Task_3_Exploratory_Data_Analysis_Retail.ipynb

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    "colab": {
      "name": "Task 3_ Exploratory_Data_Analysis.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndWporBlpEnN"
      },
      "source": [
        "#**The Sparks Foundation - GRIP June 2021**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pzv-8DygpOGq"
      },
      "source": [
        "##**Task 3**: Exploratory Data Analysis - Retail\n",
        "\n",
        "####**Level**: Beginner"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmW35fIPpswh"
      },
      "source": [
        "##**Author**: [**Aishwarya Muttineni**](https://www.linkedin.com/in/aishwarya-muttineni-047b5a200/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwJU34ujqMR-"
      },
      "source": [
        "###**Problem Statement**\n",
        "\n",
        "• Perform ‘Exploratory Data Analysis’ on dataset ‘SampleSuperstore’.\n",
        "\n",
        "• As a business manager, try to find out the weak areas where you can work to make more profit.\n",
        "\n",
        "• What all business problems you can derive by exploring the data?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ko08IzuVq98m"
      },
      "source": [
        "###**Work Flow**\n",
        "\n",
        "1) Data collection\n",
        "\n",
        "2) Data Pre-processing\n",
        "\n",
        "3) Exploratory Data analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xelY4p20PXhU"
      },
      "source": [
        "**Importing libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbAMbYn2PXhV"
      },
      "source": [
        "# Importing all libraries required\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmSqTdvTPXhV"
      },
      "source": [
        "**Data collection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77aXPdL2PXhW",
        "outputId": "f370ce15-ff2f-45e8-8250-80b7ca99b80b"
      },
      "source": [
        "# loading dataset into pandas dataframe\n",
        "store_df = pd.read_csv('SampleSuperstore.csv')\n",
        "print(\"Dataset collected successfully\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset collected successfully\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "MI04NENwPXhX",
        "outputId": "8d3c6495-977c-46f1-8bdd-eacf67d20212"
      },
      "source": [
        "# check first 10 attributes\n",
        "store_df.head(10)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>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>5</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>48.8600</td>\n",
              "      <td>7</td>\n",
              "      <td>0.00</td>\n",
              "      <td>14.1694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Art</td>\n",
              "      <td>7.2800</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Phones</td>\n",
              "      <td>907.1520</td>\n",
              "      <td>6</td>\n",
              "      <td>0.20</td>\n",
              "      <td>90.7152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>18.5040</td>\n",
              "      <td>3</td>\n",
              "      <td>0.20</td>\n",
              "      <td>5.7825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90032</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Appliances</td>\n",
              "      <td>114.9000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>34.4700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Ship Mode    Segment        Country  ... Quantity Discount    Profit\n",
              "0    Second Class   Consumer  United States  ...        2     0.00   41.9136\n",
              "1    Second Class   Consumer  United States  ...        3     0.00  219.5820\n",
              "2    Second Class  Corporate  United States  ...        2     0.00    6.8714\n",
              "3  Standard Class   Consumer  United States  ...        5     0.45 -383.0310\n",
              "4  Standard Class   Consumer  United States  ...        2     0.20    2.5164\n",
              "5  Standard Class   Consumer  United States  ...        7     0.00   14.1694\n",
              "6  Standard Class   Consumer  United States  ...        4     0.00    1.9656\n",
              "7  Standard Class   Consumer  United States  ...        6     0.20   90.7152\n",
              "8  Standard Class   Consumer  United States  ...        3     0.20    5.7825\n",
              "9  Standard Class   Consumer  United States  ...        5     0.00   34.4700\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJpa8NFAPXhZ"
      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCj5xD8rPXhZ",
        "outputId": "86df9fe7-ee0a-4912-f592-a0eabd85207f"
      },
      "source": [
        "# getting the shape of data set\n",
        "store_df.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9994, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_2DpF76PXha",
        "outputId": "c8219db2-e776-4756-a889-a9f2976d79d3"
      },
      "source": [
        "# getting the attributes\n",
        "store_df.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Ship Mode', 'Segment', 'Country', 'City', 'State', 'Postal Code',\n",
              "       'Region', 'Category', 'Sub-Category', 'Sales', 'Quantity', 'Discount',\n",
              "       'Profit'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAPBLMS-PXha",
        "outputId": "67534f1c-d0b8-4916-9c65-edc314d384d4"
      },
      "source": [
        "# getting the data types\n",
        "store_df.dtypes"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ship Mode        object\n",
              "Segment          object\n",
              "Country          object\n",
              "City             object\n",
              "State            object\n",
              "Postal Code       int64\n",
              "Region           object\n",
              "Category         object\n",
              "Sub-Category     object\n",
              "Sales           float64\n",
              "Quantity          int64\n",
              "Discount        float64\n",
              "Profit          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3g4rkF08PXha",
        "outputId": "d37f2d7d-7e47-4d59-c3b4-b8ccfeb6a2e3"
      },
      "source": [
        "# getting the information about the dataframe\n",
        "store_df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Xa2dbu2XPXhb",
        "outputId": "d5a9a3c5-1e54-412e-c77f-2a2eb11beaac"
      },
      "source": [
        "# getting the statistical values\n",
        "store_df.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "emD7dn4MPXhb"
      },
      "source": [
        "**Handling Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11oi6aerPXhc",
        "outputId": "f992188e-64d4-4db8-93f9-06da60d97c13"
      },
      "source": [
        "# checking the null values\n",
        "store_df.isnull().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ship Mode       0\n",
              "Segment         0\n",
              "Country         0\n",
              "City            0\n",
              "State           0\n",
              "Postal Code     0\n",
              "Region          0\n",
              "Category        0\n",
              "Sub-Category    0\n",
              "Sales           0\n",
              "Quantity        0\n",
              "Discount        0\n",
              "Profit          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iitk_9n1PXhc"
      },
      "source": [
        "**Handling Duplicate Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Q7jpwbzPXhc",
        "outputId": "878ee02c-4a70-45fb-feaa-1c50c6cd2a10"
      },
      "source": [
        "# checking the duplicate values\n",
        "store_df.duplicated().sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "MRYbtIs5PXhd",
        "outputId": "d5a5f100-9151-425b-bf81-220779b0d111"
      },
      "source": [
        "# getting the duplicate values\n",
        "duplicate = store_df.duplicated()\n",
        "store_df[duplicate]"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>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>950</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>19120</td>\n",
              "      <td>East</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>15.552</td>\n",
              "      <td>3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.4432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3406</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Columbus</td>\n",
              "      <td>Ohio</td>\n",
              "      <td>43229</td>\n",
              "      <td>East</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>281.372</td>\n",
              "      <td>2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>-12.0588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3670</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Salem</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>97301</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>10.368</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>3.6288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4117</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</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>Paper</td>\n",
              "      <td>19.440</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.3312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4553</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>12.840</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.7780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5905</th>\n",
              "      <td>Same Day</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>41.400</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>19.8720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6146</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Art</td>\n",
              "      <td>11.760</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.1752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6334</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10011</td>\n",
              "      <td>East</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>49.120</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>23.0864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6357</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98103</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>25.920</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.4416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7608</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94122</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>25.920</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.4416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7735</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98105</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>19.440</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.3312</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7759</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77041</td>\n",
              "      <td>Central</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>15.552</td>\n",
              "      <td>3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>5.4432</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8032</th>\n",
              "      <td>First Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77041</td>\n",
              "      <td>Central</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>47.952</td>\n",
              "      <td>3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>16.1838</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8095</th>\n",
              "      <td>Second Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98115</td>\n",
              "      <td>West</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>12.960</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.2208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9262</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Detroit</td>\n",
              "      <td>Michigan</td>\n",
              "      <td>48227</td>\n",
              "      <td>Central</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>389.970</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>35.0973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9363</th>\n",
              "      <td>Standard Class</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Seattle</td>\n",
              "      <td>Washington</td>\n",
              "      <td>98105</td>\n",
              "      <td>West</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>22.140</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.4206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9477</th>\n",
              "      <td>Second Class</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Chicago</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>60653</td>\n",
              "      <td>Central</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>3.564</td>\n",
              "      <td>3</td>\n",
              "      <td>0.8</td>\n",
              "      <td>-6.2370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Ship Mode      Segment        Country  ... Quantity Discount   Profit\n",
              "950   Standard Class  Home Office  United States  ...        3      0.2   5.4432\n",
              "3406  Standard Class  Home Office  United States  ...        2      0.3 -12.0588\n",
              "3670  Standard Class     Consumer  United States  ...        2      0.2   3.6288\n",
              "4117  Standard Class     Consumer  United States  ...        3      0.0   9.3312\n",
              "4553  Standard Class     Consumer  United States  ...        3      0.0   5.7780\n",
              "5905        Same Day  Home Office  United States  ...        4      0.0  19.8720\n",
              "6146  Standard Class    Corporate  United States  ...        4      0.0   3.1752\n",
              "6334  Standard Class     Consumer  United States  ...        4      0.0  23.0864\n",
              "6357  Standard Class    Corporate  United States  ...        4      0.0  12.4416\n",
              "7608  Standard Class     Consumer  United States  ...        4      0.0  12.4416\n",
              "7735  Standard Class    Corporate  United States  ...        3      0.0   9.3312\n",
              "7759  Standard Class    Corporate  United States  ...        3      0.2   5.4432\n",
              "8032     First Class     Consumer  United States  ...        3      0.2  16.1838\n",
              "8095    Second Class     Consumer  United States  ...        2      0.0   6.2208\n",
              "9262  Standard Class     Consumer  United States  ...        3      0.0  35.0973\n",
              "9363  Standard Class  Home Office  United States  ...        3      0.0   6.4206\n",
              "9477    Second Class    Corporate  United States  ...        3      0.8  -6.2370\n",
              "\n",
              "[17 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kg2uqfgBPXhd"
      },
      "source": [
        "# removing duplicated values\n",
        "store_df.drop_duplicates(inplace = True)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CSBtzetuPXhd",
        "outputId": "c75b92f0-1bf9-4d95-958f-ff4e4aa9b862"
      },
      "source": [
        "# checking the shape of dataframe\n",
        "store_df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9977, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGzHbXu1PXhe",
        "outputId": "ee6f79f3-2456-4109-9c33-10d0e59dc238"
      },
      "source": [
        "# now checking the duplicate values\n",
        "store_df.duplicated().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwJQx8rJPXhe"
      },
      "source": [
        "**Handling Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "18y-8k5gPXhe",
        "outputId": "6d880781-c347-46bf-ed86-77c35210a58a"
      },
      "source": [
        "# sales\n",
        "store_df.boxplot(column = ['Sales'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfd918090>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecuf2i-uPXhf"
      },
      "source": [
        "# removing outliers\n",
        "def RemoveOutlier(col):\n",
        "    sorted(col)\n",
        "    q1, q3 = col.quantile([0.25,0.75])\n",
        "    IQR = q3-q1\n",
        "    lower_range = q1-(1.5*IQR)\n",
        "    upper_range = q3+(1.5*IQR)\n",
        "    return lower_range, upper_range\n",
        "\n",
        "lowsales, upsales = RemoveOutlier(store_df['Sales'])\n",
        "store_df['Sales'] = np.where(store_df['Sales']>upsales, upsales, store_df['Sales'])\n",
        "store_df['Sales'] = np.where(store_df['Sales']<lowsales, lowsales, store_df['Sales'])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ikUcOiTQPXhf",
        "outputId": "e3030ec1-ed26-4888-8559-a7a81ba1d95f"
      },
      "source": [
        "store_df.boxplot(column = ['Sales'])"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfd3f8650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LUoeMuyR16H8B2D5N+H2GdoQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "MHEXN36aPXhf",
        "outputId": "3df2974d-2570-4b91-b739-117961fb3919"
      },
      "source": [
        "# quantity\n",
        "store_df.boxplot(column = ['Quantity'])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc930e90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aISfW1kXPXhg"
      },
      "source": [
        "# removing outliers\n",
        "def RemoveOutlier(col):\n",
        "    sorted(col)\n",
        "    q1, q3 = col.quantile([0.25,0.75])\n",
        "    IQR = q3-q1\n",
        "    lower_range = q1-(1.5*IQR)\n",
        "    upper_range = q3+(1.5*IQR)\n",
        "    return lower_range, upper_range\n",
        "\n",
        "lowquantity, upquantity = RemoveOutlier(store_df['Quantity'])\n",
        "store_df['Quantity'] = np.where(store_df['Quantity']>upquantity, upquantity, store_df['Quantity'])\n",
        "store_df['Quantity'] = np.where(store_df['Quantity']<lowquantity, lowquantity, store_df['Quantity'])"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "ul4YhXGIPXhg",
        "outputId": "19c09f28-dadd-4fe5-b664-72d80f9480c4"
      },
      "source": [
        "store_df.boxplot(column = ['Quantity'])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc8af910>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "WAYYLNiNPXhh",
        "outputId": "9b39f906-c584-4842-f3d7-f1afdc1ad7f3"
      },
      "source": [
        "# discount\n",
        "store_df.boxplot(column = ['Discount'])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc80a990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUwZS8XiPXhh"
      },
      "source": [
        "# removing outliers\n",
        "def RemoveOutlier(col):\n",
        "    sorted(col)\n",
        "    q1, q3 = col.quantile([0.25,0.75])\n",
        "    IQR = q3-q1\n",
        "    lower_range = q1-(1.5*IQR)\n",
        "    upper_range = q3+(1.5*IQR)\n",
        "    return lower_range, upper_range\n",
        "\n",
        "lowdis, updis = RemoveOutlier(store_df['Discount'])\n",
        "store_df['Discount'] = np.where(store_df['Discount']>updis, updis, store_df['Discount'])\n",
        "store_df['Discount'] = np.where(store_df['Discount']<lowdis, lowdis, store_df['Discount'])"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "dYFGzKsaPXhh",
        "outputId": "0c4c0d02-59e4-4ff8-a005-9978e97ab4c4"
      },
      "source": [
        "store_df.boxplot(column = ['Discount'])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc80ad50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "QIMVuhE6PXhi",
        "outputId": "174b9e0a-bce2-4b7e-99c7-d23f8025a1bd"
      },
      "source": [
        "# profit\n",
        "store_df.boxplot(column = ['Profit'])"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc6fd810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x55Bm_lAPXhi"
      },
      "source": [
        "# removing outliers\n",
        "def RemoveOutlier(col):\n",
        "    sorted(col)\n",
        "    q1, q3 = col.quantile([0.25,0.75])\n",
        "    IQR = q3-q1\n",
        "    lower_range = q1-(1.5*IQR)\n",
        "    upper_range = q3+(1.5*IQR)\n",
        "    return lower_range, upper_range\n",
        "\n",
        "lowprofit, upprofit = RemoveOutlier(store_df['Profit'])\n",
        "store_df['Profit'] = np.where(store_df['Profit']>upprofit, upprofit, store_df['Profit'])\n",
        "store_df['Profit'] = np.where(store_df['Profit']<lowprofit, lowprofit, store_df['Profit'])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "JsMYRQXdPXhj",
        "outputId": "a16b0801-70d1-48e1-8242-a2249161ebf0"
      },
      "source": [
        "store_df.boxplot(column = ['Profit'])"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc78ed90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "id": "O6Td_4cEPXhj",
        "outputId": "f2a5bc0c-ca26-4ebe-930e-52207f45d1c9"
      },
      "source": [
        "# heatmap\n",
        "plt.figure(figsize=(10,10))\n",
        "correletion = store_df.corr()\n",
        "sns.heatmap(correletion, annot = True, cmap = 'RdBu_r')\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGvFbi_XPXhj"
      },
      "source": [
        "From this heatmap, we an see the Discount and Profit are negatively correlated. Values closer to zero shows that there is no linear correlation between those two items. The Values close to 1 shows they are positively correlated to each other and values closer to -1 shows they are negatively correlated to each other i.e if the value of one variable increases then the value of another variable decreases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zmZNIdSnPXhk"
      },
      "source": [
        "**Inferences**\n",
        "\n",
        " •\tThe Discount and Profit are negatively correlated. \n",
        "\n",
        " •\tSales and profit are Moderately correlated.\n",
        "\n",
        " •\tQuantity and Profit are Moderately correlated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "rjZ-KOUzPXhk",
        "outputId": "bc7acecd-3e83-452d-da20-6e41eb272954"
      },
      "source": [
        "# Discount v/s Profit\n",
        "plt.figure(figsize = (10,10))\n",
        "store_df.groupby('Category')['Discount','Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "30lb7bbgPXhm",
        "outputId": "bfb5b144-5045-453b-c6bf-2a46d426180d"
      },
      "source": [
        "# Sales v/s Profit\n",
        "plt.figure(figsize = (10,10))\n",
        "store_df.groupby('Category')['Sales','Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "XAWP2CSQPXhm",
        "outputId": "6088c475-e7ad-499f-cd74-e9e00db23832"
      },
      "source": [
        "# Quantity v/s Profit\n",
        "plt.figure(figsize=(10,10))\n",
        "store_df.groupby('Category')['Quantity','Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YnMnnt4gApgNlPYJGA4/mxUelXkUDgA3pdtIMYLCkTunB8WBgRtr2gaQBqRfRqLxjmVkzlD+fQb5WrVpx1FFHUVpayj333MOpp57KwIEDGTRoEOvXr2f48OGUlJQwYMAAFi5cyJo1azjrrLN44YUXyucoOO6445g7dy7jxo1j06ZN9OnTp3xoi6oU+1wEDaGgZwaSvgCcCFyYF74eeFDSGOAtYESKPw6cDJSS63l0LkBErJd0DfBCand1RKxPyxcB9wDtgCfSy8yaoYrzGeT7+OOPmTVrFldffTWrV69m/vz5LFy4kM6dO3PJJZdw6KGH8sgjj/DUU08xatQoFixYwJ133smNN97IY489tt2xrr/+em677TYWLFhQbT5lcxEsWbIESTXOoQC5uQgWL15M586d6dmzJ+eff3757Gy33norN998M/D5XAT9+/cv/B9oJymoGETER8AeFWLryPUuqtg2gIurOM4kYIebZBExF6j/sHtm1uRVNp/BG2+8QZ8+fZDEsGHDGDp0KPfccw8nnnhi+aBws2fP5je/+Q0AAwcOZN26dXzwwQf1zid/LoJTTjmlfB6E6pTNRQDsMBdB2SB3kJuLYMmSJfXOsSF4OAozKyoV5zOAz58ZVFSXUT5rq2wuglmzZjFt2jRuu+02nnrqqaKai6AhuBiYWfUauSto/nwGbdu2LXi/Y445hqlTp/LDH/6QZ555hj333JMOHTpUu0/r1q3ZsmULrVu3BnJzEUyZMoWuXT/v3f7hhx/y8ccfc/LJJ3P00UfTs2dP4PO5CEaMGFGvuQiOPvroWu+3M3hsIjMrOmXzGdTGhAkTmDdvHiUlJYwbN47JkyfXuM/YsWMpKSlh5MiR1c5FcMopp1BSUsJXv/rV7eYi+MMf/sAhhxzCc889V+e5CBpysLn68HwGZg3N8xnUW3XzGewsxTQXQUPwfAZm1uRVN5/BztKU5yJoCH5mYGY7iAiyHjy4mOczaAg78/ZQXe74+MrAzLbTtm1b1q1bV6cPFMteRLBu3bpaPXwHXxmYWQXdunVj5cqVrF27NutUrI7atm1Lt27dam6Yx8XAzLbTunVrevTokXUa1sh8m8jMzFwMzMzMxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMwosBhI6ihpmqQlkhZLOlJSZ0kzJS1NPzultpI0UVKppIWSDss7zujUfqmk0XnxvpJeTvtMVNbfgzcza2YKvTK4BfifiPgH4BBgMTAOmBURvYBZaR1gKNArvcYCvwCQ1BkYDxwB9AfGlxWQ1OaCvP2G1O9tmZlZbdRYDCTtDhwL3AUQEZ9GxPvAMKBswPDJwPC0PAyYEjnPAx0l7QOcBMyMiPUR8R4wExiStnWIiOfTlJlT8o5lZmaNoJArgx7AWuBuSS9KulPSF4C9I+Kd1OavwN5puSuwIm//lSlWXXxlJfEdSBoraa6kuR43xcys4RRSDFoBhwG/iIhDgY/4/JYQAOkv+p0+xGFE3BER/SKiX5cuXXb26czMmo1CisFKYGVE/DmtTyNXHFanWzykn2vS9lXAvnn7d0ux6uLdKombmVkjqbEYRMRfgRWSvpxCg4BXgelAWY+g0cCjaXk6MCr1KhoAbEi3k2YAgyV1Sg+OBwMz0rYPJA1IvYhG5R3LzMwaQaFDWF8CTJW0C7AMOJdcIXlQ0hjgLWBEavs4cDJQCnyc2hIR6yVdA7yQ2l0dEevT8kXAPUA74In0MjOzRlJQMYiIBUBlkygPqqRtABdXcZxJwKRK4nOBgwrJxczMGp6/gWxmZi4GZmbmYmBmZrgYmJkZLgZmZoaLgZmZ4WJgZma4GJiZGYV/A9nMrHmYsHsjnmtD452rBr4yMDMzFwMzM3MxMDMzXAzMzAwXAzMzw8XAzMxwMTAzM1wMzMyMAouBpOWSXpa0QNLcFOssaaakpelnpxSXpImSSiUtlHRY3nFGp/ZLJY3Oi/dNxy9N+6qh36iZmVWtNlcGx0dEn4gom/5yHDArInoBs9I6wFCgV3qNBX4BueIBjAeOAPoD48sKSGpzQd5+Q+r8jszMrNbqc5toGDA5LU8GhufFp0TO80BHSfsAJwEzI2J9RLwHzASGpG0dIuL5NH/ylLxjmZlZIyi0GATwpKR5ksam2N4R8U5a/iuwd1ruCqzI23dlilUXX1lJfAeSxkqaK2nu2rVrC0zdzMxqUuhAdV+NiFWS9gJmSlqSvzEiQlI0fHrbi4g7gDsA+vXrt9PPZ2bWXBR0ZRARq9LPNcDD5O75r063eEg/16Tmq4B983bvlmLVxbtVEjczs0ZSYzGQ9AVJu5UtA4OBV4DpQFmPoNHAo2l5OjAq9SoaAGxIt5NmAIMldUoPjgcDM9K2DyQNSL2IRuUdy8zMGkEht4n2Bh5OvT1bAfdFxP9IegF4UNIY4C1gRGr/OHAyUAp8DJwLEBHrJV0DvJDaXR0R69PyRcA9QDvgifQyM7NGUmMxiIhlwCGVxNcBgyqJB3BxFceaBEyqJD4XOKiAfM3MbCfwN5DNzMzFwMzMXAzMzAwXAzMzw8XAzMxwMTAzM1wMzMwMFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzoxbFQFJLSS9Keiyt95D0Z0mlkh6QtEuKt0nrpWl797xjXJnir0k6KS8+JMVKJY1ruLdnZmaFqM2Vwb8Bi/PW/xO4KSL+HngPGJPiY4D3Uvym1A5JvYEzgAOBIcDPU4FpCfwMGAr0Bs5Mbc3MrJEUVAwkdQO+DtyZ1gUMBKalJpOB4Wl5WFonbR+U2g8D7o+ITyLiTXJzJPdPr9KIWBYRnwL3p7ZmZtZICr0yuBn4HvBZWt8DeD8itqb1lUDXtNwVWAGQtm9I7cvjFfapKr4DSWMlzZU0d+3atQWmbmZmNamxGEg6BVgTEfMaIZ9qRcQdEdEvIvp16dIl63TMzP5mtCqgzdHAqZJOBtoCHYBbgI6SWqW//rsBq1L7VcC+wEpJrYDdgXV58TL5+1QVNzOzRlDjlUFEXBkR3SKiO7kHwE9FxEjgaeD01Gw08Ghanp7WSdufiohI8TNSb6MeQC9gDvAC0Cv1TtolnWN6g7w7MzMrSCFXBlX5d+B+ST8CXgTuSvG7gHsllQLryX24ExGLJD0IvApsBS6OiG0Akv4VmAG0BCZFxKJ65GVmZrVUq2IQEc8Az6TlZeR6AlVssxn4RhX7/xj4cSXxx4HHa5OLmZk1HH8D2czMXAzMzMzFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMxwMTAzM1wMzMwMFwMzM8PFwMzMcDEwMzMKKAaS2kqaI+klSYskXZXiPST9WVKppAfSlJWkaS0fSPE/S+qed6wrU/w1SSflxYekWKmkcQ3/Ns3MrDqFXBl8AgyMiEOAPsAQSQOA/wRuioi/B94DxqT2Y4D3Uvym1A5JvclNgXkgMAT4uaSWkloCPwOGAr2BM1NbMzNrJDUWg8j5MK22Tq8ABgLTUnwyMDwtD0vrpO2DJCnF74+ITyLiTaCU3LSZ/YHSiFgWEZ8C96e2ZmbWSAp6ZpD+gl8ArAFmAm8A70fE1tRkJdA1LXcFVgCk7RuAPfLjFfapKm5mZo2koGIQEdsiog/Qjdxf8v+wU7OqgqSxkuZKmrt27dosUjAz+5tUq95EEfE+8DRwJNBRUqu0qRuwKi2vAvYFSNt3B9blxyvsU1W8svPfERH9IqJfly5dapO6mZlVo5DeRF0kdUzL7YATgcXkisLpqdlo4NG0PD2tk7Y/FRGR4mek3kY9gF7AHOAFoFfqnbQLuYfM0xvizZmZWWFa1dyEfYDJqddPC+DBiHhM0qvA/ZJ+BLwI3JXa3wXcK6kUWE/uw52IWCTpQeBVYCtwcURsA5D0r8AMoCUwKSIWNdg7NDOzGtVYDCJiIXBoJfFl5J4fVIxvBr5RxbF+DPy4kvjjwOMF5GtmZjuBv4FsZmYuBmZm5mJgZma4GJiZGS4GZmaGi4GZmeFiYGZmuBiYmRkuBmZmRmHDUVhjm7B7I59vQ+Oez8yKjq8MzMzMxcDMzFwMzMwMPzOwZqD7uN816vmWt23U05k1CF8ZmJmZi4GZmbkYmJkZhc2BvK+kpyW9KmmRpH9L8c6SZkpamn52SnFJmiipVNJCSYflHWt0ar9U0ui8eF9JL6d9JkrSznizZmZWuUKuDLYCV0REb2AAcLGk3sA4YFZE9AJmpXWAoeQmu+8FjAV+AbniAYwHjiA3Xeb4sgKS2lyQt9+Q+r81MzMrVI3FICLeiYj5aXkjsBjoCgwDJqdmk4HhaXkYMCVyngc6StoHOAmYGRHrI+I9YCYwJG3rEBHPR0QAU/KOZWZmjaBWzwwkdQcOBf4M7B0R76RNfwX2TstdgRV5u61MseriKyuJV3b+sZLmSpq7du3a2qRuZmbVKLgYSNoV+A1wWUR8kL8t/UUfDZzbDiLijojoFxH9unTpsrNPZ2bWbBRUDCS1JlcIpkbEQym8Ot3iIf1ck+KrgH3zdu+WYtXFu1USNzOzRlJIbyIBdwGLI+KneZumA2U9gkYDj+bFR6VeRQOADel20gxgsKRO6cHxYGBG2vaBpAHpXKPyjmVmZo2gkOEojgbOBl6WtCDF/i9wPfCgpDHAW8CItO1x4GSgFPgYOBcgItZLugZ4IbW7OiLWp+WLgHuAdsAT6WVmZo2kxmIQEbOBqvr9D6qkfQAXV3GsScCkSuJzgYNqysXMzHYOfwPZzMxcDMzMzMXAzMxwMTAzM1wMzMwMFwMzM8PFwMzMcDEwMzNcDMzMDBcDMzPDxcDMzHAxMDMzXAzMzAwXAzMzw8XAzMxwMTAzMwqb9nKSpDWSXsmLdZY0U9LS9LNTikvSREmlkhZKOixvn9Gp/VJJo/PifSW9nPaZmKa+NDOzRlTIlcE9wJAKsXHArIjoBcxK6wBDgV7pNRb4BeSKBzAeOALoD4wvKyCpzQV5+1U8l5mZ7WQ1FoOI+COwvkJ4GDA5LU8GhufFp0TO80BHSfsAJwEzI2J9RLwHzASGpG0dIuL5NF3mlLxjmZlZI6nrM4O9I+KdtPxXYO+03BVYkdduZYpVF19ZSbxSksZKmitp7tq1a+uYupmZVVTvB8jpL/pogFwKOdcdEdEvIvp16dKlMU5pZtYs1LUYrE63eEg/16T4KmDfvHbdUqy6eLdK4mZm1ojqWgymA2U9gkYDj+bFR6VeRQOADel20gxgsKRO6cHxYGBG2vaBpAGpF9GovGOZmVkjaVVTA0m/Ao4D9pS0klyvoOuBByWNAd4CRqTmjwMnA6XAx8C5ABGxXtI1wAup3dURUfZQ+iJyPZbaAU+kl5mZNaIai0FEnFnFpkGVtA3g4iqOMwmYVEl8LnBQTXmYmdnOU2MxMOg+7neNer7lbRv1dGZmHo7CzMx8ZWBmRc5X5o3DVwZmZuZiYGZmLgZmZoaLgZmZ4WJgZma4GJiZGS4GZmaGi4GZmeFiYGZmuBiYmRkuBmZmhouBmZnhYmBmZrgYmJkZRVQMJA2R9JqkUknjss7HzKw5KYpiIKkl8DNgKNAbOFNS72yzMjNrPoqiGAD9gdKIWBYRnwL3A8MyzsnMrNkolpnOugIr8tZXAkdUbCRpLDA2rX4o6bVGyK3RCfYE3m20E16lRjtVc+DfX9PWqL+/xv/dfamqDcVSDAoSEXcAd2Sdx84maW5E9Ms6D6sb//6atub6+yuW20SrgH3z1rulmJmZNYJiKQYvAL0k9ZC0C3AGMD3jnMzMmo2iuE0UEVsl/SswA2gJTIqIRRmnlaW/+Vthf+P8+2vamuXvTxGRdQ5mZpaxYrlNZGZmGXIxMDMzFwMza74k/aMkfw7iYmBWb5K+UPaBIukASadKap11XlaQbwJLJf1E0j9knUyW/AC5SEgSMBLoGRFXS9oP+LuImJNxalYDSfOAY4BOwLPkukp/GhEjM03MCiKpA3AmcC4QwN3AryJiY6aJNTJfGRSPnwNHkvuPEmAjucH7rPgpIj4G/hn4eUR8Azgw45ysQBHxATCN3Jho+wD/BMyXdEmmiTUyF4PicUREXAxsBoiI94Bdsk3JCiRJR5K7svcX1ccAAAwNSURBVPtdirXMMB8rULql9zDwDNAa6B8RQ4FDgCuyzK2xFcWXzgyALWko7wCQ1AX4LNuUrECXAVcCD0fEIkk9gaczzskKcxpwU0T8MT8YER9LGpNRTpnwM4MiIWkkuYdZhwGTgdOBH0TErzNNzAomqX26XWTW5LgYFIHUE2UAsB4YBAiYFRGLM03MCpJuEd0F7BoR+0k6BLgwIi7KODWrgaSNpKvxPBuAucAVEbGs8bPKhotBkZD0YkQcmnUeVnuS/kzuSm562e9Q0isRcVC2mVlNJF1Dbv6U+8j9EXYGsD8wH/g/EXFcdtk1Lj9ALh6zJJ2WuphaExMRKyqEtmWSiNXWqRHxXxGxMSI+SHOmnBQRD5DrKtxsuBgUjwuBXwOfSPpA0kZJH2SdlBVkhaSjgJDUWtJ3AN/iaxo+ljRCUov0GkHq0ceOt4/+pvk2kVk9SdoTuAU4gdythieBf4uIdZkmZjVKPb9uIfcdH4DngMvJTa7VNyJmZ5VbY3MxKBKSjq0sXrHLm5nZzuBiUCQk/TZvtS3QH5gXEQMzSslqIOl7EfETSbdSyS2FiLg0g7SsFiR1A24Fjk6h/yV3Vbcyu6yy4S+dFYmI+Mf8dUn7AjdnlI4Vpuy5wNxMs7D6uJtcT6JvpPWzUuzEzDLKiK8MilTqVbQoInpnnYvZ3ypJCyKiT02x5sBXBkWiwq2GFkAfcn2drUilW3tV/jUVEac2YjpWN+sknQX8Kq2fCTTLB/++MigSkkbnrW4FlkfEs1nlYzWT9LXqtkfEHxorF6sbSV8i98zgSHKF/U/ApRHxdqaJZcDFoEhI+reIuKWmmBUnSbsA/0DuA+W1iPg045TMasXFoEhImh8Rh1WIeYiKJkDS14HbgTfIfc+gB7mxiZ7INDGrUlU9wMo0x55gfmaQMUlnAv8C9JA0PW/TbuQGrrPi9/+A4yOiFEDS/uTmNXAxKF7uAVaBi0H2/gS8A+xJ7kOlzEZgYSYZWW1tLCsEyTJyvz8rUhExOX9d0q4p/mE2GWXPt4nM6knSL4AvAQ+Su/XwDeBt4PcAEfFQdtlZdSQdBNwLdCZ3i28tMCoiFmWaWAZcDDImaXZEfLWScdUFRER0yCg1K5Cku6vZHBFxXqMlY7Ui6U/A9yPi6bR+HHBtRByVaWIZcDEws2ZL0ksRcUhNsebAQ1gXEUktJX1R0n5lr6xzsppJ6inpt5LWSloj6dE0GqYVv2WSfiipe3r9gNwzn2bHxaBISLoEWA3MJNcT5XfAY5kmZYW6j9zzgn2AL5Kbl+JX1e5hxeI8oAvwUHp1SbFmx7eJioSkUuAIj4Hf9EhaGBElFWLN8laDNV3uWlo8VpCbiNuanickjQPuJ9cJ4JvA45I6A0SEvy9SpCQdAHwH6E7e52FzHDreVwZFQtJdwJfJ3R76pCweET/NLCkriKQ3q9kcEeHnB0VK0kvkvj0+j7x5qyNiXmZJZcRXBsXj7fTaJb2siYiIHlnnYHW2NSJ+kXUSxcDFoAhIagkcEBEjs87Fak/SqMriETGlsXOxWvutpIuAh9n+irzZ3drzbaIiIWk2MNCjXTY9adCzMm2BQcD8iDg9o5SsQFXc4muWt/ZcDIqEpCnAV4DpwEdlcT8zaHokdQTuj4ghWediVijfJioeb6RXC3IjllrT9RG5YaytCZB0FDv2Jmp2t/hcDIpERFyVdQ5WNxWmv2xJ7grvwewyskJJuhfYH1jA572JAmh2xcC3iYqEpKepZLKN5tjfuampMP3lVuCtiFiZVT5WOEmLgd7hD0JfGRSR7+QttwVOI/fBYkWubK5jSXsAxwKbAReDpuEV4O/IzSnSrPnKoIhJmhMR/bPOwyon6TFgXES8ImkfYD65GbT2B+6IiJszTdCqlHdrbzegDzCH7buWnppRapnxlUGRKBu6IGkB9AV2zygdK0yPiHglLZ8LzIyIUZJ2A54FXAyK141ZJ1BsXAyKR/7X37cCbwJjMsrFCrMlb3kQ8EuAiNgo6bNsUrJC5N3a6wG8ExGb03o7YO8sc8uKi0HGJO0XEW97SIMmaUUaenwlcBjwP1D+gdI6y8SsYL8G8mc125Zih2eTTnY8n0H2HilbkPSbLBOxWhsDHAicA3wzIt5P8QFAdVNhWvFolf+t/7TcLMcG85VB9pS33Oy+At+URcQa4FuVxJ8Gnm78jKwO1ko6NSKmA0gaBrybcU6ZcDHIXlSxbGY737eAqZJ+Ru7/v5VApQMP/q1z19KMSdpGbvgCAe2Aj8s2kRswq0NWuZk1F5J2BYiID7POJSsuBmbWbEnaG7gW+GJEDJXUGzgyIu7KOLVG5wfIZvUk6QBJsyS9ktZLJP0g67ysIPcAM4AvpvXXgcsyyyZDLgZm9fdL4ErS9w4iYiFwRqYZWbUklT0v3TMiHgQ+A4iIreRNf9mcuBiY1V/7iJhTIeZxpYpb2e/rozSmVABIGgBsyCyrDLk3kVn9vStpfz7/QDkdD3xW7Mq6dH+b3IRS+0t6FugCNMsZ6vwA2ayeJPUE7iD3Tdb3yA0lclZELM8yL6uapJVA2SyCLYA25ArEJ8C25jjDoK8MzOopIpYBJ0j6AtAiIjZmnZPVqCWwK9t/6ROgfQa5FAVfGZjVk6RrgZ+UDUchqRNwRUS4R1GRkjQ/Ig7LOo9i4gfIZvU3NG9cIiLiPeDkDPOxmlW8Imj2XAzM6q+lpDZlK2nU0jbVtLfsDco6gWLjZwZm9TcVmCWpbKTSc4HJGeZjNYiI9VnnUGz8zMCsAUgayud/bc6MiBlZ5mNWWy4GZmbm20RmdSVpdkR8VdJGth9+3CPOWpPjKwOzOpLUM33HwKzJc28is7r7NYCkWVknYlZfvk1kVnctJP1f4MuSvl1xY3Mc0sCaLl8ZmNXdGeSGO24J7FbJy6zJ8JWBWd0NiYj/lNQmIq7OOhmz+vCVgVndnZt+Ds80C7MG4CsDs7pbLGkp8EVJC/PiZV1LSzLKy6zW3LXUrB4k/R25OXRPrbgtIt5q/IzM6sbFwKyeJLUF/j6tlkbE5izzMasLPzMwqyNJrST9BFhBbmC6KcAKST+R1Drb7Mxqx8XArO5uADoDPSOib5osZX+gI3BjppmZ1ZJvE5nVUXp4fEBU+J9IUktgSUT0yiYzs9rzlYFZ3UXFQpCC29h+4DqzoudiYFZ3r0oaVTEo6SxgSQb5mNWZbxOZ1ZGkrsBDwCZgXgr3A9oB/xQRq7LKzay2XAzM6knSQODAtPpqRHgUU2tyXAzMzMzPDMzMzMXAzMxwMbBmTtLfSbpf0huS5kl6XNIBVbTtKOmixs7RrDG4GFizJUnAw8AzEbF/RPQFrgT2rmKXjsBOLwaSPJqwNToXA2vOjge2RMTtZYGIeAl4UdIsSfMlvSxpWNp8PbC/pAWSbgCQ9F1JL0haKOmqsuNI+qGk1yTNlvQrSd9J8T6Snk/tH5bUKcWfkXSzpLnA9yW9WTa+kaQO+etmO4P/ArHm7CA+/35Avs3kvifwgaQ9geclTQfGAQdFRB8ASYOBXkB/cnMYTJd0LLnvHZwGHAK0BubnnWcKcElE/EHS1cB44LK0bZeI6JeO3R34OvAIuek1H4qILQ343s2242JgtiMB16YP9s+ArlR+62hwer2Y1nclVxx2Ax5NQ1lvlvRbAEm7Ax0j4g+p/WTg13nHeyBv+U7ge+SKwbnABQ3wvsyq5GJgzdki4PRK4iOBLkDfiNgiaTnQtpJ2Aq6LiP/aLihdVknbQnxUthARz0rqLuk4oGVEvFLHY5oVxM8MrDl7CmgjaWxZQFIJ8CVgTSoEx6d1gI3k/uovMwM4T9Kuad+ukvYCngX+UVLbtO0UgIjYALwn6Zi0/9nAH6jaFOA+4O56vk+zGvnKwJqtiAhJ/wTcLOnfyT0rWA5MACZKehmYSxp0LiLWSXpW0ivAExHxXUlfAZ7LdUziQ+CsiHghPWNYCKwGXgY2pNOOBm6X1B5YRu4WUFWmAj8CftWAb9usUh6OwmwnkLRrRHyYPvT/CIyNiPm1PMbpwLCIOHunJGmWx1cGZjvHHZJ6k3vWMLkOheBWYChw8s5IzqwiXxmYmZkfIJuZmYuBmZnhYmBmZrgYmJkZLgZmZgb8fyXdCqUkUZ89AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "nMd1Hh7KPXhn",
        "outputId": "ae5feaf5-058a-4d36-844e-fdb18a1e9add"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "store_df.groupby('Sub-Category')['Sales','Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "9rVHUZYZPXhn",
        "outputId": "c99c470c-4a11-42af-9a5a-8401abae5b40"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "store_df.groupby('Category')['Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "AwJouIK1PXhn",
        "outputId": "4188249b-045c-4480-94a0-d76aad96eec1"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "store_df.groupby('Region')['Profit'].agg(['count']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "WBxfXml3PXho",
        "outputId": "9e420bc9-3cf7-4e34-b506-84cf5cb05ea6"
      },
      "source": [
        "plt.figure(figsize =(10,10))\n",
        "store_df.groupby('Sub-Category')['Profit'].agg(['sum']).plot.bar()\n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MIuIBYPM66fOBXeqkBzCxl7wmAZPKlsXMzFrjGchmZuZgYGZmDgZmZoaDgZmZ4WBgZmY4GJiZGQ4GZmaGg4GZmeFgYGZmeA9kM2uQFwzsbq4ZmJmZg4GZmTkYmJkZDgZmZoaDgZmZ4WBgZmY4GJiZGQ4GZmaGJ50ZnkxkZq4ZmJkZDgZmZoabicxa4iY26xYOBmZmA2xZuGhwM5GZmXVOMJA0XtJfJM2WdGy7y2NmNph0RDCQNBT4PrA7sClwoKRN21sqM7PBoyOCAbA1MDsiHoiIl4ALgb3aXCYzs0GjU4LBesAjhcdzcpqZmS0Fioh2lwFJ+wDjI+Kj+fGHgG0i4hM9Xnc4cDjAhhtuuOXDDz+81MtqZtZurYxOkjQ9Isb1TO+UmsFcYIPC4/Vz2mIi4qyIGBcR40aMGLHUCmdm1u06JRjcCoyWNErScsABwOQ2l8nMbNDoiElnEbFA0ieAq4ChwKSImNXmYpmZDRodEQwAImIKMKXd5TAzG4w6pZnIzMzayMHAzMw6p5nIzMwaMxAL2rlmYGZmDgZmZuZgYGZmOBiYmRkOBmZmhoOBmZnhYGBmZjgYmJkZDgZmZkaHbG5ThqR5QCO726wNPFnRr60yr07Pr5PLVnV+nVy2qvPr5LJ1en6dXLZm8tsoIpbYEGaZDQaNkjSt3q4+7c6r0/Pr5LJVnV8nl63q/Dq5bJ2eXyeXrYr83ExkZmYOBmZmNjiCwVkdmlen59fJZas6v04uW9X5dXLZOj2/Ti5by/l1fZ+BmZn1bzDUDMzMrB8OBmZm5mBgZmZdHgwkrSHpLe0uh4GkIZJWbXc5rLMo2aDd5VgWSdpY0vL5/o6SPiVp9bL5dV0wkHS9pFUlrQnMAM6W9O2SeX095zVc0jWS5kk6qIWyfa2RtCbzrOwfQtJrJA3J9zeR9F5Jw1so28/z5/ca4C7gbklHlcyr6r/F9rlcSDpI0rclbdRB+R2Z368knSNphqRdO6Rs+0paJd8/QdKlkrYok1ekESxTypall/JV/X43kvTOfH/F2nsvmVdlnx1wCfCKpDeQRhJtAPy8bNmIiK66Abflnx8FvpTv31kyr9vzz/cB5wCrAXe0ULYZddJKla1YRmAY8Abgr8A3gCkl85oOrASsBzwE/AI4v5Wy5Z8fBL4FDO+gv8WdgIDNgduAicANHZTfHfnnbsClwH/W+/9p13vNP3cArgf2BG5uIb9zga3KHj+Q7xc4DLgVuD8/Hg1c0wmfXe3/ATgK+GS+f1vZsnVdzQAYJmldYD/g8lbzyj/3BH4REU+XyUTS/5M0E3iTpDsLtwdJ/7iteDUiFpBOkt+NiKOAdUvmpYj4N/B+4IyI2Jd0EipreK5Z7A1MjoiXgbJjmWs1lJb+FgULIn179gK+FxHfB0pf8Q1Afso/9wB+GhGzCmntLtsr+eeewFkRcQWwXAv5bQP8WdL9+XsxU1Ir34sq3+9EYHvgGYCIuA94bQtlq/Kze1nSgcAEFp3rStfkh/X/kmXOycBVwB8j4lZJ/wHcVzKvyyXdCzwP/D9JI4AXSuTzc+C3wFeBYwvpz0bEUyXLVlP8h3hPTiv7DyFJbyNdyR+a04a2ULYfkGoYdwC/z1X1Z0rmNbmiv0XNs5KOAz4EvD03j5X+IhXyOwh4RwX5TZd0NTAKOC43LbzaIWWbK+mHwLuAr+VmylYuLHdr4dh6qvzbvhgRL0kpDksaRvkLGqj2szsE+BjwlYh4UNIo4KelS1ZV1axbb8CawNB8/zXA60rmMxS4dwDKtylwOnBgfjwKOKZkXv8FTK4dD/wHcHrJvIYA+/VIEzCsZF7bVfW3yMe/DvgM8Pb8eEPg4A7KbwiwBbB6frwW8JYOKdtKpNrj6Px4XWDXsvnlPHYADsn3RwCjOuFvAXwdOB64l3QC/xXp5NsRnx2wIvDGVj77hXlVkUkn3YBNgGuAu/LjtwAntPCHO4FUnYPUXvjuFsp2GbBhhe91KC206ff1vivKZ1qFZSrdFtpHnhsB7yz8rVdp4e9wXcVl26LObeMywXQgbhWfvE8EfgP8NT9+Palm3wl/2yGkfoNfAL/M99UJnx2pJeAvwIP58VhSc2y5crX7n6rqG3ADsHXx5FELDCXyugg4uhBYViJ3ZJbM7/fAszlYTc63y1p8v38Alqvos3sbcDfwt/x4c1LfQdn8TgU+RxrlsGbtVjKvbwL/3eoXsZBf1R2D1wCrVVG2nN9NwEvANFLH/ouk0XH30+CVZP5fe6bO7VngmRbKVunJmzQIQj2+s6UHVlT9t63yVuVnl/8vVqviXBcRXdlnsFJE3FJr48sWlMxr44jYP7fJExH/Vo+Mm/SFwn0BbwcOaCE/gAeAP0qaDDxXS4yIMsNp/4/Ufjs553GHpHe0ULb988+JhbQgNT816whS1f8VSc+TPr+IiLJzFyaSLhpuJmV0n6RWOgb/BcyUNJXF/w6fKpnfo8ChkTqOkbQpqT/saNLooqv7yyAiWukk7sv7gLeSghMR8Wgrwy2BlyIiJKW2xDwstAWV/W3zwI+efQRPk4L0lyNifpNZVvnZvRwRT/c4JZXtV+rKYPCkpI3Jf0BJ+wCPlczrJUkrFvLamHSFVkpE3CDprcAHgH2BB0mdrK24P9+G0NoIEQAi4pEe/1yv9PbaBvIa1Wp5CnlVfWKrumPw0nyryia1QAAQEXdLelNEPFDmekTSDqR26h9LWpvUbPJgybJVffK+OHeqri7pMOAjwNkt5Ffl3/a3pO9Abfz+AaQWgr8DP2HRoI1GVfnZzZL0AWCopNHAp4A/lc2sG4PBRNIEjDdJmks64ZadnHQicCWwgaTzSUPMPtxsJpI2AQ7MtydJzU+KiJ1KlmuhiPhS/h0rRRoW2opHJG0HRB4SeiRwT7OZSNo5Iq6V9P56z0dE0yfNXCP7IKl99RSlWavrRsQtzeaV3SDpeGBFSe8CPk6qvpcSEefmC4cNI+IvZfMpmCXpTODC/Hh/0qS95YGXm8lI0onAOOCNwI9JQxl/Rvp/LqPSk3dEfDP/DZ4h9fl9MSKmls2Pav+274yI4qSwmZJmRMQWKjfpscrP7pPA50kXqBeQRlGeUjKv7l3COkfcIRHxbIv5rAVsS2qWuCkimt6zVNKrwI2kav/snPZARJRpLumZ99tIk7BWjogNJW0OHBERHy+R19rAacA7Se/3auDIZqvCkr4UESdK+nGdpyMiPlKibGeSqsA7R8SbJa0BXB0RWzWbV85vCGn47K6k93oV8KMo+YWQ9B5Sv8ZyETFK0ljg5Ih4b8n8ViSdxHbISX8EziANp10pIv7VRF63k5smIuKtOe3OiCi9VEs+yS787Fo8eSPpdaSmnQBujYi/t5BXZX9bSXcAh9UuOiRtlfPaXNJttc+zyTwr/eyq0jXBQNJBEfEzSZ+p93yZNnRJ7wOujTzBSWmZhx0j4tdN5rM3qXq5PammcSHpH6rlZhRJNwP7kEYR1L7od0XEZq3m3UkKV2O3Fd7nHRGxeQt5Lge8iXQC+ktEvNRCXtOBnYHrO+3vIOmWiNi68Bm+BvhzK8GgSpI+CnwRuJZ0gvwvUiCd1NaCsfDkPwlYmVS2Z0irG8wC9oyIi9tQpt/QR7NX2QuQbmomqrW9Vdm2fGJE/Kr2ICL+mavcTQWDHDx+nb+EewGfBl6br3Z/FRH9dgb2k38l7fySvg58mTSx60rSsNz/iYiflS2bpD1Js5hXKJT35BJZvSxpKIv6b0bQQmdZLtcPSP0tAkZJOiIiflsyy0o78yRtD5xEGiK58HtasjZZabNObv77Gmkmrmi9M/8o4K21Gmiujf+JdBIuU76en12tfE1/dhFxKzBG0mr5cXHme8OBQNIfImIHSc+y+Im8zGf3zSZe27CuCQYR8cN8sngmIr5TUbb1ZgaW/swi4jlSR9TPczPHvsAxNDAypA+VtPNnu0bE0blG9BBpcszvSe3LTZP0A1Jn207Aj0g1mLJt/KeTJvy8VtJXcl4nlMwL0lpJOxWa7TYGriB1GJZRaWceqenvf0jDB0t34sOAtMl/HXhPRJT9P+tpPmm4a82zOa2syj47WPyCphbsm72giYgd8s8qBnncUChbdbXbbmkmqqlViSvKaxLwT+D7OWkiaZz8h6vIvwpVtfPnvO6KiM0k/Qj4ZURc2UpTTK1duvBzZeC3EfH2kvm9CdiF9D6vaeVkJOnWYn9D7qC+pYU+iJVInXnFdupTIqLUkhmSbo6Ibcoc20t+VbbJ/zEiynY+18vvPGAMaVJmbU2hO/Ot6SbeKj+73i5oIuLQPg9cMp81+3o+SixLU692S+ovLHVB043B4DukdUguYvHx3jNK5PUa0tyAd+akqaSxxc/1ftSyS9KppEXlniedOFYHLi/7xap9KSXdRKplzAdmRcQbmshj1Yh4prcvU5kvUc73TFIzwsWkE9C+wN+A3+V8qxwm2rT8txhKGq66cDhzyf/jStvkJZ1GWvLh1z3KVuozy02vvaqNmGsivyo/u0ouaJQWpQzqLzZYqglLaa2ud/es3UbEm5rNC7ozGFxXJzkiYuelXpgBJOm79N2JVGqyUz7pPh0Rr+Sr3VXLXkVK+gLwXdLV/PdzeX8UEV/o88DF87g8It7dy5ep1Jco51tvpFMx36ZGPCkNH/4cMJLF2/hL/d9V+X8s6S/Adj3b5CPijSXLVtkosYFQ8WdX63wvfUEzUCqv3XZbMKhS1V/wKkmakO9uT1qs7qL8eF/g7oj4WMl8N8v5FTt8z2uhqLV8lwdWiNaXnu5IeQjiD+jRTh0R09tWqEzSn0ij4F7Kj5cjjXrarr0lS/JggKNZcqBBJ3zP6l3QnB0RX2whz/eThgwHcGOJ0Ym1+Tvvok7tNkoMK4cuDAa51/9EoLaMwg2kKnHTJ6FO/oLX5CuWHSLtaUDuRL4xIrYtkdeJwI6kYDAF2B34Q0Ts00L5tmPJYFoquLT6JeqR1/qkL3mt7ftGUl/LnJL5TY+ILcuWp5BPZUOkC3mMpU6bfLN9X5KOjoiv91YrbaE2ejXpYuZzpCWZJwDzIuKYkvlVcg5Qmq+wbUT8KT9u+YJG0hmkjaguyEn7k9ZQmtj7UUvk0Vetlog4pEzZumY0UcEk0haL++XHHyLNuqw7G7YfCyLizKoKNkDWAFYFam3nK+e0MvYh7w4VEYdIWoeSI4kAJP2UtNLm7SwKpgE0HQzqfIk+JuldzXyJevgxaWTXvvnxQTntXSXz+42kj5NGPBXbqZvt06hyiHQtj9qSJTWXlcyv1mE/rXSJ6lsrIs6RdGQeKXODpFtbyK+Sc0BEvCrp+6QJe0TEi7SwHE22M/DmyFfhks4lzVloplylTvb96caawe0RMba/tAbzOgl4gta/4ANG0iGkMdXXkdrT3wGcFBHnlsir1j46nTR64lngntIdUtI9wKZRwT9Z7iwrfomGkNpu31wyv8r+T/Kx9db5Kd2nMZhIuikitpV0FWkI8aOk0Wwbl8yvynPAN4E/A5dW9H98OTAxIh7Ojzci7cbW7BpHSFqBNNO6Z/Naqb6bbqwZPC9ph4j4AyycgPJ8ybxq7fLFTdzLrro5ICItPPZb0taBkDamKTtscJrSLOuzSU1j/yJ9Ecq6izTqpOxCgUWzSZuUPJwfb5DTypqvtLZMraZxIC2MbY8KF+WDhe3oh7FkE1uZpTwqbZMfgL60L+emnc+Smu5WJc0TKKvKc0DVq+WuAtwjqTbfZivS9662UnAzs4d/Stp0ZzfSirYfpPwco66sGYwlbbC9GukP9xTw4Yi4o60FG0CS1mPJmaq/bzHPkaSRRE3vRatF0+VXIbVX38LiNaump8tLuoH0xVnsS0RaTrjpPPMV2XdJezgEaYLYpyLib82WLee3EumksWFEHK408eyNEVFqH+7c6XsjS/ZXXVIir6rb5CvpS8tXth8jNf/NBM6p9X21Qml9rvPowHOApP/q6/koTCjrI49hEbFAeWmWwrDX0v2F0IXBoEbSqgARUXbP3Vo+AzK6piqSvkbqhJrFouUPouQJt6q1mA4D1iGdzIreDjwWEeeUKFvLX6KBJOki0snx4EgT91YiDd8s2+xUusmqTl7TI2JLFRan6zkssUx+FZTrItIKrBQJrzsAABCKSURBVDeSBis8HBFHtppvIf+qzgHvZVFn9PVlA3whv5YmAGrRGlO1Zt3fkxY1/DtpaGmplouuayaSdCSps+hZ4GxJWwDHRon1f3obXUOJDtABtDfpCrTVji2oaC0m0miV4yJiZjFR0lPA/5KWC2hKpL0g1iHVCCD90z/RbD69jYQp/J6ym9FUvRHS5ZL2iIgpLeRRU1vy+jGlWauPknada4oWTfyrqrN804gYk/M+h/JLlfQs5/KkXfFGAsNUcgmJnNeppP+583PSkZK2j4jjSpat5wTA70oqOwHwLKVlbU4gbUi1MotvoNWUrgsGwEci4jRJu5E2Ef8QqW2tzPo/lY6uGSAPkGZcVxEMqlqLaZ2egQAgImbm5qemSdoP+AZwPYu+REdFxC+bzKrqkTA1lWyEpEULmQk4XtKLpJN5K23V9drkP10in+mFskHrfWkL92XIzR4lilTXZaTmw9p2oa3YAxgbEa/CwtE/twGlggHVLMr3Wi0aNlwbWVRbMqf0ZjndGAxq/1F7AOdFxKwWrtCez8PLFuQq5xOkjstO8m/gdknXsPhVWpkr3GmSvs3iazGVmVOxeh/PrVgiP0jr/mxVqw3kTtHfkTYpb1jPUVaqZlMgSCO6em6E1PQQwBiArSoLzRpPk0aJIanpYFB1JzmwuaRaE45Im9E8Q+udtOtHxPhKSpiszqKh26u1mFcVi/INZdGS2j2VbvfvxmAwPXeYjQKOU9pftOxSwlWPrhkIk/OtCp8kVTMvIv1T/Y7F9y9u1DRJh0XEYssk5ypy2Ql7Q3o0C82nfk2mISpsCgS0tCkQQERcnYfk1jZCOjJKbIRUKN/2wO0R8Vwe9bQF8H9lO7jr+Axpz+syZZsInB8R/8yP1wAOjIgzmsknIoaW+f0N+JOkMfVqpyV8FbhNaYmL2tDtsrUCSCPgbpa02ATA2pV+NDap8LEyTV796boOZKXx52OBB3Kb95qkK4WmR8X0yHckJUfXLCskjYoe++JK2irSmu7N5LMOqT35JRad/MeRtlt8X5mhr5K+QdpfoThz884WRsRUuimQpGsiYpf+0prI705SE+VbSHvt/gjYLyL67EhvIv9HIqJULbde57ZK7vpVJS3avH4YMJrUhPoii2oapTbzkbQui/dVtbLia8uL8g3UZ92NNYO3seQV1WnNZKC08fi9ufO553NbRInVD6sm6eKI2K/wBVhMyX/8SyS9JyLm5t/xDlKT0ZhmMomIx4HtJO0E1E6uV0TEtc0WSNIbSH0QR2nRchSQamjn935kQ+VseVOgPDxyJWDtfIVcy3BVYL0WircgIkLSXqRJSedIamrZ5H60chU4VJIiFk4AHEoK9O327qozLAT0yXXSmtbIyb4BpX53f7oxGJxJaovcnNRh9iPS6J9mrqg+S5rw8606zwVpSnm71YbgVfkFOIK0I9t7SEH0q6S+l1Ii4jrSzOhW/B+5Wh5pieRLASSNyc81PXMzq2pToCNInbGvJ9WCasHgGeB7JcsG8Kyk40jLZLwj13iHN5OBltxVa+FTlO+7gdQ3cpHS7mmQPoMrW8ivKo9T0byFgQryubmp3sVbw+eUEqO2GtKNzUS1MbhfBObmK6oZEbHEVX63UdroZn608EfNbek/JG28vmdEzKuqfCXL0+t4eEkza0MTS+Rb2aZAOb9PRsR3yxzbS36vAz5AGod+o6QNSXM+2j6sOQemI1h0hTqVtDR5y7uKtUIVzltQGqJeC/JzC089S1q1tFSgl1Scn7ECaQjsgog4ukx+VerGYHAD6SrlI6RJTk8AdzRz0tCiJWLrijZvfAIgaVvgVNIoh1NIw2fXJnWqHhwRDV+packNtjclLSHxDyg3Y7gqku6LiNG9PDc7Sq4rL2lElYFO0r7AlRHxrKQTSDWrL3dCk+JgUbw4kDSM1L5f6iJQ0lbAHGCfiPiu0pLx/03aDvakKq/OVeHujK3oxmai/UlXVB+JiL/nK6pvNJlHX00PQW6qaLPvAceThrpdC+weETcpbQ15Ac1V2wdkg+2KDMTIJIA/SnqINHLqktrImBZ8ISJ+IWkHUm3jG6Qmy6Z2iVO1G6cPCKWlNr7KkjPz271mV5XzFn4IvDMHgneQ3u8nSYNTziINPmiaFt+xbwhpYEWrw1Ur0XU1A6C27szoiPid0rIAQyPi2f6OW5YUR3RIuicKq3e2MtpAFczyrdJAjEwq5L01cABpFvfdwIURUWpSoRatE/NVYGZE/LwTRtgMBEl/IO0X8B3ShdMhpKG/pTd8qahcr7Boq9tav8i/KRFIVdj7W2kZ63kRcVJ+3OrqtrWT7gJSTePkyIvqtVPpcdqdSmldnF+SIjukzp5Sm6BIWkvS6ZJmSJou6TSlGYOdoDh3oueKjKUivNIs31tIa/zvRxoPXXpjmypExOORduT6EumL8xDwpYh4WyuBIOd9S0R8hrROzFOkBQ7Lmps7VPcHpigtidDS90vSUEmvl7Rh7dZKfhVaMSKuIV1MPpxPknu2uUxExNCIWDXfVomIYYX7zdaohuamJkh9I8WRcE23qEjaStLrImJUrkF9ibTi6L2kC5G268ZmoomkL/fNABFxn6TXlszrQuD3pLZCSEvEXkRqBmi32uzN4sxN8uMVej+sT5XM8h0IFY1MWkhpRvn7SDWDjUm1j1babfcDxgPfzPNb1mXx5RqaLd8nSVffj1NYgJA076DdXsydyPdJ+gSpg3XlNpepaheQNtl5knSxdSMsHOpcZqezH5LPG1U2O1Wp65qJJN0cEdsUqu3DgBllxt2rziSkVkawdLqe7y1/4ZvqfF9W5Or6r4GLI6KyWeX5wqPYjl52SezZwDZlRzcNpNy5eg9pmYZTSG3eX4+Im9pasIrlQRrrAldHxHM5bRNg5WYHBgxUs1OVurFmcIOk40lXy+8iLe36m5J5XS3pANKG05Ci91UVlLFTXam021Rxlm8Vq2Z2ov9oZQhuT0rLHH+LNBTxCdJGPPeSNpQp4xHKXYEOuFg0I/1flFh/aVlRL7hFxF9LZjdUeR8CUrPT4YXnOuI83I01gyGkreB2JTWZXEUaA930G80jOl7Domr6EBZ1UHXEyI6qafFZvjdGYUnrbqKKd+tS2vBlZ+B3uUa6E3BQRJSaNay0pPMbgStYfAHCRtauGRDKu3H1pp1DkDudpM+TJnA+SbpQ2CIiIjc7nRsR27e1gHRnMHgN8EJtAozSVPnlo5qVKbteHr1T23ij7aOJBooq2q2rkN+0iBiX831rpNVuFzYNlMiv7ho2Uc1yBqVImkeqsVxA6pNbbOxmtHmDoU5XZbPTQOiI6knFriF11PwrP16RNLt0uzKZFa6Ug3SlXGpk0rJA1e0ZsCxYEBFnVpjfPyWtTBpwcL6kJ1hUi2xKvoDZJCI+WGH5qvA64F2k/aI/QKq1XBARs9paqmVExc1OlevGmkG9FRVLddBIOoO0zkmxDf3+iCizrHPHy1e17+o5mqjs1W0nk3QSqW2/pd26JG0YEX/LNdLnSU2JHyR1qp5ftgM4j+XfOSJeKnP8QMtDZw8kXTx8KUouz2CdoxtrBs+psLKo0logPcfhN2pn4M21/galXY66+Sqo0j0DOtyE/LPV3bp+TWr/fU7SJRHx37Q2X6HmAdIs6ckUahjt7DOAhUFgT1IgGAmcTgqotozrxmDwaeAXkh4lNXW8jnRFX8ZsUmfPw/nxBjmtWw2a0URR3a5dxXbzKpdjuD/fhgCV735WhqTzSEuSTyHVBu5qc5GsQl3XTASgtCTxG/PDv0TEy329vo98biAtzVDbqHsr4FbS8sRdOXqi20cTSTo6Ir6e7+8bEb8oPPe/EXF8k/ktXBFXXb46rqRXKYymKz5Fl46uG0y6Lhiooi358rHFPRBEWgX1ANLcha4ePaEKlsPuRH2dvMuczAvr4RTXwoEWT5CqYN17s2Z0YzPRYRFR29CdiPhHXq+o6WAQETdIeitp5MS+wIPAD7otCKiP5bAlNbUc9jJAvdyv97hfMXD7+H6ucH/huvcD9LvMujIYtLwlXx77e2C+PUlaj0gRsVPVhe0QVS6H3emil/v1HrdNnfkOf5R0S90Xm1WgG4NBvS35fttkHveSFqZ6d0TMBpD0P9UVseMMi4irASSdXBsPHWkf6PaWrHoDscBf5bTkuvdb0iHr3lt36sZgcAxp3Y+P5cd3kkYUNeP9pL6B6yRdSVq9tOvOigWVL4fdqQawWadqxZrBAlITZamlLcwa0XUdyACFdv79SOO1LykzKSZPJNqL1Fy0M3Ae8KvaVXS36KcTdIWIaGojdiuvNomt3eWwwadrgkEv7fyfi4iNKsp/DVIn8v4RsUt/rzcro8dop9okNrMB103B4FVSO/+hhXb+B6L9+7KaNUyFrTLVpdtmWmfqpqUG3g88RmrnP1vSLnR3O791p75GO5kNmK6pGdQMlnZ+604DNYnNrD9dFwyK3M5vZtaYrg4GZmbWmG7qMzAzs5IcDMzMzMHAuo+kz0uaJelOSbdL2qaP154k6XO9Pd/jtQdLukvSTEm39XecpL0lbdps+c3aoRuXo7BBTNLbgHeTdh97MS/F3dRChb3kuztp46RdI+LRvOPXwf0ctjdwOXB3q7+/j3INiwivZmotc83Aus26wJMR8SJARDyZT94P5cCApHGSri8cs7mkP0u6Ly93Xs9xpBntj+Z8X4yIs3N+h0m6VdIdki6RtJKk7YD3At/ItZON8+1KSdMl3ZhXhSWn35RrHF+W9K+cLknfKNRG9s/pO+bjJwN3SzpZ0qdrBZX0FUlHVveR2mDgYGDd5mpgA0l/lXRGjw2KevMW0pyUtwFflPT6Oq/ZjMUXjyu6NCK2iojNgXtIs+D/BEwGjoqIsRFxP3AW8MmI2JK0X0Ftj43TgNMiYgwwp5Dv+4GxwObAO0mBZd383BbAkRGxCTCJXEuRNIS0yOLPGnjfZgu5mci6SkT8S9KWpF3pdiItZ35sP4ddFhHPA8/nHca2Jm1036jNJH0ZWB1YGbiq5wskrQxsR9qfu5a8fP75NlKTEsDPgW/m+zsAF0TEK8DjhW1YnwFuiYgH83t+SNL8vEDjOsBtETG/ifKbORhY98knz+uB6yXNBCaQloGu1YR77luwxCY3kr4C7JnzGwvMIu0pcG2dX/kTYO+IuEPSh4Ed67xmCPDPnFcVnuvx+EfAh0nLtU+q6HfYIOJmIusqkt4oaXQhaSzwMPAQ6WQOaQvJor0krSBpLdKJ/NaI+Hxu3qmdvL9KaqZ5Xf49y0n6aH5uFeAxScOBDxbyfTY/R0Q8Azwoad98vCRtnl93U6FMBxSOvxHYX9JQSSOAdwC97Xb2K2A8qeawRM3ErD8OBtZtVgbOlXS3pDuBTYGTgC8Bp0maBrzS45g7getIJ+VTap3ERRExhbQ96O8kzQJmALV1gr4A3Az8kbRLXs2FwFF5GOrGpEBxqKQ7SDWNvfLrPg18Jpf3DcDTOf1XuWx3kGokR0fE3+u96Yh4Kb+Hi3PNyKwpXo7CrM0krQQ8HxEh6QDgwIjYq7/jeuQxhBSg9o2I+wainNbd3Gdg1n5bAt9T6ln+J/CRZg7OE9suJ63O60BgpbhmYGZm7jMwMzMHAzMzw8HAzMxwMDAzMxwMzMwMBwMzMwP+PxyhO/d1SbYyAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXDEEEVYPXho"
      },
      "source": [
        "**Inferences**\n",
        "\n",
        "From the above graphs we can conclude that,\n",
        "\n",
        " •  Supplies produces the maximum profit. \n",
        "\n",
        " •\tAmong sub-category products, paper produces maximum profit.\n",
        "\n",
        " •\tAmong sub-category products, Tables are extremely loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "kEqmTkwqPXhp",
        "outputId": "989b1be6-f665-4c6c-8e30-162e01752fb6"
      },
      "source": [
        "sns.barplot(data = store_df, x = 'Discount', y = 'Profit')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bf3a7b750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "GC3ZUYT6PXhp",
        "outputId": "70606182-5299-4186-88ae-114c135aa1dc"
      },
      "source": [
        "sns.barplot(data = store_df, x = 'Quantity', y = 'Profit')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bfc61c7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "NBe4hogLPXhp",
        "outputId": "150e3a64-c7b4-45a0-d2c3-7101848ba1fb"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "sns.barplot(x = store_df.Region, y = store_df.Profit, hue = store_df.Category)\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C0toHtbPXhq",
        "outputId": "77d0bb14-29c1-4064-8be8-1d41607e7fd2"
      },
      "source": [
        "store_df['Ship Mode'].value_counts()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Standard Class    5955\n",
              "Second Class      1943\n",
              "First Class       1537\n",
              "Same Day           542\n",
              "Name: Ship Mode, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbT7oS_zPXhq",
        "outputId": "f385baf6-4461-40b9-866d-075f0bec8ac3"
      },
      "source": [
        "(store_df['Ship Mode'].value_counts()/len(store_df['Ship Mode']))*100"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Standard Class    59.687281\n",
              "Second Class      19.474792\n",
              "First Class       15.405432\n",
              "Same Day           5.432495\n",
              "Name: Ship Mode, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "K7kOeZo9PXhq",
        "outputId": "32b034bc-d807-4cf0-fc89-7bee705af1b3"
      },
      "source": [
        "((store_df['Ship Mode'].value_counts()/len(store_df['Ship Mode']))*100).plot(kind = 'bar', color = 'red')"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bf3a8a5d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ud7ZeDz_PXhr"
      },
      "source": [
        "**Inferences**\n",
        "\n",
        " •  Maximum number of shipments belongs to standard calss.\n",
        "\n",
        " •\tVery less number of shipmants are done in the same day."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq9TuEQkPXhr",
        "outputId": "ea1c060e-125f-4f0a-dde9-c58a1288b400"
      },
      "source": [
        "store_df['Category'].value_counts()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Office Supplies    6012\n",
              "Furniture          2118\n",
              "Technology         1847\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZdMnrycPXhr",
        "outputId": "8900d62d-82ed-4081-c999-a70d080223a9"
      },
      "source": [
        "(store_df['Category'].value_counts()/len(store_df['Category']))*100"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Office Supplies    60.258595\n",
              "Furniture          21.228826\n",
              "Technology         18.512579\n",
              "Name: Category, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "d8XaxWGsPXhs",
        "outputId": "7ba82d90-3670-4750-e3a6-51a32e7e1d24"
      },
      "source": [
        "((store_df['Category'].value_counts()/len(store_df['Category']))*100).plot(kind = 'bar', color = 'blue')"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bf3d14810>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUOnqLAnPXhs"
      },
      "source": [
        "**Inferences**\n",
        "\n",
        " •  Above 50% (almost 60%) of products belons to Office supplies.\n",
        "\n",
        " •\tNearly 20% of products belongs to Technology which is less."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTm8yhB7PXht"
      },
      "source": [
        "## **Task 3 Completed !** "
      ]
    }
  ]
}