Task-5/Task_5_Data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Task # 5 - To explore Business Analytics\n",
    "    Perform ‘Exploratory Data Analysis’ on the provided dataset ‘SampleSuperstore.\n",
    "You are the business owner of the retail firm and want to see how your company is performing. You are interested in finding out the weak areas where you can work to make more profit. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing packages\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Comment this if the data visualisations doesn't work on your side\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's try to read from the superstore.csv\n",
    "df = pd.read_csv('SampleSuperstore.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33180</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>25.2480</td>\n",
       "      <td>3</td>\n",
       "      <td>0.20</td>\n",
       "      <td>4.1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>91.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.6332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>258.5760</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>19.3932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>243.1600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ship Mode    Segment        Country             City       State  \\\n",
       "0       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1       Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2       Second Class  Corporate  United States      Los Angeles  California   \n",
       "3     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4     Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "...              ...        ...            ...              ...         ...   \n",
       "9989    Second Class   Consumer  United States            Miami     Florida   \n",
       "9990  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9991  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9992  Standard Class   Consumer  United States       Costa Mesa  California   \n",
       "9993    Second Class   Consumer  United States      Westminster  California   \n",
       "\n",
       "      Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0           42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1           42420  South        Furniture       Chairs  731.9400         3   \n",
       "2           90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3           33311  South        Furniture       Tables  957.5775         5   \n",
       "4           33311  South  Office Supplies      Storage   22.3680         2   \n",
       "...           ...    ...              ...          ...       ...       ...   \n",
       "9989        33180  South        Furniture  Furnishings   25.2480         3   \n",
       "9990        92627   West        Furniture  Furnishings   91.9600         2   \n",
       "9991        92627   West       Technology       Phones  258.5760         2   \n",
       "9992        92627   West  Office Supplies        Paper   29.6000         4   \n",
       "9993        92683   West  Office Supplies   Appliances  243.1600         2   \n",
       "\n",
       "      Discount    Profit  \n",
       "0         0.00   41.9136  \n",
       "1         0.00  219.5820  \n",
       "2         0.00    6.8714  \n",
       "3         0.45 -383.0310  \n",
       "4         0.20    2.5164  \n",
       "...        ...       ...  \n",
       "9989      0.20    4.1028  \n",
       "9990      0.00   15.6332  \n",
       "9991      0.20   19.3932  \n",
       "9992      0.00   13.3200  \n",
       "9993      0.00   72.9480  \n",
       "\n",
       "[9994 rows x 13 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
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       "      <th>Profit</th>\n",
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       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
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       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311</td>\n",
       "      <td>South</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Ship Mode    Segment        Country             City       State  \\\n",
       "0    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "1    Second Class   Consumer  United States        Henderson    Kentucky   \n",
       "2    Second Class  Corporate  United States      Los Angeles  California   \n",
       "3  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4  Standard Class   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region         Category Sub-Category     Sales  Quantity  \\\n",
       "0        42420  South        Furniture    Bookcases  261.9600         2   \n",
       "1        42420  South        Furniture       Chairs  731.9400         3   \n",
       "2        90036   West  Office Supplies       Labels   14.6200         2   \n",
       "3        33311  South        Furniture       Tables  957.5775         5   \n",
       "4        33311  South  Office Supplies      Storage   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9994, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Ship Mode     9994 non-null   object \n",
      " 1   Segment       9994 non-null   object \n",
      " 2   Country       9994 non-null   object \n",
      " 3   City          9994 non-null   object \n",
      " 4   State         9994 non-null   object \n",
      " 5   Postal Code   9994 non-null   int64  \n",
      " 6   Region        9994 non-null   object \n",
      " 7   Category      9994 non-null   object \n",
      " 8   Sub-Category  9994 non-null   object \n",
      " 9   Sales         9994 non-null   float64\n",
      " 10  Quantity      9994 non-null   int64  \n",
      " 11  Discount      9994 non-null   float64\n",
      " 12  Profit        9994 non-null   float64\n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 1015.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Let's print the columns data types.\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55190.379428</td>\n",
       "      <td>229.858001</td>\n",
       "      <td>3.789574</td>\n",
       "      <td>0.156203</td>\n",
       "      <td>28.656896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32063.693350</td>\n",
       "      <td>623.245101</td>\n",
       "      <td>2.225110</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>234.260108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1040.000000</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23223.000000</td>\n",
       "      <td>17.280000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>56430.500000</td>\n",
       "      <td>54.490000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>8.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90008.000000</td>\n",
       "      <td>209.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>29.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99301.000000</td>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8399.976000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Postal Code         Sales     Quantity     Discount       Profit\n",
       "count   9994.000000   9994.000000  9994.000000  9994.000000  9994.000000\n",
       "mean   55190.379428    229.858001     3.789574     0.156203    28.656896\n",
       "std    32063.693350    623.245101     2.225110     0.206452   234.260108\n",
       "min     1040.000000      0.444000     1.000000     0.000000 -6599.978000\n",
       "25%    23223.000000     17.280000     2.000000     0.000000     1.728750\n",
       "50%    56430.500000     54.490000     3.000000     0.200000     8.666500\n",
       "75%    90008.000000    209.940000     5.000000     0.200000    29.364000\n",
       "max    99301.000000  22638.480000    14.000000     0.800000  8399.976000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing statistical information on the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "      <td>9994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>49</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>New York City</td>\n",
       "      <td>California</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5968</td>\n",
       "      <td>5191</td>\n",
       "      <td>9994</td>\n",
       "      <td>915</td>\n",
       "      <td>2001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3203</td>\n",
       "      <td>6026</td>\n",
       "      <td>1523</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55190.379428</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229.858001</td>\n",
       "      <td>3.789574</td>\n",
       "      <td>0.156203</td>\n",
       "      <td>28.656896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32063.693350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>623.245101</td>\n",
       "      <td>2.225110</td>\n",
       "      <td>0.206452</td>\n",
       "      <td>234.260108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1040.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.444000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-6599.978000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23223.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.280000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.728750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56430.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.490000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>8.666500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90008.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>209.940000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>29.364000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>99301.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22638.480000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>8399.976000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ship Mode   Segment        Country           City       State  \\\n",
       "count             9994      9994           9994           9994        9994   \n",
       "unique               4         3              1            531          49   \n",
       "top     Standard Class  Consumer  United States  New York City  California   \n",
       "freq              5968      5191           9994            915        2001   \n",
       "mean               NaN       NaN            NaN            NaN         NaN   \n",
       "std                NaN       NaN            NaN            NaN         NaN   \n",
       "min                NaN       NaN            NaN            NaN         NaN   \n",
       "25%                NaN       NaN            NaN            NaN         NaN   \n",
       "50%                NaN       NaN            NaN            NaN         NaN   \n",
       "75%                NaN       NaN            NaN            NaN         NaN   \n",
       "max                NaN       NaN            NaN            NaN         NaN   \n",
       "\n",
       "         Postal Code Region         Category Sub-Category         Sales  \\\n",
       "count    9994.000000   9994             9994         9994   9994.000000   \n",
       "unique           NaN      4                3           17           NaN   \n",
       "top              NaN   West  Office Supplies      Binders           NaN   \n",
       "freq             NaN   3203             6026         1523           NaN   \n",
       "mean    55190.379428    NaN              NaN          NaN    229.858001   \n",
       "std     32063.693350    NaN              NaN          NaN    623.245101   \n",
       "min      1040.000000    NaN              NaN          NaN      0.444000   \n",
       "25%     23223.000000    NaN              NaN          NaN     17.280000   \n",
       "50%     56430.500000    NaN              NaN          NaN     54.490000   \n",
       "75%     90008.000000    NaN              NaN          NaN    209.940000   \n",
       "max     99301.000000    NaN              NaN          NaN  22638.480000   \n",
       "\n",
       "           Quantity     Discount       Profit  \n",
       "count   9994.000000  9994.000000  9994.000000  \n",
       "unique          NaN          NaN          NaN  \n",
       "top             NaN          NaN          NaN  \n",
       "freq            NaN          NaN          NaN  \n",
       "mean       3.789574     0.156203    28.656896  \n",
       "std        2.225110     0.206452   234.260108  \n",
       "min        1.000000     0.000000 -6599.978000  \n",
       "25%        2.000000     0.000000     1.728750  \n",
       "50%        3.000000     0.200000     8.666500  \n",
       "75%        5.000000     0.200000    29.364000  \n",
       "max       14.000000     0.800000  8399.976000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describing more statistical information on the dataset\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>261.96</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420</td>\n",
       "      <td>South</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>731.94</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.5820</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>29.60</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>243.16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ship Mode   Segment        Country         City       State  \\\n",
       "0       Second Class  Consumer  United States    Henderson    Kentucky   \n",
       "1       Second Class  Consumer  United States    Henderson    Kentucky   \n",
       "...              ...       ...            ...          ...         ...   \n",
       "9992  Standard Class  Consumer  United States   Costa Mesa  California   \n",
       "9993    Second Class  Consumer  United States  Westminster  California   \n",
       "\n",
       "      Postal Code Region         Category Sub-Category   Sales  Quantity  \\\n",
       "0           42420  South        Furniture    Bookcases  261.96         2   \n",
       "1           42420  South        Furniture       Chairs  731.94         3   \n",
       "...           ...    ...              ...          ...     ...       ...   \n",
       "9992        92627   West  Office Supplies        Paper   29.60         4   \n",
       "9993        92683   West  Office Supplies   Appliances  243.16         2   \n",
       "\n",
       "      Discount    Profit  \n",
       "0          0.0   41.9136  \n",
       "1          0.0  219.5820  \n",
       "...        ...       ...  \n",
       "9992       0.0   13.3200  \n",
       "9993       0.0   72.9480  \n",
       "\n",
       "[9994 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try to read from the superstore.csv\n",
    "pd.set_option('display.max_columns', 20)\n",
    "pd.set_option('display.max_rows', 5)\n",
    "# Let's try to give it a go with the new setting.\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Kentucky</td>\n",
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       "      <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>9992</th>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Costa Mesa</td>\n",
       "      <td>California</td>\n",
       "      <td>92627</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>29.60</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Westminster</td>\n",
       "      <td>California</td>\n",
       "      <td>92683</td>\n",
       "      <td>West</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>243.16</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.9480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Ship Mode   Segment        Country         City       State  \\\n",
       "0       Second Class  Consumer  United States    Henderson    Kentucky   \n",
       "1       Second Class  Consumer  United States    Henderson    Kentucky   \n",
       "...              ...       ...            ...          ...         ...   \n",
       "9992  Standard Class  Consumer  United States   Costa Mesa  California   \n",
       "9993    Second Class  Consumer  United States  Westminster  California   \n",
       "\n",
       "      PostalCode Region         Category Sub-Category   Sales  Quantity  \\\n",
       "0          42420  South        Furniture    Bookcases  261.96         2   \n",
       "1          42420  South        Furniture       Chairs  731.94         3   \n",
       "...          ...    ...              ...          ...     ...       ...   \n",
       "9992       92627   West  Office Supplies        Paper   29.60         4   \n",
       "9993       92683   West  Office Supplies   Appliances  243.16         2   \n",
       "\n",
       "      Discount    Profit  \n",
       "0          0.0   41.9136  \n",
       "1          0.0  219.5820  \n",
       "...        ...       ...  \n",
       "9992       0.0   13.3200  \n",
       "9993       0.0   72.9480  \n",
       "\n",
       "[9994 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's try to rename the column.\n",
    "df.columns = [ 'Ship Mode', 'Segment' , 'Country', 'City', 'State', 'PostalCode', 'Region', 'Category', 'Sub-Category' , 'Sales', 'Quantity', 'Discount', 'Profit']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's create a class named `display_all`, by which later we call on the next command.\n",
    "def display_all(df):\n",
    "    with pd.option_context(\"display.max_rows\", 1000, \"display.max_columns\", 1000):\n",
    "        display(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ship Mode</th>\n",
       "      <td>Second Class</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Segment</th>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Consumer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>Henderson</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>California</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Florida</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PostalCode</th>\n",
       "      <td>42420</td>\n",
       "      <td>42420</td>\n",
       "      <td>90036</td>\n",
       "      <td>33311</td>\n",
       "      <td>33311</td>\n",
       "      <td>90032</td>\n",
       "      <td>90032</td>\n",
       "      <td>90032</td>\n",
       "      <td>90032</td>\n",
       "      <td>90032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>South</td>\n",
       "      <td>South</td>\n",
       "      <td>West</td>\n",
       "      <td>South</td>\n",
       "      <td>South</td>\n",
       "      <td>West</td>\n",
       "      <td>West</td>\n",
       "      <td>West</td>\n",
       "      <td>West</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Office Supplies</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sub-Category</th>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>Art</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Appliances</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>261.96</td>\n",
       "      <td>731.94</td>\n",
       "      <td>14.62</td>\n",
       "      <td>957.577</td>\n",
       "      <td>22.368</td>\n",
       "      <td>48.86</td>\n",
       "      <td>7.28</td>\n",
       "      <td>907.152</td>\n",
       "      <td>18.504</td>\n",
       "      <td>114.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Discount</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Profit</th>\n",
       "      <td>41.9136</td>\n",
       "      <td>219.582</td>\n",
       "      <td>6.8714</td>\n",
       "      <td>-383.031</td>\n",
       "      <td>2.5164</td>\n",
       "      <td>14.1694</td>\n",
       "      <td>1.9656</td>\n",
       "      <td>90.7152</td>\n",
       "      <td>5.7825</td>\n",
       "      <td>34.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          0              1                2                3  \\\n",
       "Ship Mode      Second Class   Second Class     Second Class   Standard Class   \n",
       "Segment            Consumer       Consumer        Corporate         Consumer   \n",
       "Country       United States  United States    United States    United States   \n",
       "City              Henderson      Henderson      Los Angeles  Fort Lauderdale   \n",
       "State              Kentucky       Kentucky       California          Florida   \n",
       "PostalCode            42420          42420            90036            33311   \n",
       "Region                South          South             West            South   \n",
       "Category          Furniture      Furniture  Office Supplies        Furniture   \n",
       "Sub-Category      Bookcases         Chairs           Labels           Tables   \n",
       "Sales                261.96         731.94            14.62          957.577   \n",
       "Quantity                  2              3                2                5   \n",
       "Discount                  0              0                0             0.45   \n",
       "Profit              41.9136        219.582           6.8714         -383.031   \n",
       "\n",
       "                            4               5                6  \\\n",
       "Ship Mode      Standard Class  Standard Class   Standard Class   \n",
       "Segment              Consumer        Consumer         Consumer   \n",
       "Country         United States   United States    United States   \n",
       "City          Fort Lauderdale     Los Angeles      Los Angeles   \n",
       "State                 Florida      California       California   \n",
       "PostalCode              33311           90032            90032   \n",
       "Region                  South            West             West   \n",
       "Category      Office Supplies       Furniture  Office Supplies   \n",
       "Sub-Category          Storage     Furnishings              Art   \n",
       "Sales                  22.368           48.86             7.28   \n",
       "Quantity                    2               7                4   \n",
       "Discount                  0.2               0                0   \n",
       "Profit                 2.5164         14.1694           1.9656   \n",
       "\n",
       "                           7                8                9  \n",
       "Ship Mode     Standard Class   Standard Class   Standard Class  \n",
       "Segment             Consumer         Consumer         Consumer  \n",
       "Country        United States    United States    United States  \n",
       "City             Los Angeles      Los Angeles      Los Angeles  \n",
       "State             California       California       California  \n",
       "PostalCode             90032            90032            90032  \n",
       "Region                  West             West             West  \n",
       "Category          Technology  Office Supplies  Office Supplies  \n",
       "Sub-Category          Phones          Binders       Appliances  \n",
       "Sales                907.152           18.504            114.9  \n",
       "Quantity                   6                3                5  \n",
       "Discount                 0.2              0.2                0  \n",
       "Profit               90.7152           5.7825            34.47  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_all(df.head(10).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
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       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
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       "  <tbody>\n",
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       "      <td>55190.4</td>\n",
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       "      <td>1040</td>\n",
       "      <td>23223</td>\n",
       "      <td>56430.5</td>\n",
       "      <td>90008</td>\n",
       "      <td>99301</td>\n",
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       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <td>9994</td>\n",
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       "      <td>3203</td>\n",
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       "      <th>Sub-Category</th>\n",
       "      <td>9994</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>229.858</td>\n",
       "      <td>623.245</td>\n",
       "      <td>0.444</td>\n",
       "      <td>17.28</td>\n",
       "      <td>54.49</td>\n",
       "      <td>209.94</td>\n",
       "      <td>22638.5</td>\n",
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       "    <tr>\n",
       "      <th>Quantity</th>\n",
       "      <td>9994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Discount</th>\n",
       "      <td>9994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>9994</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>8.6665</td>\n",
       "      <td>29.364</td>\n",
       "      <td>8399.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count unique              top  freq      mean       std      min  \\\n",
       "Ship Mode     9994      4   Standard Class  5968       NaN       NaN      NaN   \n",
       "Segment       9994      3         Consumer  5191       NaN       NaN      NaN   \n",
       "Country       9994      1    United States  9994       NaN       NaN      NaN   \n",
       "City          9994    531    New York City   915       NaN       NaN      NaN   \n",
       "State         9994     49       California  2001       NaN       NaN      NaN   \n",
       "PostalCode    9994    NaN              NaN   NaN   55190.4   32063.7     1040   \n",
       "Region        9994      4             West  3203       NaN       NaN      NaN   \n",
       "Category      9994      3  Office Supplies  6026       NaN       NaN      NaN   \n",
       "Sub-Category  9994     17          Binders  1523       NaN       NaN      NaN   \n",
       "Sales         9994    NaN              NaN   NaN   229.858   623.245    0.444   \n",
       "Quantity      9994    NaN              NaN   NaN   3.78957   2.22511        1   \n",
       "Discount      9994    NaN              NaN   NaN  0.156203  0.206452        0   \n",
       "Profit        9994    NaN              NaN   NaN   28.6569    234.26 -6599.98   \n",
       "\n",
       "                  25%      50%     75%      max  \n",
       "Ship Mode         NaN      NaN     NaN      NaN  \n",
       "Segment           NaN      NaN     NaN      NaN  \n",
       "Country           NaN      NaN     NaN      NaN  \n",
       "City              NaN      NaN     NaN      NaN  \n",
       "State             NaN      NaN     NaN      NaN  \n",
       "PostalCode      23223  56430.5   90008    99301  \n",
       "Region            NaN      NaN     NaN      NaN  \n",
       "Category          NaN      NaN     NaN      NaN  \n",
       "Sub-Category      NaN      NaN     NaN      NaN  \n",
       "Sales           17.28    54.49  209.94  22638.5  \n",
       "Quantity            2        3       5       14  \n",
       "Discount            0      0.2     0.2      0.8  \n",
       "Profit        1.72875   8.6665  29.364  8399.98  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_all(df.describe(include='all').T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     9994.000000\n",
      "mean       229.858001\n",
      "             ...     \n",
      "75%        209.940000\n",
      "max      22638.480000\n",
      "Name: Sales, Length: 8, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\santosh sharma\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:218: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  color=hist_color, **hist_kws)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df['Sales'].describe())\n",
    "plt.figure(figsize=(9, 8))\n",
    "sns.distplot(df['Sales'], color='g', bins=100, hist_kws={'alpha': 0.4});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do so lets first list all the types of our data from our dataset and take only the numerical ones:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[dtype('int64'), dtype('float64'), dtype('O')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(set(df.dtypes.tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>PostalCode</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>42420</td>\n",
       "      <td>261.9600</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.9136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42420</td>\n",
       "      <td>731.9400</td>\n",
       "      <td>3</td>\n",
       "      <td>0.00</td>\n",
       "      <td>219.5820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>90036</td>\n",
       "      <td>14.6200</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.8714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33311</td>\n",
       "      <td>957.5775</td>\n",
       "      <td>5</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-383.0310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33311</td>\n",
       "      <td>22.3680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.20</td>\n",
       "      <td>2.5164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PostalCode     Sales  Quantity  Discount    Profit\n",
       "0       42420  261.9600         2      0.00   41.9136\n",
       "1       42420  731.9400         3      0.00  219.5820\n",
       "2       90036   14.6200         2      0.00    6.8714\n",
       "3       33311  957.5775         5      0.45 -383.0310\n",
       "4       33311   22.3680         2      0.20    2.5164"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_num = df.select_dtypes(include = ['float64', 'int64'])\n",
    "df_num.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization\n",
    "To check missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb66f416d8>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.heatmap(df.isnull(),cbar=False,yticklabels=False,cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets plot them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1440 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_num.hist(figsize=(16, 20), bins=50, xlabelsize=8, ylabelsize=8); # ; avoid having the matplotlib verbose informations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll try to find which features are strongly correlated with Sales. We'll store them in a var called golden_features_list. We'll reuse our df_num dataset to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is 1 strongly correlated values with Sales:\n",
      "Sales    1.0\n",
      "Name: Sales, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "df_num_corr = df_num.corr()['Sales'][:-1] # -1 because the latest row is SalePrice\n",
    "golden_features_list = df_num_corr[abs(df_num_corr) > 0.5].sort_values(ascending=False)\n",
    "print(\"There is {} strongly correlated values with Sales:\\n{}\".format(len(golden_features_list), golden_features_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, we now have a list of strongly correlated values but this list is incomplete as we know that correlation is affected by outliers. So we could proceed as follow:\n",
    "\n",
    "    1. Plot the numerical features and see which ones have very few or explainable outliers\n",
    "    2. Remove the outliers from these features and see which one can have a good correlation without their outliers\n",
    "Btw, correlation by itself does not always explain the relationship between data so ploting them could even lead us to new insights and in the same manner, check that our correlated values have a linear relationship to the Sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x180 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(0, len(df_num.columns), 5):\n",
    "    sns.pairplot(data=df_num,\n",
    "                x_vars=df_num.columns[i:i+5],\n",
    "                y_vars=['Sales'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature to feature relationship\n",
    "Trying to plot all the numerical features in a seaborn pairplot will take us too much time and will be hard to interpret. We can try to see if some variables are linked between each other and then explain their relation with common sense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_num.drop('Sales', axis=1).corr() # We already examined SalePrice correlations\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.4)], \n",
    "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
    "            annot=True, annot_kws={\"size\": 8}, square=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To check correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb64d80320>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aJSJmAsfV2GdsjfStcvMzyG6LJSKeJev47pDv0P4f4H8qDvVZ4BddOxMzs+boBZ//dWv6L7Mj4o/AR5qdTxFJM8matv5XmeUwM+vQQl0U749HeETELmWXwcwsz896MjOzQr3httd6OVCYmZWghSoUDhRmZmXoDXcz1cuBwsysBC0UJxwozMzK4M5sMzMr1EJxwoHCzKwMbnoyM7NC6vSB2r2HA4WZWQnWa6EfUjhQmJmVoJUeM+5AYWZWAvdRmJlZoRaqUDhQmJmVwb+jMDOzQv3cmd163l6xquwi9LiRmw0puwhNMf3ui8suQo8bfdBZZRehKZZOv7LsIvRabQ3eHitpP+B/A/2AayPi4or1ZwCnACuBV4CTIuLFtG4V2UijAH+NiIOL8nKgMDMrQSMtT5L6AT8D9gEWAdMlTa4Y6nk2MCoi3pL0NeBS4Oi0bllEjKw3vxaq/JiZ9R1tqn+qYldgfkQsiIh3gJuBQ/IbRMTDEfFWWnwc2LLbZe3ujmZm1n1tUt1TFVsAL+WWF6W0Wk4G7s0tbyBphqTHJR3aWVnd9GRmVoLOmp4evfM3TLvzJgC++vS8cRExIb97lV2iej46HhgF7JVL/nBELJb0UeAhSfMi4vlaZXGgMDMrQWcDF+19+HHsffhxAJw0+sMTKlYvAobllrcEFlceQ9IXgHOBvSJieUd6RCxO/y+QNBXYCagZKNz0ZGZWgrYuTFVMB4ZL2lrSB4BjgMn5DSTtBFwDHBwRf8ulD5G0fpofCnwGyHeCr8U1CjOzEjTyrKeIWCnpVOB+sttjJ0ZEu6SLgBkRMRn4ITAIuDXl1XEb7CeAayS9SxaHLq64W2otDhRmZiVo9HfZETEFmFKRdn5u/gs19vsj8Mmu5OVAYWZWAj/Cw8zMCrVOmHCgMDMrRVsLPWfcgcLMrAStdMupA4WZWQk8wp2ZmRVqnTDhQGFmVgrXKMzMrFA/BwozMyvSOmGiCR3vklZJmiOpXdITks6Q1JbWjZJ0RU/n2cXynVNm/mZmkD09tt6pbM2oUbw3cpKkfwV+AwwGLoiIGcCMJuTZFecA/11yGczsfa7RoVDXpabeypueWDgOOFWZMZLuAZC0V6p5zJE0W9KGKf07kual2sjFKW1kGmBjrqQ7JA1J6VMljUrzQyUtTPNjJd0u6T5Jz0m6NKVfDAxIed7YzHM3Myvyfq9RrCE977wN+NeKVWcC34iIxyQNAt6WtD9wKLBbGud1k7Ttr4FvRsQj6emIFwCnd5L1SLJnrC8HnpH004g4S9KpXRkr1sysGdRCNYp11Zld7RV5DPhx+mZ/e0QsSoNsXNcxzmtELJE0GNg4Ih5J+/0KuLWOPB+MiDcAJD0FfIQ1hw5cwx03TeKOmya9t3zYsWM57NixdWRjZtZ1vuspJw21twr4G9lz0AGIiIsl/Q44AHg8BQlRYzi/Glayuvlsg4p1y3Pzq+jkXB0YzGxdaqE40dw+CkmbAlcDV0ZEVKzbJiLmRcQlZB3c2wG/B06SNDBts0mqFSyVtEfa9QSgo3axENglzR9RZ7FWSOrf3XMyM+sJ7/c+igGS5gD9yb7xXw/8uMp2p0vam+zb/lPAvRGxXNJIYIakd8gG5TgHOBG4OgWQBcBX0jEuA34r6QTgoTrLNwGYK2lWRBzXvVM0M2tMK/VRqOKL/vvW9Bfe6HMvxEYD+mbFacXKd8suQo8bfdBZZRehKZZOv7LsIjTFBus1/in/4NOv1v2Z8/nthpYaVfzLbDOzEniEOzMzK9RKTU8OFGZmJWihAe4cKMzMyuAahZmZFWqhLgoHCjOzMrRQnHCgMDMrgx/hYWZmxVonTjhQmJmVwZ3ZZmZWqIVanhwozMzK0EJxwoHCzKwULRQpHCjMzErgZz2ZmVmh1gkTDhRmZuVooUjhQGFmVgLfHtuCBvfBQX7OuffpsovQFP+51zZlF6HH9dUBfoaMPrXsIjTFstmNX68W6qJwoDAzK0MrBYq2sgtgZvZ+pC78q7q/tJ+kZyTNl7TWWLqS1pd0S1r/J0lb5dadndKfkbRvZ2V1oDAzK4FU/7T2vuoH/AzYH9geOFbS9hWbnQwsjYiPAZcDl6R9tweOAUYA+wE/T8eryYHCzKwE6sJUxa7A/IhYEBHvADcDh1RscwjwqzR/G/B5SUrpN0fE8oh4AZifjleTA4WZWRk6iQ633jiRow/ck6MP3BNJ4yr23gJ4Kbe8KKVV3SYiVgJvAP9S575rcGe2mVkJOrs99qjjT+ao408GYMQWH5yw1u5rizq3qWffNThQmJmVoK2xu54WAcNyy1sCi2tss0jSesBgYEmd+65Z1oaKamZm3dNYJ8V0YLikrSV9gKxzenLFNpOBE9P8EcBDEREp/Zh0V9TWwHDgz0VFdY3CzKwEjfwyOyJWSjoVuB/oB0yMiHZJFwEzImIy8EvgeknzyWoSx6R92yX9FngKWAl8IyJWFeXnQGFmVoJGf3AXEVOAKRVp5+fm3waOrLHv94Hv15uXA4WZWQla6IfZDhRmZqVooUjhQGFmVgIPXGRmZoVaJ0w4UJiZlaOFIoUDhZlZCTxwkZmZFWqhLgoHCjOzMrRSoOiVj/CQtErSHElPSrpV0sAu7n+kpL9IeljSKElXpPQxkj7dnFKbmdWv0YGL1qVeGSiAZRExMiJ2AN4BxudXKlNU9pOBr0fE3hExIyJOS+ljAAcKMytdIwMXrWu9NVDkTQM+JmmrVEv4OTALGCbpWEnzUs2jY/Sm84HPAldL+mGqRdyThgEcD3wr1Vb2KOl8zMwaHbhonerVgSI9Gnd/YF5K2hb4dUTsBKwgG9rvc8BIYLSkQyPiImAGcFxEfLvjWBGxELgauDzVVqatuzMxM1tTK9Uoemtn9gBJc9L8NLKnIG4OvBgRj6f00cDUiHgFQNKNwJ7And3J8JbrJ3LLDRPfWz76+JM4+oSTull8M7PO9IIIUKfeGiiWRcTIfEI21Ctv5pN6MsOjT3BgMLN1p8GBi9apXt301Ik/AXtJGiqpH3As8Egn+/wD2LDpJTMz60QrNT21bKCIiJeBs4GHgSeAWRFxVye73Q0c5s5sMytbK90e2yubniJiUJW0hcAOFWm/AX5TZdsxufmpwNQ0/yywY0+W1cysW8r//K9brwwUZmZ9XQvFCQcKM7My9Ia+h3o5UJiZlUAtFCkcKMzMStA6YcKBwsysFC1UoXCgMDMrQ2+47bVeDhRmZiVwjcLMzAo5UJiZWSE3PZmZWSHXKMzMrFALxQkHCjOzUrRQpHCgMDMrQSv1UbTsY8Zb2S3XT+x8oxa0YOrtZRehx91x06Syi9AUv/zFhLKL0ONWvtpedhG6pE31T2VzoChBfsjVvuSFR/pgoLh5UtlFaIqJ1/a9QLHqtdYKFKgLU8nc9GRmVoJWanpSRJRdhvcdSeMios99peuL59UXzwn65nn1xXPqLRwozMyskPsozMyskAOFmZkVcqBIJK2SNEfSk5JulTSwG8c4vZ79JE2VNCrND5J0jaTnJbVLelTSbl3I80JJZ3a1rHUe+9xUprnptalZLkmTJB3RjHJ0haQtJd0l6TlJCyRdKWn9Hs5jjKRP55bHS/pymh8rafMezq/jvdku6QlJZ0hqS+tGSbqiJ/PrRvnOadJxG/qblHSkpL9Iejj/OlVeP+ucA8VqyyJiZETsALwDjO/GMU4HuhpgrgWWAMMjYgQwFhjajbx7lKTdgf8Ado6IHYEvAC+VW6piysaWvB24MyKGA8OBAcClPZzVGOC9D5qIuDoifp0WxwI9GihY/d4cAewDHABckPKeERGn9XB+XdWUQEEnf5PKFH2GnQx8PSL2rnidxpC7flaHiPCUdej/Mzc/Hvh5mj8DeDJNp6e0DwK/A55I6UcDp5G9mecBD6ftrgJmAO3A93LHnwqMArYBXgD61SjTWnmn9HOBZ4A/ADcBZ6b0bYD7gJnANGC7Bl6Pw4G7q6SfD0xPZZrA6hsiJgFHpPldgEdSOe4HNkvppwFPAXOBm5twDT8PPFqRthGwFDgVuDKXfg8wppPrtBD4HjArXdftgK2A/wf8X2AOsAdwIXAmcATwz3Rt5gAHAnfkjrcPcHsj7820/FHgNbI77McA96T0vVK+c4DZwIYp/Tup/E8AF6e0kcDj6VrcAQzJvzfT/FBgYZofSxaE7wOeAy5N6RcDq1KeNzb7bzK9/n9J87OBjwDHpvN7Ergk9z7tuBY/7Hidql2/nn4f9sWp9AL0lqnjTUn225K7gK+RfeDNIwsMg9IHyU7Al4Bf5PYdnP5fCAzNpW+S/u+X/gB3TMtTyQLFwfkPkory1Mq7I30g2YfgfFYHigfJaiYAuwEPNfB6DEp/SM+mP8q98ueU5q8HDkrzk8g+KPsDfwQ2TelHAxPT/GJg/TS/cROu4WnA5VXSZ5PV9moFilrXaSHwzTT/deDaNH9hx2teucyaH7QCns69Fr/peL26896sSFsKfIg1A8XdwGdy1289YP90PQZWnOvc3DW9CPhJlfJXBooFwGBgA+BFYFit8jXxb3Ir4F3g39O6zYG/Apum7R4CDq1yLvnXaY3r56nzyU1Pqw2QNIfsm+VfgV8CnyX7IH8zIv5J9o1qD7IP6i9IukTSHhHxRo1jHiVpFtkH1Qhg+y6Up1bee6T0tyLi78BkyPo6yKrTt6bzuAbYrCsvQF7KcxdgHPAKcIukscDekv4kaR7wuXReedsCOwAPpHKcB2yZ1s0FbpR0PLCyu2UrIKDa/d6d/bKp6Dp1/Nx8JtmHVN0i+1S6Hjhe0sbA7sC9XTlGgWrn9BjwY0mnkQXilWRNhtdFxFupTEskDU7rH0n7/QrYs448H4yINyLibbKa4UcaPoti1f4mAV6MiMfT/GhgakS8ks73Ruo7F+sC/zJ7tWURMTKfkNq81xIRz0rahayt+AeSfh8RF1XsuzVZc8ToiFgqaRLZN7G8duBTktoi4t2KdUUfbtU+DNuA1yvPoRERsYrsW9nUFBi+CuxI9i3tJUkXsvY5CWiPiN2rHPJAsj/ig4HvShqR/rh7SjtZbW91YaSNyL55vwZ8PLdqg7S+s+u0PP2/iu79vVxH9k3/beDWnjhfSR9N5fkb8ImO9Ii4WNLvyN6Xj0v6ArWDZy0rWd13WXltl+fmu/t6dEW1v0mAN/NJTS6D4c7szjwKHCppoKQPAocB09JdLW9FxA3AZcDOaft/ABum+Y3I3tBvSPoQWRPAGiLiebJvS9/rCEqShks6pFbeKf0wSQMkbQgclI71d+AFSUem40jSp7p74pK2lTQ8lzSSrL0X4NVUg6l2l9MzwKapMxxJ/SWNSJ2OwyLiYbI2843Jmkd60oPAwNwdSP2AHwFXkvUFjZTUJmkYsGvap9PrVEX+Oheui4jFZE1u55E1zzVE0qbA1WTNaFGxbpuImBcRl5C9r7YDfg+c1HHHkKRNUg14qaQ90q4nkPUpQdbctkuar/cuthWS+nf3nBr0J2AvSUPT9T6W1edSS9H1sypcoygQEbPSN8w/p6RrI2K2pH2BH0p6F1hB1nYKWefuvZJejoi9Jc0m+5a7gKxZoJpTyD7M5kt6i+yb77dr5Q0g6Ray/oMXyYJHh+OAqySdR9ZXcDNZB2Z3DAJ+mppMVpL1hYwDXidreltI1qm9hoh4J90me0Vq4lgP+AlZX8cNKU1kfQmvd7NsVUVESDoM+Jmk75K1W98SEd9PgfgFVnd6zkr7PFHndcq7G7gtBfRvVqybBFwtaRmwe0QsI2sO2TQinurmqXU0wfQnuxbXAz+ust3pkvYm+7b/FHBvRCyXNBKYIekdYArZXUonpnIOJDvvr6RjXAb8VtIJZO399ZgAzJU0KyKO694pdk9EvCzpbOBhsvfVlIi4q5Pd1rh+ETGtk+3f9/wID+uz0r3yNwGHR8TMEstxJTA7In7Z6cZmvZADhVkTSZpJ1rS1T0Qs72x7s97IgcLMzAq5M9vMzAo5UJiZWSEHCjMzK+RAYWZmhRwozMyskAOFmZkV+v/eWfuaboBpMwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "sns.heatmap(df.corr(),cmap='Blues',annot=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dark shades represents positive correlation while lighter shades represents negative correlation. If you set annot=True, you'll get values by which features are correlated to each other in grid-cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1fb67af77b8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Quality correlation matrix\n",
    "k = 12 #number of variables for heatmap\n",
    "cols = df.corr().nlargest(k, 'Profit')['Profit'].index\n",
    "cm = df[cols].corr()\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.heatmap(cm, annot=True, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## And finally lets look at their distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x2160 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(round(len(df_num.columns) / 3), 3, figsize=(12, 30))\n",
    "\n",
    "for i, ax in enumerate(fig.axes):\n",
    "    if i < len(df_num.columns):\n",
    "        ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)\n",
    "        sns.countplot(x=df_num.columns[i], alpha=0.7, data=df_num, ax=ax)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}