chartify_bug.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/i-ninte/data-analytics-R-python/blob/main/chartify_bug.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unBjWJHJ-ao5",
        "outputId": "6737e831-2066-4170-d214-fabe59c37dbd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting chartify\n",
            "  Downloading chartify-4.0.5-py2.py3-none-any.whl (64 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/64.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.0/64.0 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from chartify) (1.5.3)\n",
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            "Collecting selenium>=4.0.0 (from chartify)\n",
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            "Collecting ipykernel>=6.0 (from chartify)\n",
            "  Downloading ipykernel-6.25.2-py3-none-any.whl (154 kB)\n",
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            "\u001b[?25hRequirement already satisfied: ipython>=7.17.0 in /usr/local/lib/python3.10/dist-packages (from chartify) (7.34.0)\n",
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            "Collecting jupyter-bokeh>=3.0.7 (from chartify)\n",
            "  Downloading jupyter_bokeh-3.0.7-py3-none-any.whl (1.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m89.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting comm>=0.1.1 (from ipykernel>=6.0->chartify)\n",
            "  Downloading comm-0.1.4-py3-none-any.whl (6.6 kB)\n",
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            "Collecting jedi>=0.16 (from ipython>=7.17.0->chartify)\n",
            "  Downloading jedi-0.19.1-py2.py3-none-any.whl (1.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m101.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython>=7.17.0->chartify) (4.4.2)\n",
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            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=3.1.0->chartify) (2.1.3)\n",
            "Collecting ipywidgets==8.* (from jupyter-bokeh>=3.0.7->chartify)\n",
            "  Downloading ipywidgets-8.1.1-py3-none-any.whl (139 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.4/139.4 kB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting widgetsnbextension~=4.0.9 (from ipywidgets==8.*->jupyter-bokeh>=3.0.7->chartify)\n",
            "  Downloading widgetsnbextension-4.0.9-py3-none-any.whl (2.3 MB)\n",
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            "\u001b[?25hRequirement already satisfied: jupyterlab-widgets~=3.0.9 in /usr/local/lib/python3.10/dist-packages (from ipywidgets==8.*->jupyter-bokeh>=3.0.7->chartify) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.2.0->chartify) (2.8.2)\n",
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            "Requirement already satisfied: urllib3[socks]<3,>=1.26 in /usr/local/lib/python3.10/dist-packages (from selenium>=4.0.0->chartify) (2.0.6)\n",
            "Collecting trio~=0.17 (from selenium>=4.0.0->chartify)\n",
            "  Downloading trio-0.22.2-py3-none-any.whl (400 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m400.2/400.2 kB\u001b[0m \u001b[31m48.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting trio-websocket~=0.9 (from selenium>=4.0.0->chartify)\n",
            "  Downloading trio_websocket-0.11.1-py3-none-any.whl (17 kB)\n",
            "Requirement already satisfied: certifi>=2021.10.8 in /usr/local/lib/python3.10/dist-packages (from selenium>=4.0.0->chartify) (2023.7.22)\n",
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            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core!=5.0.*,>=4.12->ipykernel>=6.0->chartify) (3.11.0)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas>=1.2.0->chartify) (1.16.0)\n",
            "Requirement already satisfied: attrs>=20.1.0 in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium>=4.0.0->chartify) (23.1.0)\n",
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            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium>=4.0.0->chartify) (3.4)\n",
            "Collecting outcome (from trio~=0.17->selenium>=4.0.0->chartify)\n",
            "  Downloading outcome-1.2.0-py2.py3-none-any.whl (9.7 kB)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium>=4.0.0->chartify) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup>=1.0.0rc9 in /usr/local/lib/python3.10/dist-packages (from trio~=0.17->selenium>=4.0.0->chartify) (1.1.3)\n",
            "Collecting wsproto>=0.14 (from trio-websocket~=0.9->selenium>=4.0.0->chartify)\n",
            "  Downloading wsproto-1.2.0-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: pysocks!=1.5.7,<2.0,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from urllib3[socks]<3,>=1.26->selenium>=4.0.0->chartify) (1.7.1)\n",
            "Collecting h11<1,>=0.9.0 (from wsproto>=0.14->trio-websocket~=0.9->selenium>=4.0.0->chartify)\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: widgetsnbextension, outcome, jedi, h11, comm, wsproto, trio, trio-websocket, ipywidgets, ipykernel, selenium, jupyter-bokeh, chartify\n",
            "  Attempting uninstall: widgetsnbextension\n",
            "    Found existing installation: widgetsnbextension 3.6.6\n",
            "    Uninstalling widgetsnbextension-3.6.6:\n",
            "      Successfully uninstalled widgetsnbextension-3.6.6\n",
            "  Attempting uninstall: ipywidgets\n",
            "    Found existing installation: ipywidgets 7.7.1\n",
            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 5.5.6\n",
            "    Uninstalling ipykernel-5.5.6:\n",
            "      Successfully uninstalled ipykernel-5.5.6\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires ipykernel==5.5.6, but you have ipykernel 6.25.2 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed chartify-4.0.5 comm-0.1.4 h11-0.14.0 ipykernel-6.25.2 ipywidgets-8.1.1 jedi-0.19.1 jupyter-bokeh-3.0.7 outcome-1.2.0 selenium-4.14.0 trio-0.22.2 trio-websocket-0.11.1 widgetsnbextension-4.0.9 wsproto-1.2.0\n"
          ]
        }
      ],
      "source": [
        "pip install chartify"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import chartify\n",
        "\n",
        "# Project data\n",
        "projects = ['STG', 'GT1', 'GT2', 'HRSG 1', 'HRSG 2', 'DIVERTER DAMPER', 'OTCS', 'DEBRIS FILTER']\n",
        "days_assigned = [10, 15, 8, 12, 10, 5, 7, 3]\n",
        "days_done = [8, 10, 6, 10, 8, 4, 6, 2]\n",
        "percentage_completion = [(done / assigned) * 100 for done, assigned in zip(days_done, days_assigned)]\n",
        "\n",
        "# Create a DataFrame\n",
        "data = {'Project': projects, 'Days Assigned': days_assigned, 'Days Done': days_done, 'Completion (%)': percentage_completion}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Create a Chartify chart\n",
        "ch = chartify.Chart(blank_labels=True)\n",
        "\n",
        "# Add data to the chart\n",
        "ch.set_title('Project Progress')\n",
        "ch.set_subtitle('Number of Days Assigned, Days Done, and Percentage Completion')\n",
        "\n",
        "ch.plot.bar(data_frame=df, categorical_columns='Project', numeric_column=['Days Assigned', 'Days Done'],\n",
        "            categorical_order_ascending=True, stacked=True)\n",
        "\n",
        "ch.plot.line(data_frame=df, x_column='Project', y_column='Completion (%)', color='red')\n",
        "\n",
        "# Add labels and legends\n",
        "ch.axes.set_yaxis_label('Number of Days')\n",
        "ch.axes.set_xaxis_label('Projects')\n",
        "ch.legend.set_title('Legend')\n",
        "ch.legend.set_location('outside_bottom')\n",
        "\n",
        "# Show the chart\n",
        "ch.show('png')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "hCEYTpfgD6v5",
        "outputId": "877924ed-ea12-4223-e23c-d1bc62fc5216"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-3a0414276cba>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_subtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of Days Assigned, Days Done, and Percentage Completion'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m ch.plot.bar(data_frame=df, categorical_columns='Project', numeric_column=['Days Assigned', 'Days Done'], \n\u001b[0m\u001b[1;32m     22\u001b[0m             categorical_order_ascending=True, stacked=True)\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chartify/_core/plot.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\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    150\u001b[0m         \u001b[0;34m\"\"\"Override attribute error\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    152\u001b[0m             \"\"\"Plot `{}` not avaiable for the given Chart.\n\u001b[1;32m    153\u001b[0m             \u001b[0mTry\u001b[0m \u001b[0mchanging\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mChart\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0mx_axis_type\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my_axis_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Plot `bar` not avaiable for the given Chart.\n            Try changing the Chart parameters x_axis_type and y_axis_type.\n            "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import chartify\n",
        "\n",
        "# Project data\n",
        "projects = ['STG', 'GT1', 'GT2', 'HRSG 1', 'HRSG 2', 'DIVERTER DAMPER', 'OTCS', 'DEBRIS FILTER']\n",
        "days_assigned = [10, 15, 8, 12, 10, 5, 7, 3]\n",
        "days_done = [8, 10, 6, 10, 8, 4, 6, 2]\n",
        "percentage_completion = [(done / assigned) * 100 for done, assigned in zip(days_done, days_assigned)]\n",
        "\n",
        "# Create a DataFrame\n",
        "data = {'Project': projects, 'Days Assigned': days_assigned, 'Days Done': days_done, 'Completion (%)': percentage_completion}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Create a Chartify chart\n",
        "ch = chartify.Chart(df)\n",
        "\n",
        "# Add data to the chart\n",
        "ch.set_title('Project Progress')\n",
        "ch.set_subtitle('Number of Days Assigned, Days Done, and Percentage Completion')\n",
        "\n",
        "# Create a bar chart\n",
        "ch.plot.bar(column=['Project', 'Days Assigned', 'Days Done'], categorical_columns='Project')\n",
        "\n",
        "ch.plot.line(data_frame=df, x_column='Project', y_column='Completion (%)', color='red')\n",
        "\n",
        "# Add labels and legends\n",
        "ch.axes.set_yaxis_label('Number of Days')\n",
        "ch.axes.set_xaxis_label('Projects')\n",
        "ch.legend.set_title('Legend')\n",
        "ch.legend.set_location('outside_bottom')\n",
        "\n",
        "# Show the chart\n",
        "ch.show('png')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "CxSbdHA1ESxb",
        "outputId": "c8d23df1-30b3-4d84-ae43-95ab9eeced25"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-311c6b846aa7>\u001b[0m in \u001b[0;36m<cell line: 15>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# Create a Chartify chart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchartify\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\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     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;31m# Add data to the chart\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/chartify/_core/chart.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, blank_labels, layout, x_axis_type, y_axis_type, second_y_axis)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBasePlot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_plot_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_axis_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_axis_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCallout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBaseAxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_x_axis_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y_axis_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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    122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_second_y_axis_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvalid_second_y_axis_types\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/chartify/_core/axes.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, chart)\u001b[0m\n\u001b[1;32m     71\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_chart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchart\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseAxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\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---> 73\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize_defaults\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[0m\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\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/chartify/_core/axes.py\u001b[0m in \u001b[0;36m_initialize_defaults\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     97\u001b[0m         \u001b[0mxaxis_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\"ch.axes.set_xaxis_label('label (units)')\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     98\u001b[0m         \u001b[0myaxis_label\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\"ch.axes.set_yaxis_label('label (units)')\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_chart\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_blank_labels\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    100\u001b[0m             \u001b[0mxaxis_label\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    101\u001b[0m             \u001b[0myaxis_label\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/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1525\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1527\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m   1528\u001b[0m             \u001b[0;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m             \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import chartify\n",
        "\n",
        "# Project data\n",
        "projects = ['STG', 'GT1', 'GT2', 'HRSG 1', 'HRSG 2', 'DIVERTER DAMPER', 'OTCS', 'DEBRIS FILTER']\n",
        "days_assigned = [10, 15, 8, 12, 10, 5, 7, 3]\n",
        "days_done = [8, 10, 6, 10, 8, 4, 6, 2]\n",
        "percentage_completion = [(done / assigned) * 100 for done, assigned in zip(days_done, days_assigned)]\n",
        "\n",
        "# Create a DataFrame\n",
        "data = {'Project': projects, 'Days Assigned': days_assigned, 'Days Done': days_done, 'Completion (%)': percentage_completion}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Create a Chartify chart\n",
        "ch = chartify.Chart(blank_labels=True)\n",
        "\n",
        "# Add data to the chart\n",
        "ch.set_title('Project Progress')\n",
        "ch.set_subtitle('Number of Days Assigned, Days Done, and Percentage Completion')\n",
        "\n",
        "ch.plot.bar_stacked(data_frame=df, categorical_columns='Project', numeric_column=['Days Assigned', 'Days Done'])\n",
        "\n",
        "\n",
        "ch.plot.line(data_frame=df, x_column='Project', y_column='Completion (%)', color='red')\n",
        "\n",
        "# Add labels and legends\n",
        "ch.axes.set_yaxis_label('Number of Days')\n",
        "ch.axes.set_xaxis_label('Projects')\n",
        "ch.legend.set_title('Legend')\n",
        "ch.legend.set_location('outside_bottom')\n",
        "\n",
        "# Show the chart\n",
        "ch.show('png')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "DHbQsaxsEgEU",
        "outputId": "659e4605-9954-47f7-b21c-ab229d42feb7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-47c5805f66f4>\u001b[0m in \u001b[0;36m<cell line: 21>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0mch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_subtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of Days Assigned, Days Done, and Percentage Completion'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar_stacked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_frame\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcategorical_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Project'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_column\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Days Assigned'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Days Done'\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[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/chartify/_core/plot.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\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    150\u001b[0m         \u001b[0;34m\"\"\"Override attribute error\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         raise AttributeError(\n\u001b[0m\u001b[1;32m    152\u001b[0m             \"\"\"Plot `{}` not avaiable for the given Chart.\n\u001b[1;32m    153\u001b[0m             \u001b[0mTry\u001b[0m \u001b[0mchanging\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mChart\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0mx_axis_type\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0my_axis_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Plot `bar_stacked` not avaiable for the given Chart.\n            Try changing the Chart parameters x_axis_type and y_axis_type.\n            "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Project data\n",
        "projects = ['STG', 'GT1', 'GT2', 'HRSG 1', 'HRSG 2', 'DIVERTER DAMPER', 'OTCS', 'DEBRIS FILTER']\n",
        "days_assigned = [10, 15, 8, 12, 10, 5, 7, 3]\n",
        "days_done = [8, 10, 6, 10, 8, 4, 6, 2]\n",
        "percentage_completion = [(done / assigned) * 100 for done, assigned in zip(days_done, days_assigned)]\n",
        "\n",
        "# Create a DataFrame\n",
        "data = {'Project': projects, 'Days Assigned': days_assigned, 'Days Done': days_done, 'Completion (%)': percentage_completion}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Create a bar chart using Matplotlib\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(df['Project'], df['Days Assigned'], label='Days Assigned')\n",
        "plt.bar(df['Project'], df['Days Done'], label='Days Done')\n",
        "plt.xlabel('Projects')\n",
        "plt.ylabel('Number of Days')\n",
        "plt.title('Project Progress')\n",
        "plt.legend()\n",
        "\n",
        "# Show the chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "OG8Y7I4oE8gA",
        "outputId": "688d8449-8399-4dff-abfc-cf48f493b35d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNTaBmwWE6pIWfWdL0JgGe/",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}