Solution.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NAcQr98KvN31"
      },
      "source": [
        "# AB-inBev Assignment\n",
        "# E-Commerce\n",
        "\n",
        "[Github Link](https://github.com/aa-ryan/AbinBev_Assignment) for Assignment. (This repo is currently private will be public when submission ends).\n",
        "\n",
        "[Shareable colab link](https://colab.research.google.com/drive/1MJJEclQlrNTv2pNVJxIosmKXTCgSpbmf?usp=sharing)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## How to run this notebook\n",
        "\n",
        "* Goto **Runtime** and select **Run All**\n",
        "  - Will take almost 2-5 mins depending on your internet speed.\n",
        "  - When execution is completed it show *0s completed* at the bottom.\n",
        "* Now unhide all cells to see all output\n",
        "  - Select **Edit** then **Select All Cells** option\n",
        "  - It will select all cells as a result everything is now higlighted.\n",
        "  - Select **View** and click on **Expand all sections**\n",
        "* Now everything is done. (probably :P)\n",
        "\n",
        "\n",
        "#### Extras:\n",
        "* Using college email ID for colab, results in extra memory and better runtime\n",
        "* If seeing third party cookies error, don't worry it's nothing, if you wish enable third party cookies for colab.\n",
        "* Any other issues email me at (aryan.2023y@gmail.com)\n",
        "\n",
        "### Task Done\n",
        "* Convert the data files to csv.\n",
        "* [x] Cleaning of Data\n",
        "\t- [x] Shape of Data\n",
        "\t- [x] Check for NAs\n",
        "\t- [x] Date format\n",
        "\t- [x] Check Data Info\n",
        "* [x] Joining of tables, primary key as OrderID\n",
        "* [x] Finding profit based on OrderID, States or other params.\n",
        "* [x] Profit over a period (monthly)\n",
        "* [x] Profit Trend Analysis by category, date.\n",
        "* [x] Customer focusing\n",
        "* [x] Analysis by heirarchy, Ex - Country, State, City\n",
        "* [x] Amount vs category bar chart sales target\n",
        "* [x] Which months they hit the sales target.\n",
        "* [x] Amount vs Quantity\n",
        "* [x] Correlation matrix for Order Details.\n",
        "\n",
        "#### TODO:\n",
        "* Documentation\n",
        "* Code formatting\n",
        "* Better way for Amount vs Quantity\n",
        "* Physical notebook notes to markdown.\n",
        "* Region wise sales distribution."
      ],
      "metadata": {
        "id": "JE-Ud-vOcco3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r Data_ABinBev\n",
        "!git clone https://github.com/aa-ryan/Data_ABinBev\n",
        "## get the required dataset\n",
        "## this repository contains only datasets and is public"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YysuSVPHcAbY",
        "outputId": "850faf81-0f56-488e-cb68-6d8e67e80d2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Data_ABinBev'...\n",
            "remote: Enumerating objects: 5, done.\u001b[K\n",
            "remote: Counting objects: 100% (5/5), done.\u001b[K\n",
            "remote: Compressing objects: 100% (5/5), done.\u001b[K\n",
            "remote: Total 5 (delta 0), reused 0 (delta 0), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (5/5), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "34QfqaqY7XcG"
      },
      "source": [
        "#### Dependencies Installation.\n",
        "(Not all dependencies needed if running in Colab Environment, most of the dependencies like pandas, matplotlib are already present)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM3zqOdO7XcH",
        "outputId": "d99f5b66-320f-4183-dc6f-f4d2ae79b271"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: nbformat in /usr/local/lib/python3.7/dist-packages (5.7.0)\n",
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            "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema>=2.6->nbformat) (5.10.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
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          ]
        }
      ],
      "source": [
        "%pip install pandas\n",
        "%pip install plotly\n",
        "%pip install nbformat\n",
        "%pip install matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Sl1-hPT7XcK"
      },
      "outputs": [],
      "source": [
        "# Ignoring pandas FutureWarning\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7LUApAvOSXHZ"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "import calendar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sd9GRfC0ay2g"
      },
      "source": [
        "#### Convert CSV to Pandas Dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-BKil7fGSiP1"
      },
      "outputs": [],
      "source": [
        "list_of_orders = pd.read_csv(\"Data_ABinBev/list_of_orders.csv\")\n",
        "order_details = pd.read_csv(\"Data_ABinBev/order_details.csv\")\n",
        "sales_target = pd.read_csv(\"Data_ABinBev/sales_target.csv\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uk7Ml8rHZzbg"
      },
      "source": [
        "## Pre-processing and Data Cleansing\n",
        "* [x] Check Shape of Data\n",
        "* [x] Check NA values\n",
        "* [x] Similar Date Format\n",
        "* [x] Check Data *info*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zs7T2wdPcHBR"
      },
      "source": [
        "#### Shape of Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwkTPXNCZyR_",
        "outputId": "d4051e28-c275-4dce-ff6c-a7c6f63c9159"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensions of List of Order ->  (500, 5)\n",
            "Dimensions of Order Detail ->  (1500, 6)\n",
            "Dimensions of Sales Target ->  (36, 3)\n"
          ]
        }
      ],
      "source": [
        "print(\"Dimensions of List of Order -> \", list_of_orders.shape)\n",
        "print(\"Dimensions of Order Detail -> \", order_details.shape)\n",
        "print(\"Dimensions of Sales Target -> \", sales_target.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ldGz_fVfcKRo"
      },
      "source": [
        "#### Check NA values\n",
        "* None Found"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QF-5AVeucNhI",
        "outputId": "01dcc27e-ab85-4bfc-df25-af59e7e66763"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of Orders DataFrame\n",
            "\n",
            "Order ID        0\n",
            "Order Date      0\n",
            "CustomerName    0\n",
            "State           0\n",
            "City            0\n",
            "dtype: int64\n",
            "\n",
            "Order Details DataFrame\n",
            "\n",
            "Order ID        0\n",
            "Amount          0\n",
            "Profit          0\n",
            "Quantity        0\n",
            "Category        0\n",
            "Sub-Category    0\n",
            "dtype: int64\n",
            "\n",
            "Sales Target DataFrame\n",
            "\n",
            "Month of Order Date    0\n",
            "Category               0\n",
            "Target                 0\n",
            "dtype: int64\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"List of Orders DataFrame\\n\")\n",
        "print(list_of_orders.isnull().sum(), end=\"\\n\\n\")\n",
        "print(\"Order Details DataFrame\\n\")\n",
        "print(order_details.isnull().sum(), end='\\n\\n')\n",
        "print(\"Sales Target DataFrame\\n\")\n",
        "print(sales_target.isnull().sum(), end='\\n\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzC2OTsUXXzg"
      },
      "source": [
        "#### Same format of Date\n",
        "* YYYY-MM-DD"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pr8rkEpwTnxH"
      },
      "outputs": [],
      "source": [
        "list_of_orders['Order Date'] =  pd.to_datetime(list_of_orders['Order Date'], dayfirst=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6axrqkazcyY9"
      },
      "source": [
        "#### Check Data info\n",
        "* Focus on Dtype of Date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XUIOb0FybVZn",
        "outputId": "d35e76e1-4fcd-4f1d-f7fd-16ed7c40f6bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of Orders DataFrame\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 5 columns):\n",
            " #   Column        Non-Null Count  Dtype         \n",
            "---  ------        --------------  -----         \n",
            " 0   Order ID      500 non-null    object        \n",
            " 1   Order Date    500 non-null    datetime64[ns]\n",
            " 2   CustomerName  500 non-null    object        \n",
            " 3   State         500 non-null    object        \n",
            " 4   City          500 non-null    object        \n",
            "dtypes: datetime64[ns](1), object(4)\n",
            "memory usage: 19.7+ KB\n",
            "None\n",
            "\n",
            "Order Details DataFrame\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1500 entries, 0 to 1499\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   Order ID      1500 non-null   object\n",
            " 1   Amount        1500 non-null   int64 \n",
            " 2   Profit        1500 non-null   int64 \n",
            " 3   Quantity      1500 non-null   int64 \n",
            " 4   Category      1500 non-null   object\n",
            " 5   Sub-Category  1500 non-null   object\n",
            "dtypes: int64(3), object(3)\n",
            "memory usage: 70.4+ KB\n",
            "None\n",
            "\n",
            "Sales Target DataFrame\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 36 entries, 0 to 35\n",
            "Data columns (total 3 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   Month of Order Date  36 non-null     object\n",
            " 1   Category             36 non-null     object\n",
            " 2   Target               36 non-null     int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 992.0+ bytes\n",
            "None\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"List of Orders DataFrame\\n\")\n",
        "print(list_of_orders.info(), end=\"\\n\\n\")\n",
        "print(\"Order Details DataFrame\\n\")\n",
        "print(order_details.info(), end='\\n\\n')\n",
        "print(\"Sales Target DataFrame\\n\")\n",
        "print(sales_target.info(), end='\\n\\n')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AzXkI-3De2jz"
      },
      "source": [
        "## Dataset Overview"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Hyee7ApCbJgY",
        "outputId": "75fec24a-f2a7-4a5b-adfa-024aedb43446"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of Orders DataFrame\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID Order Date CustomerName           State       City\n",
              "0  B-25601 2018-01-04       Bharat         Gujarat  Ahmedabad\n",
              "1  B-25602 2018-01-04        Pearl     Maharashtra       Pune\n",
              "2  B-25603 2018-03-04        Jahan  Madhya Pradesh     Bhopal\n",
              "3  B-25604 2018-03-04       Divsha       Rajasthan     Jaipur\n",
              "4  B-25605 2018-05-04      Kasheen     West Bengal    Kolkata"
            ],
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ],
      "source": [
        "print(\"List of Orders DataFrame\\n\")\n",
        "list_of_orders.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OfEtsIAAfFOo",
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          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "061c9d2a-86ff-409b-8d0e-4242397bfce3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Order Details DataFrame\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID  Amount  Profit  Quantity     Category      Sub-Category\n",
              "0  B-25601    1275   -1148         7    Furniture         Bookcases\n",
              "1  B-25601      66     -12         5     Clothing             Stole\n",
              "2  B-25601       8      -2         3     Clothing       Hankerchief\n",
              "3  B-25601      80     -56         4  Electronics  Electronic Games\n",
              "4  B-25602     168    -111         2  Electronics            Phones"
            ],
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              "      <td>B-25601</td>\n",
              "      <td>66</td>\n",
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              "      <td>5</td>\n",
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              "      <td>B-25601</td>\n",
              "      <td>80</td>\n",
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              "      <td>4</td>\n",
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              "      <td>Electronics</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ],
      "source": [
        "print(\"Order Details DataFrame\\n\")\n",
        "order_details.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "SNgDgipN7Xce",
        "outputId": "8ba8915f-fbe1-4a51-81a5-b247289d298c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sales Target DataFrame\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Month of Order Date   Category  Target\n",
              "0              18-Apr  Furniture   10400\n",
              "1              18-May  Furniture   10500\n",
              "2              18-Jun  Furniture   10600\n",
              "3              18-Jul  Furniture   10800\n",
              "4              18-Aug  Furniture   10900"
            ],
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              "      <th>1</th>\n",
              "      <td>18-May</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>10500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18-Jun</td>\n",
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              "      <td>18-Jul</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18-Aug</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>10900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a751edd1-1271-40ae-8e0a-c8c5832ce688 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-a751edd1-1271-40ae-8e0a-c8c5832ce688');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "print(\"Sales Target DataFrame\\n\")\n",
        "sales_target.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYLujfJK7Xcg"
      },
      "source": [
        "## Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y7DBLzTv7Xch"
      },
      "source": [
        "#### Analyzing Sales\n",
        "* Changing the Category and Sub-Category variable to categorical data type in list_of_order df and sales_target df."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6DWFg8x77Xch",
        "outputId": "e9e4ddf6-8ebc-40da-cf3f-28105e3b7088"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1500 entries, 0 to 1499\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype   \n",
            "---  ------        --------------  -----   \n",
            " 0   Order ID      1500 non-null   object  \n",
            " 1   Amount        1500 non-null   int64   \n",
            " 2   Profit        1500 non-null   int64   \n",
            " 3   Quantity      1500 non-null   int64   \n",
            " 4   Category      1500 non-null   category\n",
            " 5   Sub-Category  1500 non-null   category\n",
            "dtypes: category(2), int64(3), object(1)\n",
            "memory usage: 50.7+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 36 entries, 0 to 35\n",
            "Data columns (total 3 columns):\n",
            " #   Column               Non-Null Count  Dtype   \n",
            "---  ------               --------------  -----   \n",
            " 0   Month of Order Date  36 non-null     object  \n",
            " 1   Category             36 non-null     category\n",
            " 2   Target               36 non-null     int64   \n",
            "dtypes: category(1), int64(1), object(1)\n",
            "memory usage: 872.0+ bytes\n"
          ]
        }
      ],
      "source": [
        "order_details['Category'] = order_details['Category'].astype('category')\n",
        "order_details['Sub-Category'] = order_details['Sub-Category'].astype('category')\n",
        "\n",
        "sales_target['Category'] = sales_target['Category'].astype('category')\n",
        "order_details.info()\n",
        "sales_target.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vr_XuI3C7Xcj"
      },
      "source": [
        "#### Making a dataframe having Amount, Profit, Quantity for different orders, joining it with list_of_order dataframe by taking OrderID as Primary Key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "AFlG3K0R7Xcn",
        "outputId": "d4326f01-baea-489c-ba4d-941fde0a6102"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID  Amount  Profit  Quantity\n",
              "0  B-25601    1429   -1218        19\n",
              "1  B-25602    3889     975        22\n",
              "2  B-25603    2025    -180        25\n",
              "3  B-25604     222      22        11\n",
              "4  B-25605      75       0         7"
            ],
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          "metadata": {},
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      ],
      "source": [
        "profits = order_details.groupby('Order ID',).sum().reset_index()\n",
        "profits.head()"
      ]
    },
    {
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              "  Order ID Order Date CustomerName           State       City  Amount  Profit  \\\n",
              "0  B-25601 2018-01-04       Bharat         Gujarat  Ahmedabad    1429   -1218   \n",
              "1  B-25602 2018-01-04        Pearl     Maharashtra       Pune    3889     975   \n",
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              "3  B-25604 2018-03-04       Divsha       Rajasthan     Jaipur     222      22   \n",
              "4  B-25605 2018-05-04      Kasheen     West Bengal    Kolkata      75       0   \n",
              "\n",
              "   Quantity  \n",
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      ],
      "source": [
        "merged_df = pd.merge(list_of_orders, profits, on='Order ID')\n",
        "merged_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5iZ_tVYv7Xcq"
      },
      "source": [
        "#### Monthly Profit Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "wEbRATwi7Xcr",
        "outputId": "c3a73647-e9e3-49b7-f8ee-4c60a3a98a31"
      },
      "outputs": [
        {
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            "text/plain": [
              "                         Amount  Profit  Quantity\n",
              "Year Month Month_Number                          \n",
              "2018 Jan   1              18035   -3296       203\n",
              "     Feb   2               6566     685        58\n",
              "     Mar   3               7434     669       144\n",
              "     Apr   4              26170   -1043       337\n",
              "     May   5              20422    -891       306\n",
              "     Jun   6              17406   -3759       353\n",
              "     Jul   7              15682   -2065       239\n",
              "     Aug   8              45269   -1059       601\n",
              "     Sep   9              20210   -3509       310\n",
              "     Oct   10             32758    5979       414\n",
              "     Nov   11             38858    4955       433\n",
              "     Dec   12             23892    1535       209\n",
              "2019 Jan   1              50448    8655       640\n",
              "     Feb   2              15894    2291       253\n",
              "     Mar   3              39700    6633       485\n",
              "     Apr   4              11079    1295       106\n",
              "     May   5               4390     943        63\n",
              "     Jun   6               3392     700        52\n",
              "     Jul   7               5116     975        67\n",
              "     Aug   8               6557     594        83\n",
              "     Sep   9               5583    1597        70\n",
              "     Oct   10             14147    1892       149\n",
              "     Nov   11              2235     122        35\n",
              "     Dec   12               259      57         5"
            ],
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              "      <td>209</td>\n",
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              "      <th rowspan=\"12\" valign=\"top\">2019</th>\n",
              "      <th>Jan</th>\n",
              "      <th>1</th>\n",
              "      <td>50448</td>\n",
              "      <td>8655</td>\n",
              "      <td>640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Feb</th>\n",
              "      <th>2</th>\n",
              "      <td>15894</td>\n",
              "      <td>2291</td>\n",
              "      <td>253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mar</th>\n",
              "      <th>3</th>\n",
              "      <td>39700</td>\n",
              "      <td>6633</td>\n",
              "      <td>485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Apr</th>\n",
              "      <th>4</th>\n",
              "      <td>11079</td>\n",
              "      <td>1295</td>\n",
              "      <td>106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>May</th>\n",
              "      <th>5</th>\n",
              "      <td>4390</td>\n",
              "      <td>943</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jun</th>\n",
              "      <th>6</th>\n",
              "      <td>3392</td>\n",
              "      <td>700</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jul</th>\n",
              "      <th>7</th>\n",
              "      <td>5116</td>\n",
              "      <td>975</td>\n",
              "      <td>67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aug</th>\n",
              "      <th>8</th>\n",
              "      <td>6557</td>\n",
              "      <td>594</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sep</th>\n",
              "      <th>9</th>\n",
              "      <td>5583</td>\n",
              "      <td>1597</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Oct</th>\n",
              "      <th>10</th>\n",
              "      <td>14147</td>\n",
              "      <td>1892</td>\n",
              "      <td>149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nov</th>\n",
              "      <th>11</th>\n",
              "      <td>2235</td>\n",
              "      <td>122</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dec</th>\n",
              "      <th>12</th>\n",
              "      <td>259</td>\n",
              "      <td>57</td>\n",
              "      <td>5</td>\n",
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        "merged_df['Year'] = pd.DatetimeIndex(merged_df['Order Date']).year\n",
        "merged_df['Month_Number'] = pd.DatetimeIndex(merged_df['Order Date']).month\n",
        "merged_df['Month'] = merged_df['Month_Number'].apply(lambda x: calendar.month_abbr[x])\n",
        "\n",
        "year_wiseDF = merged_df.groupby(['Year', 'Month','Month_Number']).sum().sort_values(['Year','Month_Number'])\n",
        "year_wiseDF"
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        "year_wiseDF[\"Color\"] = np.where(year_wiseDF[\"Profit\"]<0, 'Loss', 'Profit')\n",
        "\n",
        "year_month_2018 = year_wiseDF[year_wiseDF['Year']==2018]\n",
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        "fig.update_layout(yaxis_tickprefix = '', yaxis_tickformat = ',.0f')\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis = dict(\n",
        "        tickvals = [1, 2, 3, 4, 5, 6, 7,8 ,9, 10, 11, 12],\n",
        "        ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    )\n",
        ")\n",
        "fig.show()"
      ]
    },
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "year_month_2019 = year_wiseDF[year_wiseDF['Year'] == 2019]\n",
        "fig = px.bar(year_month_2019, x='Month_Number', y='Profit', color='Color',\n",
        "             title=\"Monthly Profit in 2019\",\n",
        "             labels=dict(Month_Number=\"Month\", Profit=\"Profit\", Color=\"Results\"),\n",
        "             color_discrete_map={\n",
        "                 'Loss': '#EC2049',\n",
        "                 'Profit': '#358701'},\n",
        "             hover_data=[\"Month\", \"Profit\"],\n",
        "             template='plotly_white')\n",
        "\n",
        "fig.update_layout(yaxis_tickprefix = '₹', yaxis_tickformat = ',.0f')\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis = dict(\n",
        "        tickvals = [1, 2, 3, 4, 5, 6, 7,8 ,9, 10, 11, 12],\n",
        "        ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    )\n",
        ")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Alternative Analysis of Monthly Data"
      ],
      "metadata": {
        "id": "e2zE77tb_L_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "date_orders = list_of_orders.groupby('Order Date').size().reset_index(name=\"Orders\")\n",
        "date_orders['Month'] = pd.DatetimeIndex(date_orders['Order Date']).month\n",
        "date_orders['Year'] = pd.DatetimeIndex(date_orders['Order Date']).year\n",
        "\n",
        "date_orders_2018 = date_orders[date_orders['Year']==2018]\n",
        "date_orders_2019 = date_orders[date_orders['Year']==2019]\n",
        "\n",
        "month_2018 = date_orders_2018.groupby('Month').sum().reset_index()\n",
        "month_2019 = date_orders_2019.groupby('Month').sum().reset_index()\n",
        "\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2018',\n",
        "    x=month_2018['Month'],\n",
        "    y=month_2018['Orders'],\n",
        "    connectgaps=True \n",
        "))\n",
        "fig.add_trace(go.Scatter(\n",
        "    name='2019',\n",
        "    x=month_2019['Month'],\n",
        "    y=month_2019['Orders'],\n",
        "    connectgaps=True \n",
        "))\n",
        "fig.update_layout(title_text='Monthly Quantity Sold',\n",
        "                 template='plotly_dark')\n",
        "fig.update_xaxes(title_text='Time')\n",
        "fig.update_yaxes(title_text='Orders')\n",
        "fig.update_layout(\n",
        "    xaxis = dict(\n",
        "        tickvals = [1, 2, 3, 4, 5, 6, 7,8 ,9, 10, 11, 12],\n",
        "        ticktext = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.layout.xaxis.fixedrange = True\n",
        "fig.layout.yaxis.fixedrange = True\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "HX38y8SC_Wew",
        "outputId": "97fc09d5-7d44-4eb7-afcd-579fcaf74ec9"
      },
      "execution_count": null,
      "outputs": [
        {
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              "<body>\n",
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      "source": [
        "### State wise sales analysis"
      ]
    },
    {
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      "execution_count": null,
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              "  Order ID  Amount  Profit  Quantity     Category      Sub-Category  \\\n",
              "0  B-25601    1275   -1148         7    Furniture         Bookcases   \n",
              "1  B-25601      66     -12         5     Clothing             Stole   \n",
              "2  B-25601       8      -2         3     Clothing       Hankerchief   \n",
              "3  B-25601      80     -56         4  Electronics  Electronic Games   \n",
              "4  B-25602     168    -111         2  Electronics            Phones   \n",
              "\n",
              "  Order Date CustomerName        State       City  \n",
              "0 2018-01-04       Bharat      Gujarat  Ahmedabad  \n",
              "1 2018-01-04       Bharat      Gujarat  Ahmedabad  \n",
              "2 2018-01-04       Bharat      Gujarat  Ahmedabad  \n",
              "3 2018-01-04       Bharat      Gujarat  Ahmedabad  \n",
              "4 2018-01-04        Pearl  Maharashtra       Pune  "
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              "      <td>B-25601</td>\n",
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              "      <td>2018-01-04</td>\n",
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              "      <td>B-25602</td>\n",
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              "                            \n",
              "var gd = document.getElementById('5761bc37-4ae2-461e-b642-bb4920b4a6ca');\n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "px.histogram(state_wise_cat_df, x='State', color='Category', text_auto=True)\n",
        "# y - axis count of orders not to be confused with quantity.\n",
        "# x - axis states"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Total number of order by States."
      ],
      "metadata": {
        "id": "CcwQKP8matEI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_order_df = state_wise_cat_df.groupby(['State'])['Category'].count().reset_index()\n",
        "total_order_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "Gfa2j7TNasNy",
        "outputId": "160211ae-afe1-4c48-d6d9-684a200e5335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                State  Category\n",
              "0      Andhra Pradesh        42\n",
              "1               Bihar        62\n",
              "2               Delhi        74\n",
              "3                 Goa        43\n",
              "4             Gujarat        87\n",
              "5             Haryana        26\n",
              "6    Himachal Pradesh        29\n",
              "7   Jammu and Kashmir        49\n",
              "8           Karnataka        54\n",
              "9             Kerala         45\n",
              "10     Madhya Pradesh       340\n",
              "11        Maharashtra       290\n",
              "12           Nagaland        45\n",
              "13             Punjab        60\n",
              "14          Rajasthan        74\n",
              "15             Sikkim        24\n",
              "16         Tamil Nadu        25\n",
              "17      Uttar Pradesh        68\n",
              "18        West Bengal        63"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f13112ab-248c-4116-927f-f6b747bbbbbc\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>State</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Andhra Pradesh</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bihar</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Goa</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Gujarat</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Haryana</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Himachal Pradesh</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Jammu and Kashmir</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Karnataka</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kerala</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Madhya Pradesh</td>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Maharashtra</td>\n",
              "      <td>290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Nagaland</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Punjab</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Rajasthan</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Sikkim</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Tamil Nadu</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Uttar Pradesh</td>\n",
              "      <td>68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>West Bengal</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Top 10 States with most number of orders"
      ],
      "metadata": {
        "id": "40s06p26bK4D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "top_state_df = total_order_df.sort_values(by='Category', ascending=False).head(10)\n",
        "top_fig = px.bar(top_state_df, x='State', y='Category', color='State', barmode='relative', hover_data=['State'])\n",
        "top_fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "BNH-Wr19bAWX",
        "outputId": "a498ade1-81d1-4831-c0f7-2fefa82da7d2"
      },
      "execution_count": null,
      "outputs": [
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Profit by State"
      ],
      "metadata": {
        "id": "2I77zAK3dJjR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "profit_by_state = merged_df.groupby('State').sum().reset_index().sort_values(['Profit'])\n",
        "profit_by_state[\"Color\"] = np.where(profit_by_state[\"Profit\"]<0, 'Loss', 'Profit')\n",
        "\n",
        "\n",
        "fig = px.bar(profit_by_state, x='State', y='Profit',\n",
        "             color='Color', color_discrete_map={\n",
        "                 'Loss': '#EC2049',\n",
        "                 'Profit': '#358701'},\n",
        "             title=\"Profit by State\",\n",
        "             labels=dict(Color=\"Results\"),\n",
        "             template='plotly_white')\n",
        "\n",
        "fig.layout.xaxis.fixedrange = True\n",
        "fig.layout.yaxis.fixedrange = True\n",
        "\n",
        "fig.update_layout(yaxis_tickprefix = '₹', yaxis_tickformat = ',.2f')\n",
        "\n",
        "fig.update_xaxes(\n",
        "        tickangle = -90,\n",
        "        title_text = \"States\",\n",
        ")\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "FSsdd3vxbrau",
        "outputId": "b9310390-b260-4a9e-b64a-14063eff20e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### City Wise Analysis"
      ],
      "metadata": {
        "id": "MVpUGBeukcYi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.histogram(state_wise_cat_df, x='City', color='Category', text_auto=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "MIl7MniedYyG",
        "outputId": "712ea836-8414-4393-b059-02743ea1584a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Total Number of Orders by Cities"
      ],
      "metadata": {
        "id": "r-Dxuqx_obfi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "total_order_city_df = state_wise_cat_df.groupby(['City'])['Category'].count().reset_index()\n",
        "total_order_city_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "id": "uggB_LbbkjJN",
        "outputId": "545405d4-1f26-49af-9a5e-aeb9e88ec856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  City  Category\n",
              "0            Ahmedabad        62\n",
              "1            Allahabad        30\n",
              "2             Amritsar        15\n",
              "3            Bangalore        54\n",
              "4               Bhopal        66\n",
              "5           Chandigarh        71\n",
              "6              Chennai        25\n",
              "7                Delhi        81\n",
              "8              Gangtok        24\n",
              "9                  Goa        43\n",
              "10           Hyderabad        42\n",
              "11              Indore       267\n",
              "12              Jaipur        44\n",
              "13             Kashmir        49\n",
              "14              Kohima        45\n",
              "15             Kolkata        63\n",
              "16             Lucknow        38\n",
              "17              Mumbai       207\n",
              "18               Patna        62\n",
              "19                Pune        83\n",
              "20               Simla        29\n",
              "21               Surat        25\n",
              "22  Thiruvananthapuram        45\n",
              "23             Udaipur        30"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>City</th>\n",
              "      <th>Category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Ahmedabad</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Allahabad</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Amritsar</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bangalore</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bhopal</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Chandigarh</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Chennai</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Delhi</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Gangtok</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Goa</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Indore</td>\n",
              "      <td>267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Jaipur</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kashmir</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Kohima</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Kolkata</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Lucknow</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Mumbai</td>\n",
              "      <td>207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Patna</td>\n",
              "      <td>62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Pune</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Simla</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Surat</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Thiruvananthapuram</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Udaipur</td>\n",
              "      <td>30</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Top 10 City with most sales"
      ],
      "metadata": {
        "id": "_S5MTvPClEEv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_city_df = total_order_city_df.sort_values(by='Category', ascending=False).head(10)\n",
        "top_fig = px.bar(top_city_df, x='City', y='Category', color='City', barmode='relative', hover_data=['City'])\n",
        "top_fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "k84YCMT2k5Ln",
        "outputId": "03187dd6-79a5-4488-c92b-a7803bacdb3c"
      },
      "execution_count": null,
      "outputs": [
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Profit by city"
      ],
      "metadata": {
        "id": "p5pbNT7aldOR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "profit_by_city = merged_df.groupby('City').sum().reset_index().sort_values(['Profit'])\n",
        "profit_by_city[\"Color\"] = np.where(profit_by_city[\"Profit\"]<0, 'Loss', 'Profit')\n",
        "\n",
        "\n",
        "fig = px.bar(profit_by_city, x='City', y='Profit',\n",
        "             color='Color', color_discrete_map={\n",
        "                 'Loss': '#EC2049',\n",
        "                 'Profit': '#358701'},\n",
        "             title=\"Profit by City\",\n",
        "             labels=dict(Color=\"Results\"),\n",
        "             template='plotly_white')\n",
        "\n",
        "fig.layout.xaxis.fixedrange = True\n",
        "fig.layout.yaxis.fixedrange = True\n",
        "\n",
        "fig.update_layout(yaxis_tickprefix = '₹', yaxis_tickformat = ',.0f')\n",
        "\n",
        "fig.update_xaxes(\n",
        "        tickangle = -90,\n",
        "        title_text = \"Cities\",\n",
        ")\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
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        "id": "qEInT84DlQaW",
        "outputId": "91445451-ed01-466b-8281-ef3c3d7fda18"
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      "execution_count": null,
      "outputs": [
        {
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "## Analysis based on Category"
      ],
      "metadata": {
        "id": "E_S67sSCzYHW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "category = order_details.groupby('Category').sum().reset_index()\n",
        "fig = px.pie(category, values='Quantity', names='Category', color='Category',\n",
        "             color_discrete_map={'Clothing':'cyan',\n",
        "                                 'Electronics':'royalblue',\n",
        "                                 'Furniture':'darkblue'},\n",
        "            title='Total Quantity Sold per Category')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "QZl3iKZ2lsNT",
        "outputId": "287efbee-b16d-498e-ab18-7c213d115c87"
      },
      "execution_count": null,
      "outputs": [
        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subcategory = order_details.groupby('Sub-Category').sum().reset_index()\n",
        "fig = px.pie(subcategory, values='Quantity', names='Sub-Category', color='Sub-Category',\n",
        "            title='Total Quantity Sold per Sub-Category')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "A1ARTTN3yINH",
        "outputId": "14ff2623-8526-4e49-b91a-d70a58151a95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Quantity Sold per Sub-Category\"}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</html>"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Profitable and Non-Profitable Categories"
      ],
      "metadata": {
        "id": "e0_9Qut1Gwom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "profit_orders = order_details.merge(list_of_orders[['Order ID','Order Date']], left_on='Order ID',right_on='Order ID', how='inner')\n",
        "profit_orders.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "yHhSKqgQzKdD",
        "outputId": "bbd0347c-3b04-4bb4-f41c-29884f817efb"
      },
      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "  Order ID  Amount  Profit  Quantity     Category      Sub-Category Order Date\n",
              "0  B-25601    1275   -1148         7    Furniture         Bookcases 2018-01-04\n",
              "1  B-25601      66     -12         5     Clothing             Stole 2018-01-04\n",
              "2  B-25601       8      -2         3     Clothing       Hankerchief 2018-01-04\n",
              "3  B-25601      80     -56         4  Electronics  Electronic Games 2018-01-04\n",
              "4  B-25602     168    -111         2  Electronics            Phones 2018-01-04"
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              "      <td>B-25601</td>\n",
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              "      <td>2018-01-04</td>\n",
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              "      <th>1</th>\n",
              "      <td>B-25601</td>\n",
              "      <td>66</td>\n",
              "      <td>-12</td>\n",
              "      <td>5</td>\n",
              "      <td>Clothing</td>\n",
              "      <td>Stole</td>\n",
              "      <td>2018-01-04</td>\n",
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              "      <td>B-25601</td>\n",
              "      <td>8</td>\n",
              "      <td>-2</td>\n",
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              "      <td>2018-01-04</td>\n",
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              "      <td>B-25601</td>\n",
              "      <td>80</td>\n",
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              "      <td>4</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>Electronic Games</td>\n",
              "      <td>2018-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>B-25602</td>\n",
              "      <td>168</td>\n",
              "      <td>-111</td>\n",
              "      <td>2</td>\n",
              "      <td>Electronics</td>\n",
              "      <td>Phones</td>\n",
              "      <td>2018-01-04</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import copy\n",
        "copy_profit_orders = copy.deepcopy(profit_orders)"
      ],
      "metadata": {
        "id": "bb9sClQ0OO10"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating row with month and year of order"
      ],
      "metadata": {
        "id": "65FIbLNUPWaC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "copy_profit_orders['Date'] = copy_profit_orders['Order Date'].dt.strftime('%Y-%m')\n",
        "copy_profit_orders"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "sh4bUxjeOkNe",
        "outputId": "f25b2b08-d720-4333-baeb-909ad786079c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Order ID  Amount  Profit  Quantity     Category      Sub-Category  \\\n",
              "0     B-25601    1275   -1148         7    Furniture         Bookcases   \n",
              "1     B-25601      66     -12         5     Clothing             Stole   \n",
              "2     B-25601       8      -2         3     Clothing       Hankerchief   \n",
              "3     B-25601      80     -56         4  Electronics  Electronic Games   \n",
              "4     B-25602     168    -111         2  Electronics            Phones   \n",
              "...       ...     ...     ...       ...          ...               ...   \n",
              "1495  B-26099     835     267         5  Electronics            Phones   \n",
              "1496  B-26099    2366     552         5     Clothing          Trousers   \n",
              "1497  B-26100     828     230         2    Furniture            Chairs   \n",
              "1498  B-26100      34      10         2     Clothing           T-shirt   \n",
              "1499  B-26100      72      16         2     Clothing             Shirt   \n",
              "\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sub_order = copy_profit_orders.groupby(['Sub-Category', 'Date']).Profit.sum().unstack().reset_index()\n",
        "sub_order.head()"
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      "execution_count": null,
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-2e54feb3-2ac8-4afd-ab77-da34e6b721d7');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sub_order['Profit Summary'] = sub_order.iloc[:, 1:14].sum(axis = 1)\n",
        "sub_order"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        },
        "id": "yefu4TYsWNOK",
        "outputId": "bfa09947-57d4-4764-a8d4-fd42536ace55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date      Sub-Category  2018-01  2018-02  2018-03  2018-04  2018-05  2018-06  \\\n",
              "0          Accessories      -66       -2       30       76     -531      -60   \n",
              "1            Bookcases    -3129      293        0      289      -15       71   \n",
              "2               Chairs      -21     -280      -86     -373     -231     -667   \n",
              "3     Electronic Games     -159      213       44     -957     -894     -714   \n",
              "4          Furnishings       13       21       92      -98     -169      -17   \n",
              "5          Hankerchief      -40       61       89     -121       21     -301   \n",
              "6                Kurti        0        0      104      -54       73      -37   \n",
              "7             Leggings      -16        0        7       28       17        2   \n",
              "8               Phones      515      -87      -46     -418      -26     -155   \n",
              "9             Printers      340      476      596        9      741     -165   \n",
              "10               Saree      110      -10     -174     -190     -169    -1227   \n",
              "11               Shirt       48        0      -25      109        0     -227   \n",
              "12               Skirt       11        0       -4       15        0      -14   \n",
              "13               Stole     -199        0       69      167       66     -110   \n",
              "14             T-shirt       -2        0       28       -9      153      107   \n",
              "15              Tables     -187        0        0        0      -66     -317   \n",
              "16            Trousers     -514        0      -55      484      139       72   \n",
              "\n",
              "Date  2018-07  2018-08  2018-09  ...  2019-04  2019-05  2019-06  2019-07  \\\n",
              "0          30       18      235  ...       81      382       27       88   \n",
              "1        -239      649      334  ...      469      352        0      269   \n",
              "2        -195      848     -456  ...      457        0       93        0   \n",
              "3         -78     -377       26  ...       99      -16        0      102   \n",
              "4        -160      206     -228  ...       30       41        0       17   \n",
              "5           1      314      -19  ...       58      102       47       24   \n",
              "6          33     -119      -45  ...        0        0        0        0   \n",
              "7         -42      -34       -8  ...        0        0        0        0   \n",
              "8       -1957     -342        0  ...       50       11      143        0   \n",
              "9         408     -477    -1076  ...        0        0      243      147   \n",
              "10       -246     -564     -707  ...     -475       -8       87      119   \n",
              "11        147       20       -9  ...      140       62        6       41   \n",
              "12        -30      -10      -12  ...       18        0        9       11   \n",
              "13        -32      129     -221  ...      -43       17       13       72   \n",
              "14         -5      100       43  ...        0        0       32       85   \n",
              "15        242    -1864     -994  ...      202        0        0        0   \n",
              "16         58      444     -372  ...      209        0        0        0   \n",
              "\n",
              "Date  2019-08  2019-09  2019-10  2019-11  2019-12  Profit Summary  \n",
              "0         170      944       61        0        0             851  \n",
              "1           0      222      238       10        0            2264  \n",
              "2          -8        1       64        0        0            -366  \n",
              "3          73        0      174      -10        0           -1373  \n",
              "4          54        0        0      -13        0             138  \n",
              "5          47       96      171       99        0            1109  \n",
              "6          51        0      -18        0        0              89  \n",
              "7           0       24        0        0        0              70  \n",
              "8           0        0      204        0        7            1913  \n",
              "9         -23        0      462        0        0            3475  \n",
              "10         15      222      230        3        0            -822  \n",
              "11          1        0       42       27        0             417  \n",
              "12          2        7        0        0        0             138  \n",
              "13         39       81      142        3       44            1534  \n",
              "14         51        0       35        3        6             780  \n",
              "15        122        0        0        0        0           -4640  \n",
              "16          0        0       87        0        0            1279  \n",
              "\n",
              "[17 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5009fb7e-b50f-4cb8-8429-6118a4294dc2\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Date</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>2018-01</th>\n",
              "      <th>2018-02</th>\n",
              "      <th>2018-03</th>\n",
              "      <th>2018-04</th>\n",
              "      <th>2018-05</th>\n",
              "      <th>2018-06</th>\n",
              "      <th>2018-07</th>\n",
              "      <th>2018-08</th>\n",
              "      <th>2018-09</th>\n",
              "      <th>...</th>\n",
              "      <th>2019-04</th>\n",
              "      <th>2019-05</th>\n",
              "      <th>2019-06</th>\n",
              "      <th>2019-07</th>\n",
              "      <th>2019-08</th>\n",
              "      <th>2019-09</th>\n",
              "      <th>2019-10</th>\n",
              "      <th>2019-11</th>\n",
              "      <th>2019-12</th>\n",
              "      <th>Profit Summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Accessories</td>\n",
              "      <td>-66</td>\n",
              "      <td>-2</td>\n",
              "      <td>30</td>\n",
              "      <td>76</td>\n",
              "      <td>-531</td>\n",
              "      <td>-60</td>\n",
              "      <td>30</td>\n",
              "      <td>18</td>\n",
              "      <td>235</td>\n",
              "      <td>...</td>\n",
              "      <td>81</td>\n",
              "      <td>382</td>\n",
              "      <td>27</td>\n",
              "      <td>88</td>\n",
              "      <td>170</td>\n",
              "      <td>944</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bookcases</td>\n",
              "      <td>-3129</td>\n",
              "      <td>293</td>\n",
              "      <td>0</td>\n",
              "      <td>289</td>\n",
              "      <td>-15</td>\n",
              "      <td>71</td>\n",
              "      <td>-239</td>\n",
              "      <td>649</td>\n",
              "      <td>334</td>\n",
              "      <td>...</td>\n",
              "      <td>469</td>\n",
              "      <td>352</td>\n",
              "      <td>0</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>238</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chairs</td>\n",
              "      <td>-21</td>\n",
              "      <td>-280</td>\n",
              "      <td>-86</td>\n",
              "      <td>-373</td>\n",
              "      <td>-231</td>\n",
              "      <td>-667</td>\n",
              "      <td>-195</td>\n",
              "      <td>848</td>\n",
              "      <td>-456</td>\n",
              "      <td>...</td>\n",
              "      <td>457</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>0</td>\n",
              "      <td>-8</td>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Electronic Games</td>\n",
              "      <td>-159</td>\n",
              "      <td>213</td>\n",
              "      <td>44</td>\n",
              "      <td>-957</td>\n",
              "      <td>-894</td>\n",
              "      <td>-714</td>\n",
              "      <td>-78</td>\n",
              "      <td>-377</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>99</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>102</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>-10</td>\n",
              "      <td>0</td>\n",
              "      <td>-1373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Furnishings</td>\n",
              "      <td>13</td>\n",
              "      <td>21</td>\n",
              "      <td>92</td>\n",
              "      <td>-98</td>\n",
              "      <td>-169</td>\n",
              "      <td>-17</td>\n",
              "      <td>-160</td>\n",
              "      <td>206</td>\n",
              "      <td>-228</td>\n",
              "      <td>...</td>\n",
              "      <td>30</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-13</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Hankerchief</td>\n",
              "      <td>-40</td>\n",
              "      <td>61</td>\n",
              "      <td>89</td>\n",
              "      <td>-121</td>\n",
              "      <td>21</td>\n",
              "      <td>-301</td>\n",
              "      <td>1</td>\n",
              "      <td>314</td>\n",
              "      <td>-19</td>\n",
              "      <td>...</td>\n",
              "      <td>58</td>\n",
              "      <td>102</td>\n",
              "      <td>47</td>\n",
              "      <td>24</td>\n",
              "      <td>47</td>\n",
              "      <td>96</td>\n",
              "      <td>171</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>1109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Kurti</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>104</td>\n",
              "      <td>-54</td>\n",
              "      <td>73</td>\n",
              "      <td>-37</td>\n",
              "      <td>33</td>\n",
              "      <td>-119</td>\n",
              "      <td>-45</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Leggings</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>-42</td>\n",
              "      <td>-34</td>\n",
              "      <td>-8</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Phones</td>\n",
              "      <td>515</td>\n",
              "      <td>-87</td>\n",
              "      <td>-46</td>\n",
              "      <td>-418</td>\n",
              "      <td>-26</td>\n",
              "      <td>-155</td>\n",
              "      <td>-1957</td>\n",
              "      <td>-342</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>50</td>\n",
              "      <td>11</td>\n",
              "      <td>143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Printers</td>\n",
              "      <td>340</td>\n",
              "      <td>476</td>\n",
              "      <td>596</td>\n",
              "      <td>9</td>\n",
              "      <td>741</td>\n",
              "      <td>-165</td>\n",
              "      <td>408</td>\n",
              "      <td>-477</td>\n",
              "      <td>-1076</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>243</td>\n",
              "      <td>147</td>\n",
              "      <td>-23</td>\n",
              "      <td>0</td>\n",
              "      <td>462</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Saree</td>\n",
              "      <td>110</td>\n",
              "      <td>-10</td>\n",
              "      <td>-174</td>\n",
              "      <td>-190</td>\n",
              "      <td>-169</td>\n",
              "      <td>-1227</td>\n",
              "      <td>-246</td>\n",
              "      <td>-564</td>\n",
              "      <td>-707</td>\n",
              "      <td>...</td>\n",
              "      <td>-475</td>\n",
              "      <td>-8</td>\n",
              "      <td>87</td>\n",
              "      <td>119</td>\n",
              "      <td>15</td>\n",
              "      <td>222</td>\n",
              "      <td>230</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>-822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Shirt</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>-25</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>-227</td>\n",
              "      <td>147</td>\n",
              "      <td>20</td>\n",
              "      <td>-9</td>\n",
              "      <td>...</td>\n",
              "      <td>140</td>\n",
              "      <td>62</td>\n",
              "      <td>6</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Skirt</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>-14</td>\n",
              "      <td>-30</td>\n",
              "      <td>-10</td>\n",
              "      <td>-12</td>\n",
              "      <td>...</td>\n",
              "      <td>18</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Stole</td>\n",
              "      <td>-199</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>167</td>\n",
              "      <td>66</td>\n",
              "      <td>-110</td>\n",
              "      <td>-32</td>\n",
              "      <td>129</td>\n",
              "      <td>-221</td>\n",
              "      <td>...</td>\n",
              "      <td>-43</td>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>72</td>\n",
              "      <td>39</td>\n",
              "      <td>81</td>\n",
              "      <td>142</td>\n",
              "      <td>3</td>\n",
              "      <td>44</td>\n",
              "      <td>1534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>T-shirt</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>-9</td>\n",
              "      <td>153</td>\n",
              "      <td>107</td>\n",
              "      <td>-5</td>\n",
              "      <td>100</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>85</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Tables</td>\n",
              "      <td>-187</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-66</td>\n",
              "      <td>-317</td>\n",
              "      <td>242</td>\n",
              "      <td>-1864</td>\n",
              "      <td>-994</td>\n",
              "      <td>...</td>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-4640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Trousers</td>\n",
              "      <td>-514</td>\n",
              "      <td>0</td>\n",
              "      <td>-55</td>\n",
              "      <td>484</td>\n",
              "      <td>139</td>\n",
              "      <td>72</td>\n",
              "      <td>58</td>\n",
              "      <td>444</td>\n",
              "      <td>-372</td>\n",
              "      <td>...</td>\n",
              "      <td>209</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1279</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17 rows × 26 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Sub-Category that causes loss"
      ],
      "metadata": {
        "id": "7TfdwfS8Y8RB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sub_order.loc[sub_order['Profit Summary'] < 0]['Sub-Category'].unique()"
      ],
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        },
        "id": "EOLP1JluYlls",
        "outputId": "68886fdb-73ae-480a-a476-632489f143ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Chairs', 'Electronic Games', 'Saree', 'Tables']\n",
              "Categories (17, object): ['Accessories', 'Bookcases', 'Chairs', 'Electronic Games', ..., 'Stole',\n",
              "                          'T-shirt', 'Tables', 'Trousers']"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = go.Figure(data=[go.Bar(x=sub_order[sub_order['Profit Summary'] < 0]['Sub-Category'], y=sub_order[sub_order['Profit Summary'] < 0]['Profit Summary'])])\n",
        "\n",
        "fig.update_traces(marker_color='rgb(255,0,17)', marker_line_color='rgb(173,0,0)',\n",
        "                  marker_line_width=1.5, opacity=0.6)\n",
        "fig.update_layout(title_text='Non profitable sub-categories: Loss')\n",
        "fig.show()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "w-XuhpyOZGHX",
        "outputId": "65aaccb5-e702-45fb-9906-90b19ba42d86"
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      "execution_count": null,
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    {
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        "sub_order[sub_order['Profit Summary'] < 0]"
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        "id": "uzPTw8ljiuwP",
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              "Date      Sub-Category  2018-01  2018-02  2018-03  2018-04  2018-05  2018-06  \\\n",
              "2               Chairs      -21     -280      -86     -373     -231     -667   \n",
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              "15              Tables     -187        0        0        0      -66     -317   \n",
              "\n",
              "Date  2018-07  2018-08  2018-09  ...  2019-04  2019-05  2019-06  2019-07  \\\n",
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              "Date  2019-08  2019-09  2019-10  2019-11  2019-12  Profit Summary  \n",
              "2          -8        1       64        0        0            -366  \n",
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              "10         15      222      230        3        0            -822  \n",
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              "      <th>2019-09</th>\n",
              "      <th>2019-10</th>\n",
              "      <th>2019-11</th>\n",
              "      <th>2019-12</th>\n",
              "      <th>Profit Summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Chairs</td>\n",
              "      <td>-21</td>\n",
              "      <td>-280</td>\n",
              "      <td>-86</td>\n",
              "      <td>-373</td>\n",
              "      <td>-231</td>\n",
              "      <td>-667</td>\n",
              "      <td>-195</td>\n",
              "      <td>848</td>\n",
              "      <td>-456</td>\n",
              "      <td>...</td>\n",
              "      <td>457</td>\n",
              "      <td>0</td>\n",
              "      <td>93</td>\n",
              "      <td>0</td>\n",
              "      <td>-8</td>\n",
              "      <td>1</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Electronic Games</td>\n",
              "      <td>-159</td>\n",
              "      <td>213</td>\n",
              "      <td>44</td>\n",
              "      <td>-957</td>\n",
              "      <td>-894</td>\n",
              "      <td>-714</td>\n",
              "      <td>-78</td>\n",
              "      <td>-377</td>\n",
              "      <td>26</td>\n",
              "      <td>...</td>\n",
              "      <td>99</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>102</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>-10</td>\n",
              "      <td>0</td>\n",
              "      <td>-1373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Saree</td>\n",
              "      <td>110</td>\n",
              "      <td>-10</td>\n",
              "      <td>-174</td>\n",
              "      <td>-190</td>\n",
              "      <td>-169</td>\n",
              "      <td>-1227</td>\n",
              "      <td>-246</td>\n",
              "      <td>-564</td>\n",
              "      <td>-707</td>\n",
              "      <td>...</td>\n",
              "      <td>-475</td>\n",
              "      <td>-8</td>\n",
              "      <td>87</td>\n",
              "      <td>119</td>\n",
              "      <td>15</td>\n",
              "      <td>222</td>\n",
              "      <td>230</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>-822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Tables</td>\n",
              "      <td>-187</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-66</td>\n",
              "      <td>-317</td>\n",
              "      <td>242</td>\n",
              "      <td>-1864</td>\n",
              "      <td>-994</td>\n",
              "      <td>...</td>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-4640</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 26 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop Sub-categories making loss\n",
        "sub_order = sub_order.loc[sub_order['Profit Summary'] > 0]\n",
        "sub_order = sub_order.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "xGAbUD2tjBY3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating new columns of profit share showing contribution of category for profits and cumulative share is total cumlative."
      ],
      "metadata": {
        "id": "o0yMQ137jUsx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sub_order['Profit share'] = sub_order['Profit Summary'] / sub_order['Profit Summary'].values.sum()*100\n",
        "sub_order = sub_order.sort_values('Profit share', ascending = False)\n",
        "sub_order['Cumulative share'] = sub_order['Profit share'].cumsum()\n",
        "sub_order"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "tqQJzevIjPYK",
        "outputId": "3c55d4a1-6753-4f99-d654-d2ae6e723468"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date Sub-Category  2018-01  2018-02  2018-03  2018-04  2018-05  2018-06  \\\n",
              "7        Printers      340      476      596        9      741     -165   \n",
              "1       Bookcases    -3129      293        0      289      -15       71   \n",
              "6          Phones      515      -87      -46     -418      -26     -155   \n",
              "10          Stole     -199        0       69      167       66     -110   \n",
              "12       Trousers     -514        0      -55      484      139       72   \n",
              "3     Hankerchief      -40       61       89     -121       21     -301   \n",
              "0     Accessories      -66       -2       30       76     -531      -60   \n",
              "11        T-shirt       -2        0       28       -9      153      107   \n",
              "8           Shirt       48        0      -25      109        0     -227   \n",
              "2     Furnishings       13       21       92      -98     -169      -17   \n",
              "9           Skirt       11        0       -4       15        0      -14   \n",
              "4           Kurti        0        0      104      -54       73      -37   \n",
              "5        Leggings      -16        0        7       28       17        2   \n",
              "\n",
              "Date  2018-07  2018-08  2018-09  ...  2019-06  2019-07  2019-08  2019-09  \\\n",
              "7         408     -477    -1076  ...      243      147      -23        0   \n",
              "1        -239      649      334  ...        0      269        0      222   \n",
              "6       -1957     -342        0  ...      143        0        0        0   \n",
              "10        -32      129     -221  ...       13       72       39       81   \n",
              "12         58      444     -372  ...        0        0        0        0   \n",
              "3           1      314      -19  ...       47       24       47       96   \n",
              "0          30       18      235  ...       27       88      170      944   \n",
              "11         -5      100       43  ...       32       85       51        0   \n",
              "8         147       20       -9  ...        6       41        1        0   \n",
              "2        -160      206     -228  ...        0       17       54        0   \n",
              "9         -30      -10      -12  ...        9       11        2        7   \n",
              "4          33     -119      -45  ...        0        0       51        0   \n",
              "5         -42      -34       -8  ...        0        0        0       24   \n",
              "\n",
              "Date  2019-10  2019-11  2019-12  Profit Summary  Profit share  \\\n",
              "7         462        0        0            3475     24.720780   \n",
              "1         238       10        0            2264     16.105855   \n",
              "6         204        0        7            1913     13.608878   \n",
              "10        142        3       44            1534     10.912713   \n",
              "12         87        0        0            1279      9.098670   \n",
              "3         171       99        0            1109      7.889308   \n",
              "0          61        0        0             851      6.053923   \n",
              "11         35        3        6             780      5.548837   \n",
              "8          42       27        0             417      2.966494   \n",
              "2           0      -13        0             138      0.981717   \n",
              "9           0        0        0             138      0.981717   \n",
              "4         -18        0        0              89      0.633137   \n",
              "5           0        0        0              70      0.497973   \n",
              "\n",
              "Date  Cumulative share  \n",
              "7            24.720780  \n",
              "1            40.826634  \n",
              "6            54.435513  \n",
              "10           65.348225  \n",
              "12           74.446895  \n",
              "3            82.336203  \n",
              "0            88.390126  \n",
              "11           93.938963  \n",
              "8            96.905456  \n",
              "2            97.887174  \n",
              "9            98.868891  \n",
              "4            99.502027  \n",
              "5           100.000000  \n",
              "\n",
              "[13 rows x 28 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th>Date</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>2018-01</th>\n",
              "      <th>2018-02</th>\n",
              "      <th>2018-03</th>\n",
              "      <th>2018-04</th>\n",
              "      <th>2018-05</th>\n",
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              "      <th>2019-11</th>\n",
              "      <th>2019-12</th>\n",
              "      <th>Profit Summary</th>\n",
              "      <th>Profit share</th>\n",
              "      <th>Cumulative share</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Printers</td>\n",
              "      <td>340</td>\n",
              "      <td>476</td>\n",
              "      <td>596</td>\n",
              "      <td>9</td>\n",
              "      <td>741</td>\n",
              "      <td>-165</td>\n",
              "      <td>408</td>\n",
              "      <td>-477</td>\n",
              "      <td>-1076</td>\n",
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              "      <td>243</td>\n",
              "      <td>147</td>\n",
              "      <td>-23</td>\n",
              "      <td>0</td>\n",
              "      <td>462</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3475</td>\n",
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              "      <td>24.720780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bookcases</td>\n",
              "      <td>-3129</td>\n",
              "      <td>293</td>\n",
              "      <td>0</td>\n",
              "      <td>289</td>\n",
              "      <td>-15</td>\n",
              "      <td>71</td>\n",
              "      <td>-239</td>\n",
              "      <td>649</td>\n",
              "      <td>334</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>238</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>2264</td>\n",
              "      <td>16.105855</td>\n",
              "      <td>40.826634</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Phones</td>\n",
              "      <td>515</td>\n",
              "      <td>-87</td>\n",
              "      <td>-46</td>\n",
              "      <td>-418</td>\n",
              "      <td>-26</td>\n",
              "      <td>-155</td>\n",
              "      <td>-1957</td>\n",
              "      <td>-342</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>143</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>1913</td>\n",
              "      <td>13.608878</td>\n",
              "      <td>54.435513</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Stole</td>\n",
              "      <td>-199</td>\n",
              "      <td>0</td>\n",
              "      <td>69</td>\n",
              "      <td>167</td>\n",
              "      <td>66</td>\n",
              "      <td>-110</td>\n",
              "      <td>-32</td>\n",
              "      <td>129</td>\n",
              "      <td>-221</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>72</td>\n",
              "      <td>39</td>\n",
              "      <td>81</td>\n",
              "      <td>142</td>\n",
              "      <td>3</td>\n",
              "      <td>44</td>\n",
              "      <td>1534</td>\n",
              "      <td>10.912713</td>\n",
              "      <td>65.348225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Trousers</td>\n",
              "      <td>-514</td>\n",
              "      <td>0</td>\n",
              "      <td>-55</td>\n",
              "      <td>484</td>\n",
              "      <td>139</td>\n",
              "      <td>72</td>\n",
              "      <td>58</td>\n",
              "      <td>444</td>\n",
              "      <td>-372</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1279</td>\n",
              "      <td>9.098670</td>\n",
              "      <td>74.446895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hankerchief</td>\n",
              "      <td>-40</td>\n",
              "      <td>61</td>\n",
              "      <td>89</td>\n",
              "      <td>-121</td>\n",
              "      <td>21</td>\n",
              "      <td>-301</td>\n",
              "      <td>1</td>\n",
              "      <td>314</td>\n",
              "      <td>-19</td>\n",
              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>24</td>\n",
              "      <td>47</td>\n",
              "      <td>96</td>\n",
              "      <td>171</td>\n",
              "      <td>99</td>\n",
              "      <td>0</td>\n",
              "      <td>1109</td>\n",
              "      <td>7.889308</td>\n",
              "      <td>82.336203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Accessories</td>\n",
              "      <td>-66</td>\n",
              "      <td>-2</td>\n",
              "      <td>30</td>\n",
              "      <td>76</td>\n",
              "      <td>-531</td>\n",
              "      <td>-60</td>\n",
              "      <td>30</td>\n",
              "      <td>18</td>\n",
              "      <td>235</td>\n",
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              "      <td>27</td>\n",
              "      <td>88</td>\n",
              "      <td>170</td>\n",
              "      <td>944</td>\n",
              "      <td>61</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>851</td>\n",
              "      <td>6.053923</td>\n",
              "      <td>88.390126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>T-shirt</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "      <td>28</td>\n",
              "      <td>-9</td>\n",
              "      <td>153</td>\n",
              "      <td>107</td>\n",
              "      <td>-5</td>\n",
              "      <td>100</td>\n",
              "      <td>43</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>85</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>780</td>\n",
              "      <td>5.548837</td>\n",
              "      <td>93.938963</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Shirt</td>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>-25</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>-227</td>\n",
              "      <td>147</td>\n",
              "      <td>20</td>\n",
              "      <td>-9</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>42</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>417</td>\n",
              "      <td>2.966494</td>\n",
              "      <td>96.905456</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Furnishings</td>\n",
              "      <td>13</td>\n",
              "      <td>21</td>\n",
              "      <td>92</td>\n",
              "      <td>-98</td>\n",
              "      <td>-169</td>\n",
              "      <td>-17</td>\n",
              "      <td>-160</td>\n",
              "      <td>206</td>\n",
              "      <td>-228</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-13</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>0.981717</td>\n",
              "      <td>97.887174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Skirt</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>-4</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>-14</td>\n",
              "      <td>-30</td>\n",
              "      <td>-10</td>\n",
              "      <td>-12</td>\n",
              "      <td>...</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>0.981717</td>\n",
              "      <td>98.868891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kurti</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>104</td>\n",
              "      <td>-54</td>\n",
              "      <td>73</td>\n",
              "      <td>-37</td>\n",
              "      <td>33</td>\n",
              "      <td>-119</td>\n",
              "      <td>-45</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>-18</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>89</td>\n",
              "      <td>0.633137</td>\n",
              "      <td>99.502027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Leggings</td>\n",
              "      <td>-16</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>28</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>-42</td>\n",
              "      <td>-34</td>\n",
              "      <td>-8</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>0.497973</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13 rows × 28 columns</p>\n",
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          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(sub_order, y='Profit Summary', x='Sub-Category', title='Profit by sub-category', color='Cumulative share')\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 562
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        "id": "O-VnT-1nkFXf",
        "outputId": "35c238f5-a7e8-45aa-836c-d9faf2cac373"
      },
      "execution_count": null,
      "outputs": [
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    {
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        "fig = go.Figure()\n",
        "for i in range(sub_order['Sub-Category'].nunique()):\n",
        "    x = sub_order.iloc[:, 1:13].columns\n",
        "    fig.add_trace(go.Scatter(x = x, y = sub_order.iloc[i, 1:13], mode = 'lines+markers', name = sub_order['Sub-Category'].unique()[i]))\n",
        "    i+=1  \n",
        "fig.update_layout(title = 'Monthly Sub-categories sales')\n",
        "fig.show()"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## Customer Analysis\n",
        "\n"
      ],
      "metadata": {
        "id": "qCxJ9kurqTTu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Customer who spent the most amount"
      ],
      "metadata": {
        "id": "uJoM-fHL8lqD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Total amt spent by each consumer\n",
        "cus_amt = merged_df.groupby([\n",
        "    'CustomerName',\n",
        "    'State'\n",
        "])['Amount'].sum().reset_index()\n",
        "### sort to get who spent most, get top 10\n",
        "\n",
        "sorted_cus_amt = cus_amt.sort_values(by='Amount', ascending=False).head(10)"
      ],
      "metadata": {
        "id": "-IyeMm3Slfk3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Plotting customer based on amount top 10"
      ],
      "metadata": {
        "id": "nyJSRIfe9beR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.bar(sorted_cus_amt, x = \"CustomerName\", y = \"Amount\", color=\"CustomerName\", barmode=\"relative\", hover_data=['State'])\n",
        "fig.show()"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Customer with more Orders\n",
        "* Even the max amount is spent by Yaanvi, Abhishek is the one whose order count is most."
      ],
      "metadata": {
        "id": "pKo99gtK-ksk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_customers = merged_df.groupby('CustomerName').sum().reset_index() \\\n",
        "                         .sort_values(['Quantity'], ascending=False).head(5)\n",
        "\n",
        "fig = go.Figure(data=[go.Bar(\n",
        "    x=top_customers['CustomerName'],\n",
        "    y=top_customers['Quantity'],\n",
        ")])\n",
        "fig.update_layout(title_text='Top 5 Customers', \n",
        "                  template='plotly_white')\n",
        "fig.update_xaxes(title_text='Customers')\n",
        "fig.update_yaxes(title_text='Total Orders')\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "N9J_vicz9jnu",
        "outputId": "44f169fc-2f11-40db-845c-0089f774342f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sales Target Analysis\n"
      ],
      "metadata": {
        "id": "lLfxjEI9Caj0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "target = sales_target.groupby('Category').max().reset_index()\n",
        "details = order_details.groupby('Category').sum().reset_index()\n",
        "\n",
        "target['Actual_Amount'] = details['Profit']\n",
        "\n",
        "fig = go.Figure(data=[\n",
        "    go.Bar(name='Target', x=target['Category'], y=target['Target'],\n",
        "          marker_color='green'),\n",
        "    go.Bar(name='Actual Amount', x=target['Category'], y=target['Actual_Amount'],\n",
        "          marker_color='red')\n",
        "])\n",
        "\n",
        "fig.update_layout(title_text='Actual Sales vs Target Sales',\n",
        "                 template='plotly_white')\n",
        "\n",
        "fig.update_xaxes(title_text='Categories')\n",
        "fig.update_yaxes(title_text='Amount')\n",
        "\n",
        "fig.update_layout(yaxis_tickprefix = '₹', yaxis_tickformat = ',.0f')\n",
        "\n",
        "\n",
        "fig.layout.xaxis.fixedrange = True\n",
        "fig.layout.yaxis.fixedrange = True\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "Yjd534H2-NDr",
        "outputId": "b218268f-aed9-4d32-918a-bde0fc86145f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation Matrix of Details of Amount, Profit, Quantity"
      ],
      "metadata": {
        "id": "XjTbbiiAJhC4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plotCorrelationMatrix(df, graphWidth):\n",
        "    filename = df.dataframeName\n",
        "    df = df.dropna('columns') \n",
        "    df = df[[col for col in df if df[col].nunique() > 1]] \n",
        "    if df.shape[1] < 2:\n",
        "        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n",
        "        return\n",
        "    corr = df.corr()\n",
        "    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n",
        "    corrMat = plt.matshow(corr, fignum = 1)\n",
        "    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n",
        "    plt.yticks(range(len(corr.columns)), corr.columns)\n",
        "    plt.gca().xaxis.tick_bottom()\n",
        "    plt.colorbar(corrMat)\n",
        "    plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "i9RRap_YJgne"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "order_details.dataframeName = \"order_details.csv\"\n",
        "plotCorrelationMatrix(order_details, 8)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "XwfvaVz4Cgte",
        "outputId": "b87298c4-4777-4c15-8d3b-8d489466a694"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x640 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Customer Segmentation using Clusters"
      ],
      "metadata": {
        "id": "rKv3TwXDMsUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.cluster import KMeans\n"
      ],
      "metadata": {
        "id": "Uoxzv8TUNtOu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "segment_customer = merged_df.groupby('CustomerName').sum().reset_index()\n",
        "segment_customer = segment_customer[['CustomerName', 'Amount', 'Quantity']]\n",
        "segment_customer.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "t5KjNtxLLiXl",
        "outputId": "ac51d2c9-f984-4917-de39-8471d62db117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  CustomerName  Amount  Quantity\n",
              "0    Aakanksha      74         8\n",
              "1      Aarushi    4701        49\n",
              "2       Aashna    1931        32\n",
              "3       Aastha    3276        28\n",
              "4       Aayush     556        18"
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              "      <td>Aakanksha</td>\n",
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              "      <th>2</th>\n",
              "      <td>Aashna</td>\n",
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              "      <td>Aastha</td>\n",
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              "      <th>4</th>\n",
              "      <td>Aayush</td>\n",
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        "# Follow documentation\n",
        "\n",
        "segment_copy_customer = segment_customer[['Amount', 'Quantity']]\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(segment_copy_customer)\n",
        "\n",
        "customers_normalizedDF = scaler.transform(segment_copy_customer)\n",
        "customers_normalizedDF\n",
        "\n",
        "# Elbow Method to find best number of clusters GFG zindabad\n",
        "sse = {}\n",
        "for k in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
        "    kmeans.fit(customers_normalizedDF)\n",
        "    sse[k] = kmeans.inertia_ # SSE -> sum of square error to closest cluster centroid\n",
        "\n",
        "# Plotting SSE\n",
        "fig = go.Figure()\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=list(sse.keys()),\n",
        "    y=list(sse.values()),\n",
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        "fig.update_xaxes(title_text='k')\n",
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        "Due to linear trend k-value as 3 is suitable hyperparameter"
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    {
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        "model = KMeans(n_clusters=3)\n",
        "model.fit(customers_normalizedDF)\n",
        "segment_customer['Cluster'] = model.labels_ + 1\n",
        "segment_customer['Cluster'] = segment_customer['Cluster'].astype('category')\n",
        "segment_customer.head()"
      ],
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      "execution_count": null,
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              "  CustomerName  Amount  Quantity Cluster\n",
              "0    Aakanksha      74         8       1\n",
              "1      Aarushi    4701        49       2\n",
              "2       Aashna    1931        32       3\n",
              "3       Aastha    3276        28       3\n",
              "4       Aayush     556        18       1"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerName</th>\n",
              "      <th>Amount</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Aakanksha</td>\n",
              "      <td>74</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Aarushi</td>\n",
              "      <td>4701</td>\n",
              "      <td>49</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Aashna</td>\n",
              "      <td>1931</td>\n",
              "      <td>32</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Aastha</td>\n",
              "      <td>3276</td>\n",
              "      <td>28</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aayush</td>\n",
              "      <td>556</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "segment_customer.groupby('Cluster').agg ({\n",
        "    'Amount':'mean',\n",
        "    'Quantity':'count'\n",
        "}).round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "9veCDliWPrVl",
        "outputId": "65ab9c1b-dcd8-4b8a-b379-b8748bb77346"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Amount  Quantity\n",
              "Cluster                   \n",
              "1         451.36       208\n",
              "2        5237.32        28\n",
              "3        1989.32        96"
            ],
            "text/html": [
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              "      <th>Cluster</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>451.36</td>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5237.32</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1989.32</td>\n",
              "      <td>96</td>\n",
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              "  </style>\n",
              "\n",
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              "          const element = document.querySelector('#df-eeec1e0c-0d86-4d19-ad63-56d0f6352666');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Cost vs Quantity Scatter Plot"
      ],
      "metadata": {
        "id": "Kwgq18IFQSrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(segment_customer, x=\"Quantity\", y=\"Amount\",\n",
        "                 color=\"Cluster\",\n",
        "                 template='plotly_white',\n",
        "                 title=\"Cost vs Quantity - Customer Segmentation\")\n",
        "fig.layout.xaxis.fixedrange = True\n",
        "fig.layout.yaxis.fixedrange = True\n",
        "\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "FP0k_pFEQEci",
        "outputId": "94633e05-2b5e-4d6f-ef53-17d3eb5b9418"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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