SampleSuperstore_dashboard.ipynb

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              "None",
              "South",
              "West",
              "Central",
              "East"
            ],
            "_view_count": null,
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            "_view_module_version": "1.5.0",
            "_view_name": "DropdownView",
            "description": "Region:",
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            "_model_name": "DropdownModel",
            "_options_labels": [
              "None",
              "Furniture",
              "Office Supplies",
              "Technology"
            ],
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            "outputs": [
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "Interactive Dashboard:\n",
                  "----------------------\n",
                  "\n",
                  "1. Total Sales by Region\n"
                ]
              },
              {
                "output_type": "stream",
                "name": "stderr",
                "text": [
                  "<ipython-input-4-fd74a4af01ac>:9: FutureWarning: \n",
                  "\n",
                  "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
                  "\n",
                  "  sns.barplot(x='Region', y='Sales', data=filtered_data, ci=None, estimator=sum, palette='viridis')\n",
                  "<ipython-input-4-fd74a4af01ac>:9: FutureWarning: \n",
                  "\n",
                  "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
                  "\n",
                  "  sns.barplot(x='Region', y='Sales', data=filtered_data, ci=None, estimator=sum, palette='viridis')\n"
                ]
              },
              {
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                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
                  "image/png": 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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "2. Profit Distribution by Category\n"
                ]
              },
              {
                "output_type": "stream",
                "name": "stderr",
                "text": [
                  "<ipython-input-4-fd74a4af01ac>:23: FutureWarning: \n",
                  "\n",
                  "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
                  "\n",
                  "  sns.boxplot(x='Category', y='Profit', data=filtered_data, palette='muted')\n"
                ]
              },
              {
                "output_type": "display_data",
                "data": {
                  "text/plain": "<Figure size 1000x600 with 1 Axes>",
                  "image/png": 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\n"
                },
                "metadata": {}
              },
              {
                "output_type": "stream",
                "name": "stdout",
                "text": [
                  "\n",
                  "3. Sales Trend Over Time\n"
                ]
              },
              {
                "output_type": "error",
                "ename": "KeyError",
                "evalue": "'Order Date'",
                "traceback": [
                  "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
                  "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
                  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
                  "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
                  "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
                  "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
                  "\u001b[0;31mKeyError\u001b[0m: 'Order Date'",
                  "\nThe above exception was the direct cause of the following exception:\n",
                  "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
                  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/ipywidgets/widgets/interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    255\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;32m<ipython-input-4-fd74a4af01ac>\u001b[0m in \u001b[0;36mdashboard\u001b[0;34m(region, category)\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\\n3. Sales Trend Over Time\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0mplot_sales_trend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcategory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;31m# Widgets for interactivity\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;32m<ipython-input-4-fd74a4af01ac>\u001b[0m in \u001b[0;36mplot_sales_trend\u001b[0;34m(category)\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mfiltered_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 36\u001b[0;31m     \u001b[0mfiltered_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     37\u001b[0m     \u001b[0mfiltered_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubset\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0mfiltered_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiltered_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_period\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'M'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
                  "\u001b[0;31mKeyError\u001b[0m: 'Order Date'"
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      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LatiefDataVisionary/data-mining-college-task/blob/main/SampleSuperstore_dashboard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "l_DKq5aIPv0C"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from IPython.display import display\n",
        "from ipywidgets import interact, widgets\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "file_path = 'SampleSuperstore.csv'\n",
        "data = pd.read_csv(file_path)"
      ],
      "metadata": {
        "id": "foFNBHm3QCXk"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the style for seaborn\n",
        "sns.set(style=\"whitegrid\")"
      ],
      "metadata": {
        "id": "f_Mx59bFQCGu"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dashboard Functions\n",
        "def plot_sales_by_region(region=None):\n",
        "    if region:\n",
        "        filtered_data = data[data['Region'] == region]\n",
        "    else:\n",
        "        filtered_data = data\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.barplot(x='Region', y='Sales', data=filtered_data, ci=None, estimator=sum, palette='viridis')\n",
        "    plt.title(\"Total Sales by Region\", fontsize=16)\n",
        "    plt.ylabel(\"Total Sales\")\n",
        "    plt.xlabel(\"Region\")\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.show()\n",
        "\n",
        "def plot_category_performance(region=None):\n",
        "    if region:\n",
        "        filtered_data = data[data['Region'] == region]\n",
        "    else:\n",
        "        filtered_data = data\n",
        "\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.boxplot(x='Category', y='Profit', data=filtered_data, palette='muted')\n",
        "    plt.title(\"Profit Distribution by Category\", fontsize=16)\n",
        "    plt.ylabel(\"Profit\")\n",
        "    plt.xlabel(\"Category\")\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.show()\n",
        "\n",
        "def plot_sales_trend(category=None):\n",
        "    if category:\n",
        "        filtered_data = data[data['Category'] == category]\n",
        "    else:\n",
        "        filtered_data = data\n",
        "\n",
        "    filtered_data['Order Date'] = pd.to_datetime(filtered_data['Order Date'], errors='coerce')\n",
        "    filtered_data = filtered_data.dropna(subset=['Order Date'])\n",
        "    filtered_data = filtered_data.groupby(filtered_data['Order Date'].dt.to_period('M')).sum().reset_index()\n",
        "\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    sns.lineplot(x='Order Date', y='Sales', data=filtered_data, marker='o', label='Sales', color='b')\n",
        "    plt.title(\"Sales Trend Over Time\", fontsize=16)\n",
        "    plt.ylabel(\"Sales\")\n",
        "    plt.xlabel(\"Date\")\n",
        "    plt.xticks(rotation=45)\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Interactive Dashboard\n",
        "def dashboard(region=None, category=None):\n",
        "    print(\"Interactive Dashboard:\")\n",
        "    print(\"----------------------\")\n",
        "\n",
        "    print(\"\\n1. Total Sales by Region\")\n",
        "    plot_sales_by_region(region)\n",
        "\n",
        "    print(\"\\n2. Profit Distribution by Category\")\n",
        "    plot_category_performance(region)\n",
        "\n",
        "    print(\"\\n3. Sales Trend Over Time\")\n",
        "    plot_sales_trend(category)\n",
        "\n",
        "# Widgets for interactivity\n",
        "region_widget = widgets.Dropdown(\n",
        "    options=[None] + list(data['Region'].unique()),\n",
        "    description='Region:',\n",
        ")\n",
        "\n",
        "category_widget = widgets.Dropdown(\n",
        "    options=[None] + list(data['Category'].unique()),\n",
        "    description='Category:',\n",
        ")\n",
        "\n",
        "interact(dashboard, region=region_widget, category=category_widget)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "e9a15f9dbead470a97066c9a6e7733a9",
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            "24f7b1ae42a0410890a4b7f5450a7579"
          ]
        },
        "id": "5KNtZmc0QBV0",
        "outputId": "7b592760-cd51-44d0-cedc-da77ce992e3b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='Region:', options=(None, 'South', 'West', 'Central', 'East'), valu…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e9a15f9dbead470a97066c9a6e7733a9"
            }
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        }
      ]
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}