SMEkfyr+fv+blW/27rTfvH6sT57GSXpK0fqGs+Dt9UNJ3c/k2kv4maa1C3f+VdEhfLZ/K+lWo9+nK7yN3T9Rr24g7JL230O9GSdMK3WMl3Vg1T8Xf1gdqxPWY0jZsen7fv6r/6767XFb3t5A/L1v+kjbMy/XwXi2wiPCrD17A3wufLwT+HRBwB3B4Lu8HnAt8J3efBJxZGG4rYI12z0ud+Xs3cFslPtJ/TDfNn78A3ALcWDXMY8DgqrJPA2fVGP944NfAarl7ODCoRr1xwG/z53WAh4EdgE2AJ4DtC/HdBfyf3P17YP/CeN7eznWj0H1JXnaTC2V75zKRbh41GxgEjMqf7ynUPRKYXlmmwGTg+DrLbQ5we6Hst8C4Wsu2Fcsjz8+TwCxgQC47C/h0oe4o4N6q4c8DPlZjvMvWLWAj0n+gR3RXL3f/kvwbzd1vA2YCc4F1qqb7BHBkYR17rDpO4FBgRvW63671Jvdbi7QtWp30t+XhuXwNYKsG5325ZZ773ZGnW/zelq1LedoPArvk7tOBU/PnA4Br+3L5FMtrrBcfIm0XBufu7fN3uknuvjF375O7x5K3azT4+6Cw3SNt0x+v6l/ruxtFN7+FyvIH1if9O+3I3i4vH9k3xy3AW4D3Ay9FxM8AIuIV4FjgM3kvdCjpR0Xu/1BEvNyGeBsxFHi6El9EPB0RlXsdjAeOA4ZLWtE7Gw4F5kfEq3n8XRHxbHcDRMQ/SD/cLYCjgPMi4u5KfMAkoPLQpKGk+zVUhm3XI+4q6wZKrTi7AEeQ/k4KQET8DpgPHAZ8j7RRqCyLJcADkir/7z2YtPFoxF+B5yV98I3ORB9aCFwPTOjLkUbEM6QN59Du6knqT9ppLK5rnwB+DlwLfLhqkO8Dx+bhao3v46R1bs+8DvaVN7rejCMlsoGkC7OfycO8HBHFm491N+/LkbQFsC7wH6TtwHIiYglph7Sybfg6cJCkMaTEf1RP02nAsuXTgxOAr1S+m7y9OL8qhu+Q5qcvrEdh3ar33WU9/RbWBa4BfhkRZ/c2ECf7PpY3AvuQ9o63ISWjZSLiBdKe41uAKcAJkm6TdKqk0a2OtxeuBUbkJrgfS3ofgKQRpL3iO0hJ5+AGxnVwVVPYWnnY/XL3GZLe2dNIJG0E7AzcR41lDUzL5ZA2fjdIuiY3O27QQJx9qmrdgHRU87uI+BuwSNL2herHAKcBQyLi51Wjugg4RNJw4BWWv8HUsYVl+8eqfqfSdxuyvnI6cJzSA68a9Z3CPF5Y3VPSSGBN0hF2LQdLmk7a2d4QuKrYD7gYmMryCewJ4FbS0Xu1zUhHY3tGxIJezEu3+mi92ScPs4h0j5LHJU2V9ElJxTzQ3bzXMj7XvQXYStKbasQ/CBgN3AwQES8Cx+fuiyLi4QamU1eN5bNWcftC2rmo6Gk7AakF82VJu9eY3K5V264t6oT1R6XTOjfx+t9bd98ddP9bOBO4NSK+V2ea3XKy7ztr5RVrGmmDcC6pOa3W3x0ERERMB95M2pPckPQ0v7e1KN5eiYi/k5rLJ5L2QC/O55MO4bUjy4tobANxcUSMKbyWREQXqcnrJOBV4HpJe9QZfldJ95B2QE6PdM+Fess6cvw/IzVR/op0lHO78rnXFqi1bkBaVhflz69bdrnV5Aag1h7870hPgBxP2jBX+15h2b5ugxURtwBI2nXFZ6dvRcSjpKbgT/RisK8U5vGThfKDJd0HPAL8ICJeqjP8xRExhnT6ZybwFQBJ7wIWRsTjpKOs7bX8efdv5vrV28+FpO/3472Yj+705XqzC2knhUi3Hd+DtMyPJx10NDrv1Q4hJexXgcuAgwr9dpU0A1hAav5etgMUEVcBzwE/7mH83am3fJYUty/A13oYT61tR72d4luqtl2z64xz94jYFng7cJZeuxar7ncHPf4WbgD2r7VD1YiO+Z99B1iSV6xl8kbno1Vl65Fu/TsbliXRy4DLJL0K7As80JKIeymfhrgRuFHSTFJz0zBgY0mVDe6mkkavyN56PkVwDXCNpCeBA/LyqlyE99n8fktEfKhq8PtI59eKd1bcAVj2GOS8IZwCTMl73duy/F5+M9RaNzYinebZVlKQrucISZMin6Qj7fS8Wj2yiPinpLtIp062AfarrtOD04CvAkt7OVwzfZN0zcbNb3A8F0fE0ZLeDfyvpGu6O8qOiJB0FfBF0lHVeOCtyhfekZphPwr8T2GYWTnJVCf1F0lHmLdKeioilmtx6KU+WW8kvRmYE+mJoZV5mAnMlPRz4FHSee0e570qlneQjtivkwQwgLST9aNc5ZaI+JCkLUnL5PJ8gFNRc/3uheWWTwPuJ20XbiiUbU9hOwEQETcoXQi78xuIj4iYnbdlW0uaTZ3vrmqwer+Fi0g7bFdL2j0iFvcmFh/ZN9f1wNqSDgPITTNnkM4tvyhpl8qes9IV+lsDj7ct2m4oXc1ePM0whrSzuE5EDIuIURExCvgvlj8X1cj4t5e0af68GvAO0oUtlxf2oqd1M4ofAZ/O5wErG8VvAd/O3XtLWj1/3oR0AdfcOuNqhY8BF0TEZnnZjSBtdN/bw3AVZwAn5HPTvRIR15Iu+Nuut8M2S0Q8SNrgVu/Erej4biOde/5yA9XfC8zO691BwDsK6/P+1G6tOo10VFw93YWkC+W+KWmvFQy/Oyuy3uxDag1C0rqSxhX6jSE16fdm3ivGk64LGJVfmwLDJG1WrJSbrP+LdL683b4NfCtvH8jbi09Tu4XhNNJ1PyssH4VvTtquN/TddfdbiIjvk/LK5TlnNMzJvonynvaBpItRHgb+BrwEnJyrbAHclI+S7yE1R13ajlgbsC5wvtJfdWaQdkxmA5dX1buU128gZkjqyq8zc1n1Ofv3AG8CrspH3DNIR51n0aCImA98CvippAeBP5Nuq1w5F7sncK+kv5KuzP9KX55XXQHjqb3sGmrKjoj7IuL8Or2PrVq+o2rUOY30j4eVSW9i+k7VPNba8H0LOFzSwBr9KuvgDOCdwDeA3YC5EVHcCbyZdFT2ugv98qmju2sFlptiP0xqQdqpwflp1IqsN3uTkz2pyXqSpIdy68R/kpJdI/P+34Xf8m2knfrqWC6n9s7+T4DdVOPvtK0UEVeSWvf+nLcTPwU+lbcf1XWvJp2aKao+Z1/z74ikc/bTgT8CJ0bEk/Tuu6v7W4iIE0j/rPm5Xn+9Rbd8Bz0zs5LK16X8KTro6WzWHE72ZmZmJedmfDMzs5JzsjczMys5J3szM7OSc7I3MzMrOSd7MzOzknOyN+tQSo+u/UITx/+6R4M2Yfyfr9xwqps641R41G4jw5jZ8ny7XLPOtQHp8cJv5P7ibSGpf0T8pOeajAP+TrpJEg0OY2ZV/D97sw4l6SLSLU0fIj2m9XHSA3I2IT1tazfS3dM+DKxNumPj5RFR9xagkg4nPYxoPumOjy/ne80PId0FbWSuekxE/Enp6Yc/yGUB7BYRi/P9vg8l3fv8mog4UdKNpKS9C+kZBgNJzx7/bu43HdiRdE/2zwBPAbeTnuy3kHT/+j0Kw4zJMa1NupvjZyLi2TyuvwC7k3aIjqg8AMhsVeVmfLPOdSIwOz83YDTp6WJHkW4BekrhdsBjSI8ufTvpNrEjao0s3xb1P0nJ+IOkWyJX/ID0NL138fqHoxwPHJUfSLIrsETSPqRHee4UEduRn0+QbRAR74uIM2qEsE5EvIfUWjElIh4jJfPKU/yqE/YFpOcDvIP05LpTCv36R8SOpEe+noLZKs7N+Gbl8UXgXuD2iJhaKL8+Ip4HkHQ/6bnrc2oMvxNwY36YC5IuBrbM/T5Auk96pe56+Z7zfwLOVHqm/GUR0SXpA8DPIj23nEjPUK+o9Ujeiqm5/s2S1pO0Qb2KktYn7TjclIvOJz2+uOKy/H4XMKqbaZqtEpzszcpjGKnZfGNJq0V6xjjAy4U6r9D9777eeb3VgHdHxJKq8tMl/S/p0cy350Rf6/ngFf/oxbTfyDnGyjz3NL9mqwQ345t1rsWk895I6g/8jPQErQeAf1+B8f0FGCdpo/w44IMK/a4Fjq50FB4lvEVEzIyIb5Ge2vjWXPczktbOdTZscPoH5/rvBZ7PrRHL5rEo93tW0q656FDSdQpmVoP3eM06VEQ8I+lP+bHA25DObd+SH615Zz7i7s345kuaDNxGukDvbqBf7v0l4Ef5kbD9SY8//TxwjKTdSUfQ95Muxns57wxMk/RP4Gpee6xzd56V9Gdeu0AP4Crg15L2J52mKJoA/CTvVDwCHN6b+TVblfhqfDNru3wF/fERMa3dsZiVkZvxzczMSs5H9marIEl/AdaoKj40Ima2Ix4zay4nezMzs5JzM76ZmVnJOdmbmZmVnJO9mZlZyTnZm5mZlZyTvZmZWcn9fzXCQ17GYlLTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.barplot(x = \"txn_description\", y = \"amount\", data = data)\n",
    "plt.title(\"Average transaction volume by category\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pay/salary has the largest average transaction volume. Seems logical. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.6 Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    6285\n",
       "F    5758\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.gender.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transactions by gender')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAAFNCAYAAAC9l4yfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbCUlEQVR4nO3de5RedX3v8fdHwkUEBSQgEjS2UhW8oE0BL6sHxQJS29BWWqiXiGh6oT12rbbW9pxTFLTqaStql9JDK3KpFiktJbqomoOix7YKoVIR0JWIStJwCYaboLTg9/yxfwMP42QySX5PZgber7WeNXv/9m/v/X2e2fOZvfdvnmdSVUiSts1jZrsASXokMEwlqQPDVJI6MEwlqQPDVJI6MEwlqQPD9BEqyTlJ3jFL+06SjyS5PckVs1HD9pDk1Uk+s533+e0kL9+e+9xWSV6f5IuzXce4GabbSfshuCXJ40ba3pjk8lksa1xeAvwMsKiqDp28cD7+cCVZnKSSLJhoq6qPVtVRs1mX5g7DdPtaALx5tovYUkl22MJVngp8u6ru2Y771CPQfDoODNPt60+B30uyx+QFU535JLk8yRvb9OuT/HOSM5LckeSGJC9q7WuT3Jpk2aTN7p1kZZK7k3w+yVNHtv3Mtmxjkm8k+eWRZeckOTPJpUnuAV46Rb1PTrKirb8myZta+8nAXwMvTPK9JG+ftN6zgL8cWX7HpvaZ5GeTfCXJXe05vm2K12tZkhuT3Jbkf4wsPzTJqrbuLUneO7Ls75LcnOTOJF9IcvDIsscm+fMk32nLv5jkscAXWpc7Wt0vnHyG3b4fV7b1rkzyoknfy9Pb9/DuJJ9JsndbtkuSv0ny3fa9vTLJvpNf8xE/leS6dhvlI0l2adv5WpKfG9nnju11OWSqjSR5S5KbkqxvV0mV5Olt2c5J/qy9trck+cv2OpDkiCTrkvxuO+5uSnLSyHaf2I6NuzLc5vnxSfvdpmNvzqoqH9vhAXwbeDnwD8A7Wtsbgcvb9GKggAUj61wOvLFNvx64HzgJ2AF4B3Aj8EFgZ+Ao4G5gt9b/nDb/0235+4EvtmWPA9a2bS0AXgDcBhw8su6dwIsZfuHuMsXz+TzwIWAX4BBgA3DkSK1fnOa1+JHlU+0TOAJ4Tpt/LnALcNyk1+uvgMcCzwPuA57Vlv8r8No2vRtw+Mi+3gDs3l6X9wFXjyz7YHvd92+v84tav6m+Pw8+D2Av4Hbgte01PbHNP3Hke/lN4CdavZcD727Lfg34BLBr2+dPAo+f5jj6GnBA2+c/89Dx9Bbg4yN9lwLXbGI7xwA3Awe3/Z7fnt/T2/L3ASvaPnZv9b2rLTuC4Vg8DdgROBa4F9izLb8AuJDhOHs28B90PPbm6mPWC3i0PHgoTJ/dDpaFbHmYrh5Z9pzWf9+Rtu8Ch7Tpc4ALRpbtBjzQfgh/Bfh/k+r7P8CpI+ueN81zOaBta/eRtncB54zUujVhusl9tj7vA86Y9HotGll+BXBCm/4C8HZg781sc4+2nSe0H97vA8+bot9U358HnwdDiF4xaZ1/BV4/8r38nyPLfhP4VJt+A/AvwHNneBz9+sj8scA32/STGX6BPr7NXwS8ZRPbOZsWjm3+6e35PR0IcA/w4yPLXwh8q00f0V6n0dfiVuBwhl8G/wU8c2TZn4y8Ttt07M3lh5f521lVfQ34JPDWrVj9lpHp77ftTW7bbWR+7ch+vwdsZPiBeypwWLukvKNdar8aeNJU607hycDGqrp7pO07DGdz2+Jh+0xyWJLPJdmQ5E7g14G9J61z88j0vTz0/E9mOAv8ertsfmXb5g5J3p3km0nuYggn2nb3Zjgj/uZW1P5khtdg1OTXZFO1ng98GrigXXL/7yQ7TrOv0dfpO23fVNV6hjPVX8pwK+kVwEenqXd0O6PTCxnOVq8aOT4+1donfLeq7p/i+SxkOOOcXOOEbT325izDdHacCryJh/+gTQzW7DrSNnqAbY0DJiaS7MZwybae4WD9fFXtMfLYrap+Y2Td6T5ObD2wV5LdR9qewnA5NxOb2vbk9o8xXGoeUFVPYLjXmhntoGp1VZ0I7AO8B7gow19S/CrD5e/LGc5GF7dVwnC5+QMm3ePbTM0T1jMExagZvSZV9V9V9faqOojhtsIrgddNs8oBI9NPafuecC7wGuB44F+ralP7vwlYtIlt3sbwi/ngkePjCVW1G5u3geEWwOQaJ2zrsTdnGaazoKrWAB8H/vtI2waGH7zXtLOnNzD1D/WWODbJS5LsBJwOfLmq1jKcGf9Ekte2QYodk/xUhsGhmdS/luGy9F1t8OS5DGeCmzoLmuwWYFGrazq7M5wB/yDJoQxBOCNJXpNkYVX9ELijNT/Qtnkfwy2RXRkuQSee1w8ZLn/fm2GAbYc20LQzQ0j8EPixTezyUobX9FeTLEjyK8BBDK/15mp9aZLnZBi5vovhMvmBaVY5JcmiJHsBf8RwLE34R4b7kG8GzptmGxcCJyV5VpJdgT+eWNBeh78CzkiyT6tx/yRHb+65VNUDDOMCb0uya5KDgNGB0W069uYyw3T2nMZwM37Um4DfZ/hBP5ghsLbFxxjOgjcyDGq8GqBdnh8FnMBwVnMzw9nbzluw7RMZzurWAxcz3PNaOcN1PwtcC9yc5LZp+v0mcFqSuxl+2C/cgvqOAa5N8j2GwbcTquoHDAHzHYZfXNcBX5q03u8B1wBXMrxu7wEeU1X3Au8E/rldnh4+ulJVfZfhjPJ3Gb5/bwFeWVXTPb8JT2K4v3kXcD3D4N7fTNP/Y8BngBva48E3Z1TV94G/B57GEGpTqqp/Aj4AfA5Yw3B/F4ZfNAB/0Nq/1G6H/F/gGTN4LgC/xXDJfzPDPdCPjOy3x7E3J6Xd9JX0CJHkj4GfqKrXbME6z2L4K4GdJ90L1Qx5Zio9grRL/5OBs2bQ9xeS7JRkT4azw08YpFvPMJUeITK8cWIt8E9V9YXN9Wf4+9YNDH+98ADwG9N313S8zJekDjwzlaQODFNJ6mDB5rvMP3vvvXctXrx4tsuQ9Ahz1VVX3VZVC6da9ogM08WLF7Nq1arZLkPSI0ySyW8ZfpCX+ZLUgWEqSR0YppLUgWEqSR0YppLUgWEqSR0YppLUgWEqSR0YppLUgWEqSR0YppLUwSPyvfnS9nDjac+Z7RK0lZ7yx9d036ZnppLUgWEqSR0YppLUgWEqSR0YppLUgWEqSR0YppLUwVjDNMkeSS5K8vUk1yd5YZK9kqxMsrp93bP1TZIPJFmT5KtJXjCynWWt/+oky8ZZsyRtjXGfmb4f+FRVPRN4HnA98Fbgsqo6ELiszQO8AjiwPZYDZwIk2Qs4FTgMOBQ4dSKAJWmuGFuYJnk88NPAhwGq6j+r6g5gKXBu63YucFybXgqcV4MvAXsk2Q84GlhZVRur6nZgJXDMuOqWpK0xzjPTHwM2AB9J8pUkf53kccC+VXUTQPu6T+u/P7B2ZP11rW1T7ZI0Z4wzTBcALwDOrKrnA/fw0CX9VDJFW03T/vCVk+VJViVZtWHDhq2pV5K22jjDdB2wrqq+3OYvYgjXW9rlO+3rrSP9DxhZfxGwfpr2h6mqs6pqSVUtWbhwYdcnIkmbM7YwraqbgbVJntGajgSuA1YAEyPyy4BL2vQK4HVtVP9w4M52G+DTwFFJ9mwDT0e1NkmaM8b9EXy/DXw0yU7ADcBJDAF+YZKTgRuB41vfS4FjgTXAva0vVbUxyenAla3faVW1ccx1S9IWGWuYVtXVwJIpFh05Rd8CTtnEds4Gzu5bnST14zugJKkDw1SSOjBMJakDw1SSOjBMJakD/zvpiJ/8/fNmuwRtg6v+9HWzXYIexTwzlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6mCsYZrk20muSXJ1klWtba8kK5Osbl/3bO1J8oEka5J8NckLRrazrPVfnWTZOGuWpK2xPc5MX1pVh1TVkjb/VuCyqjoQuKzNA7wCOLA9lgNnwhC+wKnAYcChwKkTASxJc8VsXOYvBc5t0+cCx420n1eDLwF7JNkPOBpYWVUbq+p2YCVwzPYuWpKmM+4wLeAzSa5Ksry17VtVNwG0r/u09v2BtSPrrmttm2qXpDljwZi3/+KqWp9kH2Blkq9P0zdTtNU07Q9feQjr5QBPecpTtqZWSdpqYz0zrar17eutwMUM9zxvaZfvtK+3tu7rgANGVl8ErJ+mffK+zqqqJVW1ZOHChb2fiiRNa2xhmuRxSXafmAaOAr4GrAAmRuSXAZe06RXA69qo/uHAne02wKeBo5Ls2QaejmptkjRnjPMyf1/g4iQT+/lYVX0qyZXAhUlOBm4Ejm/9LwWOBdYA9wInAVTVxiSnA1e2fqdV1cYx1i1JW2xsYVpVNwDPm6L9u8CRU7QXcMomtnU2cHbvGiWpF98BJUkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1IFhKkkdGKaS1MHYwzTJDkm+kuSTbf5pSb6cZHWSjyfZqbXv3ObXtOWLR7bxh639G0mOHnfNkrSltseZ6ZuB60fm3wOcUVUHArcDJ7f2k4Hbq+rpwBmtH0kOAk4ADgaOAT6UZIftULckzdhYwzTJIuBngb9u8wFeBlzUupwLHNeml7Z52vIjW/+lwAVVdV9VfQtYAxw6zrolaUuN+8z0fcBbgB+2+ScCd1TV/W1+HbB/m94fWAvQlt/Z+j/YPsU6kjQnjC1Mk7wSuLWqrhptnqJrbWbZdOuM7m95klVJVm3YsGGL65WkbTHOM9MXAz+f5NvABQyX9+8D9kiyoPVZBKxv0+uAAwDa8icAG0fbp1jnQVV1VlUtqaolCxcu7P9sJGkaYwvTqvrDqlpUVYsZBpA+W1WvBj4HvKp1WwZc0qZXtHna8s9WVbX2E9po/9OAA4ErxlW3JG2NBZvv0t0fABckeQfwFeDDrf3DwPlJ1jCckZ4AUFXXJrkQuA64Hzilqh7Y/mVL0qZtlzCtqsuBy9v0DUwxGl9VPwCO38T67wTeOb4KJWnb+A4oSerAMJWkDgxTSerAMJWkDgxTSerAMJWkDgxTSerAMJWkDgxTSerAMJWkDgxTSerAMJWkDmYUpkkum0mbJD1aTfupUUl2AXYF9k6yJw996v3jgSePuTZJmjc29xF8vwb8DkNwXsVDYXoX8MEx1iVJ88q0YVpV7wfen+S3q+ovtlNNkjTvzOjDoavqL5K8CFg8uk5VnTemuiRpXplRmCY5H/hx4Gpg4l+GFGCYShIz/7clS4CD2j+4kyRNMtO/M/0a8KRxFiJJ89lMz0z3Bq5LcgVw30RjVf38WKqSpHlmpmH6tnEWIUnz3UxH8z8/7kIkaT6b6Wj+3Qyj9wA7ATsC91TV48dVmCTNJzM9M919dD7JccChY6lIkuahrfrUqKr6R+BlnWuRpHlrppf5vzgy+xiGvzv1b04lqZnpaP7PjUzfD3wbWNq9Gkmap2Z6z/SkcRciSfPZTD8celGSi5PcmuSWJH+fZNG4i5Ok+WKmA1AfAVYwfK7p/sAnWpskiZmH6cKq+khV3d8e5wALx1iXJM0rMw3T25K8JskO7fEa4LvTrZBklyRXJPn3JNcmeXtrf1qSLydZneTjSXZq7Tu3+TVt+eKRbf1ha/9GkqO37qlK0vjMNEzfAPwycDNwE/AqYHODUvcBL6uq5wGHAMckORx4D3BGVR0I3A6c3PqfDNxeVU8Hzmj9SHIQcAJwMHAM8KEkO8ywbknaLmYapqcDy6pqYVXtwxCub5tuhRp8r83u2B7F8Mf+F7X2c4Hj2vTSNk9bfmSStPYLquq+qvoWsAbffSVpjplpmD63qm6fmKmqjcDzN7dSuyVwNXArsBL4JnBHVd3fuqxjGNCifV3btn8/cCfwxNH2KdaRpDlhpmH6mPavngFIshcz+BvVqnqgqg4BFjGcTT5rqm4Tm93Esk21P0yS5UlWJVm1YcOGzZUmSV3N9B1Qfw78S5KLGILsl4F3znQnVXVHksuBw4E9kixoZ5+LgPWt2zrgAGBdkgXAE4CNI+0TRtcZ3cdZwFkAS5Ys8a2ukrarGZ2Ztv9C+kvALcAG4Ber6vzp1kmyMMkebfqxwMuB64HPMQxgASwDLmnTK9o8bfln2/+cWgGc0Eb7nwYcCFwxs6cnSdvHTM9MqarrgOu2YNv7Aee2kffHABdW1SeTXAdckOQdwFeAD7f+HwbOT7KG4Yz0hLbfa5Nc2PZ9P3BKVT2AJM0hMw7TLVVVX2WKQaqquoEpRuOr6gfA8ZvY1jvZgtsKkrS9bdXnmUqSHs4wlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6mBsYZrkgCSfS3J9kmuTvLm175VkZZLV7euerT1JPpBkTZKvJnnByLaWtf6rkywbV82StLXGeWZ6P/C7VfUs4HDglCQHAW8FLquqA4HL2jzAK4AD22M5cCYM4QucChwGHAqcOhHAkjRXjC1Mq+qmqvq3Nn03cD2wP7AUOLd1Oxc4rk0vBc6rwZeAPZLsBxwNrKyqjVV1O7ASOGZcdUvS1tgu90yTLAaeD3wZ2LeqboIhcIF9Wrf9gbUjq61rbZtqn7yP5UlWJVm1YcOG3k9BkqY19jBNshvw98DvVNVd03Wdoq2maX94Q9VZVbWkqpYsXLhw64qVpK001jBNsiNDkH60qv6hNd/SLt9pX29t7euAA0ZWXwSsn6ZdkuaMcY7mB/gwcH1VvXdk0QpgYkR+GXDJSPvr2qj+4cCd7TbAp4GjkuzZBp6Oam2SNGcsGOO2Xwy8FrgmydWt7Y+AdwMXJjkZuBE4vi27FDgWWAPcC5wEUFUbk5wOXNn6nVZVG8dYtyRtsbGFaVV9kanvdwIcOUX/Ak7ZxLbOBs7uV50k9eU7oCSpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjowTCWpA8NUkjoYW5gmOTvJrUm+NtK2V5KVSVa3r3u29iT5QJI1Sb6a5AUj6yxr/VcnWTaueiVpW4zzzPQc4JhJbW8FLquqA4HL2jzAK4AD22M5cCYM4QucChwGHAqcOhHAkjSXjC1Mq+oLwMZJzUuBc9v0ucBxI+3n1eBLwB5J9gOOBlZW1caquh1YyY8GtCTNuu19z3TfqroJoH3dp7XvD6wd6beutW2qXZLmlLkyAJUp2mqa9h/dQLI8yaokqzZs2NC1OEnanO0dpre0y3fa11tb+zrggJF+i4D107T/iKo6q6qWVNWShQsXdi9ckqazvcN0BTAxIr8MuGSk/XVtVP9w4M52G+DTwFFJ9mwDT0e1NkmaUxaMa8NJ/hY4Atg7yTqGUfl3AxcmORm4ETi+db8UOBZYA9wLnARQVRuTnA5c2fqdVlWTB7UkadaNLUyr6sRNLDpyir4FnLKJ7ZwNnN2xNEnqbq4MQEnSvGaYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIHhqkkdWCYSlIH8yZMkxyT5BtJ1iR562zXI0mj5kWYJtkB+CDwCuAg4MQkB81uVZL0kHkRpsChwJqquqGq/hO4AFg6yzVJ0oPmS5juD6wdmV/X2iRpTlgw2wXMUKZoq4d1SJYDy9vs95J8Y+xVzT97A7fNdhHjkj9bNtslPNI8co+XU6eKlBl56qYWzJcwXQccMDK/CFg/2qGqzgLO2p5FzTdJVlXVktmuQ/ODx8uWmS+X+VcCByZ5WpKdgBOAFbNckyQ9aF6cmVbV/Ul+C/g0sANwdlVdO8tlSdKD5kWYAlTVpcCls13HPOdtEG0Jj5ctkKrafC9J0rTmyz1TSZrTDNNHgSQPJLl65LF4tmvS3JOkkpw/Mr8gyYYkn5zNuuaLeXPPVNvk+1V1yGwXoTnvHuDZSR5bVd8Hfgb4j1muad7wzFTSqH8CfrZNnwj87SzWMq8Ypo8Ojx25xL94tovRnHYBcEKSXYDnAl+e5XrmDS/zHx28zNeMVNVX2z31E/FPEbeIYSppshXAnwFHAE+c3VLmD8NU0mRnA3dW1TVJjpjtYuYLw1TSw1TVOuD9s13HfOM7oCSpA0fzJakDw1SSOjBMJakDw1SSOjBMJakDw1SaQpJzkrxqtuvQ/GGYSh0k8W+2H+U8ADTvJflfwKuBtQz/mvgq4GLgg8BC4F7gTVX19STnAHcBS4AnAW+pqouSBPgL4GXAtxj59+JJfhJ4L7Bb2/7rq+qmJJcD/wK8mOEtmH8+9ierOcsw1byWZAnwS8DzGY7nf2MI07OAX6+q1UkOAz7EEJQA+wEvAZ7JEIIXAb8APAN4DrAvcB1wdpIdGUJ2aVVtSPIrwDuBN7Rt7VFV/23sT1RznmGq+e4lwCXtw4xJ8glgF+BFwN8NJ5wA7Dyyzj9W1Q+B65Ls29p+GvjbqnoAWJ/ks639GcCzgZVtWzsAN41s6+P9n5LmI8NU812maHsMcMc0Hzt43ybWn+q91QGuraoXbmJb92y+RD0aOACl+e6LwM8l2SXJbgyfEn8v8K0kxwNk8LzNbOcLDB+KvEOS/YCXtvZvAAuTvLBta8ckB4/lmWheM0w1r1XVlQz3Pf8d+AdgFXAnw4DUyUn+HbgWWLqZTV0MrAauAc4EPt+2/5/Aq4D3tG1dzXALQXoYPzVK816S3arqe0l2ZTjDXF5V/zbbdenRxXumeiQ4K8lBDANP5xqkmg2emUpSB94zlaQODFNJ6sAwlaQODFNJ6sAwlaQODFNJ6uD/A2dYE7pP5EAyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 5))\n",
    "sns.countplot(data.gender)\n",
    "plt.title(\"Number of transactions by gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are more male customer transactions than there are female. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>205.721809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>168.517303</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender      amount\n",
       "1      M  205.721809\n",
       "0      F  168.517303"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"gender\", \"amount\"]].groupby(\"gender\", as_index = False).mean().sort_values(by = \"amount\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average transaction volume by gender')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 5))\n",
    "sns.barplot(x = \"gender\", y = \"amount\", data = data)\n",
    "plt.title(\"Average transaction volume by gender\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Male has a higher average transaction volume. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.7 Merchant suburb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN                4326\n",
       "Melbourne           255\n",
       "Sydney              233\n",
       "Southport            82\n",
       "Brisbane City        79\n",
       "                   ... \n",
       "Blackburn South       1\n",
       "Quakers Hill          1\n",
       "Weston                1\n",
       "Carey Park            1\n",
       "Toombul               1\n",
       "Name: merchant_suburb, Length: 1610, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.merchant_suburb.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the suburbs where the transactions were made. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.8 Merchant state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NaN    4326\n",
       "NSW    2169\n",
       "VIC    2131\n",
       "QLD    1556\n",
       "WA     1100\n",
       "SA      415\n",
       "NT      205\n",
       "ACT      73\n",
       "TAS      68\n",
       "Name: merchant_state, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.merchant_state.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transactions by state')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 7))\n",
    "sns.countplot(data.merchant_state)\n",
    "plt.title(\"Number of transactions by state\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to merchant suburb, these are the states where the transactions were made.\n",
    "\n",
    "NSW and VIC are the top 2 states in number of transactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>merchant_state</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACT</td>\n",
       "      <td>66.803836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSW</td>\n",
       "      <td>47.036316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NT</td>\n",
       "      <td>44.726293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>VIC</td>\n",
       "      <td>41.099953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SA</td>\n",
       "      <td>40.425470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>QLD</td>\n",
       "      <td>34.372397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WA</td>\n",
       "      <td>30.901873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TAS</td>\n",
       "      <td>28.866618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  merchant_state     amount\n",
       "0            ACT  66.803836\n",
       "1            NSW  47.036316\n",
       "2             NT  44.726293\n",
       "6            VIC  41.099953\n",
       "4             SA  40.425470\n",
       "3            QLD  34.372397\n",
       "7             WA  30.901873\n",
       "5            TAS  28.866618"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"merchant_state\", \"amount\"]].groupby(\"merchant_state\", as_index = False).mean().sort_values(by = \"amount\", ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average transaction volume by state')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 7))\n",
    "sns.barplot(x = \"merchant_state\", y = \"amount\", data = data)\n",
    "plt.title(\"Average transaction volume by state\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ACT has the highest average transaction volume but the variance is quite large. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.9 Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2018-08-01T01:01:15.000+0000\n",
       "1    2018-08-01T01:13:45.000+0000\n",
       "2    2018-08-01T01:26:15.000+0000\n",
       "3    2018-08-01T01:38:45.000+0000\n",
       "4    2018-08-01T01:51:15.000+0000\n",
       "Name: extraction, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.extraction.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This appears to be the timestamp for each transaction. We can cross-reference this with the date column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>date</th>\n",
       "      <th>extraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018-08-01T01:01:15.000+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018-08-01T01:13:45.000+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018-08-01T01:26:15.000+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018-08-01T01:38:45.000+0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>2018-08-01T01:51:15.000+0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date                    extraction\n",
       "0 2018-08-01  2018-08-01T01:01:15.000+0000\n",
       "1 2018-08-01  2018-08-01T01:13:45.000+0000\n",
       "2 2018-08-01  2018-08-01T01:26:15.000+0000\n",
       "3 2018-08-01  2018-08-01T01:38:45.000+0000\n",
       "4 2018-08-01  2018-08-01T01:51:15.000+0000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"date\", \"extraction\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already have an existing date column, we can potentially extract only the time component out of the extraction column. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.10 Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Australia    12043\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.country.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All transactions were recorded within Australia. Therefore, can consider dropping this column since it does not provide us with any information."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.11 Merchant longitude and latitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    153.38 -27.99\n",
       "1    151.21 -33.87\n",
       "2    151.21 -33.87\n",
       "3    153.05 -26.68\n",
       "4    153.44 -28.06\n",
       "Name: merchant_long_lat, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.merchant_long_lat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the coordinates of the merchants' location."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1.12 Movement (debit/credit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "debit     11160\n",
       "credit      883\n",
       "Name: movement, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.movement.value_counts(dropna = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of transactions by movement')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5 ,5))\n",
    "sns.countplot(data.movement)\n",
    "plt.title(\"Number of transactions by movement\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most transactions are overwhelmingly debit transactions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average transaction volume by movement')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 5))\n",
    "sns.barplot(x = \"movement\", y = \"amount\", data = data)\n",
    "plt.title(\"Average transaction volume by movement\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, the average transaction amount for credit transactions are much higher."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Numerical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2.1 Balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Balance distribution')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(data.balance)\n",
    "plt.title(\"Balance distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23e3e60b248>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 4))\n",
    "sns.boxplot(data.balance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of the balance feature has a long tail. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2.2 Age of customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age distribution')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(data.age)\n",
    "plt.title(\"Age distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Majority of the customers belong in the 20-25 age bracket. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2.3 Transaction amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Amount distribution')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(data.amount)\n",
    "plt.title(\"Amount distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Feature engineering\n",
    "\n",
    "In this section, we are going to focus on dropping unwanted columns, deal with missing values and finally create some additional features that will make our data analysis more detailed and robust. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Drop unwanted columns\n",
    "\n",
    "Drop currency and country columns as they do not provide any aditional information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before:  (12043, 23)\n",
      "After:  (12043, 21)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before: \", data.shape)\n",
    "data = data.drop([\"currency\", \"country\"], axis = 1)\n",
    "print(\"After: \", data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Deal with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of missing values</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bpay_biller_code</th>\n",
       "      <td>11158</td>\n",
       "      <td>92.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_code</th>\n",
       "      <td>11160</td>\n",
       "      <td>92.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>card_present_flag</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_id</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_suburb</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_state</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>merchant_long_lat</th>\n",
       "      <td>4326</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Number of missing values  Percentage\n",
       "bpay_biller_code                      11158        92.7\n",
       "merchant_code                         11160        92.7\n",
       "card_present_flag                      4326        35.9\n",
       "merchant_id                            4326        35.9\n",
       "merchant_suburb                        4326        35.9\n",
       "merchant_state                         4326        35.9\n",
       "merchant_long_lat                      4326        35.9"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Number of missing values\": missing, \"Percentage\": missing_percentage}).sort_values(by = \"Percentage\", ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's investigate if there is a reason behind these missing values. \n",
    "\n",
    "Recall that not all transactions are purchases-related, could this imply the missing values in the merchant columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "merchant_code        11160\n",
       "bpay_biller_code     11158\n",
       "card_present_flag     4326\n",
       "merchant_state        4326\n",
       "merchant_suburb       4326\n",
       "merchant_id           4326\n",
       "merchant_long_lat     4326\n",
       "movement                 0\n",
       "first_name               0\n",
       "account                  0\n",
       "long_lat                 0\n",
       "txn_description          0\n",
       "date                     0\n",
       "balance                  0\n",
       "gender                   0\n",
       "age                      0\n",
       "extraction               0\n",
       "amount                   0\n",
       "transaction_id           0\n",
       "customer_id              0\n",
       "status                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonSales = data.loc[(data.txn_description != \"SALES-POS\") | (data.txn_description != \"POS\"), :]\n",
    "nonSales.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, missing values in the following columns all originate from non-purchases transactions:\n",
    "\n",
    "- card_present_flag\n",
    "- merchant_state\n",
    "- merchant_suburb\n",
    "- merchant_id\n",
    "- merchant_long_lat\n",
    "\n",
    "We will proceed to fill these missing values with n/a."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"card_present_flag\", \"merchant_state\", \"merchant_suburb\", \"merchant_id\", \"merchant_long_lat\"]\n",
    "for col in cols:\n",
    "    data[col].fillna(\"n/a\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "merchant_code       11160\n",
       "bpay_biller_code    11158\n",
       "dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = data.isnull().sum()\n",
    "missing = missing[missing > 0]\n",
    "missing.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both merchant_code and bpay_biller_code are severely missing, around 93%. Therefore, we will drop those 2 columns from the dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop([\"merchant_code\", \"bpay_biller_code\"], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, make sure there are no more missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum().max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Create features for month, dayofweek and hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>29867.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-02</th>\n",
       "      <td>21786.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-03</th>\n",
       "      <td>38096.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-04</th>\n",
       "      <td>6296.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-05</th>\n",
       "      <td>4426.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              amount\n",
       "date                \n",
       "2018-08-01  29867.94\n",
       "2018-08-02  21786.32\n",
       "2018-08-03  38096.58\n",
       "2018-08-04   6296.05\n",
       "2018-08-05   4426.50"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_amount = pd.DataFrame(data.groupby(\"date\").amount.sum())\n",
    "daily_amount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Transaction volume')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (12, 5))\n",
    "ax.plot(daily_amount.index, daily_amount.amount)\n",
    "plt.title(\"Transaction volume from 1/8/2018 to 31/10/2018\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Transaction volume\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There appears to be a cyclical pattern in transaction volume over the 3 month period. My hypothesis for this occurence is:\n",
    "- Salaries are paid out only on certain days of the week\n",
    "- People tend to spend more during the weekends (shopping, going out with friends etc)\n",
    "\n",
    "Therefore, to motivate this analysis, we will create 3 new features from both the date and extraction columns:\n",
    "- month\n",
    "- dayofweek\n",
    "- hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  month  dayofweek\n",
       "0 2018-08-01      8          2\n",
       "1 2018-08-01      8          2\n",
       "2 2018-08-01      8          2\n",
       "3 2018-08-01      8          2\n",
       "4 2018-08-01      8          2"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"month\"] = pd.DatetimeIndex(data.date).month\n",
    "data[\"dayofweek\"] = pd.DatetimeIndex(data.date).dayofweek\n",
    "data[[\"date\", \"month\", \"dayofweek\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition to month and day of week, we can also get the hour in which those transactions took place from the extraction column.\n",
    "\n",
    "First, I will update the extraction column such that it only contains the time. Then, I will create a new hour column which contains only the hour component of each transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2018-08-01T01:01:15.000+0000\n",
       "1    2018-08-01T01:13:45.000+0000\n",
       "2    2018-08-01T01:26:15.000+0000\n",
       "3    2018-08-01T01:38:45.000+0000\n",
       "4    2018-08-01T01:51:15.000+0000\n",
       "Name: extraction, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.extraction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    01:01:15\n",
       "1    01:13:45\n",
       "2    01:26:15\n",
       "3    01:38:45\n",
       "4    01:51:15\n",
       "Name: extraction, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"extraction\"] = [timestamp.split(\"T\")[1].split(\".\")[0] for timestamp in data.extraction]\n",
    "data.extraction.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "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>extraction</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01:01:15</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01:13:45</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01:26:15</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01:38:45</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01:51:15</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  extraction hour\n",
       "0   01:01:15   01\n",
       "1   01:13:45   01\n",
       "2   01:26:15   01\n",
       "3   01:38:45   01\n",
       "4   01:51:15   01"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"hour\"] = [time.split(\":\")[0] for time in data.extraction]\n",
    "data[[\"extraction\", \"hour\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before:  object\n",
      "After:  int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Before: \", data.hour.dtype)\n",
    "data[\"hour\"] = pd.to_numeric(data.hour)\n",
    "print(\"After: \", data.hour.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Deep dive \n",
    "\n",
    "Now that our dataframe is in a much better shape and we have new features to work with, let's now do a deep dive and see if we can gather any interesting insights about customers' transactional behaviour. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "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>status</th>\n",
       "      <th>card_present_flag</th>\n",
       "      <th>account</th>\n",
       "      <th>long_lat</th>\n",
       "      <th>txn_description</th>\n",
       "      <th>merchant_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>balance</th>\n",
       "      <th>date</th>\n",
       "      <th>gender</th>\n",
       "      <th>...</th>\n",
       "      <th>merchant_state</th>\n",
       "      <th>extraction</th>\n",
       "      <th>amount</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>merchant_long_lat</th>\n",
       "      <th>movement</th>\n",
       "      <th>month</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>POS</td>\n",
       "      <td>81c48296-73be-44a7-befa-d053f48ce7cd</td>\n",
       "      <td>Diana</td>\n",
       "      <td>35.39</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>01:01:15</td>\n",
       "      <td>16.25</td>\n",
       "      <td>a623070bfead4541a6b0fff8a09e706c</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.38 -27.99</td>\n",
       "      <td>debit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>authorized</td>\n",
       "      <td>0</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>830a451c-316e-4a6a-bf25-e37caedca49e</td>\n",
       "      <td>Diana</td>\n",
       "      <td>21.20</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>01:13:45</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13270a2a902145da9db4c951e04b51b9</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1</td>\n",
       "      <td>ACC-1222300524</td>\n",
       "      <td>151.23 -33.94</td>\n",
       "      <td>POS</td>\n",
       "      <td>835c231d-8cdf-4e96-859d-e9d571760cf0</td>\n",
       "      <td>Michael</td>\n",
       "      <td>5.71</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>M</td>\n",
       "      <td>...</td>\n",
       "      <td>NSW</td>\n",
       "      <td>01:26:15</td>\n",
       "      <td>6.42</td>\n",
       "      <td>feb79e7ecd7048a5a36ec889d1a94270</td>\n",
       "      <td>CUS-2142601169</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "      <td>debit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1</td>\n",
       "      <td>ACC-1037050564</td>\n",
       "      <td>153.10 -27.66</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>48514682-c78a-4a88-b0da-2d6302e64673</td>\n",
       "      <td>Rhonda</td>\n",
       "      <td>2117.22</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>01:38:45</td>\n",
       "      <td>40.90</td>\n",
       "      <td>2698170da3704fd981b15e64a006079e</td>\n",
       "      <td>CUS-1614226872</td>\n",
       "      <td>153.05 -26.68</td>\n",
       "      <td>debit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>authorized</td>\n",
       "      <td>1</td>\n",
       "      <td>ACC-1598451071</td>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>b4e02c10-0852-4273-b8fd-7b3395e32eb0</td>\n",
       "      <td>Diana</td>\n",
       "      <td>17.95</td>\n",
       "      <td>2018-08-01</td>\n",
       "      <td>F</td>\n",
       "      <td>...</td>\n",
       "      <td>QLD</td>\n",
       "      <td>01:51:15</td>\n",
       "      <td>3.25</td>\n",
       "      <td>329adf79878c4cf0aeb4188b4691c266</td>\n",
       "      <td>CUS-2487424745</td>\n",
       "      <td>153.44 -28.06</td>\n",
       "      <td>debit</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       status card_present_flag         account       long_lat  \\\n",
       "0  authorized                 1  ACC-1598451071  153.41 -27.95   \n",
       "1  authorized                 0  ACC-1598451071  153.41 -27.95   \n",
       "2  authorized                 1  ACC-1222300524  151.23 -33.94   \n",
       "3  authorized                 1  ACC-1037050564  153.10 -27.66   \n",
       "4  authorized                 1  ACC-1598451071  153.41 -27.95   \n",
       "\n",
       "  txn_description                           merchant_id first_name  balance  \\\n",
       "0             POS  81c48296-73be-44a7-befa-d053f48ce7cd      Diana    35.39   \n",
       "1       SALES-POS  830a451c-316e-4a6a-bf25-e37caedca49e      Diana    21.20   \n",
       "2             POS  835c231d-8cdf-4e96-859d-e9d571760cf0    Michael     5.71   \n",
       "3       SALES-POS  48514682-c78a-4a88-b0da-2d6302e64673     Rhonda  2117.22   \n",
       "4       SALES-POS  b4e02c10-0852-4273-b8fd-7b3395e32eb0      Diana    17.95   \n",
       "\n",
       "        date gender  ...  merchant_state extraction amount  \\\n",
       "0 2018-08-01      F  ...             QLD   01:01:15  16.25   \n",
       "1 2018-08-01      F  ...             NSW   01:13:45  14.19   \n",
       "2 2018-08-01      M  ...             NSW   01:26:15   6.42   \n",
       "3 2018-08-01      F  ...             QLD   01:38:45  40.90   \n",
       "4 2018-08-01      F  ...             QLD   01:51:15   3.25   \n",
       "\n",
       "                     transaction_id     customer_id merchant_long_lat  \\\n",
       "0  a623070bfead4541a6b0fff8a09e706c  CUS-2487424745     153.38 -27.99   \n",
       "1  13270a2a902145da9db4c951e04b51b9  CUS-2487424745     151.21 -33.87   \n",
       "2  feb79e7ecd7048a5a36ec889d1a94270  CUS-2142601169     151.21 -33.87   \n",
       "3  2698170da3704fd981b15e64a006079e  CUS-1614226872     153.05 -26.68   \n",
       "4  329adf79878c4cf0aeb4188b4691c266  CUS-2487424745     153.44 -28.06   \n",
       "\n",
       "  movement month dayofweek  hour  \n",
       "0    debit     8         2     1  \n",
       "1    debit     8         2     1  \n",
       "2    debit     8         2     1  \n",
       "3    debit     8         2     1  \n",
       "4    debit     8         2     1  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Purchases amount and overall amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    16.25\n",
       "1    14.19\n",
       "2     6.42\n",
       "3    40.90\n",
       "4     3.25\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases_amount = data.loc[(data.txn_description == \"POS\") | (data.txn_description == \"SALES-POS\"), \"amount\"]\n",
    "purchases_amount.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Purchase transaction amount')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(purchases_amount)\n",
    "plt.title(\"Purchase transaction amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    7717.000000\n",
       "mean       40.153732\n",
       "std       149.833070\n",
       "min         0.100000\n",
       "25%        12.080000\n",
       "50%        19.700000\n",
       "75%        33.910000\n",
       "max      7081.090000\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchases_amount.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Overall transaction amount')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "sns.distplot(data.amount)\n",
    "plt.title(\"Overall transaction amount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    12043.000000\n",
       "mean       187.933588\n",
       "std        592.599934\n",
       "min          0.100000\n",
       "25%         16.000000\n",
       "50%         29.000000\n",
       "75%         53.655000\n",
       "max       8835.980000\n",
       "Name: amount, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.amount.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Variance in overall transaction amount is much higher than purchases transaction amount due to the inclusion of salary payments. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Transaction volume per customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CUS-1005756958</th>\n",
       "      <td>5422.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1117979751</th>\n",
       "      <td>11328.123333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1140341822</th>\n",
       "      <td>5670.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1147642491</th>\n",
       "      <td>9660.273333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CUS-1196156254</th>\n",
       "      <td>12016.906667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      amount\n",
       "customer_id                 \n",
       "CUS-1005756958   5422.990000\n",
       "CUS-1117979751  11328.123333\n",
       "CUS-1140341822   5670.200000\n",
       "CUS-1147642491   9660.273333\n",
       "CUS-1196156254  12016.906667"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_monthly_volume = pd.DataFrame(data.groupby(\"customer_id\").amount.sum() / 3) \n",
    "customer_monthly_volume.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"Customers' monthly transaction volume\")"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 5))\n",
    "sns.distplot(customer_monthly_volume.amount)\n",
    "plt.title(\"Customers' monthly transaction volume\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Transaction volume over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average transaction volume by month')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (5, 5))\n",
    "sns.barplot(x = \"month\", y = \"amount\", data = data)\n",
    "plt.title(\"Average transaction volume by month\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dayofweek</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>373.221000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>221.576456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>195.215570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>184.010422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>249.353517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               amount\n",
       "dayofweek            \n",
       "0          373.221000\n",
       "1          221.576456\n",
       "2          195.215570\n",
       "3          184.010422\n",
       "4          249.353517"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_daily_volume = pd.DataFrame(data.groupby(\"dayofweek\").amount.mean())\n",
    "average_daily_volume.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Day of week')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 5))\n",
    "ax.plot(average_daily_volume.index, average_daily_volume.amount)\n",
    "plt.title(\"Average transaction volume per day\")\n",
    "plt.ylabel(\"Transaction volume\")\n",
    "plt.xlabel(\"Day of week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Friday and Saturday have the lowest average transaction volume. Let's see a breakdown of these transactions by types of transactions. \n",
    "\n",
    "To simplify the categories, I will group all transactions into 3 categories:\n",
    "- Salary\n",
    "- Purchase\n",
    "- Others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SALES-POS     3934\n",
       "POS           3783\n",
       "PAYMENT       2600\n",
       "PAY/SALARY     883\n",
       "INTER BANK     742\n",
       "PHONE BANK     101\n",
       "Name: txn_description, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.txn_description.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>txn_description</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SALES-POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>POS</td>\n",
       "      <td>Purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>INTER BANK</td>\n",
       "      <td>Others</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  txn_description  category\n",
       "0             POS  Purchase\n",
       "1       SALES-POS  Purchase\n",
       "2             POS  Purchase\n",
       "3       SALES-POS  Purchase\n",
       "4       SALES-POS  Purchase\n",
       "5         PAYMENT    Others\n",
       "6       SALES-POS  Purchase\n",
       "7             POS  Purchase\n",
       "8             POS  Purchase\n",
       "9      INTER BANK    Others"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[data.txn_description == \"PAY/SALARY\", \"category\"] = \"Salary\"\n",
    "data.loc[(data.txn_description == \"SALES-POS\") | (data.txn_description == \"POS\"), \"category\"] = \"Purchase\"\n",
    "data.category.fillna(\"Others\", inplace = True)\n",
    "data[[\"txn_description\", \"category\"]].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Day of week')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_barplot = pd.DataFrame(data.groupby([\"dayofweek\", \"category\"]).amount.count())\n",
    "stacked_barplot.unstack().plot(kind = \"bar\", stacked = True, figsize = (12, 7))\n",
    "plt.title(\"Number of transactions each day by category\")\n",
    "plt.legend([\"Others\", \"Purchases\", \"Salaries\"])\n",
    "plt.ylabel(\"Number of transactions\")\n",
    "plt.xlabel(\"Day of week\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No salaries were paid on Friday and Saturday, therefore lower average transaction volume on those days.\n",
    "\n",
    "Also, it does not seem to appear that people spend more on purchases over the weekends. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>28.274907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.716402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>75.269764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84.725918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61.301845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         amount\n",
       "hour           \n",
       "0     28.274907\n",
       "1     48.716402\n",
       "2     75.269764\n",
       "3     84.725918\n",
       "4     61.301845"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_hourly_volume = pd.DataFrame(data.groupby(\"hour\").amount.mean())\n",
    "average_hourly_volume.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8, 5))\n",
    "ax.plot(average_hourly_volume.index, average_hourly_volume.amount)\n",
    "plt.title(\"Average transaction volume per hour\")\n",
    "plt.ylabel(\"Transaction volume\")\n",
    "plt.xlabel(\"Hour\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Highest transaction volumes happen during the midday and late afternoon. Let's see a breakdown of these transactions by categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Hour')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stacked_barplot = pd.DataFrame(data.groupby([\"hour\", \"category\"]).amount.count())\n",
    "stacked_barplot.unstack().plot(kind = \"bar\", stacked = True, figsize = (12, 7))\n",
    "plt.title(\"Number of transactions each hour by category\")\n",
    "plt.legend([\"Others\", \"Purchases\", \"Salaries\"])\n",
    "plt.ylabel(\"Number of transactions\")\n",
    "plt.xlabel(\"Hour\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Homework: distance between customers and merchants \n",
    "\n",
    "An additional challenge that we could do is figuring out the distance between the customers and the merchants and analyse its distribution.\n",
    "\n",
    "This can be done via the [haversine](https://pypi.org/project/haversine/) library in Python which calculates the distance between two points on the Earth using their latitude and longitude. We can obtain the latitude and longitude of both the customers and merchants from the following columns:\n",
    "- long_lat\n",
    "- merchant_long_lat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>long_lat</th>\n",
       "      <th>merchant_long_lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>153.38 -27.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>151.23 -33.94</td>\n",
       "      <td>151.21 -33.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>153.10 -27.66</td>\n",
       "      <td>153.05 -26.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>153.41 -27.95</td>\n",
       "      <td>153.44 -28.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        long_lat merchant_long_lat\n",
       "0  153.41 -27.95     153.38 -27.99\n",
       "1  153.41 -27.95     151.21 -33.87\n",
       "2  151.23 -33.94     151.21 -33.87\n",
       "3  153.10 -27.66     153.05 -26.68\n",
       "4  153.41 -27.95     153.44 -28.06"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[[\"long_lat\", \"merchant_long_lat\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Save data\n",
    "\n",
    "Task 1 is done! Save data for task 2. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_pickle(\"C:/Users/Jason Chong/Documents/Forage ANZ/Data/data.pkl\")"
   ]
  }
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