Demographic_Data_Analyzer.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/WillNRL/Demographic-Data-Analyzer/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",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.DataFrame(pd.read_csv('https://raw.githubusercontent.com/WillNRL/Demographic-Data-Analyzer/main/adult.data'))\n",
        "df.head(20)"
      ],
      "metadata": {
        "id": "_39Irkq8YuFO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "outputId": "50cb9e10-f180-4507-cfa7-a8e9adac0c06"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    39          State-gov   77516      Bachelors   13           Never-married  \\\n",
              "0   50   Self-emp-not-inc   83311      Bachelors   13      Married-civ-spouse   \n",
              "1   38            Private  215646        HS-grad    9                Divorced   \n",
              "2   53            Private  234721           11th    7      Married-civ-spouse   \n",
              "3   28            Private  338409      Bachelors   13      Married-civ-spouse   \n",
              "4   37            Private  284582        Masters   14      Married-civ-spouse   \n",
              "5   49            Private  160187            9th    5   Married-spouse-absent   \n",
              "6   52   Self-emp-not-inc  209642        HS-grad    9      Married-civ-spouse   \n",
              "7   31            Private   45781        Masters   14           Never-married   \n",
              "8   42            Private  159449      Bachelors   13      Married-civ-spouse   \n",
              "9   37            Private  280464   Some-college   10      Married-civ-spouse   \n",
              "10  30          State-gov  141297      Bachelors   13      Married-civ-spouse   \n",
              "11  23            Private  122272      Bachelors   13           Never-married   \n",
              "12  32            Private  205019     Assoc-acdm   12           Never-married   \n",
              "13  40            Private  121772      Assoc-voc   11      Married-civ-spouse   \n",
              "14  34            Private  245487        7th-8th    4      Married-civ-spouse   \n",
              "15  25   Self-emp-not-inc  176756        HS-grad    9           Never-married   \n",
              "16  32            Private  186824        HS-grad    9           Never-married   \n",
              "17  38            Private   28887           11th    7      Married-civ-spouse   \n",
              "18  43   Self-emp-not-inc  292175        Masters   14                Divorced   \n",
              "19  40            Private  193524      Doctorate   16      Married-civ-spouse   \n",
              "\n",
              "          Adm-clerical   Not-in-family                White     Male   2174  \\\n",
              "0      Exec-managerial         Husband                White     Male      0   \n",
              "1    Handlers-cleaners   Not-in-family                White     Male      0   \n",
              "2    Handlers-cleaners         Husband                Black     Male      0   \n",
              "3       Prof-specialty            Wife                Black   Female      0   \n",
              "4      Exec-managerial            Wife                White   Female      0   \n",
              "5        Other-service   Not-in-family                Black   Female      0   \n",
              "6      Exec-managerial         Husband                White     Male      0   \n",
              "7       Prof-specialty   Not-in-family                White   Female  14084   \n",
              "8      Exec-managerial         Husband                White     Male   5178   \n",
              "9      Exec-managerial         Husband                Black     Male      0   \n",
              "10      Prof-specialty         Husband   Asian-Pac-Islander     Male      0   \n",
              "11        Adm-clerical       Own-child                White   Female      0   \n",
              "12               Sales   Not-in-family                Black     Male      0   \n",
              "13        Craft-repair         Husband   Asian-Pac-Islander     Male      0   \n",
              "14    Transport-moving         Husband   Amer-Indian-Eskimo     Male      0   \n",
              "15     Farming-fishing       Own-child                White     Male      0   \n",
              "16   Machine-op-inspct       Unmarried                White     Male      0   \n",
              "17               Sales         Husband                White     Male      0   \n",
              "18     Exec-managerial       Unmarried                White   Female      0   \n",
              "19      Prof-specialty         Husband                White     Male      0   \n",
              "\n",
              "     0   40   United-States   <=50K  \n",
              "0    0   13   United-States   <=50K  \n",
              "1    0   40   United-States   <=50K  \n",
              "2    0   40   United-States   <=50K  \n",
              "3    0   40            Cuba   <=50K  \n",
              "4    0   40   United-States   <=50K  \n",
              "5    0   16         Jamaica   <=50K  \n",
              "6    0   45   United-States    >50K  \n",
              "7    0   50   United-States    >50K  \n",
              "8    0   40   United-States    >50K  \n",
              "9    0   80   United-States    >50K  \n",
              "10   0   40           India    >50K  \n",
              "11   0   30   United-States   <=50K  \n",
              "12   0   50   United-States   <=50K  \n",
              "13   0   40               ?    >50K  \n",
              "14   0   45          Mexico   <=50K  \n",
              "15   0   35   United-States   <=50K  \n",
              "16   0   40   United-States   <=50K  \n",
              "17   0   50   United-States   <=50K  \n",
              "18   0   45   United-States    >50K  \n",
              "19   0   60   United-States    >50K  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>39</th>\n",
              "      <th>State-gov</th>\n",
              "      <th>77516</th>\n",
              "      <th>Bachelors</th>\n",
              "      <th>13</th>\n",
              "      <th>Never-married</th>\n",
              "      <th>Adm-clerical</th>\n",
              "      <th>Not-in-family</th>\n",
              "      <th>White</th>\n",
              "      <th>Male</th>\n",
              "      <th>2174</th>\n",
              "      <th>0</th>\n",
              "      <th>40</th>\n",
              "      <th>United-States</th>\n",
              "      <th>&lt;=50K</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>50</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>83311</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>215646</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>53</td>\n",
              "      <td>Private</td>\n",
              "      <td>234721</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Handlers-cleaners</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>Private</td>\n",
              "      <td>338409</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Wife</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>Cuba</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>37</td>\n",
              "      <td>Private</td>\n",
              "      <td>284582</td>\n",
              "      <td>Masters</td>\n",
              "      <td>14</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Wife</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>49</td>\n",
              "      <td>Private</td>\n",
              "      <td>160187</td>\n",
              "      <td>9th</td>\n",
              "      <td>5</td>\n",
              "      <td>Married-spouse-absent</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Black</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>Jamaica</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>52</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>209642</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>31</td>\n",
              "      <td>Private</td>\n",
              "      <td>45781</td>\n",
              "      <td>Masters</td>\n",
              "      <td>14</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>14084</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>42</td>\n",
              "      <td>Private</td>\n",
              "      <td>159449</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>5178</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>37</td>\n",
              "      <td>Private</td>\n",
              "      <td>280464</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>10</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>30</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>141297</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>India</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>23</td>\n",
              "      <td>Private</td>\n",
              "      <td>122272</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>13</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>32</td>\n",
              "      <td>Private</td>\n",
              "      <td>205019</td>\n",
              "      <td>Assoc-acdm</td>\n",
              "      <td>12</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Not-in-family</td>\n",
              "      <td>Black</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>121772</td>\n",
              "      <td>Assoc-voc</td>\n",
              "      <td>11</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Craft-repair</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Asian-Pac-Islander</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>?</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>34</td>\n",
              "      <td>Private</td>\n",
              "      <td>245487</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>4</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Transport-moving</td>\n",
              "      <td>Husband</td>\n",
              "      <td>Amer-Indian-Eskimo</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>25</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>176756</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Farming-fishing</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>32</td>\n",
              "      <td>Private</td>\n",
              "      <td>186824</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>9</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>38</td>\n",
              "      <td>Private</td>\n",
              "      <td>28887</td>\n",
              "      <td>11th</td>\n",
              "      <td>7</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Sales</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>43</td>\n",
              "      <td>Self-emp-not-inc</td>\n",
              "      <td>292175</td>\n",
              "      <td>Masters</td>\n",
              "      <td>14</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Exec-managerial</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>White</td>\n",
              "      <td>Female</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>40</td>\n",
              "      <td>Private</td>\n",
              "      <td>193524</td>\n",
              "      <td>Doctorate</td>\n",
              "      <td>16</td>\n",
              "      <td>Married-civ-spouse</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Husband</td>\n",
              "      <td>White</td>\n",
              "      <td>Male</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>60</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "How many people of each race are represented in this dataset? This should be a Pandas series with race names as the index labels. (race column)"
      ],
      "metadata": {
        "id": "mjjsziuWdtLa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyzuksJo2tli",
        "outputId": "522d4ef4-7af9-43d6-a8e3-af57278a322f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['39', ' State-gov', ' 77516', ' Bachelors', ' 13', ' Never-married',\n",
              "       ' Adm-clerical', ' Not-in-family', ' White', ' Male', ' 2174', ' 0',\n",
              "       ' 40', ' United-States', ' <=50K'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[' White'].value_counts()"
      ],
      "metadata": {
        "id": "Xf32u_rWeA4X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66ea6abe-48f0-4eb5-bbf4-f230e92ed573"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " White                 27815\n",
              " Black                  3124\n",
              " Asian-Pac-Islander     1039\n",
              " Amer-Indian-Eskimo      311\n",
              " Other                   271\n",
              "Name:  White, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the average age of men?"
      ],
      "metadata": {
        "id": "xU5xM9CmeBck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df.loc[df[' Male'] == ' Male']['39']).mean() #Coloco toda operação de colunas entre parenteses, e depois uso mean(). Dentro da operação de colunas aconatece: uso df.loc[df[coluna] == dado especifico que contem nas linhas][mais dado especifico que contem nas linhas separado por dados especificos]"
      ],
      "metadata": {
        "id": "zuDs0bnOeDat",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4d7a1ef-ff86-42f1-a687-a3466d26f913"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39.433567396392675"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the percentage of people who have a Bachelor's degree?"
      ],
      "metadata": {
        "id": "u2R0qrayeFoY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Na primeira parte estou usando df.loc para selecionar coluna e linha que contem bachelors para separar valores com uma condição para achar só Bachelors.\n",
        "Segunda são as operações normais.\n",
        "Terceira parte sendo uma simples seleção total da columa Bachelors e fazendo \n",
        "'''\n",
        "df.loc[df[' Bachelors'] == ' Bachelors'][' Bachelors'].value_counts() / df[[' Bachelors']].value_counts().sum() * 100 "
      ],
      "metadata": {
        "id": "fDJ4ae43eHo1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "081c6417-f24e-4fb5-9904-a3d9f65b825a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Bachelors    16.443489\n",
              "Name:  Bachelors, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What percentage of people with advanced education (Bachelors, Masters, or Doctorate) make more than 50K?"
      ],
      "metadata": {
        "id": "lUvL6mXceIKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Primeiro uso df.loc[] para selecionar linhas da coluna ' Bachelors' para pegar os valores ' Bachelors',\n",
        "' Masters' e ' Doctorate' usando estruturas de condição com operadores do tipo OR para filtrar os valores. \n",
        "A premissa de sintaxe é df.loc[(df[coluna] == valor dentro das linhas da coluna) | (df[coluna] == valor dentro das linhas da coluna)].\n",
        "No final eu somo o resultado das operações de porcentagem para saber se é maior do 50k.\n",
        "# .value_counts()  / df[[' Bachelors']].value_counts().sum() * 100) .sum()\n",
        "'''\n",
        "percent = df.loc[((df[' Bachelors'] == ' Bachelors') | (df[' Bachelors'] == ' Masters') | (df[' Bachelors'] == ' Doctorate'))] \n",
        "percent = percent.loc[df[' <=50K'] == ' >50K']\n",
        "(percent[' Bachelors'].value_counts() / df[[' Bachelors']].value_counts().sum() * 100).sum()"
      ],
      "metadata": {
        "id": "L8B_7iOvfYi_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92a4bb28-4ef0-4849-d7a8-c12e6b317189"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10.706388206388205"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What percentage of people without advanced education make more than 50K?"
      ],
      "metadata": {
        "id": "lRUUTJvkfbk0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "((df.loc[(df[' Bachelors'] == ' HS-grad') & (df[' <=50K'] == ' >50K')][' Bachelors']).value_counts() / df[[' Bachelors']].value_counts().sum() * 100).sum()"
      ],
      "metadata": {
        "id": "lrhqkmcPfdjZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51f8deb3-56c3-46c1-a870-31993a6342a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.144348894348894"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the minimum number of hours a person works per week?"
      ],
      "metadata": {
        "id": "_STp-aCsfg2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df[[' 40']]).min()"
      ],
      "metadata": {
        "id": "FdZ4iBsEfjFi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d31e53b-c909-4cfc-c6cb-f7248f0218f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " 40    1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What percentage of the people who work the minimum number of hours per week have a salary of more than 50K?"
      ],
      "metadata": {
        "id": "giK4VZywfjdb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "((df.loc[(df[' 40'] == 1)][' 40']).value_counts() / df[' 40'].value_counts().sum() * 100).sum()"
      ],
      "metadata": {
        "id": "6yNdDLxNflGT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1701900-5cc2-4087-8b4b-93d406501e7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06142506142506143"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What country has the highest percentage of people that earn >50K and what is that percentage?"
      ],
      "metadata": {
        "id": "YeDwfkHpfqM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "((df.loc[df[' <=50K'] == ' >50K'][' United-States']).value_counts() / df[' 40'].value_counts().sum() * 100).idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "kFkGbCbeJ7Zw",
        "outputId": "6147b38d-8a70-4c2a-e34b-0dcddd6972e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' United-States'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "((df.loc[df[' <=50K'] == ' >50K'][' United-States']).value_counts() / df[' 40'].value_counts().sum() * 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNLMfZvwGume",
        "outputId": "9daed650-8834-4e9c-aedd-3c7e4cc59116"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " United-States         22.023956\n",
              " ?                      0.448403\n",
              " Philippines            0.187346\n",
              " Germany                0.135135\n",
              " India                  0.122850\n",
              " Canada                 0.119779\n",
              " Mexico                 0.101351\n",
              " England                0.092138\n",
              " Italy                  0.076781\n",
              " Cuba                   0.076781\n",
              " Japan                  0.073710\n",
              " Taiwan                 0.061425\n",
              " China                  0.061425\n",
              " Iran                   0.055283\n",
              " South                  0.049140\n",
              " Puerto-Rico            0.036855\n",
              " Poland                 0.036855\n",
              " France                 0.036855\n",
              " Jamaica                0.030713\n",
              " El-Salvador            0.027641\n",
              " Greece                 0.024570\n",
              " Cambodia               0.021499\n",
              " Hong                   0.018428\n",
              " Yugoslavia             0.018428\n",
              " Ireland                0.015356\n",
              " Vietnam                0.015356\n",
              " Portugal               0.012285\n",
              " Haiti                  0.012285\n",
              " Ecuador                0.012285\n",
              " Thailand               0.009214\n",
              " Hungary                0.009214\n",
              " Guatemala              0.009214\n",
              " Scotland               0.009214\n",
              " Nicaragua              0.006143\n",
              " Trinadad&Tobago        0.006143\n",
              " Laos                   0.006143\n",
              " Columbia               0.006143\n",
              " Dominican-Republic     0.006143\n",
              " Peru                   0.006143\n",
              " Honduras               0.003071\n",
              "Name:  United-States, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identify the most popular occupation for those who earn >50K in India."
      ],
      "metadata": {
        "id": "1uXiRYhHfsox"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df.loc[(df[' <=50K'] == ' >50K') & (df[' United-States'] == ' India')][' Adm-clerical']).value_counts().idxmax()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "4oBKPpBxGquO",
        "outputId": "2509688f-59f4-4fa7-ed5c-cc17b481edc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "' Prof-specialty'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ],
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    "colab": {
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
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