IT496_ViktoriiaO_Pareto_Chart_for_ITSkills.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IT496_ViktoriiaO_Pareto_Chart_for_ITSkills.ipynb",
      "provenance": [],
      "mount_file_id": "1bzy5Sb1FTuFNlwzHHmeH6-FvadSlwJHF",
      "authorship_tag": "ABX9TyO19I6f5pqCld6xXdhurtnI",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vovsiienko/IT496_Course_acitvities/blob/master/IT496_ViktoriiaO_Pareto_Chart_for_ITSkills.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GubJFW2ErDx2",
        "outputId": "3d069f9b-629a-47db-a87e-fee44862ffc2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Skills  No of people\n",
            "0  Programming language skills            10\n",
            "1                        UI/UX             6\n",
            "2             Machine learning             7\n",
            "3            Quality assurance             5\n",
            "4    Mobile application skills             9\n",
            "5                   Networking             4\n",
            "6              Cloud Computing             8\n",
            "7            Technical support             1\n",
            "8                        Linux             2\n",
            "9                Risk analysis             3\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import PercentFormatter\n",
        "ITSkillsDF=pd.read_csv(\"/content/drive/MyDrive/IT496/ITSkills.csv\")\n",
        "print(ITSkillsDF)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ITSkillsDF.index = ITSkillsDF['Skills']\n",
        "ITSkillsDF = ITSkillsDF.sort_values(by='No of people', ascending=False)\n",
        "\n",
        "print(ITSkillsDF)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8kQmDlgjthjV",
        "outputId": "0fe7dfd3-e4e5-442f-d307-c32a6a27496c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  Skills  No of people\n",
            "Skills                                                                \n",
            "Programming language skills  Programming language skills            10\n",
            "Mobile application skills      Mobile application skills             9\n",
            "Cloud Computing                          Cloud Computing             8\n",
            " Machine learning                       Machine learning             7\n",
            "UI/UX                                              UI/UX             6\n",
            "Quality assurance                      Quality assurance             5\n",
            "Networking                                    Networking             4\n",
            "Risk analysis                              Risk analysis             3\n",
            "Linux                                              Linux             2\n",
            "Technical support                      Technical support             1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ITSkillsDF['cumulativePercentage'] = ITSkillsDF['No of people'].cumsum()/ITSkillsDF['No of people'].sum()*100\n",
        "\n",
        "ITSkillsDF['cumulativePercentage'] = ITSkillsDF['cumulativePercentage'].apply(lambda x: round(x,2))\n",
        "\n",
        "print(ITSkillsDF)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DFzw2znVuFf5",
        "outputId": "f376f6b0-81b5-4b07-c2b1-ced664a84864"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                                  Skills  No of people  \\\n",
            "Skills                                                                   \n",
            "Programming language skills  Programming language skills            10   \n",
            "Mobile application skills      Mobile application skills             9   \n",
            "Cloud Computing                          Cloud Computing             8   \n",
            " Machine learning                       Machine learning             7   \n",
            "UI/UX                                              UI/UX             6   \n",
            "Quality assurance                      Quality assurance             5   \n",
            "Networking                                    Networking             4   \n",
            "Risk analysis                              Risk analysis             3   \n",
            "Linux                                              Linux             2   \n",
            "Technical support                      Technical support             1   \n",
            "\n",
            "                             cumulativePercentage  \n",
            "Skills                                             \n",
            "Programming language skills                 18.18  \n",
            "Mobile application skills                   34.55  \n",
            "Cloud Computing                             49.09  \n",
            " Machine learning                           61.82  \n",
            "UI/UX                                       72.73  \n",
            "Quality assurance                           81.82  \n",
            "Networking                                  89.09  \n",
            "Risk analysis                               94.55  \n",
            "Linux                                       98.18  \n",
            "Technical support                          100.00  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.bar(ITSkillsDF.index, ITSkillsDF['No of people'], color=\"C0\")\n",
        "\n",
        "ax.set_xticklabels(ITSkillsDF['Skills'], rotation=60)\n",
        "\n",
        "ax2 = ax.twinx()\n",
        "\n",
        "ax2.plot(ITSkillsDF.index, ITSkillsDF['cumulativePercentage'], color=\"C1\", marker=\"D\", ms=7, label=ITSkillsDF['cumulativePercentage'])\n",
        "ax2.yaxis.set_major_formatter(PercentFormatter())\n",
        "\n",
        "ax.tick_params(axis=\"y\", colors=\"C0\")\n",
        "ax2.tick_params(axis=\"y\", colors=\"C1\")\n",
        "\n",
        "for index, v in ITSkillsDF.iterrows():\n",
        "  label = round(v['cumulativePercentage'],1)\n",
        "  plt.annotate(label, xy=(v['Skills'],v['cumulativePercentage']))\n",
        "  \n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "g1p7RvVgvEh0",
        "outputId": "42df618b-84a2-4e4d-81a4-a7eaec9db788"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}