ML-MINOR-APRIL .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "      <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",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>02-03-2018 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>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>1/18/2018 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>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",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</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>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>1/20/2016 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>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",
       "    </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>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>1/20/2016 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>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>1/18/2016 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>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 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 53 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 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           02-03-2018 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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv(\"C:\\ML\\DataCoSupplyChainDataset.csv\",sep=\",\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <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",
       "      <td>Smart watch</td>\n",
       "      <td>327.75</td>\n",
       "      <td>0</td>\n",
       "      <td>02-03-2018 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>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>327.75</td>\n",
       "      <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",
       "      <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>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>327.75</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>327.75</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",
       "      <td>Smart watch</td>\n",
       "      <td>327.75</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 11:24</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 53 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",
       "   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",
       "   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",
       "  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",
       "                                  Product Image  Product Name Product Price  \\\n",
       "0  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "1  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "2  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "3  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "4  http://images.acmesports.sports/Smart+watch   Smart watch         327.75   \n",
       "\n",
       "  Product Status shipping date (DateOrders)   Shipping Mode  \n",
       "0              0           02-03-2018 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",
       "[5 rows x 53 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#head takes integer as an argument and this integer tells number of rows to be displayed starting from top,by default haas value of 5\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180519, 53)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The shape attribute is used to check the dimension of data.\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 180519 entries, 0 to 180518\n",
      "Data columns (total 53 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 Country               180519 non-null  object \n",
      " 11  Customer Email                 180519 non-null  object \n",
      " 12  Customer Fname                 180519 non-null  object \n",
      " 13  Customer Id                    180519 non-null  int64  \n",
      " 14  Customer Lname                 180511 non-null  object \n",
      " 15  Customer Password              180519 non-null  object \n",
      " 16  Customer Segment               180519 non-null  object \n",
      " 17  Customer State                 180519 non-null  object \n",
      " 18  Customer Street                180519 non-null  object \n",
      " 19  Customer Zipcode               180516 non-null  float64\n",
      " 20  Department Id                  180519 non-null  int64  \n",
      " 21  Department Name                180519 non-null  object \n",
      " 22  Latitude                       180519 non-null  float64\n",
      " 23  Longitude                      180519 non-null  float64\n",
      " 24  Market                         180519 non-null  object \n",
      " 25  Order City                     180519 non-null  object \n",
      " 26  Order Country                  180519 non-null  object \n",
      " 27  Order Customer Id              180519 non-null  int64  \n",
      " 28  order date (DateOrders)        180519 non-null  object \n",
      " 29  Order Id                       180519 non-null  int64  \n",
      " 30  Order Item Cardprod Id         180519 non-null  int64  \n",
      " 31  Order Item Discount            180519 non-null  float64\n",
      " 32  Order Item Discount Rate       180519 non-null  float64\n",
      " 33  Order Item Id                  180519 non-null  int64  \n",
      " 34  Order Item Product Price       180519 non-null  float64\n",
      " 35  Order Item Profit Ratio        180519 non-null  float64\n",
      " 36  Order Item Quantity            180519 non-null  int64  \n",
      " 37  Sales                          180519 non-null  float64\n",
      " 38  Order Item Total               180519 non-null  float64\n",
      " 39  Order Profit Per Order         180519 non-null  float64\n",
      " 40  Order Region                   180519 non-null  object \n",
      " 41  Order State                    180519 non-null  object \n",
      " 42  Order Status                   180519 non-null  object \n",
      " 43  Order Zipcode                  24840 non-null   float64\n",
      " 44  Product Card Id                180519 non-null  int64  \n",
      " 45  Product Category Id            180519 non-null  int64  \n",
      " 46  Product Description            0 non-null       float64\n",
      " 47  Product Image                  180519 non-null  object \n",
      " 48  Product Name                   180519 non-null  object \n",
      " 49  Product Price                  180519 non-null  float64\n",
      " 50  Product Status                 180519 non-null  int64  \n",
      " 51  shipping date (DateOrders)     180519 non-null  object \n",
      " 52  Shipping Mode                  180519 non-null  object \n",
      "dtypes: float64(15), int64(14), object(24)\n",
      "memory usage: 73.0+ MB\n"
     ]
    }
   ],
   "source": [
    "#info() is used to check the Information about the data and the datatypes of each respective attribute.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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 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",
       "      <th>Product Status</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>24840.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>180519.000000</td>\n",
       "      <td>180519.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</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>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",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 29 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 Quantity          Sales  \\\n",
       "count  180519.000000  ...        180519.000000  180519.000000   \n",
       "mean       29.719955  ...             2.127638     203.772096   \n",
       "std         9.813646  ...             1.453451     132.273077   \n",
       "min       -33.937553  ...             1.000000       9.990000   \n",
       "25%        18.265432  ...             1.000000     119.980003   \n",
       "50%        33.144863  ...             1.000000     199.919998   \n",
       "75%        39.279617  ...             3.000000     299.950012   \n",
       "max        48.781933  ...             5.000000    1999.989990   \n",
       "\n",
       "       Order Item Total  Order Profit Per Order  Order Zipcode  \\\n",
       "count     180519.000000           180519.000000   24840.000000   \n",
       "mean         183.107609               21.974989   55426.132327   \n",
       "std          120.043670              104.433526   31919.279101   \n",
       "min            7.490000            -4274.979980    1040.000000   \n",
       "25%          104.379997                7.000000   23464.000000   \n",
       "50%          163.990005               31.520000   59405.000000   \n",
       "75%          247.399994               64.800003   90008.000000   \n",
       "max         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  Product Status  \n",
       "count  180519.000000        180519.0  \n",
       "mean      141.232550             0.0  \n",
       "std       139.732492             0.0  \n",
       "min         9.990000             0.0  \n",
       "25%        50.000000             0.0  \n",
       "50%        59.990002             0.0  \n",
       "75%       199.990005             0.0  \n",
       "max      1999.989990             0.0  \n",
       "\n",
       "[8 rows x 29 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The described method will display statistics of numerical columns. We can clearly see the minimum value, mean values, different percentile values, and maximum values.\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Smart watch ', 'Perfect Fitness Perfect Rip Deck',\n",
       "       \"Under Armour Girls' Toddler Spine Surge Runni\",\n",
       "       \"Nike Men's Dri-FIT Victory Golf Polo\",\n",
       "       \"Under Armour Men's Compression EV SL Slide\",\n",
       "       \"Under Armour Women's Micro G Skulpt Running S\",\n",
       "       \"Nike Men's Free 5.0+ Running Shoe\",\n",
       "       \"Glove It Women's Mod Oval 3-Zip Carry All Gol\",\n",
       "       'Bridgestone e6 Straight Distance NFL San Dieg',\n",
       "       \"Columbia Men's PFG Anchor Tough T-Shirt\",\n",
       "       'Titleist Pro V1x Golf Balls',\n",
       "       'Bridgestone e6 Straight Distance NFL Tennesse',\n",
       "       'Polar FT4 Heart Rate Monitor', 'ENO Atlas Hammock Straps',\n",
       "       \"adidas Men's F10 Messi TRX FG Soccer Cleat\",\n",
       "       \"Brooks Women's Ghost 6 Running Shoe\",\n",
       "       \"Nike Men's CJ Elite 2 TD Football Cleat\",\n",
       "       \"Diamondback Women's Serene Classic Comfort Bi\",\n",
       "       'Industrial consumer electronics', 'Web Camera', 'Dell Laptop',\n",
       "       'SOLE E25 Elliptical', 'Elevation Training Mask 2.0',\n",
       "       \"adidas Men's Germany Black Crest Away Tee\",\n",
       "       'Team Golf Pittsburgh Steelers Putter Grip',\n",
       "       'Glove It Urban Brick Golf Towel',\n",
       "       'Team Golf Texas Longhorns Putter Grip',\n",
       "       \"Nike Men's Deutschland Weltmeister Winners Bl\",\n",
       "       'Team Golf St. Louis Cardinals Putter Grip', 'Summer dresses',\n",
       "       'Porcelain crafts', \"Men's gala suit\",\n",
       "       'Team Golf Tennessee Volunteers Putter Grip',\n",
       "       'Team Golf San Francisco Giants Putter Grip',\n",
       "       'Glove It Imperial Golf Towel', \"Nike Men's Comfort 2 Slide\",\n",
       "       'Under Armour Hustle Storm Medium Duffle Bag',\n",
       "       \"Under Armour Kids' Mercenary Slide\",\n",
       "       \"Under Armour Women's Ignite PIP VI Slide\",\n",
       "       \"Nike Men's Free TR 5.0 TB Training Shoe\",\n",
       "       'adidas Youth Germany Black/Red Away Match Soc',\n",
       "       \"TYR Boys' Team Digi Jammer\",\n",
       "       \"Glove It Women's Imperial Golf Glove\",\n",
       "       'Titleist Pro V1x High Numbers Golf Balls',\n",
       "       'Bridgestone e6 Straight Distance NFL Carolina',\n",
       "       \"Under Armour Women's Ignite Slide\",\n",
       "       'Titleist Pro V1x High Numbers Personalized Go',\n",
       "       'GoPro HERO3+ Black Edition Camera', 'Total Gym 1400',\n",
       "       \"Children's heaters\", 'Team Golf New England Patriots Putter Grip',\n",
       "       \"adidas Kids' F5 Messi FG Soccer Cleat\",\n",
       "       \"Nike Women's Tempo Shorts\",\n",
       "       \"Glove It Women's Mod Oval Golf Glove\",\n",
       "       'Titleist Pro V1 High Numbers Personalized Gol',\n",
       "       \"Under Armour Men's Tech II T-Shirt\", 'Baby sweater',\n",
       "       'Mio ALPHA Heart Rate Monitor/Sport Watch',\n",
       "       'Field & Stream Sportsman 16 Gun Fire Safe', 'Sports Books ',\n",
       "       \"Diamondback Boys' Insight 24 Performance Hybr\",\n",
       "       'Polar Loop Activity Tracker', 'Garmin Forerunner 910XT GPS Watch',\n",
       "       'DVDs ', 'CDs of rock',\n",
       "       \"Nike Kids' Grade School KD VI Basketball Shoe\",\n",
       "       \"Nike Women's Free 5.0 TR FIT PRT 4 Training S\",\n",
       "       \"Hirzl Women's Soffft Flex Golf Glove\",\n",
       "       \"The North Face Women's Recon Backpack\", 'Lawn mower',\n",
       "       'Nike Dri-FIT Crew Sock 6 Pack',\n",
       "       \"Nike Women's Legend V-Neck T-Shirt\",\n",
       "       'Garmin Approach S4 Golf GPS Watch',\n",
       "       'insta-bed Neverflat Air Mattress',\n",
       "       \"Nike Men's Kobe IX Elite Low Basketball Shoe\",\n",
       "       'Adult dog supplies', 'First aid kit',\n",
       "       'Garmin Approach S3 Golf GPS Watch', 'Rock music',\n",
       "       'Fighting video games',\n",
       "       'Fitbit The One Wireless Activity & Sleep Trac',\n",
       "       'Stiga Master Series ST3100 Competition Indoor',\n",
       "       \"Diamondback Girls' Clarity 24 Hybrid Bike 201\",\n",
       "       'adidas Brazuca 2014 Official Match Ball',\n",
       "       'GolfBuddy VT3 GPS Watch',\n",
       "       'Bushnell Pro X7 Jolt Slope Rangefinder',\n",
       "       'Yakima DoubleDown Ace Hitch Mount 4-Bike Rack',\n",
       "       \"Nike Men's Fingertrap Max Training Shoe\",\n",
       "       'Bowflex SelectTech 1090 Dumbbells', 'SOLE E35 Elliptical',\n",
       "       \"Hirzl Women's Hybrid Golf Glove\", \"Hirzl Men's Hybrid Golf Glove\",\n",
       "       'TaylorMade 2014 Purelite Stand Bag', 'Bag Boy Beverage Holder',\n",
       "       'Bag Boy M330 Push Cart', 'Clicgear 8.0 Shoe Brush',\n",
       "       'Titleist Small Wheeled Travel Cover', 'Clicgear Rovic Cooler Bag',\n",
       "       'Titleist Club Glove Travel Cover', 'Ogio Race Golf Shoes',\n",
       "       \"LIJA Women's Argyle Golf Polo\",\n",
       "       \"LIJA Women's Eyelet Sleeveless Golf Polo\",\n",
       "       \"LIJA Women's Button Golf Dress\",\n",
       "       \"LIJA Women's Mid-Length Panel Golf Shorts\",\n",
       "       \"TaylorMade Women's RBZ SL Rescue\",\n",
       "       \"Cleveland Golf Women's 588 RTX CB Satin Chrom\",\n",
       "       \"Top Flite Women's 2014 XL Hybrid\",\n",
       "       'MDGolf Pittsburgh Penguins Putter',\n",
       "       'TaylorMade White Smoke IN-12 Putter',\n",
       "       'Cleveland Golf Collegiate My Custom Wedge 588',\n",
       "       \"Merrell Men's All Out Flash Trail Running Sho\",\n",
       "       \"Merrell Women's Grassbow Sport Waterproof Hik\",\n",
       "       \"Merrell Women's Siren Mid Waterproof Hiking B\",\n",
       "       \"Merrell Women's Grassbow Sport Hiking Shoe\", 'Toys ',\n",
       "       'Pelican Sunstream 100 Kayak', 'Pelican Maverick 100X Kayak',\n",
       "       \"O'Brien Men's Neoprene Life Vest\"], dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.unique() is used to check unique values of a particular column\n",
    "df[\"Product Name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Perfect Fitness Perfect Rip Deck                 24515\n",
       "Nike Men's CJ Elite 2 TD Football Cleat          22246\n",
       "Nike Men's Dri-FIT Victory Golf Polo             21035\n",
       "O'Brien Men's Neoprene Life Vest                 19298\n",
       "Field & Stream Sportsman 16 Gun Fire Safe        17325\n",
       "                                                 ...  \n",
       "Stiga Master Series ST3100 Competition Indoor       27\n",
       "SOLE E35 Elliptical                                 15\n",
       "Bushnell Pro X7 Jolt Slope Rangefinder              11\n",
       "Bowflex SelectTech 1090 Dumbbells                   10\n",
       "SOLE E25 Elliptical                                 10\n",
       "Name: Product Name, Length: 118, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#value_counts() is used to print how many times a value is repeated in that column\n",
    "df[\"Product Name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                                  0\n",
       "Days for shipping (real)              0\n",
       "Days for shipment (scheduled)         0\n",
       "Benefit per order                     0\n",
       "Sales per customer                    0\n",
       "Delivery Status                       0\n",
       "Late_delivery_risk                    0\n",
       "Category Id                           0\n",
       "Category Name                         0\n",
       "Customer City                         0\n",
       "Customer Country                      0\n",
       "Customer Email                        0\n",
       "Customer Fname                        0\n",
       "Customer Id                           0\n",
       "Customer Lname                        8\n",
       "Customer Password                     0\n",
       "Customer Segment                      0\n",
       "Customer State                        0\n",
       "Customer Street                       0\n",
       "Customer Zipcode                      3\n",
       "Department Id                         0\n",
       "Department Name                       0\n",
       "Latitude                              0\n",
       "Longitude                             0\n",
       "Market                                0\n",
       "Order City                            0\n",
       "Order Country                         0\n",
       "Order Customer Id                     0\n",
       "order date (DateOrders)               0\n",
       "Order Id                              0\n",
       "Order Item Cardprod Id                0\n",
       "Order Item Discount                   0\n",
       "Order Item Discount Rate              0\n",
       "Order Item Id                         0\n",
       "Order Item Product Price              0\n",
       "Order Item Profit Ratio               0\n",
       "Order Item Quantity                   0\n",
       "Sales                                 0\n",
       "Order Item Total                      0\n",
       "Order Profit Per Order                0\n",
       "Order Region                          0\n",
       "Order State                           0\n",
       "Order Status                          0\n",
       "Order Zipcode                    155679\n",
       "Product Card Id                       0\n",
       "Product Category Id                   0\n",
       "Product Description              180519\n",
       "Product Image                         0\n",
       "Product Name                          0\n",
       "Product Price                         0\n",
       "Product Status                        0\n",
       "shipping date (DateOrders)            0\n",
       "Shipping Mode                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#isnull.sum() function is used to check if any NaN values are present in values of any column.If present tells how many NaN value are present in that column \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Type', 'Days for shipping (real)', 'Days for shipment (scheduled)',\n",
       "       'Benefit per order', 'Sales per customer', 'Delivery Status',\n",
       "       'Late_delivery_risk', 'Category Id', 'Category Name', 'Customer City',\n",
       "       'Customer Country', 'Customer Email', 'Customer Fname', 'Customer Id',\n",
       "       'Customer Lname', 'Customer Password', 'Customer Segment',\n",
       "       'Customer State', 'Customer Street', 'Customer Zipcode',\n",
       "       'Department Id', 'Department Name', 'Latitude', 'Longitude', 'Market',\n",
       "       'Order City', 'Order Country', 'Order Customer Id',\n",
       "       'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id',\n",
       "       'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id',\n",
       "       'Order Item Product Price', 'Order Item Profit Ratio',\n",
       "       'Order Item Quantity', 'Sales', 'Order Item Total',\n",
       "       'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status',\n",
       "       'Order Zipcode', 'Product Card Id', 'Product Category Id',\n",
       "       'Product Description', 'Product Image', 'Product Name', 'Product Price',\n",
       "       'Product Status', 'shipping date (DateOrders)', 'Shipping Mode'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displays the columns present in dataset\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping entire column as Product Description consists of all null values\n",
    "df=df.drop([\"Product Description\"],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                                  0\n",
       "Days for shipping (real)              0\n",
       "Days for shipment (scheduled)         0\n",
       "Benefit per order                     0\n",
       "Sales per customer                    0\n",
       "Delivery Status                       0\n",
       "Late_delivery_risk                    0\n",
       "Category Id                           0\n",
       "Category Name                         0\n",
       "Customer City                         0\n",
       "Customer Country                      0\n",
       "Customer Email                        0\n",
       "Customer Fname                        0\n",
       "Customer Id                           0\n",
       "Customer Lname                        8\n",
       "Customer Password                     0\n",
       "Customer Segment                      0\n",
       "Customer State                        0\n",
       "Customer Street                       0\n",
       "Customer Zipcode                      3\n",
       "Department Id                         0\n",
       "Department Name                       0\n",
       "Latitude                              0\n",
       "Longitude                             0\n",
       "Market                                0\n",
       "Order City                            0\n",
       "Order Country                         0\n",
       "Order Customer Id                     0\n",
       "order date (DateOrders)               0\n",
       "Order Id                              0\n",
       "Order Item Cardprod Id                0\n",
       "Order Item Discount                   0\n",
       "Order Item Discount Rate              0\n",
       "Order Item Id                         0\n",
       "Order Item Product Price              0\n",
       "Order Item Profit Ratio               0\n",
       "Order Item Quantity                   0\n",
       "Sales                                 0\n",
       "Order Item Total                      0\n",
       "Order Profit Per Order                0\n",
       "Order Region                          0\n",
       "Order State                           0\n",
       "Order Status                          0\n",
       "Order Zipcode                    155679\n",
       "Product Card Id                       0\n",
       "Product Category Id                   0\n",
       "Product Image                         0\n",
       "Product Name                          0\n",
       "Product Price                         0\n",
       "Product Status                        0\n",
       "shipping date (DateOrders)            0\n",
       "Shipping Mode                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking whether column \"Product Description is removed or not\"\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replacing Order ZipCode null values with its mean\n",
    "x=df[\"Order Zipcode\"].mean()\n",
    "df[\"Order Zipcode\"].replace(np.nan,x,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                             0\n",
       "Days for shipping (real)         0\n",
       "Days for shipment (scheduled)    0\n",
       "Benefit per order                0\n",
       "Sales per customer               0\n",
       "Delivery Status                  0\n",
       "Late_delivery_risk               0\n",
       "Category Id                      0\n",
       "Category Name                    0\n",
       "Customer City                    0\n",
       "Customer Country                 0\n",
       "Customer Email                   0\n",
       "Customer Fname                   0\n",
       "Customer Id                      0\n",
       "Customer Lname                   8\n",
       "Customer Password                0\n",
       "Customer Segment                 0\n",
       "Customer State                   0\n",
       "Customer Street                  0\n",
       "Customer Zipcode                 3\n",
       "Department Id                    0\n",
       "Department Name                  0\n",
       "Latitude                         0\n",
       "Longitude                        0\n",
       "Market                           0\n",
       "Order City                       0\n",
       "Order Country                    0\n",
       "Order Customer Id                0\n",
       "order date (DateOrders)          0\n",
       "Order Id                         0\n",
       "Order Item Cardprod Id           0\n",
       "Order Item Discount              0\n",
       "Order Item Discount Rate         0\n",
       "Order Item Id                    0\n",
       "Order Item Product Price         0\n",
       "Order Item Profit Ratio          0\n",
       "Order Item Quantity              0\n",
       "Sales                            0\n",
       "Order Item Total                 0\n",
       "Order Profit Per Order           0\n",
       "Order Region                     0\n",
       "Order State                      0\n",
       "Order Status                     0\n",
       "Order Zipcode                    0\n",
       "Product Card Id                  0\n",
       "Product Category Id              0\n",
       "Product Image                    0\n",
       "Product Name                     0\n",
       "Product Price                    0\n",
       "Product Status                   0\n",
       "shipping date (DateOrders)       0\n",
       "Shipping Mode                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking whether NaN values of column \" Order Zipcode\" are replaced with mean or not\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping rows of null values as many of the remaining rows consists of only one null value\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                             0\n",
       "Days for shipping (real)         0\n",
       "Days for shipment (scheduled)    0\n",
       "Benefit per order                0\n",
       "Sales per customer               0\n",
       "Delivery Status                  0\n",
       "Late_delivery_risk               0\n",
       "Category Id                      0\n",
       "Category Name                    0\n",
       "Customer City                    0\n",
       "Customer Country                 0\n",
       "Customer Email                   0\n",
       "Customer Fname                   0\n",
       "Customer Id                      0\n",
       "Customer Lname                   0\n",
       "Customer Password                0\n",
       "Customer Segment                 0\n",
       "Customer State                   0\n",
       "Customer Street                  0\n",
       "Customer Zipcode                 0\n",
       "Department Id                    0\n",
       "Department Name                  0\n",
       "Latitude                         0\n",
       "Longitude                        0\n",
       "Market                           0\n",
       "Order City                       0\n",
       "Order Country                    0\n",
       "Order Customer Id                0\n",
       "order date (DateOrders)          0\n",
       "Order Id                         0\n",
       "Order Item Cardprod Id           0\n",
       "Order Item Discount              0\n",
       "Order Item Discount Rate         0\n",
       "Order Item Id                    0\n",
       "Order Item Product Price         0\n",
       "Order Item Profit Ratio          0\n",
       "Order Item Quantity              0\n",
       "Sales                            0\n",
       "Order Item Total                 0\n",
       "Order Profit Per Order           0\n",
       "Order Region                     0\n",
       "Order State                      0\n",
       "Order Status                     0\n",
       "Order Zipcode                    0\n",
       "Product Card Id                  0\n",
       "Product Category Id              0\n",
       "Product Image                    0\n",
       "Product Name                     0\n",
       "Product Price                    0\n",
       "Product Status                   0\n",
       "shipping date (DateOrders)       0\n",
       "Shipping Mode                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values again \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#check for duplicates \n",
    "duplicates=df.duplicated()\n",
    "print(duplicates.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 44,
     "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": [
    "#check for outliers for a particular column(work only for columns having numerical values)using Box Plot analysis \n",
    "#Benefit per order has many outliers present\n",
    "df.boxplot(column=[\"Benefit per order\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing outliers\n",
    "#As it consists of  many outliers we removed Benefit per order column\n",
    "df=df.drop(['Benefit per order'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</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>Longitude</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 Price</th>\n",
       "      <th>Product Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.515884</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.401418</td>\n",
       "      <td>-0.000316</td>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-0.004068</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004645</td>\n",
       "      <td>-0.000820</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.001760</td>\n",
       "      <td>-0.005108</td>\n",
       "      <td>-0.000302</td>\n",
       "      <td>-0.000835</td>\n",
       "      <td>-0.000316</td>\n",
       "      <td>0.002196</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <td>0.515884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>-0.369343</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>-0.003026</td>\n",
       "      <td>-0.000227</td>\n",
       "      <td>-0.005318</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.002919</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>-0.000210</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales per customer</th>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>-0.001331</td>\n",
       "      <td>0.232448</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>0.105458</td>\n",
       "      <td>0.989744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133435</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <td>0.401418</td>\n",
       "      <td>-0.369343</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.003166</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>-0.001953</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002319</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>-0.003554</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>-0.003729</td>\n",
       "      <td>-0.005240</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>-0.002160</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category Id</th>\n",
       "      <td>-0.000316</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>0.889035</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>-0.302355</td>\n",
       "      <td>0.227429</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.031826</td>\n",
       "      <td>-0.001927</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Id</th>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>0.136940</td>\n",
       "      <td>0.002321</td>\n",
       "      <td>-0.005006</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>-0.091234</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>0.009286</td>\n",
       "      <td>-0.001813</td>\n",
       "      <td>0.227822</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.110975</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Zipcode</th>\n",
       "      <td>0.000122</td>\n",
       "      <td>-0.003026</td>\n",
       "      <td>-0.001331</td>\n",
       "      <td>0.003166</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002876</td>\n",
       "      <td>0.584564</td>\n",
       "      <td>-0.923925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003364</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>-0.001518</td>\n",
       "      <td>-0.001331</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department Id</th>\n",
       "      <td>-0.001634</td>\n",
       "      <td>-0.000227</td>\n",
       "      <td>0.232448</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.889035</td>\n",
       "      <td>0.136940</td>\n",
       "      <td>0.002876</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003055</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>0.234797</td>\n",
       "      <td>0.232448</td>\n",
       "      <td>0.031455</td>\n",
       "      <td>-0.002009</td>\n",
       "      <td>0.906283</td>\n",
       "      <td>0.889035</td>\n",
       "      <td>0.446853</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>-0.004068</td>\n",
       "      <td>-0.005318</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.000700</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>0.002321</td>\n",
       "      <td>0.584564</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.525148</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.001846</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>-0.001081</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.003676</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>-0.001953</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>-0.005006</td>\n",
       "      <td>-0.923925</td>\n",
       "      <td>-0.002407</td>\n",
       "      <td>-0.525148</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003583</td>\n",
       "      <td>0.004423</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>-0.002523</td>\n",
       "      <td>-0.002200</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>-0.000835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Customer Id</th>\n",
       "      <td>0.003460</td>\n",
       "      <td>0.000877</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>0.001512</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>0.136940</td>\n",
       "      <td>0.002321</td>\n",
       "      <td>-0.005006</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>-0.091234</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>0.009286</td>\n",
       "      <td>-0.001813</td>\n",
       "      <td>0.227822</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.110975</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Id</th>\n",
       "      <td>-0.001690</td>\n",
       "      <td>-0.001891</td>\n",
       "      <td>0.078926</td>\n",
       "      <td>-0.001273</td>\n",
       "      <td>0.198302</td>\n",
       "      <td>0.204622</td>\n",
       "      <td>-0.001253</td>\n",
       "      <td>0.106772</td>\n",
       "      <td>-0.003000</td>\n",
       "      <td>0.002642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002732</td>\n",
       "      <td>-0.087000</td>\n",
       "      <td>0.079760</td>\n",
       "      <td>0.078926</td>\n",
       "      <td>0.013662</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>0.163241</td>\n",
       "      <td>0.198302</td>\n",
       "      <td>0.115214</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Cardprod Id</th>\n",
       "      <td>-0.000835</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>0.227822</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.906283</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>-0.312504</td>\n",
       "      <td>0.238731</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.033102</td>\n",
       "      <td>-0.001929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>0.480232</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Discount</th>\n",
       "      <td>0.002248</td>\n",
       "      <td>0.002906</td>\n",
       "      <td>0.498727</td>\n",
       "      <td>-0.000739</td>\n",
       "      <td>0.140142</td>\n",
       "      <td>0.038427</td>\n",
       "      <td>-0.001880</td>\n",
       "      <td>0.144648</td>\n",
       "      <td>-0.003006</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002793</td>\n",
       "      <td>0.065409</td>\n",
       "      <td>0.617433</td>\n",
       "      <td>0.498727</td>\n",
       "      <td>0.064744</td>\n",
       "      <td>0.001302</td>\n",
       "      <td>0.147251</td>\n",
       "      <td>0.140142</td>\n",
       "      <td>0.488085</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Discount Rate</th>\n",
       "      <td>0.001483</td>\n",
       "      <td>0.000091</td>\n",
       "      <td>-0.119480</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.001719</td>\n",
       "      <td>-0.000134</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>-0.003890</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002684</td>\n",
       "      <td>-0.000028</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>-0.119480</td>\n",
       "      <td>-0.018634</td>\n",
       "      <td>0.000998</td>\n",
       "      <td>0.000731</td>\n",
       "      <td>0.000579</td>\n",
       "      <td>0.000336</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Id</th>\n",
       "      <td>-0.001750</td>\n",
       "      <td>-0.001861</td>\n",
       "      <td>0.078106</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.186611</td>\n",
       "      <td>0.191625</td>\n",
       "      <td>-0.001248</td>\n",
       "      <td>0.101687</td>\n",
       "      <td>-0.003074</td>\n",
       "      <td>0.002627</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002758</td>\n",
       "      <td>-0.083573</td>\n",
       "      <td>0.078931</td>\n",
       "      <td>0.078106</td>\n",
       "      <td>0.013504</td>\n",
       "      <td>0.000708</td>\n",
       "      <td>0.153910</td>\n",
       "      <td>0.186611</td>\n",
       "      <td>0.112343</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Product Price</th>\n",
       "      <td>0.002196</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>-0.002160</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>0.110975</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>0.446853</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>-0.000835</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>-0.476217</td>\n",
       "      <td>0.789928</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>0.103401</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>0.480232</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Profit Ratio</th>\n",
       "      <td>-0.004645</td>\n",
       "      <td>-0.001834</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>-0.002319</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>-0.000371</td>\n",
       "      <td>0.003364</td>\n",
       "      <td>-0.003055</td>\n",
       "      <td>-0.000094</td>\n",
       "      <td>-0.003583</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>-0.001787</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>0.823691</td>\n",
       "      <td>-0.002109</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Quantity</th>\n",
       "      <td>-0.000820</td>\n",
       "      <td>-0.002919</td>\n",
       "      <td>0.105458</td>\n",
       "      <td>-0.000148</td>\n",
       "      <td>-0.302355</td>\n",
       "      <td>-0.091234</td>\n",
       "      <td>-0.006156</td>\n",
       "      <td>-0.265458</td>\n",
       "      <td>-0.001846</td>\n",
       "      <td>0.004423</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.106488</td>\n",
       "      <td>0.105458</td>\n",
       "      <td>0.015722</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>-0.312504</td>\n",
       "      <td>-0.302355</td>\n",
       "      <td>-0.476217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>0.989744</td>\n",
       "      <td>-0.003554</td>\n",
       "      <td>0.227429</td>\n",
       "      <td>0.060862</td>\n",
       "      <td>-0.001518</td>\n",
       "      <td>0.234797</td>\n",
       "      <td>-0.000706</td>\n",
       "      <td>0.001732</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001787</td>\n",
       "      <td>0.106488</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.989744</td>\n",
       "      <td>0.131769</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>0.238731</td>\n",
       "      <td>0.227429</td>\n",
       "      <td>0.789928</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Item Total</th>\n",
       "      <td>0.001760</td>\n",
       "      <td>0.006421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003781</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.060084</td>\n",
       "      <td>-0.001331</td>\n",
       "      <td>0.232448</td>\n",
       "      <td>-0.000232</td>\n",
       "      <td>0.001471</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001462</td>\n",
       "      <td>0.105458</td>\n",
       "      <td>0.989744</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133435</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Profit Per Order</th>\n",
       "      <td>-0.005108</td>\n",
       "      <td>-0.000210</td>\n",
       "      <td>0.133435</td>\n",
       "      <td>-0.003729</td>\n",
       "      <td>0.031826</td>\n",
       "      <td>0.009286</td>\n",
       "      <td>0.002229</td>\n",
       "      <td>0.031455</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>-0.002523</td>\n",
       "      <td>...</td>\n",
       "      <td>0.823691</td>\n",
       "      <td>0.015722</td>\n",
       "      <td>0.131769</td>\n",
       "      <td>0.133435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>0.033102</td>\n",
       "      <td>0.031826</td>\n",
       "      <td>0.103401</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order Zipcode</th>\n",
       "      <td>-0.000302</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.005240</td>\n",
       "      <td>-0.001927</td>\n",
       "      <td>-0.001813</td>\n",
       "      <td>0.004401</td>\n",
       "      <td>-0.002009</td>\n",
       "      <td>-0.001081</td>\n",
       "      <td>-0.002200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002109</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.000043</td>\n",
       "      <td>-0.001696</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.001929</td>\n",
       "      <td>-0.001927</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Card Id</th>\n",
       "      <td>-0.000835</td>\n",
       "      <td>-0.000445</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.001513</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>0.227822</td>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.906283</td>\n",
       "      <td>0.002011</td>\n",
       "      <td>-0.002468</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001817</td>\n",
       "      <td>-0.312504</td>\n",
       "      <td>0.238731</td>\n",
       "      <td>0.236311</td>\n",
       "      <td>0.033102</td>\n",
       "      <td>-0.001929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>0.480232</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Category Id</th>\n",
       "      <td>-0.000316</td>\n",
       "      <td>-0.000383</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.001780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.274391</td>\n",
       "      <td>0.002846</td>\n",
       "      <td>0.889035</td>\n",
       "      <td>0.002172</td>\n",
       "      <td>-0.002193</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001650</td>\n",
       "      <td>-0.302355</td>\n",
       "      <td>0.227429</td>\n",
       "      <td>0.225149</td>\n",
       "      <td>0.031826</td>\n",
       "      <td>-0.001927</td>\n",
       "      <td>0.991096</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Price</th>\n",
       "      <td>0.002196</td>\n",
       "      <td>0.006890</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>-0.002160</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>0.110975</td>\n",
       "      <td>0.002155</td>\n",
       "      <td>0.446853</td>\n",
       "      <td>0.000458</td>\n",
       "      <td>-0.000835</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002071</td>\n",
       "      <td>-0.476217</td>\n",
       "      <td>0.789928</td>\n",
       "      <td>0.781762</td>\n",
       "      <td>0.103401</td>\n",
       "      <td>-0.000038</td>\n",
       "      <td>0.480232</td>\n",
       "      <td>0.461007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Status</th>\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>...</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",
       "<p>27 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Days for shipping (real)  \\\n",
       "Days for shipping (real)                       1.000000   \n",
       "Days for shipment (scheduled)                  0.515884   \n",
       "Sales per customer                             0.001760   \n",
       "Late_delivery_risk                             0.401418   \n",
       "Category Id                                   -0.000316   \n",
       "Customer Id                                    0.003460   \n",
       "Customer Zipcode                               0.000122   \n",
       "Department Id                                 -0.001634   \n",
       "Latitude                                      -0.004068   \n",
       "Longitude                                      0.003891   \n",
       "Order Customer Id                              0.003460   \n",
       "Order Id                                      -0.001690   \n",
       "Order Item Cardprod Id                        -0.000835   \n",
       "Order Item Discount                            0.002248   \n",
       "Order Item Discount Rate                       0.001483   \n",
       "Order Item Id                                 -0.001750   \n",
       "Order Item Product Price                       0.002196   \n",
       "Order Item Profit Ratio                       -0.004645   \n",
       "Order Item Quantity                           -0.000820   \n",
       "Sales                                          0.001968   \n",
       "Order Item Total                               0.001760   \n",
       "Order Profit Per Order                        -0.005108   \n",
       "Order Zipcode                                 -0.000302   \n",
       "Product Card Id                               -0.000835   \n",
       "Product Category Id                           -0.000316   \n",
       "Product Price                                  0.002196   \n",
       "Product Status                                      NaN   \n",
       "\n",
       "                               Days for shipment (scheduled)  \\\n",
       "Days for shipping (real)                            0.515884   \n",
       "Days for shipment (scheduled)                       1.000000   \n",
       "Sales per customer                                  0.006421   \n",
       "Late_delivery_risk                                 -0.369343   \n",
       "Category Id                                        -0.000383   \n",
       "Customer Id                                         0.000877   \n",
       "Customer Zipcode                                   -0.003026   \n",
       "Department Id                                      -0.000227   \n",
       "Latitude                                           -0.005318   \n",
       "Longitude                                           0.003676   \n",
       "Order Customer Id                                   0.000877   \n",
       "Order Id                                           -0.001891   \n",
       "Order Item Cardprod Id                             -0.000445   \n",
       "Order Item Discount                                 0.002906   \n",
       "Order Item Discount Rate                            0.000091   \n",
       "Order Item Id                                      -0.001861   \n",
       "Order Item Product Price                            0.006890   \n",
       "Order Item Profit Ratio                            -0.001834   \n",
       "Order Item Quantity                                -0.002919   \n",
       "Sales                                               0.006306   \n",
       "Order Item Total                                    0.006421   \n",
       "Order Profit Per Order                             -0.000210   \n",
       "Order Zipcode                                       0.001956   \n",
       "Product Card Id                                    -0.000445   \n",
       "Product Category Id                                -0.000383   \n",
       "Product Price                                       0.006890   \n",
       "Product Status                                           NaN   \n",
       "\n",
       "                               Sales per customer  Late_delivery_risk  \\\n",
       "Days for shipping (real)                 0.001760            0.401418   \n",
       "Days for shipment (scheduled)            0.006421           -0.369343   \n",
       "Sales per customer                       1.000000           -0.003781   \n",
       "Late_delivery_risk                      -0.003781            1.000000   \n",
       "Category Id                              0.225149            0.001780   \n",
       "Customer Id                              0.060084            0.001512   \n",
       "Customer Zipcode                        -0.001331            0.003166   \n",
       "Department Id                            0.232448            0.001049   \n",
       "Latitude                                -0.000232            0.000700   \n",
       "Longitude                                0.001471           -0.001953   \n",
       "Order Customer Id                        0.060084            0.001512   \n",
       "Order Id                                 0.078926           -0.001273   \n",
       "Order Item Cardprod Id                   0.236311            0.001513   \n",
       "Order Item Discount                      0.498727           -0.000739   \n",
       "Order Item Discount Rate                -0.119480            0.000399   \n",
       "Order Item Id                            0.078106           -0.001357   \n",
       "Order Item Product Price                 0.781762           -0.002160   \n",
       "Order Item Profit Ratio                 -0.001462           -0.002319   \n",
       "Order Item Quantity                      0.105458           -0.000148   \n",
       "Sales                                    0.989744           -0.003554   \n",
       "Order Item Total                         1.000000           -0.003781   \n",
       "Order Profit Per Order                   0.133435           -0.003729   \n",
       "Order Zipcode                           -0.000043           -0.005240   \n",
       "Product Card Id                          0.236311            0.001513   \n",
       "Product Category Id                      0.225149            0.001780   \n",
       "Product Price                            0.781762           -0.002160   \n",
       "Product Status                                NaN                 NaN   \n",
       "\n",
       "                               Category Id  Customer Id  Customer Zipcode  \\\n",
       "Days for shipping (real)         -0.000316     0.003460          0.000122   \n",
       "Days for shipment (scheduled)    -0.000383     0.000877         -0.003026   \n",
       "Sales per customer                0.225149     0.060084         -0.001331   \n",
       "Late_delivery_risk                0.001780     0.001512          0.003166   \n",
       "Category Id                       1.000000     0.274391          0.002846   \n",
       "Customer Id                       0.274391     1.000000          0.001959   \n",
       "Customer Zipcode                  0.002846     0.001959          1.000000   \n",
       "Department Id                     0.889035     0.136940          0.002876   \n",
       "Latitude                          0.002172     0.002321          0.584564   \n",
       "Longitude                        -0.002193    -0.005006         -0.923925   \n",
       "Order Customer Id                 0.274391     1.000000          0.001959   \n",
       "Order Id                          0.198302     0.204622         -0.001253   \n",
       "Order Item Cardprod Id            0.991096     0.227822          0.003127   \n",
       "Order Item Discount               0.140142     0.038427         -0.001880   \n",
       "Order Item Discount Rate          0.000579     0.001719         -0.000134   \n",
       "Order Item Id                     0.186611     0.191625         -0.001248   \n",
       "Order Item Product Price          0.461007     0.110975          0.002155   \n",
       "Order Item Profit Ratio          -0.001650    -0.000371          0.003364   \n",
       "Order Item Quantity              -0.302355    -0.091234         -0.006156   \n",
       "Sales                             0.227429     0.060862         -0.001518   \n",
       "Order Item Total                  0.225149     0.060084         -0.001331   \n",
       "Order Profit Per Order            0.031826     0.009286          0.002229   \n",
       "Order Zipcode                    -0.001927    -0.001813          0.004401   \n",
       "Product Card Id                   0.991096     0.227822          0.003127   \n",
       "Product Category Id               1.000000     0.274391          0.002846   \n",
       "Product Price                     0.461007     0.110975          0.002155   \n",
       "Product Status                         NaN          NaN               NaN   \n",
       "\n",
       "                               Department Id  Latitude  Longitude  ...  \\\n",
       "Days for shipping (real)           -0.001634 -0.004068   0.003891  ...   \n",
       "Days for shipment (scheduled)      -0.000227 -0.005318   0.003676  ...   \n",
       "Sales per customer                  0.232448 -0.000232   0.001471  ...   \n",
       "Late_delivery_risk                  0.001049  0.000700  -0.001953  ...   \n",
       "Category Id                         0.889035  0.002172  -0.002193  ...   \n",
       "Customer Id                         0.136940  0.002321  -0.005006  ...   \n",
       "Customer Zipcode                    0.002876  0.584564  -0.923925  ...   \n",
       "Department Id                       1.000000  0.002435  -0.002407  ...   \n",
       "Latitude                            0.002435  1.000000  -0.525148  ...   \n",
       "Longitude                          -0.002407 -0.525148   1.000000  ...   \n",
       "Order Customer Id                   0.136940  0.002321  -0.005006  ...   \n",
       "Order Id                            0.106772 -0.003000   0.002642  ...   \n",
       "Order Item Cardprod Id              0.906283  0.002011  -0.002468  ...   \n",
       "Order Item Discount                 0.144648 -0.003006   0.002406  ...   \n",
       "Order Item Discount Rate            0.000250 -0.003890   0.000556  ...   \n",
       "Order Item Id                       0.101687 -0.003074   0.002627  ...   \n",
       "Order Item Product Price            0.446853  0.000458  -0.000835  ...   \n",
       "Order Item Profit Ratio            -0.003055 -0.000094  -0.003583  ...   \n",
       "Order Item Quantity                -0.265458 -0.001846   0.004423  ...   \n",
       "Sales                               0.234797 -0.000706   0.001732  ...   \n",
       "Order Item Total                    0.232448 -0.000232   0.001471  ...   \n",
       "Order Profit Per Order              0.031455  0.000320  -0.002523  ...   \n",
       "Order Zipcode                      -0.002009 -0.001081  -0.002200  ...   \n",
       "Product Card Id                     0.906283  0.002011  -0.002468  ...   \n",
       "Product Category Id                 0.889035  0.002172  -0.002193  ...   \n",
       "Product Price                       0.446853  0.000458  -0.000835  ...   \n",
       "Product Status                           NaN       NaN        NaN  ...   \n",
       "\n",
       "                               Order Item Profit Ratio  Order Item Quantity  \\\n",
       "Days for shipping (real)                     -0.004645            -0.000820   \n",
       "Days for shipment (scheduled)                -0.001834            -0.002919   \n",
       "Sales per customer                           -0.001462             0.105458   \n",
       "Late_delivery_risk                           -0.002319            -0.000148   \n",
       "Category Id                                  -0.001650            -0.302355   \n",
       "Customer Id                                  -0.000371            -0.091234   \n",
       "Customer Zipcode                              0.003364            -0.006156   \n",
       "Department Id                                -0.003055            -0.265458   \n",
       "Latitude                                     -0.000094            -0.001846   \n",
       "Longitude                                    -0.003583             0.004423   \n",
       "Order Customer Id                            -0.000371            -0.091234   \n",
       "Order Id                                      0.002732            -0.087000   \n",
       "Order Item Cardprod Id                       -0.001817            -0.312504   \n",
       "Order Item Discount                          -0.002793             0.065409   \n",
       "Order Item Discount Rate                     -0.002684            -0.000028   \n",
       "Order Item Id                                 0.002758            -0.083573   \n",
       "Order Item Product Price                     -0.002071            -0.476217   \n",
       "Order Item Profit Ratio                       1.000000             0.001141   \n",
       "Order Item Quantity                           0.001141             1.000000   \n",
       "Sales                                        -0.001787             0.106488   \n",
       "Order Item Total                             -0.001462             0.105458   \n",
       "Order Profit Per Order                        0.823691             0.015722   \n",
       "Order Zipcode                                -0.002109             0.000622   \n",
       "Product Card Id                              -0.001817            -0.312504   \n",
       "Product Category Id                          -0.001650            -0.302355   \n",
       "Product Price                                -0.002071            -0.476217   \n",
       "Product Status                                     NaN                  NaN   \n",
       "\n",
       "                                  Sales  Order Item Total  \\\n",
       "Days for shipping (real)       0.001968          0.001760   \n",
       "Days for shipment (scheduled)  0.006306          0.006421   \n",
       "Sales per customer             0.989744          1.000000   \n",
       "Late_delivery_risk            -0.003554         -0.003781   \n",
       "Category Id                    0.227429          0.225149   \n",
       "Customer Id                    0.060862          0.060084   \n",
       "Customer Zipcode              -0.001518         -0.001331   \n",
       "Department Id                  0.234797          0.232448   \n",
       "Latitude                      -0.000706         -0.000232   \n",
       "Longitude                      0.001732          0.001471   \n",
       "Order Customer Id              0.060862          0.060084   \n",
       "Order Id                       0.079760          0.078926   \n",
       "Order Item Cardprod Id         0.238731          0.236311   \n",
       "Order Item Discount            0.617433          0.498727   \n",
       "Order Item Discount Rate       0.000336         -0.119480   \n",
       "Order Item Id                  0.078931          0.078106   \n",
       "Order Item Product Price       0.789928          0.781762   \n",
       "Order Item Profit Ratio       -0.001787         -0.001462   \n",
       "Order Item Quantity            0.106488          0.105458   \n",
       "Sales                          1.000000          0.989744   \n",
       "Order Item Total               0.989744          1.000000   \n",
       "Order Profit Per Order         0.131769          0.133435   \n",
       "Order Zipcode                  0.000176         -0.000043   \n",
       "Product Card Id                0.238731          0.236311   \n",
       "Product Category Id            0.227429          0.225149   \n",
       "Product Price                  0.789928          0.781762   \n",
       "Product Status                      NaN               NaN   \n",
       "\n",
       "                               Order Profit Per Order  Order Zipcode  \\\n",
       "Days for shipping (real)                    -0.005108      -0.000302   \n",
       "Days for shipment (scheduled)               -0.000210       0.001956   \n",
       "Sales per customer                           0.133435      -0.000043   \n",
       "Late_delivery_risk                          -0.003729      -0.005240   \n",
       "Category Id                                  0.031826      -0.001927   \n",
       "Customer Id                                  0.009286      -0.001813   \n",
       "Customer Zipcode                             0.002229       0.004401   \n",
       "Department Id                                0.031455      -0.002009   \n",
       "Latitude                                     0.000320      -0.001081   \n",
       "Longitude                                   -0.002523      -0.002200   \n",
       "Order Customer Id                            0.009286      -0.001813   \n",
       "Order Id                                     0.013662       0.000712   \n",
       "Order Item Cardprod Id                       0.033102      -0.001929   \n",
       "Order Item Discount                          0.064744       0.001302   \n",
       "Order Item Discount Rate                    -0.018634       0.000998   \n",
       "Order Item Id                                0.013504       0.000708   \n",
       "Order Item Product Price                     0.103401      -0.000038   \n",
       "Order Item Profit Ratio                      0.823691      -0.002109   \n",
       "Order Item Quantity                          0.015722       0.000622   \n",
       "Sales                                        0.131769       0.000176   \n",
       "Order Item Total                             0.133435      -0.000043   \n",
       "Order Profit Per Order                       1.000000      -0.001696   \n",
       "Order Zipcode                               -0.001696       1.000000   \n",
       "Product Card Id                              0.033102      -0.001929   \n",
       "Product Category Id                          0.031826      -0.001927   \n",
       "Product Price                                0.103401      -0.000038   \n",
       "Product Status                                    NaN            NaN   \n",
       "\n",
       "                               Product Card Id  Product Category Id  \\\n",
       "Days for shipping (real)             -0.000835            -0.000316   \n",
       "Days for shipment (scheduled)        -0.000445            -0.000383   \n",
       "Sales per customer                    0.236311             0.225149   \n",
       "Late_delivery_risk                    0.001513             0.001780   \n",
       "Category Id                           0.991096             1.000000   \n",
       "Customer Id                           0.227822             0.274391   \n",
       "Customer Zipcode                      0.003127             0.002846   \n",
       "Department Id                         0.906283             0.889035   \n",
       "Latitude                              0.002011             0.002172   \n",
       "Longitude                            -0.002468            -0.002193   \n",
       "Order Customer Id                     0.227822             0.274391   \n",
       "Order Id                              0.163241             0.198302   \n",
       "Order Item Cardprod Id                1.000000             0.991096   \n",
       "Order Item Discount                   0.147251             0.140142   \n",
       "Order Item Discount Rate              0.000731             0.000579   \n",
       "Order Item Id                         0.153910             0.186611   \n",
       "Order Item Product Price              0.480232             0.461007   \n",
       "Order Item Profit Ratio              -0.001817            -0.001650   \n",
       "Order Item Quantity                  -0.312504            -0.302355   \n",
       "Sales                                 0.238731             0.227429   \n",
       "Order Item Total                      0.236311             0.225149   \n",
       "Order Profit Per Order                0.033102             0.031826   \n",
       "Order Zipcode                        -0.001929            -0.001927   \n",
       "Product Card Id                       1.000000             0.991096   \n",
       "Product Category Id                   0.991096             1.000000   \n",
       "Product Price                         0.480232             0.461007   \n",
       "Product Status                             NaN                  NaN   \n",
       "\n",
       "                               Product Price  Product Status  \n",
       "Days for shipping (real)            0.002196             NaN  \n",
       "Days for shipment (scheduled)       0.006890             NaN  \n",
       "Sales per customer                  0.781762             NaN  \n",
       "Late_delivery_risk                 -0.002160             NaN  \n",
       "Category Id                         0.461007             NaN  \n",
       "Customer Id                         0.110975             NaN  \n",
       "Customer Zipcode                    0.002155             NaN  \n",
       "Department Id                       0.446853             NaN  \n",
       "Latitude                            0.000458             NaN  \n",
       "Longitude                          -0.000835             NaN  \n",
       "Order Customer Id                   0.110975             NaN  \n",
       "Order Id                            0.115214             NaN  \n",
       "Order Item Cardprod Id              0.480232             NaN  \n",
       "Order Item Discount                 0.488085             NaN  \n",
       "Order Item Discount Rate            0.000336             NaN  \n",
       "Order Item Id                       0.112343             NaN  \n",
       "Order Item Product Price            1.000000             NaN  \n",
       "Order Item Profit Ratio            -0.002071             NaN  \n",
       "Order Item Quantity                -0.476217             NaN  \n",
       "Sales                               0.789928             NaN  \n",
       "Order Item Total                    0.781762             NaN  \n",
       "Order Profit Per Order              0.103401             NaN  \n",
       "Order Zipcode                      -0.000038             NaN  \n",
       "Product Card Id                     0.480232             NaN  \n",
       "Product Category Id                 0.461007             NaN  \n",
       "Product Price                       1.000000             NaN  \n",
       "Product Status                           NaN             NaN  \n",
       "\n",
       "[27 rows x 27 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#correlation between numerical columns\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "#bivariate analysis\n",
    "#pyplot\n",
    "#for plotting visulaization use matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "x=df[\"Order Item Quantity\"]\n",
    "y=df[\"Product Price\"]\n",
    "plt.plot(x,y,marker=\"H\",linewidth=2,markersize=12)\n",
    "plt.xlabel(\"Order Item Quantity\")\n",
    "plt.ylabel(\"Product Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Order City\n",
      "Kanpur        25\n",
      "Pune          24\n",
      "Bangalore     24\n",
      "Lucknow       21\n",
      "Mumbai        18\n",
      "              ..\n",
      "Chandigarh     1\n",
      "Hisar          1\n",
      "Gulbarga       1\n",
      "Ghaziabad      1\n",
      "Warangal       1\n",
      "Length: 91, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#1.List all the cities and the respective number of pending orders in 'India'?\n",
    "x=df.loc[(df[\"Order Country\"]==\"India\")&(df[\"Order Status\"]==\"PENDING\"),[\"Order City\"]]\n",
    "print(x.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estados Unidos    573\n",
       "Name: Order Country, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2.Which Country has the most suspected Fraud?\n",
    "x=df.loc[df[\"Order Status\"]==\"SUSPECTED_FRAUD\",\"Order Country\"].value_counts()\n",
    "x.iloc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Product Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12606</th>\n",
       "      <td>SOLE E35 Elliptical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Product Name\n",
       "12606  SOLE E35 Elliptical"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3.Which products(s) has /have the maximum discount?\n",
    "x=df[\"Order Item Discount\"].max()\n",
    "x=df.loc[df[\"Order Item Discount\"]==x,[\"Product Name\"]]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#4.Which year had the minimum sales for \"Nike\" products?\n",
    "x=df.loc[(df[\"Product Name\"].str.startswith(\"Nike\")==True),[\"shipping date (DateOrders)\",\"Sales\"]]\n",
    "z=pd.DataFrame(pd.DatetimeIndex(x[\"shipping date (DateOrders)\"]).year,x[\"Sales\"])\n",
    "z[\"shipping date (DateOrders)\"].value_counts(ascending=True).index.tolist()[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34197"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5.How many successfully processed orders have days for shipping(Real)>Days for shipment(Scheduled)?\n",
    "x=df.loc[((df[\"Days for shipping (real)\"]>df[\"Days for shipment (scheduled)\"])&(df[\"Order Status\"]==\"COMPLETE\")),[\"Order Status\"]]\n",
    "x.shape[0]"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}