Demographic-Data-Analyzer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "first-colab-project.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPyuORnXGkHTUvBUQQDWHym",
      "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/Mukeshvudayagiri/keep-coding-python/blob/main/Demographic-Data-Analyzer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RZGHQo7tOpE",
        "outputId": "6d0f4edb-4510-4b55-94a6-12feaac65c3f"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/adult.data.csv')\n",
        "#df.head()\n",
        "#1.\n",
        "#race_count = df['race'].value_counts()\n",
        "#race_count\n",
        "#2.\n",
        "#avg_men = round(df[df[\"sex\"]=='Male']['age'].mean(),1)\n",
        "#avg_men\n",
        "\n",
        "#3.\n",
        "\n",
        "# bacherlors_degree = len(df[df['education']== 'Bachelors'])\n",
        "# print(bacherlors_degree)\n",
        "# total_degree = len(df)\n",
        "# #print(total_degree)\n",
        "# percentage_bachelors = round(bacherlors_degree/total_degree * 100 , 1)\n",
        "# print(percentage_bachelors)\n",
        "\n",
        "#4.\n",
        "\n",
        "# more_than_one = df[df['education'].isin(['Bachelors', 'Masters', 'Masters'])]\n",
        "# print(more_than_one) #7078 rows\n",
        "# not_more_than_one = df[~df['education'].isin(['Bachelors', 'Masters', 'Masters'])]\n",
        "# print(not_more_than_one) #25483 rows\n",
        "\n",
        "# num_of_higher = len(more_than_one[more_than_one.salary == '>50K'])\n",
        "# print(num_of_higher)\n",
        "# num_of_lower = len(not_more_than_one[not_more_than_one.salary == '>50K'])\n",
        "# print(num_of_lower)\n",
        "\n",
        "# high_education_rich = round(num_of_higher / len(more_than_one) * 100 , 1)\n",
        "# lower_education_rich = round(num_of_lower / len(not_more_than_one) * 100 , 1)\n",
        "# print(high_education_rich)\n",
        "# print(lower_education_rich)\n",
        "\n",
        "#5. \n",
        "\n",
        "# min_work = df['hours-per-week'].min()\n",
        "# print(min_work)\n",
        "\n",
        "#6. \n",
        "\n",
        "# min_num_workers = (df[df['hours-per-week'] == min_work])\n",
        "# # print(min_num_workers)\n",
        "# rich_min_work_percentage = len(min_num_workers[min_num_workers['salary']== '>50K']) / len(min_num_workers) * 100 \n",
        "# print(rich_min_work_percentage)\n",
        "\n",
        "#7.\n",
        "\n",
        "country_count = df['native-country'].value_counts()\n",
        "# print(country_count)\n",
        "country_rich_count = df[df['salary'] == '>50K']['native-country'].value_counts()\n",
        "# print(country_rich_count)\n",
        "\n",
        "# country_count = len(df['native-country'])\n",
        "# print(country_count)\n",
        "# country_rich_count = len(df[df['salary'] == '>50K']['native-country'])\n",
        "# print(country_rich_count)\n",
        "\n",
        "\n",
        "highest_earning_country = (country_rich_count / country_count * 100 ).max() #sort_values(ascending = False)\n",
        "print(highest_earning_country)\n",
        "\n",
        "#8. \n",
        "\n",
        "occupation_values = df[(df['native-country'] == 'India') & (df['salary'] == '>50K')]\n",
        "occupation_counts = occupation_values.occupation.value_counts()\n",
        "top_IN_occupation = occupation_counts.idxmax()\n",
        "print(top_IN_occupation)\n",
        "\n",
        "\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "41.86046511627907\n",
            "Prof-specialty\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}