Source Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the required functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   # Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Order Zipcode</th>\n",
       "      <th>Product Card Id</th>\n",
       "      <th>Product Category Id</th>\n",
       "      <th>Product Description</th>\n",
       "      <th>Product Image</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>314.640015</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
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       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
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       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
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       "      <td>1360</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
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       "      <td>0</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
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       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>134.210007</td>\n",
       "      <td>298.250000</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
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       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
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       "      <th>180514</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>399.980011</td>\n",
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       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 3:40</td>\n",
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       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
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       "      <td>TRANSFER</td>\n",
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       "      <td>391.980011</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 21:00</td>\n",
       "      <td>Standard Class</td>\n",
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       "      <th>180517</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>186.229996</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 20:18</td>\n",
       "      <td>Standard Class</td>\n",
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       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168.949997</td>\n",
       "      <td>383.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 53 columns</p>\n",
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      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0          DEBIT                         3                              4   \n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "3          DEBIT                         3                              4   \n",
       "4        PAYMENT                         2                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180514      CASH                         4                              4   \n",
       "180515     DEBIT                         3                              2   \n",
       "180516  TRANSFER                         5                              4   \n",
       "180517   PAYMENT                         3                              4   \n",
       "180518   PAYMENT                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "4              134.210007          298.250000  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180514          40.000000          399.980011  Shipping on time   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "180516         141.110001          391.980011     Late delivery   \n",
       "180517         186.229996          387.980011  Advance shipping   \n",
       "180518         168.949997          383.980011  Shipping on time   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                        0           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180514                   0           45         Fishing      Brooklyn  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "180516                   1           45         Fishing       Bristol  ...   \n",
       "180517                   0           45         Fishing        Caguas  ...   \n",
       "180518                   0           45         Fishing        Caguas  ...   \n",
       "\n",
       "       Order Zipcode Product Card Id Product Category Id  Product Description  \\\n",
       "0                NaN            1360                  73                  NaN   \n",
       "1                NaN            1360                  73                  NaN   \n",
       "2                NaN            1360                  73                  NaN   \n",
       "3                NaN            1360                  73                  NaN   \n",
       "4                NaN            1360                  73                  NaN   \n",
       "...              ...             ...                 ...                  ...   \n",
       "180514           NaN            1004                  45                  NaN   \n",
       "180515           NaN            1004                  45                  NaN   \n",
       "180516           NaN            1004                  45                  NaN   \n",
       "180517           NaN            1004                  45                  NaN   \n",
       "180518           NaN            1004                  45                  NaN   \n",
       "\n",
       "                                            Product Image  \\\n",
       "0            http://images.acmesports.sports/Smart+watch    \n",
       "1            http://images.acmesports.sports/Smart+watch    \n",
       "2            http://images.acmesports.sports/Smart+watch    \n",
       "3            http://images.acmesports.sports/Smart+watch    \n",
       "4            http://images.acmesports.sports/Smart+watch    \n",
       "...                                                   ...   \n",
       "180514  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180515  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180516  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180517  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180518  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "\n",
       "                                     Product Name Product Price  \\\n",
       "0                                    Smart watch     327.750000   \n",
       "1                                    Smart watch     327.750000   \n",
       "2                                    Smart watch     327.750000   \n",
       "3                                    Smart watch     327.750000   \n",
       "4                                    Smart watch     327.750000   \n",
       "...                                           ...           ...   \n",
       "180514  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180516  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180517  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180518  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status shipping date (DateOrders)   Shipping Mode  \n",
       "0                   0             2/3/2018 22:56  Standard Class  \n",
       "1                   0            1/18/2018 12:27  Standard Class  \n",
       "2                   0            1/17/2018 12:06  Standard Class  \n",
       "3                   0            1/16/2018 11:45  Standard Class  \n",
       "4                   0            1/15/2018 11:24  Standard Class  \n",
       "...               ...                        ...             ...  \n",
       "180514              0             1/20/2016 3:40  Standard Class  \n",
       "180515              0             1/19/2016 1:34    Second Class  \n",
       "180516              0            1/20/2016 21:00  Standard Class  \n",
       "180517              0            1/18/2016 20:18  Standard Class  \n",
       "180518              0            1/19/2016 18:54  Standard Class  \n",
       "\n",
       "[180519 rows x 53 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main = pd.read_csv('C:/Users/Tanvi/Documents/SEM 1/ATDM/DataCoSupplyChainDataset.csv')\n",
    "main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180519, 53)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Order Status</th>\n",
       "      <th>Order Zipcode</th>\n",
       "      <th>Product Card Id</th>\n",
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       "      <th>Product Description</th>\n",
       "      <th>Product Image</th>\n",
       "      <th>Product Name</th>\n",
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       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
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       "      <td>91.250000</td>\n",
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       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>PENDING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>CLOSED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>134.210007</td>\n",
       "      <td>298.250000</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>PENDING_PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>1/15/2018 11:24</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>...</td>\n",
       "      <td>CLOSED</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>1/20/2016 3:40</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>COMPLETE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>141.110001</td>\n",
       "      <td>391.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>...</td>\n",
       "      <td>PENDING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>1/20/2016 21:00</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>186.229996</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>PENDING_PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>1/18/2016 20:18</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168.949997</td>\n",
       "      <td>383.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>PENDING_PAYMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>1/19/2016 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0          DEBIT                         3                              4   \n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "3          DEBIT                         3                              4   \n",
       "4        PAYMENT                         2                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180514      CASH                         4                              4   \n",
       "180515     DEBIT                         3                              2   \n",
       "180516  TRANSFER                         5                              4   \n",
       "180517   PAYMENT                         3                              4   \n",
       "180518   PAYMENT                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "4              134.210007          298.250000  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180514          40.000000          399.980011  Shipping on time   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "180516         141.110001          391.980011     Late delivery   \n",
       "180517         186.229996          387.980011  Advance shipping   \n",
       "180518         168.949997          383.980011  Shipping on time   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                        0           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180514                   0           45         Fishing      Brooklyn  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "180516                   1           45         Fishing       Bristol  ...   \n",
       "180517                   0           45         Fishing        Caguas  ...   \n",
       "180518                   0           45         Fishing        Caguas  ...   \n",
       "\n",
       "           Order Status Order Zipcode  Product Card Id Product Category Id  \\\n",
       "0              COMPLETE           NaN             1360                  73   \n",
       "1               PENDING           NaN             1360                  73   \n",
       "2                CLOSED           NaN             1360                  73   \n",
       "3              COMPLETE           NaN             1360                  73   \n",
       "4       PENDING_PAYMENT           NaN             1360                  73   \n",
       "...                 ...           ...              ...                 ...   \n",
       "180514           CLOSED           NaN             1004                  45   \n",
       "180515         COMPLETE           NaN             1004                  45   \n",
       "180516          PENDING           NaN             1004                  45   \n",
       "180517  PENDING_PAYMENT           NaN             1004                  45   \n",
       "180518  PENDING_PAYMENT           NaN             1004                  45   \n",
       "\n",
       "       Product Description                                      Product Image  \\\n",
       "0                      NaN       http://images.acmesports.sports/Smart+watch    \n",
       "1                      NaN       http://images.acmesports.sports/Smart+watch    \n",
       "2                      NaN       http://images.acmesports.sports/Smart+watch    \n",
       "3                      NaN       http://images.acmesports.sports/Smart+watch    \n",
       "4                      NaN       http://images.acmesports.sports/Smart+watch    \n",
       "...                    ...                                                ...   \n",
       "180514                 NaN  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180515                 NaN  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180516                 NaN  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180517                 NaN  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180518                 NaN  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "\n",
       "                                     Product Name  Product Price  \\\n",
       "0                                    Smart watch      327.750000   \n",
       "1                                    Smart watch      327.750000   \n",
       "2                                    Smart watch      327.750000   \n",
       "3                                    Smart watch      327.750000   \n",
       "4                                    Smart watch      327.750000   \n",
       "...                                           ...            ...   \n",
       "180514  Field & Stream Sportsman 16 Gun Fire Safe     399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe     399.980011   \n",
       "180516  Field & Stream Sportsman 16 Gun Fire Safe     399.980011   \n",
       "180517  Field & Stream Sportsman 16 Gun Fire Safe     399.980011   \n",
       "180518  Field & Stream Sportsman 16 Gun Fire Safe     399.980011   \n",
       "\n",
       "        shipping date (DateOrders)   Shipping Mode  \n",
       "0                   2/3/2018 22:56  Standard Class  \n",
       "1                  1/18/2018 12:27  Standard Class  \n",
       "2                  1/17/2018 12:06  Standard Class  \n",
       "3                  1/16/2018 11:45  Standard Class  \n",
       "4                  1/15/2018 11:24  Standard Class  \n",
       "...                            ...             ...  \n",
       "180514              1/20/2016 3:40  Standard Class  \n",
       "180515              1/19/2016 1:34    Second Class  \n",
       "180516             1/20/2016 21:00  Standard Class  \n",
       "180517             1/18/2016 20:18  Standard Class  \n",
       "180518             1/19/2016 18:54  Standard Class  \n",
       "\n",
       "[180519 rows x 50 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_new = main.drop(columns = [\"Product Status\", \"Customer Country\", \"Customer State\"])\n",
    "main_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180519 entries, 0 to 180518\n",
      "Data columns (total 50 columns):\n",
      " #   Column                         Non-Null Count   Dtype  \n",
      "---  ------                         --------------   -----  \n",
      " 0   Type                           180519 non-null  object \n",
      " 1   Days for shipping (real)       180519 non-null  int64  \n",
      " 2   Days for shipment (scheduled)  180519 non-null  int64  \n",
      " 3   Benefit per order              180519 non-null  float64\n",
      " 4   Sales per customer             180519 non-null  float64\n",
      " 5   Delivery Status                180519 non-null  object \n",
      " 6   Late_delivery_risk             180519 non-null  int64  \n",
      " 7   Category Id                    180519 non-null  int64  \n",
      " 8   Category Name                  180519 non-null  object \n",
      " 9   Customer City                  180519 non-null  object \n",
      " 10  Customer Email                 180519 non-null  object \n",
      " 11  Customer Fname                 180519 non-null  object \n",
      " 12  Customer Id                    180519 non-null  int64  \n",
      " 13  Customer Lname                 180511 non-null  object \n",
      " 14  Customer Password              180519 non-null  object \n",
      " 15  Customer Segment               180519 non-null  object \n",
      " 16  Customer Street                180519 non-null  object \n",
      " 17  Customer Zipcode               180516 non-null  float64\n",
      " 18  Department Id                  180519 non-null  int64  \n",
      " 19  Department Name                180519 non-null  object \n",
      " 20  Latitude                       180519 non-null  float64\n",
      " 21  Longitude                      180519 non-null  float64\n",
      " 22  Market                         180519 non-null  object \n",
      " 23  Order City                     180519 non-null  object \n",
      " 24  Order Country                  180519 non-null  object \n",
      " 25  Order Customer Id              180519 non-null  int64  \n",
      " 26  order date (DateOrders)        180519 non-null  object \n",
      " 27  Order Id                       180519 non-null  int64  \n",
      " 28  Order Item Cardprod Id         180519 non-null  int64  \n",
      " 29  Order Item Discount            180519 non-null  float64\n",
      " 30  Order Item Discount Rate       180519 non-null  float64\n",
      " 31  Order Item Id                  180519 non-null  int64  \n",
      " 32  Order Item Product Price       180519 non-null  float64\n",
      " 33  Order Item Profit Ratio        180519 non-null  float64\n",
      " 34  Order Item Quantity            180519 non-null  int64  \n",
      " 35  Sales                          180519 non-null  float64\n",
      " 36  Order Item Total               180519 non-null  float64\n",
      " 37  Order Profit Per Order         180519 non-null  float64\n",
      " 38  Order Region                   180519 non-null  object \n",
      " 39  Order State                    180519 non-null  object \n",
      " 40  Order Status                   180519 non-null  object \n",
      " 41  Order Zipcode                  24840 non-null   float64\n",
      " 42  Product Card Id                180519 non-null  int64  \n",
      " 43  Product Category Id            180519 non-null  int64  \n",
      " 44  Product Description            0 non-null       float64\n",
      " 45  Product Image                  180519 non-null  object \n",
      " 46  Product Name                   180519 non-null  object \n",
      " 47  Product Price                  180519 non-null  float64\n",
      " 48  shipping date (DateOrders)     180519 non-null  object \n",
      " 49  Shipping Mode                  180519 non-null  object \n",
      "dtypes: float64(15), int64(13), object(22)\n",
      "memory usage: 68.9+ MB\n"
     ]
    }
   ],
   "source": [
    "main_new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Customer Zipcode</th>\n",
       "      <th>Department Id</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Order Item Profit Ratio</th>\n",
       "      <th>Order Item Quantity</th>\n",
       "      <th>Sales</th>\n",
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       "      <th>Order Profit Per Order</th>\n",
       "      <th>Order Zipcode</th>\n",
       "      <th>Product Card Id</th>\n",
       "      <th>Product Category Id</th>\n",
       "      <th>Product Description</th>\n",
       "      <th>Product Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>180516.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>24840.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.497654</td>\n",
       "      <td>2.931847</td>\n",
       "      <td>21.974989</td>\n",
       "      <td>183.107609</td>\n",
       "      <td>0.548291</td>\n",
       "      <td>31.851451</td>\n",
       "      <td>6691.379495</td>\n",
       "      <td>35921.126914</td>\n",
       "      <td>5.443460</td>\n",
       "      <td>29.719955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120647</td>\n",
       "      <td>2.127638</td>\n",
       "      <td>203.772096</td>\n",
       "      <td>183.107609</td>\n",
       "      <td>21.974989</td>\n",
       "      <td>55426.132327</td>\n",
       "      <td>692.509764</td>\n",
       "      <td>31.851451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.232550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.623722</td>\n",
       "      <td>1.374449</td>\n",
       "      <td>104.433526</td>\n",
       "      <td>120.043670</td>\n",
       "      <td>0.497664</td>\n",
       "      <td>15.640064</td>\n",
       "      <td>4162.918106</td>\n",
       "      <td>37542.461122</td>\n",
       "      <td>1.629246</td>\n",
       "      <td>9.813646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466796</td>\n",
       "      <td>1.453451</td>\n",
       "      <td>132.273077</td>\n",
       "      <td>120.043670</td>\n",
       "      <td>104.433526</td>\n",
       "      <td>31919.279101</td>\n",
       "      <td>336.446807</td>\n",
       "      <td>15.640064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.732492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4274.979980</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-33.937553</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>-4274.979980</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>104.379997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3258.500000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.265432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>104.379997</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23464.000000</td>\n",
       "      <td>403.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>31.520000</td>\n",
       "      <td>163.990005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6457.000000</td>\n",
       "      <td>19380.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>33.144863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199.919998</td>\n",
       "      <td>163.990005</td>\n",
       "      <td>31.520000</td>\n",
       "      <td>59405.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>64.800003</td>\n",
       "      <td>247.399994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9779.000000</td>\n",
       "      <td>78207.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>39.279617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>299.950012</td>\n",
       "      <td>247.399994</td>\n",
       "      <td>64.800003</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.990005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>911.799988</td>\n",
       "      <td>1939.989990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>20757.000000</td>\n",
       "      <td>99205.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>48.781933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1999.989990</td>\n",
       "      <td>1939.989990</td>\n",
       "      <td>911.799988</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.989990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "count             180519.000000                  180519.000000   \n",
       "mean                   3.497654                       2.931847   \n",
       "std                    1.623722                       1.374449   \n",
       "min                    0.000000                       0.000000   \n",
       "25%                    2.000000                       2.000000   \n",
       "50%                    3.000000                       4.000000   \n",
       "75%                    5.000000                       4.000000   \n",
       "max                    6.000000                       4.000000   \n",
       "\n",
       "       Benefit per order  Sales per customer  Late_delivery_risk  \\\n",
       "count      180519.000000       180519.000000       180519.000000   \n",
       "mean           21.974989          183.107609            0.548291   \n",
       "std           104.433526          120.043670            0.497664   \n",
       "min         -4274.979980            7.490000            0.000000   \n",
       "25%             7.000000          104.379997            0.000000   \n",
       "50%            31.520000          163.990005            1.000000   \n",
       "75%            64.800003          247.399994            1.000000   \n",
       "max           911.799988         1939.989990            1.000000   \n",
       "\n",
       "         Category Id    Customer Id  Customer Zipcode  Department Id  \\\n",
       "count  180519.000000  180519.000000     180516.000000  180519.000000   \n",
       "mean       31.851451    6691.379495      35921.126914       5.443460   \n",
       "std        15.640064    4162.918106      37542.461122       1.629246   \n",
       "min         2.000000       1.000000        603.000000       2.000000   \n",
       "25%        18.000000    3258.500000        725.000000       4.000000   \n",
       "50%        29.000000    6457.000000      19380.000000       5.000000   \n",
       "75%        45.000000    9779.000000      78207.000000       7.000000   \n",
       "max        76.000000   20757.000000      99205.000000      12.000000   \n",
       "\n",
       "            Latitude  ...  Order Item Profit Ratio  Order Item Quantity  \\\n",
       "count  180519.000000  ...            180519.000000        180519.000000   \n",
       "mean       29.719955  ...                 0.120647             2.127638   \n",
       "std         9.813646  ...                 0.466796             1.453451   \n",
       "min       -33.937553  ...                -2.750000             1.000000   \n",
       "25%        18.265432  ...                 0.080000             1.000000   \n",
       "50%        33.144863  ...                 0.270000             1.000000   \n",
       "75%        39.279617  ...                 0.360000             3.000000   \n",
       "max        48.781933  ...                 0.500000             5.000000   \n",
       "\n",
       "               Sales  Order Item Total  Order Profit Per Order  Order Zipcode  \\\n",
       "count  180519.000000     180519.000000           180519.000000   24840.000000   \n",
       "mean      203.772096        183.107609               21.974989   55426.132327   \n",
       "std       132.273077        120.043670              104.433526   31919.279101   \n",
       "min         9.990000          7.490000            -4274.979980    1040.000000   \n",
       "25%       119.980003        104.379997                7.000000   23464.000000   \n",
       "50%       199.919998        163.990005               31.520000   59405.000000   \n",
       "75%       299.950012        247.399994               64.800003   90008.000000   \n",
       "max      1999.989990       1939.989990              911.799988   99301.000000   \n",
       "\n",
       "       Product Card Id  Product Category Id  Product Description  \\\n",
       "count    180519.000000        180519.000000                  0.0   \n",
       "mean        692.509764            31.851451                  NaN   \n",
       "std         336.446807            15.640064                  NaN   \n",
       "min          19.000000             2.000000                  NaN   \n",
       "25%         403.000000            18.000000                  NaN   \n",
       "50%         627.000000            29.000000                  NaN   \n",
       "75%        1004.000000            45.000000                  NaN   \n",
       "max        1363.000000            76.000000                  NaN   \n",
       "\n",
       "       Product Price  \n",
       "count  180519.000000  \n",
       "mean      141.232550  \n",
       "std       139.732492  \n",
       "min         9.990000  \n",
       "25%        50.000000  \n",
       "50%        59.990002  \n",
       "75%       199.990005  \n",
       "max      1999.989990  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Customer Zipcode</th>\n",
       "      <th>Department Id</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>Order Item Profit Ratio</th>\n",
       "      <th>Order Item Quantity</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order Item Total</th>\n",
       "      <th>Order Profit Per Order</th>\n",
       "      <th>Order Zipcode</th>\n",
       "      <th>Product Card Id</th>\n",
       "      <th>Product Category Id</th>\n",
       "      <th>Product Description</th>\n",
       "      <th>Product Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180516.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>24840.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180519.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.497654</td>\n",
       "      <td>2.931847</td>\n",
       "      <td>21.974989</td>\n",
       "      <td>183.107609</td>\n",
       "      <td>0.548291</td>\n",
       "      <td>31.851451</td>\n",
       "      <td>6691.379495</td>\n",
       "      <td>35921.126914</td>\n",
       "      <td>5.443460</td>\n",
       "      <td>29.719955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120647</td>\n",
       "      <td>2.127638</td>\n",
       "      <td>203.772096</td>\n",
       "      <td>183.107609</td>\n",
       "      <td>21.974989</td>\n",
       "      <td>55426.132327</td>\n",
       "      <td>692.509764</td>\n",
       "      <td>31.851451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.232550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.623722</td>\n",
       "      <td>1.374449</td>\n",
       "      <td>104.433526</td>\n",
       "      <td>120.043670</td>\n",
       "      <td>0.497664</td>\n",
       "      <td>15.640064</td>\n",
       "      <td>4162.918106</td>\n",
       "      <td>37542.461122</td>\n",
       "      <td>1.629246</td>\n",
       "      <td>9.813646</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466796</td>\n",
       "      <td>1.453451</td>\n",
       "      <td>132.273077</td>\n",
       "      <td>120.043670</td>\n",
       "      <td>104.433526</td>\n",
       "      <td>31919.279101</td>\n",
       "      <td>336.446807</td>\n",
       "      <td>15.640064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.732492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-4274.979980</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-33.937553</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.990000</td>\n",
       "      <td>7.490000</td>\n",
       "      <td>-4274.979980</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.990000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>104.379997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>3258.500000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18.265432</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>119.980003</td>\n",
       "      <td>104.379997</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>23464.000000</td>\n",
       "      <td>403.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>31.520000</td>\n",
       "      <td>163.990005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>6457.000000</td>\n",
       "      <td>19380.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>33.144863</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>199.919998</td>\n",
       "      <td>163.990005</td>\n",
       "      <td>31.520000</td>\n",
       "      <td>59405.000000</td>\n",
       "      <td>627.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>64.800003</td>\n",
       "      <td>247.399994</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>9779.000000</td>\n",
       "      <td>78207.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>39.279617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>299.950012</td>\n",
       "      <td>247.399994</td>\n",
       "      <td>64.800003</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>199.990005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>911.799988</td>\n",
       "      <td>1939.989990</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>20757.000000</td>\n",
       "      <td>99205.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>48.781933</td>\n",
       "      <td>...</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1999.989990</td>\n",
       "      <td>1939.989990</td>\n",
       "      <td>911.799988</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>1363.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1999.989990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "count             180519.000000                  180519.000000   \n",
       "mean                   3.497654                       2.931847   \n",
       "std                    1.623722                       1.374449   \n",
       "min                    0.000000                       0.000000   \n",
       "25%                    2.000000                       2.000000   \n",
       "50%                    3.000000                       4.000000   \n",
       "75%                    5.000000                       4.000000   \n",
       "max                    6.000000                       4.000000   \n",
       "\n",
       "       Benefit per order  Sales per customer  Late_delivery_risk  \\\n",
       "count      180519.000000       180519.000000       180519.000000   \n",
       "mean           21.974989          183.107609            0.548291   \n",
       "std           104.433526          120.043670            0.497664   \n",
       "min         -4274.979980            7.490000            0.000000   \n",
       "25%             7.000000          104.379997            0.000000   \n",
       "50%            31.520000          163.990005            1.000000   \n",
       "75%            64.800003          247.399994            1.000000   \n",
       "max           911.799988         1939.989990            1.000000   \n",
       "\n",
       "         Category Id    Customer Id  Customer Zipcode  Department Id  \\\n",
       "count  180519.000000  180519.000000     180516.000000  180519.000000   \n",
       "mean       31.851451    6691.379495      35921.126914       5.443460   \n",
       "std        15.640064    4162.918106      37542.461122       1.629246   \n",
       "min         2.000000       1.000000        603.000000       2.000000   \n",
       "25%        18.000000    3258.500000        725.000000       4.000000   \n",
       "50%        29.000000    6457.000000      19380.000000       5.000000   \n",
       "75%        45.000000    9779.000000      78207.000000       7.000000   \n",
       "max        76.000000   20757.000000      99205.000000      12.000000   \n",
       "\n",
       "            Latitude  ...  Order Item Profit Ratio  Order Item Quantity  \\\n",
       "count  180519.000000  ...            180519.000000        180519.000000   \n",
       "mean       29.719955  ...                 0.120647             2.127638   \n",
       "std         9.813646  ...                 0.466796             1.453451   \n",
       "min       -33.937553  ...                -2.750000             1.000000   \n",
       "25%        18.265432  ...                 0.080000             1.000000   \n",
       "50%        33.144863  ...                 0.270000             1.000000   \n",
       "75%        39.279617  ...                 0.360000             3.000000   \n",
       "max        48.781933  ...                 0.500000             5.000000   \n",
       "\n",
       "               Sales  Order Item Total  Order Profit Per Order  Order Zipcode  \\\n",
       "count  180519.000000     180519.000000           180519.000000   24840.000000   \n",
       "mean      203.772096        183.107609               21.974989   55426.132327   \n",
       "std       132.273077        120.043670              104.433526   31919.279101   \n",
       "min         9.990000          7.490000            -4274.979980    1040.000000   \n",
       "25%       119.980003        104.379997                7.000000   23464.000000   \n",
       "50%       199.919998        163.990005               31.520000   59405.000000   \n",
       "75%       299.950012        247.399994               64.800003   90008.000000   \n",
       "max      1999.989990       1939.989990              911.799988   99301.000000   \n",
       "\n",
       "       Product Card Id  Product Category Id  Product Description  \\\n",
       "count    180519.000000        180519.000000                  0.0   \n",
       "mean        692.509764            31.851451                  NaN   \n",
       "std         336.446807            15.640064                  NaN   \n",
       "min          19.000000             2.000000                  NaN   \n",
       "25%         403.000000            18.000000                  NaN   \n",
       "50%         627.000000            29.000000                  NaN   \n",
       "75%        1004.000000            45.000000                  NaN   \n",
       "max        1363.000000            76.000000                  NaN   \n",
       "\n",
       "       Product Price  \n",
       "count  180519.000000  \n",
       "mean      141.232550  \n",
       "std       139.732492  \n",
       "min         9.990000  \n",
       "25%        50.000000  \n",
       "50%        59.990002  \n",
       "75%       199.990005  \n",
       "max      1999.989990  \n",
       "\n",
       "[8 rows x 28 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats = main_new.describe()\n",
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.99000168"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_new['Product Price'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days for shipping (real)             3.248827\n",
       "Days for shipment (scheduled)        3.465923\n",
       "Benefit per order                   48.160002\n",
       "Sales per customer                 173.548807\n",
       "Late_delivery_risk                   0.774146\n",
       "Category Id                         30.425725\n",
       "Customer Id                       6574.189747\n",
       "Customer Zipcode                 36731.794018\n",
       "Department Id                        5.221730\n",
       "Latitude                            31.432409\n",
       "Longitude                          -71.609245\n",
       "Order Customer Id                 6574.189747\n",
       "Order Id                         36180.947452\n",
       "Order Item Cardprod Id             659.754882\n",
       "Order Item Discount                 21.232821\n",
       "Order Item Discount Rate             0.100834\n",
       "Order Item Id                    90260.000000\n",
       "Order Item Product Price           140.482521\n",
       "Order Item Profit Ratio              0.315000\n",
       "Order Item Quantity                  1.790545\n",
       "Sales                              201.846047\n",
       "Order Item Total                   173.548807\n",
       "Order Profit Per Order              48.160002\n",
       "Order Zipcode                    43672.705714\n",
       "Product Card Id                    659.754882\n",
       "Product Category Id                 30.425725\n",
       "Product Description                       NaN\n",
       "Product Price                      140.482521\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.iloc[:,:].apply(np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type                                  4\n",
      "Days for shipping (real)              7\n",
      "Days for shipment (scheduled)         4\n",
      "Benefit per order                 21998\n",
      "Sales per customer                 2927\n",
      "Delivery Status                       4\n",
      "Late_delivery_risk                    2\n",
      "Category Id                          51\n",
      "Category Name                        50\n",
      "Customer City                       563\n",
      "Customer Email                        1\n",
      "Customer Fname                      782\n",
      "Customer Id                       20652\n",
      "Customer Lname                     1109\n",
      "Customer Password                     1\n",
      "Customer Segment                      3\n",
      "Customer Street                    7458\n",
      "Customer Zipcode                    995\n",
      "Department Id                        11\n",
      "Department Name                      11\n",
      "Latitude                          11250\n",
      "Longitude                          4487\n",
      "Market                                5\n",
      "Order City                         3597\n",
      "Order Country                       164\n",
      "Order Customer Id                 20652\n",
      "order date (DateOrders)           65752\n",
      "Order Id                          65752\n",
      "Order Item Cardprod Id              118\n",
      "Order Item Discount                1017\n",
      "Order Item Discount Rate             18\n",
      "Order Item Id                    180519\n",
      "Order Item Product Price             75\n",
      "Order Item Profit Ratio             162\n",
      "Order Item Quantity                   5\n",
      "Sales                               193\n",
      "Order Item Total                   2927\n",
      "Order Profit Per Order            21998\n",
      "Order Region                         23\n",
      "Order State                        1089\n",
      "Order Status                          9\n",
      "Order Zipcode                       609\n",
      "Product Card Id                     118\n",
      "Product Category Id                  51\n",
      "Product Description                   0\n",
      "Product Image                       118\n",
      "Product Name                        118\n",
      "Product Price                        75\n",
      "shipping date (DateOrders)        63701\n",
      "Shipping Mode                         4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(main_new.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days for shipping (real)             2.000000\n",
       "Days for shipment (scheduled)        2.000000\n",
       "Benefit per order                    7.000000\n",
       "Sales per customer                 104.379997\n",
       "Late_delivery_risk                   0.000000\n",
       "Category Id                         18.000000\n",
       "Customer Id                       3258.500000\n",
       "Customer Zipcode                   725.000000\n",
       "Department Id                        4.000000\n",
       "Latitude                            18.265432\n",
       "Longitude                          -98.446312\n",
       "Order Customer Id                 3258.500000\n",
       "Order Id                         18057.000000\n",
       "Order Item Cardprod Id             403.000000\n",
       "Order Item Discount                  5.400000\n",
       "Order Item Discount Rate             0.040000\n",
       "Order Item Id                    45130.500000\n",
       "Order Item Product Price            50.000000\n",
       "Order Item Profit Ratio              0.080000\n",
       "Order Item Quantity                  1.000000\n",
       "Sales                              119.980003\n",
       "Order Item Total                   104.379997\n",
       "Order Profit Per Order               7.000000\n",
       "Order Zipcode                    23464.000000\n",
       "Product Card Id                    403.000000\n",
       "Product Category Id                 18.000000\n",
       "Product Description                       NaN\n",
       "Product Price                       50.000000\n",
       "Name: 0.25, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = main_new.quantile(0.25)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days for shipping (real)              5.000000\n",
       "Days for shipment (scheduled)         4.000000\n",
       "Benefit per order                    64.800003\n",
       "Sales per customer                  247.399994\n",
       "Late_delivery_risk                    1.000000\n",
       "Category Id                          45.000000\n",
       "Customer Id                        9779.000000\n",
       "Customer Zipcode                  78207.000000\n",
       "Department Id                         7.000000\n",
       "Latitude                             39.279617\n",
       "Longitude                           -66.370583\n",
       "Order Customer Id                  9779.000000\n",
       "Order Id                          54144.000000\n",
       "Order Item Cardprod Id             1004.000000\n",
       "Order Item Discount                  29.990000\n",
       "Order Item Discount Rate              0.160000\n",
       "Order Item Id                    135389.500000\n",
       "Order Item Product Price            199.990005\n",
       "Order Item Profit Ratio               0.360000\n",
       "Order Item Quantity                   3.000000\n",
       "Sales                               299.950012\n",
       "Order Item Total                    247.399994\n",
       "Order Profit Per Order               64.800003\n",
       "Order Zipcode                     90008.000000\n",
       "Product Card Id                    1004.000000\n",
       "Product Category Id                  45.000000\n",
       "Product Description                        NaN\n",
       "Product Price                       199.990005\n",
       "Name: 0.75, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = main_new.quantile(0.75)\n",
    "q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Days for shipping (real)             3.000000\n",
       "Days for shipment (scheduled)        2.000000\n",
       "Benefit per order                   57.800003\n",
       "Sales per customer                 143.019997\n",
       "Late_delivery_risk                   1.000000\n",
       "Category Id                         27.000000\n",
       "Customer Id                       6520.500000\n",
       "Customer Zipcode                 77482.000000\n",
       "Department Id                        3.000000\n",
       "Latitude                            21.014185\n",
       "Longitude                           32.075729\n",
       "Order Customer Id                 6520.500000\n",
       "Order Id                         36087.000000\n",
       "Order Item Cardprod Id             601.000000\n",
       "Order Item Discount                 24.590000\n",
       "Order Item Discount Rate             0.120000\n",
       "Order Item Id                    90259.000000\n",
       "Order Item Product Price           149.990005\n",
       "Order Item Profit Ratio              0.280000\n",
       "Order Item Quantity                  2.000000\n",
       "Sales                              179.970009\n",
       "Order Item Total                   143.019997\n",
       "Order Profit Per Order              57.800003\n",
       "Order Zipcode                    66544.000000\n",
       "Product Card Id                    601.000000\n",
       "Product Category Id                 27.000000\n",
       "Product Description                       NaN\n",
       "Product Price                      149.990005\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IQR = q3 - q1\n",
    "IQR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Total Sales from each Customer')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "p1 = sns.boxplot(x = main_new[\"Sales per customer\"]) \n",
    "p1.set(title = \"Total Sales from each Customer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Boxplot of Sales after discount'),\n",
       " Text(0.5, 0, 'Sales after discount')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "p2  = sns.boxplot(x = main_new[\"Sales\"])    \n",
    "p2.set(title = \"Boxplot of Sales after discount\",xlabel ='Sales after discount')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Frequency Distribution of Mode of Payment'),\n",
       " Text(0, 0.5, 'Number of Transactions')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "p3 = sns.countplot(x = main_new[\"Type\"]) \n",
    "p3.set(title = \"Frequency Distribution of Mode of Payment\",ylabel = \"Number of Transactions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Number of products sold '),\n",
       " Text(0.5, 1.0, ' Department-wise sales distribution')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "p4 = sns.countplot(x = main_new[\"Department Name\"]) \n",
    "p4.set_xticklabels(p4.get_xticklabels(),rotation = 90)\n",
    "p4.set(ylabel = \"Number of products sold \",title = \" Department-wise sales distribution\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Number of products sold'),\n",
       " Text(0.5, 1.0, 'Market-wise sales distribution'),\n",
       " Text(0.5, 0, 'Market(where each order is delivered)')]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "p5 = sns.countplot(x = main_new[\"Market\"])  \n",
    "p5.set(ylabel = \"Number of products sold\",title = \"Market-wise sales distribution\",xlabel = \"Market(where each order is delivered)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of discounts by sales '),\n",
       " Text(0.5, 0, 'discount offered'),\n",
       " Text(0, 0.5, 'Number Of Products')]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p6 = sns.histplot(x = main_new[\"Order Item Discount\"],bins=25) \n",
    "p6.set(title = \"Distribution of discounts by sales \",xlabel = \"discount offered\",ylabel = \"Number Of Products\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Distribution of Profit from each order'),\n",
       " Text(0, 0.5, 'Number of Products sold')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p7= sns.histplot(x = main_new[\"Benefit per order\"],bins = 50) \n",
    "p7.set(title = \"Distribution of Profit from each order\",ylabel = \"Number of Products sold\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}