Sales Performance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "42f7cefe",
   "metadata": {},
   "source": [
    "# Sales Performance Report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d79450",
   "metadata": {},
   "source": [
    "### Objectives\n",
    "\n",
    "##### To analyze sales performance of retail store\n",
    "\n",
    "Including metrics :\n",
    "\n",
    "1. Overall performance by year and by sub category product\n",
    "\n",
    "2. Burn Rate\n",
    "\n",
    "3. Customer acquisition and Customer Retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ca60389",
   "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>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>customer</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_quantity</th>\n",
       "      <th>sales</th>\n",
       "      <th>discount</th>\n",
       "      <th>discount_value</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_sub_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Muhammed MacIntyre</td>\n",
       "      <td>2010-Oct-13</td>\n",
       "      <td>6</td>\n",
       "      <td>523080</td>\n",
       "      <td>0.04</td>\n",
       "      <td>20923</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage &amp; Organization</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>293</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Barry French</td>\n",
       "      <td>2012-Oct-01</td>\n",
       "      <td>49</td>\n",
       "      <td>20246040</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1417223</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>483</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Clay Rozendal</td>\n",
       "      <td>2011-Jul-10</td>\n",
       "      <td>30</td>\n",
       "      <td>9931519</td>\n",
       "      <td>0.08</td>\n",
       "      <td>794522</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Telephones and Communication</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>515</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>2010-Aug-28</td>\n",
       "      <td>19</td>\n",
       "      <td>788540</td>\n",
       "      <td>0.08</td>\n",
       "      <td>63083</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>613</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Carl Jackson</td>\n",
       "      <td>2011-Jun-17</td>\n",
       "      <td>12</td>\n",
       "      <td>187080</td>\n",
       "      <td>0.03</td>\n",
       "      <td>5612</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id    order_status            customer   order_date  order_quantity  \\\n",
       "0         3  Order Finished  Muhammed MacIntyre  2010-Oct-13               6   \n",
       "1       293  Order Finished        Barry French  2012-Oct-01              49   \n",
       "2       483  Order Finished       Clay Rozendal  2011-Jul-10              30   \n",
       "3       515  Order Finished      Carlos Soltero  2010-Aug-28              19   \n",
       "4       613  Order Finished        Carl Jackson  2011-Jun-17              12   \n",
       "\n",
       "      sales  discount  discount_value product_category  \\\n",
       "0    523080      0.04           20923  Office Supplies   \n",
       "1  20246040      0.07         1417223  Office Supplies   \n",
       "2   9931519      0.08          794522       Technology   \n",
       "3    788540      0.08           63083  Office Supplies   \n",
       "4    187080      0.03            5612  Office Supplies   \n",
       "\n",
       "             product_sub_category  \n",
       "0          Storage & Organization  \n",
       "1                      Appliances  \n",
       "2    Telephones and Communication  \n",
       "3                      Appliances  \n",
       "4  Binders and Binder Accessories  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv('D:\\Projects\\dqlab_sales_store_.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7644d60e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Info dataset:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5499 entries, 0 to 5498\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   order_id              5499 non-null   int64  \n",
      " 1   order_status          5499 non-null   object \n",
      " 2   customer              5498 non-null   object \n",
      " 3   order_date            5499 non-null   object \n",
      " 4   order_quantity        5499 non-null   int64  \n",
      " 5   sales                 5499 non-null   int64  \n",
      " 6   discount              5499 non-null   float64\n",
      " 7   discount_value        5499 non-null   int64  \n",
      " 8   product_category      5499 non-null   object \n",
      " 9   product_sub_category  5499 non-null   object \n",
      "dtypes: float64(1), int64(4), object(5)\n",
      "memory usage: 429.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('\\nInfo dataset:')\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d594560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Info dataset:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5499 entries, 0 to 5498\n",
      "Data columns (total 10 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   order_id              5499 non-null   int64         \n",
      " 1   order_status          5499 non-null   object        \n",
      " 2   customer              5498 non-null   object        \n",
      " 3   order_date            5499 non-null   datetime64[ns]\n",
      " 4   order_quantity        5499 non-null   int64         \n",
      " 5   sales                 5499 non-null   int64         \n",
      " 6   discount              5499 non-null   float64       \n",
      " 7   discount_value        5499 non-null   int64         \n",
      " 8   product_category      5499 non-null   object        \n",
      " 9   product_sub_category  5499 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(1), int64(4), object(4)\n",
      "memory usage: 429.7+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "data['order_date'] = pd.to_datetime(data['order_date'])\n",
    "\n",
    "print('\\nInfo dataset:')\n",
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d274a0f6",
   "metadata": {},
   "source": [
    "## Overall Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "245db6a4",
   "metadata": {},
   "source": [
    "### 1. Overall Performance by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6dc6a40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Total Order\n",
    "\n",
    "data['year_transaction'] = data['order_date'].dt.year\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data_year = data[data['order_status'] == 'Order Finished'].groupby(['year_transaction'])['order_status'].count()\n",
    "data_year.plot(x='year_transaction',y='order_status', kind='bar', title='Total Order by Year')\n",
    "plt.xlabel('Year_Transaction')\n",
    "plt.ylabel('Num_of_Order')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18617849",
   "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>year_transaction</th>\n",
       "      <th>order_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>1178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1254</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_transaction  order_status\n",
       "0              2009          1244\n",
       "1              2010          1248\n",
       "2              2011          1178\n",
       "3              2012          1254"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall Performance by Year\n",
    "\n",
    "## Total Order\n",
    "data_year1 = data[data['order_status'] == 'Order Finished'].groupby(['year_transaction'], as_index = False)['order_status'].count()\n",
    "data_year1 = pd.DataFrame(data_year1)\n",
    "data_year1.columns\n",
    "data_year1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bac8b0b1",
   "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>year</th>\n",
       "      <th>number_of_order</th>\n",
       "      <th>number_of_order_lag1</th>\n",
       "      <th>growth_of_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1248</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>0.321543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>1178</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>-5.608974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1254</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>6.451613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  number_of_order  number_of_order_lag1  growth_of_order\n",
       "0  2009             1244                   NaN              NaN\n",
       "1  2010             1248                1244.0         0.321543\n",
       "2  2011             1178                1248.0        -5.608974\n",
       "3  2012             1254                1178.0         6.451613"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_year1.rename(columns={'year_transaction': 'year', 'order_status': 'number_of_order'}, inplace=True)\n",
    "data_year1['number_of_order_lag1'] = data_year1['number_of_order'].shift(1)\n",
    "data_year1['growth_of_order'] = ((data_year1['number_of_order'] / data_year1['number_of_order_lag1']) -1)*100\n",
    "data_year1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80dc9f9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.pointplot(data = data_year1, \n",
    "              x='year', \n",
    "              y='growth_of_order')\n",
    "plt.ylim(-15,15)\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Growth of Order (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "84d68a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overall Performance by Year\n",
    "\n",
    "## Total Sales\n",
    "data_year = data[data['order_status'] == 'Order Finished'].groupby(['year_transaction'])['sales'].sum()\n",
    "data_year.plot(x='year_transaction', y='sales', kind='bar', title='Total Sales by Year')\n",
    "plt.xlabel('Year_Transaction')\n",
    "plt.ylabel('Total Sales')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0d0be615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['year_transaction', 'sales'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Total Sales\n",
    "\n",
    "data_year2 = data[data['order_status'] == 'Order Finished'].groupby(['year_transaction'], as_index=False)['sales'].sum()\n",
    "data_year2 = pd.DataFrame(data_year2)\n",
    "data_year2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "92cc6bb9",
   "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>year</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>total_sales_lag1</th>\n",
       "      <th>growth_of_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>4613872681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>4059100607</td>\n",
       "      <td>4.613873e+09</td>\n",
       "      <td>-12.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>4112036186</td>\n",
       "      <td>4.059101e+09</td>\n",
       "      <td>1.304121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>4482983158</td>\n",
       "      <td>4.112036e+09</td>\n",
       "      <td>9.021005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  total_sales  total_sales_lag1  growth_of_sales\n",
       "0  2009   4613872681               NaN              NaN\n",
       "1  2010   4059100607      4.613873e+09       -12.024000\n",
       "2  2011   4112036186      4.059101e+09         1.304121\n",
       "3  2012   4482983158      4.112036e+09         9.021005"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_year2.rename(columns={'year_transaction': 'year', 'sales': 'total_sales'}, inplace=True)\n",
    "data_year2['total_sales_lag1'] = data_year2['total_sales'].shift(1)\n",
    "data_year2['growth_of_sales'] = ((data_year2['total_sales'] / data_year2['total_sales_lag1']) -1)*100\n",
    "data_year2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16631f4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(data = data_year2, \n",
    "              x='year', \n",
    "              y='growth_of_sales')\n",
    "plt.ylim(-15,15)\n",
    "plt.tight_layout()\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Growth of Sales (%)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b998dbce",
   "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>year</th>\n",
       "      <th>number_of_order</th>\n",
       "      <th>number_of_order_lag1</th>\n",
       "      <th>growth_of_order</th>\n",
       "      <th>total_sales</th>\n",
       "      <th>total_sales_lag1</th>\n",
       "      <th>growth_of_sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4613872681</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1248</td>\n",
       "      <td>1244.0</td>\n",
       "      <td>0.321543</td>\n",
       "      <td>4059100607</td>\n",
       "      <td>4.613873e+09</td>\n",
       "      <td>-12.024000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>1178</td>\n",
       "      <td>1248.0</td>\n",
       "      <td>-5.608974</td>\n",
       "      <td>4112036186</td>\n",
       "      <td>4.059101e+09</td>\n",
       "      <td>1.304121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>1254</td>\n",
       "      <td>1178.0</td>\n",
       "      <td>6.451613</td>\n",
       "      <td>4482983158</td>\n",
       "      <td>4.112036e+09</td>\n",
       "      <td>9.021005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  number_of_order  number_of_order_lag1  growth_of_order  total_sales  \\\n",
       "0  2009             1244                   NaN              NaN   4613872681   \n",
       "1  2010             1248                1244.0         0.321543   4059100607   \n",
       "2  2011             1178                1248.0        -5.608974   4112036186   \n",
       "3  2012             1254                1178.0         6.451613   4482983158   \n",
       "\n",
       "   total_sales_lag1  growth_of_sales  \n",
       "0               NaN              NaN  \n",
       "1      4.613873e+09       -12.024000  \n",
       "2      4.059101e+09         1.304121  \n",
       "3      4.112036e+09         9.021005  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_year = pd.merge(data_year1, data_year2, on='year', how='inner')\n",
    "data_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05c1227d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_year.plot(x='year', y=['growth_of_order','growth_of_sales'], kind='bar', title='Overall Sales Performance by Year')\n",
    "plt.xlabel('Year')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(['Growth of Order (%)','Growth of Sales (%)'])\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8847f72",
   "metadata": {},
   "source": [
    "### 2. Overall Performance by Product Sub Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b8d3776c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp/ipykernel_14040/3818788355.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['year_transaction'] = data1['year_transaction'].astype(str)\n"
     ]
    },
    {
     "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>year_transaction</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>%SalesGrowth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>product_sub_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Scissors, Rulers and Trimmers</th>\n",
       "      <td>12638340</td>\n",
       "      <td>36776400</td>\n",
       "      <td>190.990747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>36463900</td>\n",
       "      <td>58629280</td>\n",
       "      <td>60.787190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Machines</th>\n",
       "      <td>545856280</td>\n",
       "      <td>811427140</td>\n",
       "      <td>48.652158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Peripherals</th>\n",
       "      <td>232677960</td>\n",
       "      <td>308014340</td>\n",
       "      <td>32.377961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage &amp; Organization</th>\n",
       "      <td>285991820</td>\n",
       "      <td>356714140</td>\n",
       "      <td>24.728791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubber Bands</th>\n",
       "      <td>3090120</td>\n",
       "      <td>3837880</td>\n",
       "      <td>24.198413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binders and Binder Accessories</th>\n",
       "      <td>298023200</td>\n",
       "      <td>363879200</td>\n",
       "      <td>22.097609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>111080380</td>\n",
       "      <td>126896160</td>\n",
       "      <td>14.238140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Furnishings</th>\n",
       "      <td>160471500</td>\n",
       "      <td>178927480</td>\n",
       "      <td>11.501095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telephones and Communication</th>\n",
       "      <td>392194658</td>\n",
       "      <td>422287514</td>\n",
       "      <td>7.672939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs &amp; Chairmats</th>\n",
       "      <td>622962720</td>\n",
       "      <td>654168740</td>\n",
       "      <td>5.009292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pens &amp; Art Supplies</th>\n",
       "      <td>43093800</td>\n",
       "      <td>43818480</td>\n",
       "      <td>1.681634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appliances</th>\n",
       "      <td>272630020</td>\n",
       "      <td>266131100</td>\n",
       "      <td>-2.383787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookcases</th>\n",
       "      <td>169304620</td>\n",
       "      <td>159984680</td>\n",
       "      <td>-5.504835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>505875008</td>\n",
       "      <td>388993784</td>\n",
       "      <td>-23.104763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers and Fax</th>\n",
       "      <td>404074080</td>\n",
       "      <td>292489800</td>\n",
       "      <td>-27.614808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>15607780</td>\n",
       "      <td>10007040</td>\n",
       "      <td>-35.884283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_transaction                     2011       2012  %SalesGrowth\n",
       "product_sub_category                                              \n",
       "Scissors, Rulers and Trimmers    12638340   36776400    190.990747\n",
       "Envelopes                        36463900   58629280     60.787190\n",
       "Office Machines                 545856280  811427140     48.652158\n",
       "Computer Peripherals            232677960  308014340     32.377961\n",
       "Storage & Organization          285991820  356714140     24.728791\n",
       "Rubber Bands                      3090120    3837880     24.198413\n",
       "Binders and Binder Accessories  298023200  363879200     22.097609\n",
       "Paper                           111080380  126896160     14.238140\n",
       "Office Furnishings              160471500  178927480     11.501095\n",
       "Telephones and Communication    392194658  422287514      7.672939\n",
       "Chairs & Chairmats              622962720  654168740      5.009292\n",
       "Pens & Art Supplies              43093800   43818480      1.681634\n",
       "Appliances                      272630020  266131100     -2.383787\n",
       "Bookcases                       169304620  159984680     -5.504835\n",
       "Tables                          505875008  388993784    -23.104763\n",
       "Copiers and Fax                 404074080  292489800    -27.614808\n",
       "Labels                           15607780   10007040    -35.884283"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot data using pivot_table\n",
    "\n",
    "data1 = data[data['order_status'] == 'Order Finished']\n",
    "data1['year_transaction'] = data1['year_transaction'].astype(str)\n",
    "data_piv1 = data1.loc[(data1['year_transaction'] == '2011') | (data1['year_transaction'] == '2012')].pivot_table(\n",
    "                        index='year_transaction',\n",
    "                        columns=['product_sub_category'],\n",
    "                        values='sales', \n",
    "                        aggfunc=sum, \n",
    "                        fill_value=0).T\n",
    "\n",
    "data_piv1['%SalesGrowth'] = ((data_piv1['2012'] / data_piv1['2011']) -1) * 100\n",
    "data_piv1.sort_values('%SalesGrowth', ascending=False, inplace=True)\n",
    "data_piv1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "29dd2248",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        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>year_transaction</th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th>Sales_2011</th>\n",
       "      <th>Sales_2012</th>\n",
       "      <th>%SalesGrowth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scissors, Rulers and Trimmers</td>\n",
       "      <td>12638340</td>\n",
       "      <td>36776400</td>\n",
       "      <td>190.990747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Envelopes</td>\n",
       "      <td>36463900</td>\n",
       "      <td>58629280</td>\n",
       "      <td>60.787190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Office Machines</td>\n",
       "      <td>545856280</td>\n",
       "      <td>811427140</td>\n",
       "      <td>48.652158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Peripherals</td>\n",
       "      <td>232677960</td>\n",
       "      <td>308014340</td>\n",
       "      <td>32.377961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Storage &amp; Organization</td>\n",
       "      <td>285991820</td>\n",
       "      <td>356714140</td>\n",
       "      <td>24.728791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rubber Bands</td>\n",
       "      <td>3090120</td>\n",
       "      <td>3837880</td>\n",
       "      <td>24.198413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>298023200</td>\n",
       "      <td>363879200</td>\n",
       "      <td>22.097609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paper</td>\n",
       "      <td>111080380</td>\n",
       "      <td>126896160</td>\n",
       "      <td>14.238140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Furnishings</td>\n",
       "      <td>160471500</td>\n",
       "      <td>178927480</td>\n",
       "      <td>11.501095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>392194658</td>\n",
       "      <td>422287514</td>\n",
       "      <td>7.672939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chairs &amp; Chairmats</td>\n",
       "      <td>622962720</td>\n",
       "      <td>654168740</td>\n",
       "      <td>5.009292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pens &amp; Art Supplies</td>\n",
       "      <td>43093800</td>\n",
       "      <td>43818480</td>\n",
       "      <td>1.681634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Appliances</td>\n",
       "      <td>272630020</td>\n",
       "      <td>266131100</td>\n",
       "      <td>-2.383787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bookcases</td>\n",
       "      <td>169304620</td>\n",
       "      <td>159984680</td>\n",
       "      <td>-5.504835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tables</td>\n",
       "      <td>505875008</td>\n",
       "      <td>388993784</td>\n",
       "      <td>-23.104763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copiers and Fax</td>\n",
       "      <td>404074080</td>\n",
       "      <td>292489800</td>\n",
       "      <td>-27.614808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Labels</td>\n",
       "      <td>15607780</td>\n",
       "      <td>10007040</td>\n",
       "      <td>-35.884283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_transaction            product_sub_category  Sales_2011  Sales_2012  \\\n",
       "0                  Scissors, Rulers and Trimmers    12638340    36776400   \n",
       "1                                      Envelopes    36463900    58629280   \n",
       "2                                Office Machines   545856280   811427140   \n",
       "3                           Computer Peripherals   232677960   308014340   \n",
       "4                         Storage & Organization   285991820   356714140   \n",
       "5                                   Rubber Bands     3090120     3837880   \n",
       "6                 Binders and Binder Accessories   298023200   363879200   \n",
       "7                                          Paper   111080380   126896160   \n",
       "8                             Office Furnishings   160471500   178927480   \n",
       "9                   Telephones and Communication   392194658   422287514   \n",
       "10                            Chairs & Chairmats   622962720   654168740   \n",
       "11                           Pens & Art Supplies    43093800    43818480   \n",
       "12                                    Appliances   272630020   266131100   \n",
       "13                                     Bookcases   169304620   159984680   \n",
       "14                                        Tables   505875008   388993784   \n",
       "15                               Copiers and Fax   404074080   292489800   \n",
       "16                                        Labels    15607780    10007040   \n",
       "\n",
       "year_transaction  %SalesGrowth  \n",
       "0                   190.990747  \n",
       "1                    60.787190  \n",
       "2                    48.652158  \n",
       "3                    32.377961  \n",
       "4                    24.728791  \n",
       "5                    24.198413  \n",
       "6                    22.097609  \n",
       "7                    14.238140  \n",
       "8                    11.501095  \n",
       "9                     7.672939  \n",
       "10                    5.009292  \n",
       "11                    1.681634  \n",
       "12                   -2.383787  \n",
       "13                   -5.504835  \n",
       "14                  -23.104763  \n",
       "15                  -27.614808  \n",
       "16                  -35.884283  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subcat1 = data_piv1.reset_index()\n",
    "data_subcat1.rename(columns={'2011': 'Sales_2011', '2012': 'Sales_2012'}, inplace=True)\n",
    "data_subcat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3dc7745b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_transaction</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>%OrderGrowth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"17\" valign=\"top\">order_status</th>\n",
       "      <th>Office Machines</th>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>37.209302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Computer Peripherals</th>\n",
       "      <td>106</td>\n",
       "      <td>132</td>\n",
       "      <td>24.528302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Envelopes</th>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "      <td>23.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Storage &amp; Organization</th>\n",
       "      <td>58</td>\n",
       "      <td>71</td>\n",
       "      <td>22.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rubber Bands</th>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>22.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Scissors, Rulers and Trimmers</th>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bookcases</th>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>7.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Telephones and Communication</th>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>7.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Labels</th>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pens &amp; Art Supplies</th>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paper</th>\n",
       "      <td>162</td>\n",
       "      <td>167</td>\n",
       "      <td>3.086420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binders and Binder Accessories</th>\n",
       "      <td>178</td>\n",
       "      <td>176</td>\n",
       "      <td>-1.123596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tables</th>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>-2.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appliances</th>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "      <td>-2.469136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Furnishings</th>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>-2.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chairs &amp; Chairmats</th>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>-4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Copiers and Fax</th>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>-14.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_transaction                             2011  2012  %OrderGrowth\n",
       "             product_sub_category                                    \n",
       "order_status Office Machines                   43    59     37.209302\n",
       "             Computer Peripherals             106   132     24.528302\n",
       "             Envelopes                         34    42     23.529412\n",
       "             Storage & Organization            58    71     22.413793\n",
       "             Rubber Bands                      18    22     22.222222\n",
       "             Scissors, Rulers and Trimmers     19    21     10.526316\n",
       "             Bookcases                         26    28      7.692308\n",
       "             Telephones and Communication      92    99      7.608696\n",
       "             Labels                            45    48      6.666667\n",
       "             Pens & Art Supplies               78    81      3.846154\n",
       "             Paper                            162   167      3.086420\n",
       "             Binders and Binder Accessories   178   176     -1.123596\n",
       "             Tables                            41    40     -2.439024\n",
       "             Appliances                        81    79     -2.469136\n",
       "             Office Furnishings               111   108     -2.702703\n",
       "             Chairs & Chairmats                72    69     -4.166667\n",
       "             Copiers and Fax                   14    12    -14.285714"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_piv2 = data1.loc[(data1['year_transaction'] == '2011') | (data1['year_transaction'] == '2012')].pivot_table(\n",
    "                        index='year_transaction',\n",
    "                        columns=['product_sub_category'],\n",
    "                        values=['order_status'], \n",
    "                        aggfunc={'order_status':'count'}, \n",
    "                        fill_value=0).T\n",
    "\n",
    "data_piv2['%OrderGrowth'] = ((data_piv2['2012'] / data_piv2['2011']) -1) * 100\n",
    "data_piv2.sort_values('%OrderGrowth', ascending=False, inplace=True)\n",
    "data_piv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "87e158aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>year_transaction</th>\n",
       "      <th>level_0</th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th>Order_2011</th>\n",
       "      <th>Order_2012</th>\n",
       "      <th>%OrderGrowth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Office Machines</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>37.209302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Computer Peripherals</td>\n",
       "      <td>106</td>\n",
       "      <td>132</td>\n",
       "      <td>24.528302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "      <td>23.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Storage &amp; Organization</td>\n",
       "      <td>58</td>\n",
       "      <td>71</td>\n",
       "      <td>22.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Rubber Bands</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>22.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Scissors, Rulers and Trimmers</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>7.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>7.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Labels</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Pens &amp; Art Supplies</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Paper</td>\n",
       "      <td>162</td>\n",
       "      <td>167</td>\n",
       "      <td>3.086420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>178</td>\n",
       "      <td>176</td>\n",
       "      <td>-1.123596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Tables</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>-2.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "      <td>-2.469136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Office Furnishings</td>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>-2.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Chairs &amp; Chairmats</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>-4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>order_status</td>\n",
       "      <td>Copiers and Fax</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>-14.285714</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_transaction       level_0            product_sub_category  Order_2011  \\\n",
       "0                 order_status                 Office Machines          43   \n",
       "1                 order_status            Computer Peripherals         106   \n",
       "2                 order_status                       Envelopes          34   \n",
       "3                 order_status          Storage & Organization          58   \n",
       "4                 order_status                    Rubber Bands          18   \n",
       "5                 order_status   Scissors, Rulers and Trimmers          19   \n",
       "6                 order_status                       Bookcases          26   \n",
       "7                 order_status    Telephones and Communication          92   \n",
       "8                 order_status                          Labels          45   \n",
       "9                 order_status             Pens & Art Supplies          78   \n",
       "10                order_status                           Paper         162   \n",
       "11                order_status  Binders and Binder Accessories         178   \n",
       "12                order_status                          Tables          41   \n",
       "13                order_status                      Appliances          81   \n",
       "14                order_status              Office Furnishings         111   \n",
       "15                order_status              Chairs & Chairmats          72   \n",
       "16                order_status                 Copiers and Fax          14   \n",
       "\n",
       "year_transaction  Order_2012  %OrderGrowth  \n",
       "0                         59     37.209302  \n",
       "1                        132     24.528302  \n",
       "2                         42     23.529412  \n",
       "3                         71     22.413793  \n",
       "4                         22     22.222222  \n",
       "5                         21     10.526316  \n",
       "6                         28      7.692308  \n",
       "7                         99      7.608696  \n",
       "8                         48      6.666667  \n",
       "9                         81      3.846154  \n",
       "10                       167      3.086420  \n",
       "11                       176     -1.123596  \n",
       "12                        40     -2.439024  \n",
       "13                        79     -2.469136  \n",
       "14                       108     -2.702703  \n",
       "15                        69     -4.166667  \n",
       "16                        12    -14.285714  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subcat2 = data_piv2.reset_index()\n",
    "data_subcat2.rename(columns={'2011': 'Order_2011', '2012': 'Order_2012'}, inplace=True)\n",
    "data_subcat2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "087b6cfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>year_transaction</th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th>Sales_2011</th>\n",
       "      <th>Sales_2012</th>\n",
       "      <th>%SalesGrowth</th>\n",
       "      <th>Order_2011</th>\n",
       "      <th>Order_2012</th>\n",
       "      <th>%OrderGrowth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Scissors, Rulers and Trimmers</td>\n",
       "      <td>12638340</td>\n",
       "      <td>36776400</td>\n",
       "      <td>190.990747</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>10.526316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Envelopes</td>\n",
       "      <td>36463900</td>\n",
       "      <td>58629280</td>\n",
       "      <td>60.787190</td>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "      <td>23.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Office Machines</td>\n",
       "      <td>545856280</td>\n",
       "      <td>811427140</td>\n",
       "      <td>48.652158</td>\n",
       "      <td>43</td>\n",
       "      <td>59</td>\n",
       "      <td>37.209302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Computer Peripherals</td>\n",
       "      <td>232677960</td>\n",
       "      <td>308014340</td>\n",
       "      <td>32.377961</td>\n",
       "      <td>106</td>\n",
       "      <td>132</td>\n",
       "      <td>24.528302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Storage &amp; Organization</td>\n",
       "      <td>285991820</td>\n",
       "      <td>356714140</td>\n",
       "      <td>24.728791</td>\n",
       "      <td>58</td>\n",
       "      <td>71</td>\n",
       "      <td>22.413793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Rubber Bands</td>\n",
       "      <td>3090120</td>\n",
       "      <td>3837880</td>\n",
       "      <td>24.198413</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>22.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>298023200</td>\n",
       "      <td>363879200</td>\n",
       "      <td>22.097609</td>\n",
       "      <td>178</td>\n",
       "      <td>176</td>\n",
       "      <td>-1.123596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Paper</td>\n",
       "      <td>111080380</td>\n",
       "      <td>126896160</td>\n",
       "      <td>14.238140</td>\n",
       "      <td>162</td>\n",
       "      <td>167</td>\n",
       "      <td>3.086420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Furnishings</td>\n",
       "      <td>160471500</td>\n",
       "      <td>178927480</td>\n",
       "      <td>11.501095</td>\n",
       "      <td>111</td>\n",
       "      <td>108</td>\n",
       "      <td>-2.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>392194658</td>\n",
       "      <td>422287514</td>\n",
       "      <td>7.672939</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>7.608696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Chairs &amp; Chairmats</td>\n",
       "      <td>622962720</td>\n",
       "      <td>654168740</td>\n",
       "      <td>5.009292</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>-4.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pens &amp; Art Supplies</td>\n",
       "      <td>43093800</td>\n",
       "      <td>43818480</td>\n",
       "      <td>1.681634</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Appliances</td>\n",
       "      <td>272630020</td>\n",
       "      <td>266131100</td>\n",
       "      <td>-2.383787</td>\n",
       "      <td>81</td>\n",
       "      <td>79</td>\n",
       "      <td>-2.469136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Bookcases</td>\n",
       "      <td>169304620</td>\n",
       "      <td>159984680</td>\n",
       "      <td>-5.504835</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "      <td>7.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Tables</td>\n",
       "      <td>505875008</td>\n",
       "      <td>388993784</td>\n",
       "      <td>-23.104763</td>\n",
       "      <td>41</td>\n",
       "      <td>40</td>\n",
       "      <td>-2.439024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Copiers and Fax</td>\n",
       "      <td>404074080</td>\n",
       "      <td>292489800</td>\n",
       "      <td>-27.614808</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>-14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Labels</td>\n",
       "      <td>15607780</td>\n",
       "      <td>10007040</td>\n",
       "      <td>-35.884283</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year_transaction            product_sub_category  Sales_2011  Sales_2012  \\\n",
       "0                  Scissors, Rulers and Trimmers    12638340    36776400   \n",
       "1                                      Envelopes    36463900    58629280   \n",
       "2                                Office Machines   545856280   811427140   \n",
       "3                           Computer Peripherals   232677960   308014340   \n",
       "4                         Storage & Organization   285991820   356714140   \n",
       "5                                   Rubber Bands     3090120     3837880   \n",
       "6                 Binders and Binder Accessories   298023200   363879200   \n",
       "7                                          Paper   111080380   126896160   \n",
       "8                             Office Furnishings   160471500   178927480   \n",
       "9                   Telephones and Communication   392194658   422287514   \n",
       "10                            Chairs & Chairmats   622962720   654168740   \n",
       "11                           Pens & Art Supplies    43093800    43818480   \n",
       "12                                    Appliances   272630020   266131100   \n",
       "13                                     Bookcases   169304620   159984680   \n",
       "14                                        Tables   505875008   388993784   \n",
       "15                               Copiers and Fax   404074080   292489800   \n",
       "16                                        Labels    15607780    10007040   \n",
       "\n",
       "year_transaction  %SalesGrowth  Order_2011  Order_2012  %OrderGrowth  \n",
       "0                   190.990747          19          21     10.526316  \n",
       "1                    60.787190          34          42     23.529412  \n",
       "2                    48.652158          43          59     37.209302  \n",
       "3                    32.377961         106         132     24.528302  \n",
       "4                    24.728791          58          71     22.413793  \n",
       "5                    24.198413          18          22     22.222222  \n",
       "6                    22.097609         178         176     -1.123596  \n",
       "7                    14.238140         162         167      3.086420  \n",
       "8                    11.501095         111         108     -2.702703  \n",
       "9                     7.672939          92          99      7.608696  \n",
       "10                    5.009292          72          69     -4.166667  \n",
       "11                    1.681634          78          81      3.846154  \n",
       "12                   -2.383787          81          79     -2.469136  \n",
       "13                   -5.504835          26          28      7.692308  \n",
       "14                  -23.104763          41          40     -2.439024  \n",
       "15                  -27.614808          14          12    -14.285714  \n",
       "16                  -35.884283          45          48      6.666667  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_subcat = pd.merge(data_subcat1, data_subcat2, on ='product_sub_category', how='inner')\n",
    "data_subcat = data_subcat.drop(data_subcat.columns[[4]], axis=1)\n",
    "data_subcat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b81775",
   "metadata": {},
   "source": [
    "select year(order_date) as years, product_sub_category, sum(sales) as sales from dqlab_sales_store\n",
    "where (year(order_date) between 2011 and 2012) and order_status = 'Order Finished'\n",
    "group by year(order_date),product_sub_category\n",
    "order by years asc, sales desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ebf1bd1",
   "metadata": {},
   "source": [
    "## Promotion Effectiveness and Efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a212fc1",
   "metadata": {},
   "source": [
    "### 1. Promotion Effectiveness and Efficiency by Years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "43a214d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year_transaction</th>\n",
       "      <th>discount_value</th>\n",
       "      <th>sales</th>\n",
       "      <th>burn_rate_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>214330327</td>\n",
       "      <td>4613872681</td>\n",
       "      <td>4.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>197506939</td>\n",
       "      <td>4059100607</td>\n",
       "      <td>4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>214611556</td>\n",
       "      <td>4112036186</td>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>225867642</td>\n",
       "      <td>4482983158</td>\n",
       "      <td>5.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_transaction  discount_value       sales  burn_rate_pct\n",
       "0              2009       214330327  4613872681           4.65\n",
       "1              2010       197506939  4059100607           4.87\n",
       "2              2011       214611556  4112036186           5.22\n",
       "3              2012       225867642  4482983158           5.04"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the burn rate of the promotion performed by overalls based on the year\n",
    "\n",
    "\"\"\"The effectiveness and efficiency of the promotions carried out will be analyzed based on the Burn Rate, \n",
    "Burn Rate is calculated by comparing the total value of the promotions issued to the total sales obtained.\"\"\"\n",
    "\n",
    "# DQLab expected maximum burn rate is 4.5%\n",
    "\n",
    "burn_rate_year = data[data['order_status'] == 'Order Finished'].groupby('year_transaction', \n",
    "                                                                        as_index=False)[['discount_value','sales']].sum()\n",
    "burn_rate_year['burn_rate_pct'] = round(burn_rate_year['discount_value']/burn_rate_year['sales'] * 100, 2)\n",
    "burn_rate_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b46e909a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize burn rate percentage \n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.pointplot(data = burn_rate_year, \n",
    "              x='year_transaction', \n",
    "              y='burn_rate_pct')\n",
    "plt.ylim(4,6)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdcb3d2",
   "metadata": {},
   "source": [
    "select year(order_date) as years, sum(sales) as sales, sum(discount_value) as promotion_value, round(sum(discount_value)/sum(sales) * 100, 2) as burn_rate_percentage from dqlab_sales_store\n",
    "where order_status = 'Order Finished'\n",
    "group by years"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5e1ab4",
   "metadata": {},
   "source": [
    "### 2. Promotion Effectiveness and Efficiency by Product Sub Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ab657f9c",
   "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>year_transaction</th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th>discount_value</th>\n",
       "      <th>sales</th>\n",
       "      <th>burn_rate_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>2012</td>\n",
       "      <td>Scissors, Rulers and Trimmers</td>\n",
       "      <td>2349280</td>\n",
       "      <td>36776400</td>\n",
       "      <td>6.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2012</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>10024365</td>\n",
       "      <td>159984680</td>\n",
       "      <td>6.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2012</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>22338980</td>\n",
       "      <td>363879200</td>\n",
       "      <td>6.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>2012</td>\n",
       "      <td>Office Machines</td>\n",
       "      <td>46616695</td>\n",
       "      <td>811427140</td>\n",
       "      <td>5.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2012</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>14393300</td>\n",
       "      <td>266131100</td>\n",
       "      <td>5.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>2012</td>\n",
       "      <td>Pens &amp; Art Supplies</td>\n",
       "      <td>2343501</td>\n",
       "      <td>43818480</td>\n",
       "      <td>5.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2012</td>\n",
       "      <td>Storage &amp; Organization</td>\n",
       "      <td>18802166</td>\n",
       "      <td>356714140</td>\n",
       "      <td>5.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2012</td>\n",
       "      <td>Computer Peripherals</td>\n",
       "      <td>15333293</td>\n",
       "      <td>308014340</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2012</td>\n",
       "      <td>Copiers and Fax</td>\n",
       "      <td>14530870</td>\n",
       "      <td>292489800</td>\n",
       "      <td>4.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2012</td>\n",
       "      <td>Paper</td>\n",
       "      <td>6224694</td>\n",
       "      <td>126896160</td>\n",
       "      <td>4.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2012</td>\n",
       "      <td>Office Furnishings</td>\n",
       "      <td>8233849</td>\n",
       "      <td>178927480</td>\n",
       "      <td>4.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2012</td>\n",
       "      <td>Labels</td>\n",
       "      <td>452245</td>\n",
       "      <td>10007040</td>\n",
       "      <td>4.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2012</td>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>18800188</td>\n",
       "      <td>422287514</td>\n",
       "      <td>4.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>2012</td>\n",
       "      <td>Tables</td>\n",
       "      <td>16348689</td>\n",
       "      <td>388993784</td>\n",
       "      <td>4.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2012</td>\n",
       "      <td>Chairs &amp; Chairmats</td>\n",
       "      <td>26623882</td>\n",
       "      <td>654168740</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2012</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>2334321</td>\n",
       "      <td>58629280</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>2012</td>\n",
       "      <td>Rubber Bands</td>\n",
       "      <td>117324</td>\n",
       "      <td>3837880</td>\n",
       "      <td>3.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year_transaction            product_sub_category  discount_value  \\\n",
       "64              2012   Scissors, Rulers and Trimmers         2349280   \n",
       "53              2012                       Bookcases        10024365   \n",
       "52              2012  Binders and Binder Accessories        22338980   \n",
       "60              2012                 Office Machines        46616695   \n",
       "51              2012                      Appliances        14393300   \n",
       "62              2012             Pens & Art Supplies         2343501   \n",
       "65              2012          Storage & Organization        18802166   \n",
       "55              2012            Computer Peripherals        15333293   \n",
       "56              2012                 Copiers and Fax        14530870   \n",
       "61              2012                           Paper         6224694   \n",
       "59              2012              Office Furnishings         8233849   \n",
       "58              2012                          Labels          452245   \n",
       "67              2012    Telephones and Communication        18800188   \n",
       "66              2012                          Tables        16348689   \n",
       "54              2012              Chairs & Chairmats        26623882   \n",
       "57              2012                       Envelopes         2334321   \n",
       "63              2012                    Rubber Bands          117324   \n",
       "\n",
       "        sales  burn_rate_pct  \n",
       "64   36776400           6.39  \n",
       "53  159984680           6.27  \n",
       "52  363879200           6.14  \n",
       "60  811427140           5.75  \n",
       "51  266131100           5.41  \n",
       "62   43818480           5.35  \n",
       "65  356714140           5.27  \n",
       "55  308014340           4.98  \n",
       "56  292489800           4.97  \n",
       "61  126896160           4.91  \n",
       "59  178927480           4.60  \n",
       "58   10007040           4.52  \n",
       "67  422287514           4.45  \n",
       "66  388993784           4.20  \n",
       "54  654168740           4.07  \n",
       "57   58629280           3.98  \n",
       "63    3837880           3.06  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the burn rate of the promotion performed by overalls based on sub-category\n",
    "\n",
    "burn_rate_sub_category = data[data['order_status'] == 'Order Finished'].groupby(['year_transaction', 'product_sub_category'], as_index=False)[['discount_value','sales']].sum()\n",
    "burn_rate_sub_category['burn_rate_pct'] = round(burn_rate_sub_category['discount_value']/burn_rate_sub_category['sales'] * 100, 2)\n",
    "burn_rate_sub_category = pd.DataFrame(burn_rate_sub_category)\n",
    "burn_rate_sub_category = burn_rate_sub_category.loc[burn_rate_sub_category['year_transaction'] == 2012]\n",
    "burn_rate_sub_category = burn_rate_sub_category.sort_values(by ='burn_rate_pct', ascending=False)\n",
    "burn_rate_sub_category\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "500973c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot burn rate percentage by sub category\n",
    "\n",
    "burn_rate_sub_category.plot(x='product_sub_category', y='burn_rate_pct', kind='barh', title='Burn Rate by Sub-Category in 2012')\n",
    "plt.xlabel('Product Sub-Category')\n",
    "plt.ylabel('Burn Rate Percentage')\n",
    "plt.legend().set_visible(False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f1d5701",
   "metadata": {},
   "source": [
    "select year(order_date) as years, product_sub_category, product_category, sum(sales) as sales, sum(discount_value) as promotion_value, round(sum(discount_value)/sum(sales) * 100, 2) as burn_rate_percentage from dqlab_sales_store\n",
    "where year(order_date) = '2012' and order_status = 'Order Finished'\n",
    "group by year(order_date), product_sub_category, product_category\n",
    "order by sales desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3834506",
   "metadata": {},
   "source": [
    "## Customers Transactions per Year"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f26f4485",
   "metadata": {},
   "source": [
    "### 1. Number of Customer who Make Transaction by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "72ce7749",
   "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>year_transaction</th>\n",
       "      <th>customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year_transaction  customer\n",
       "0              2009       585\n",
       "1              2010       593\n",
       "2              2011       581\n",
       "3              2012       594"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of Customer who Make Transaction by Year\n",
    "\n",
    "num_customer = data[data['order_status'] == 'Order Finished'].groupby('year_transaction', as_index=False)['customer'].nunique()\n",
    "num_customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d03ed02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Number of Customer who Make Transaction by Year\n",
    "\n",
    "sns.pointplot(data = num_customer, \n",
    "              x='year_transaction', \n",
    "              y='customer')\n",
    "plt.ylim(570, 600)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bf95ee9",
   "metadata": {},
   "source": [
    "select year(order_date) as years, count(distinct customer) as number_of_customer\n",
    "from dqlab_sales_store\n",
    "where order_status = 'Order Finished'\n",
    "group by year(order_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85299cf7",
   "metadata": {},
   "source": [
    "### 2. Customer Aquisition per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e3f169a",
   "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>customer</th>\n",
       "      <th>first_year_as_customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Muhammed MacIntyre</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Barry French</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Clay Rozendal</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Carlos Soltero</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carl Jackson</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>Catherine Glotzbach</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>Frank Olsen</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>Alice McCarthy</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>Frank Merwin</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>Bruce Money</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>775 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                customer  first_year_as_customer\n",
       "0     Muhammed MacIntyre                    2009\n",
       "1           Barry French                    2010\n",
       "2          Clay Rozendal                    2009\n",
       "3         Carlos Soltero                    2009\n",
       "4           Carl Jackson                    2010\n",
       "..                   ...                     ...\n",
       "770  Catherine Glotzbach                    2010\n",
       "771          Frank Olsen                    2009\n",
       "772       Alice McCarthy                    2010\n",
       "773         Frank Merwin                    2010\n",
       "774          Bruce Money                    2012\n",
       "\n",
       "[775 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of new customers every year\n",
    "\n",
    "customer = pd.DataFrame(data['customer'].unique())\n",
    "customer.columns = ['customer']\n",
    "\n",
    "first_year = data[data['order_status'] == 'Order Finished'].groupby('customer').year_transaction.min().reset_index()\n",
    "first_year.columns = ['customer', 'first_year_as_customer']\n",
    "\n",
    "customer = pd.merge(customer, first_year[['customer','first_year_as_customer']], on='customer')\n",
    "customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "73688cbc",
   "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>first_year_as_customer</th>\n",
       "      <th>customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first_year_as_customer  customer\n",
       "0                    2009       585\n",
       "1                    2010       141\n",
       "2                    2011        38\n",
       "3                    2012        11"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of new customers every year\n",
    "customer = pd.DataFrame(customer)\n",
    "customer = customer.groupby('first_year_as_customer', as_index=False)['customer'].count()\n",
    "customer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4332132e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Number of new customers every year\n",
    "\n",
    "customer.plot(x='first_year_as_customer', y='customer', kind='bar', title='Number of New Customers per Year')\n",
    "plt.xlabel('Year_Acquisition')\n",
    "plt.ylabel('Num_of_Customer')\n",
    "plt.legend().set_visible(False)\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "530b32a4",
   "metadata": {},
   "source": [
    "### 3, Customer Retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "72e5108e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ASUS\\AppData\\Local\\Temp/ipykernel_14040/1239184740.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data1['year_transaction'] = data1['year_transaction'].astype(int)\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp/ipykernel_14040/1239184740.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['month_transaction'] = data2['order_date'].dt.month\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp/ipykernel_14040/1239184740.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['first_transaction_month'] = customer_by_month.transform('min')\n",
      "C:\\Users\\ASUS\\AppData\\Local\\Temp/ipykernel_14040/1239184740.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data2['lifetime'] = data2['month_transaction'] - data2['first_transaction_month']\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>order_status</th>\n",
       "      <th>customer</th>\n",
       "      <th>order_date</th>\n",
       "      <th>order_quantity</th>\n",
       "      <th>sales</th>\n",
       "      <th>discount</th>\n",
       "      <th>discount_value</th>\n",
       "      <th>product_category</th>\n",
       "      <th>product_sub_category</th>\n",
       "      <th>year_transaction</th>\n",
       "      <th>month_transaction</th>\n",
       "      <th>first_transaction_month</th>\n",
       "      <th>lifetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>293</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Barry French</td>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>49</td>\n",
       "      <td>20246040</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1417223</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>2012</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>868</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Carlos Daly</td>\n",
       "      <td>2012-06-08</td>\n",
       "      <td>32</td>\n",
       "      <td>1433680</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>2012</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>933</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Claudia Miner</td>\n",
       "      <td>2012-08-04</td>\n",
       "      <td>15</td>\n",
       "      <td>161220</td>\n",
       "      <td>0.02</td>\n",
       "      <td>3224</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>2012</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1154</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Sylvia Foulston</td>\n",
       "      <td>2012-02-14</td>\n",
       "      <td>44</td>\n",
       "      <td>8924460</td>\n",
       "      <td>0.04</td>\n",
       "      <td>356978</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1344</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Jim Radford</td>\n",
       "      <td>2012-04-15</td>\n",
       "      <td>15</td>\n",
       "      <td>1669808</td>\n",
       "      <td>0.06</td>\n",
       "      <td>100188</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5475</th>\n",
       "      <td>52673</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Jas O'Carroll</td>\n",
       "      <td>2012-05-08</td>\n",
       "      <td>14</td>\n",
       "      <td>4090132</td>\n",
       "      <td>0.07</td>\n",
       "      <td>286309</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Telephones and Communication</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5476</th>\n",
       "      <td>53026</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Eva Jacobs</td>\n",
       "      <td>2012-02-26</td>\n",
       "      <td>35</td>\n",
       "      <td>645640</td>\n",
       "      <td>0.05</td>\n",
       "      <td>32282</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Scissors, Rulers and Trimmers</td>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5483</th>\n",
       "      <td>57152</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Philip Brown</td>\n",
       "      <td>2012-09-03</td>\n",
       "      <td>6</td>\n",
       "      <td>45780</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3662</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5489</th>\n",
       "      <td>5767</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Lisa DeCherney</td>\n",
       "      <td>2012-04-28</td>\n",
       "      <td>36</td>\n",
       "      <td>327080</td>\n",
       "      <td>0.03</td>\n",
       "      <td>9812</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>2012</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>52706</td>\n",
       "      <td>Order Finished</td>\n",
       "      <td>Paul Prost</td>\n",
       "      <td>2012-07-09</td>\n",
       "      <td>34</td>\n",
       "      <td>2083320</td>\n",
       "      <td>0.02</td>\n",
       "      <td>41666</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders and Binder Accessories</td>\n",
       "      <td>2012</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1254 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      order_id    order_status         customer order_date  order_quantity  \\\n",
       "1          293  Order Finished     Barry French 2012-10-01              49   \n",
       "8          868  Order Finished      Carlos Daly 2012-06-08              32   \n",
       "9          933  Order Finished    Claudia Miner 2012-08-04              15   \n",
       "12        1154  Order Finished  Sylvia Foulston 2012-02-14              44   \n",
       "13        1344  Order Finished      Jim Radford 2012-04-15              15   \n",
       "...        ...             ...              ...        ...             ...   \n",
       "5475     52673  Order Finished    Jas O'Carroll 2012-05-08              14   \n",
       "5476     53026  Order Finished       Eva Jacobs 2012-02-26              35   \n",
       "5483     57152  Order Finished     Philip Brown 2012-09-03               6   \n",
       "5489      5767  Order Finished   Lisa DeCherney 2012-04-28              36   \n",
       "5495     52706  Order Finished       Paul Prost 2012-07-09              34   \n",
       "\n",
       "         sales  discount  discount_value product_category  \\\n",
       "1     20246040      0.07         1417223  Office Supplies   \n",
       "8      1433680      0.00               0  Office Supplies   \n",
       "9       161220      0.02            3224  Office Supplies   \n",
       "12     8924460      0.04          356978        Furniture   \n",
       "13     1669808      0.06          100188       Technology   \n",
       "...        ...       ...             ...              ...   \n",
       "5475   4090132      0.07          286309       Technology   \n",
       "5476    645640      0.05           32282  Office Supplies   \n",
       "5483     45780      0.08            3662  Office Supplies   \n",
       "5489    327080      0.03            9812  Office Supplies   \n",
       "5495   2083320      0.02           41666  Office Supplies   \n",
       "\n",
       "                product_sub_category  year_transaction  month_transaction  \\\n",
       "1                         Appliances              2012                 10   \n",
       "8                         Appliances              2012                  6   \n",
       "9     Binders and Binder Accessories              2012                  8   \n",
       "12                         Bookcases              2012                  2   \n",
       "13      Telephones and Communication              2012                  4   \n",
       "...                              ...               ...                ...   \n",
       "5475    Telephones and Communication              2012                  5   \n",
       "5476   Scissors, Rulers and Trimmers              2012                  2   \n",
       "5483  Binders and Binder Accessories              2012                  9   \n",
       "5489  Binders and Binder Accessories              2012                  4   \n",
       "5495  Binders and Binder Accessories              2012                  7   \n",
       "\n",
       "      first_transaction_month  lifetime  \n",
       "1                           2         8  \n",
       "8                           6         0  \n",
       "9                           4         4  \n",
       "12                          2         0  \n",
       "13                          4         0  \n",
       "...                       ...       ...  \n",
       "5475                        5         0  \n",
       "5476                        1         1  \n",
       "5483                        9         0  \n",
       "5489                        4         0  \n",
       "5495                        7         0  \n",
       "\n",
       "[1254 rows x 14 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1['year_transaction'] = data1['year_transaction'].astype(int)\n",
    "data2 = data1.loc[data1['year_transaction'] == 2012]\n",
    "data2['month_transaction'] = data2['order_date'].dt.month\n",
    "\n",
    "customer_by_month = data2.groupby('customer')['month_transaction']\n",
    "data2['first_transaction_month'] = customer_by_month.transform('min')\n",
    "data2['lifetime'] = data2['month_transaction'] - data2['first_transaction_month']\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b1bc8d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>first_transaction_month</th>\n",
       "      <th>lifetime</th>\n",
       "      <th>customer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    first_transaction_month  lifetime  customer\n",
       "0                         1         0        95\n",
       "1                         1         1        14\n",
       "2                         1         2        20\n",
       "3                         1         3        17\n",
       "4                         1         4        12\n",
       "..                      ...       ...       ...\n",
       "73                       10         1         5\n",
       "74                       10         2         2\n",
       "75                       11         0        18\n",
       "76                       11         1         3\n",
       "77                       12         0        15\n",
       "\n",
       "[78 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_by_month = data2.groupby(['first_transaction_month','lifetime'])\n",
    "\n",
    "cohort_data = customer_by_month['customer'].apply(pd.Series.nunique)\n",
    "cohort_data = cohort_data.reset_index()\n",
    "\n",
    "# Assigning column names to the dataframe created above\n",
    "cohort_counts = cohort_data.pivot(index='first_transaction_month',\n",
    "                                 columns ='lifetime',\n",
    "                                 values = 'customer')\n",
    "# Printing top 5 rows of Dataframe\n",
    "cohort_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "14740054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>12</th>\n",
       "      <td>100.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "lifetime                    0     1     2     3     4     5     6     7   \\\n",
       "first_transaction_month                                                    \n",
       "1                        100.0  14.7  21.1  17.9  12.6   8.4  10.5  14.7   \n",
       "2                        100.0  13.9  13.9  11.1  20.8   6.9  15.3  15.3   \n",
       "3                        100.0  13.4  18.3  12.2  17.1  13.4   9.8   9.8   \n",
       "4                        100.0  23.1   7.7  12.3  13.8  21.5  12.3  10.8   \n",
       "5                        100.0   9.4  14.1  17.2   9.4  10.9  12.5   9.4   \n",
       "6                        100.0   7.1  19.0   9.5  26.2  16.7   9.5   NaN   \n",
       "7                        100.0   9.1  11.4  11.4   2.3   6.8   NaN   NaN   \n",
       "8                        100.0  18.9  13.5   5.4   2.7   NaN   NaN   NaN   \n",
       "9                        100.0  12.9   6.5  12.9   NaN   NaN   NaN   NaN   \n",
       "10                       100.0  17.2   6.9   NaN   NaN   NaN   NaN   NaN   \n",
       "11                       100.0  16.7   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "12                       100.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   \n",
       "\n",
       "lifetime                   8     9     10    11  \n",
       "first_transaction_month                          \n",
       "1                        21.1  18.9  13.7  18.9  \n",
       "2                        16.7  11.1   9.7   NaN  \n",
       "3                         8.5   7.3   NaN   NaN  \n",
       "4                        15.4   NaN   NaN   NaN  \n",
       "5                         NaN   NaN   NaN   NaN  \n",
       "6                         NaN   NaN   NaN   NaN  \n",
       "7                         NaN   NaN   NaN   NaN  \n",
       "8                         NaN   NaN   NaN   NaN  \n",
       "9                         NaN   NaN   NaN   NaN  \n",
       "10                        NaN   NaN   NaN   NaN  \n",
       "11                        NaN   NaN   NaN   NaN  \n",
       "12                        NaN   NaN   NaN   NaN  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cohort_sizes = cohort_counts.iloc[:,0]\n",
    "retention = cohort_counts.divide(cohort_sizes, axis=0)\n",
    "\n",
    "# Coverting the retention rate into percentage and rounding off.\n",
    "retention = retention.round(3)*100\n",
    "retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "873297cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Customer Retention Rate (%) - Monthly Cohort in 2012')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16,9))\n",
    "sns.heatmap(retention, annot=True, fmt='g', cmap='RdPu')\n",
    "plt.ylabel('First Transaction Month')\n",
    "plt.xlabel('Lifetime')\n",
    "plt.yticks(rotation=0)\n",
    "plt.title('Customer Retention Rate (%) - Monthly Cohort in 2012')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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