financialDataSet.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "financialDataSet.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ChrisAchinga/Introduction-to-data-science/blob/master/financialDataSet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asGLvi4Z4KXy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "7be0f050-4952-41f3-8789-b5af0f40febf"
      },
      "source": [
        "# Import Libraries we use for analysis\n",
        "import pandas as pd\n",
        "#add a document\n",
        "myDataSet = pd.read_csv('https://bit.ly/FinancialDataset')\n",
        "\n",
        "#view observations\n",
        "myDataSet.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>year</th>\n",
              "      <th>uniqueid</th>\n",
              "      <th>Has a Bank account</th>\n",
              "      <th>Type of Location</th>\n",
              "      <th>Cell Phone Access</th>\n",
              "      <th>household_size</th>\n",
              "      <th>Respondent Age</th>\n",
              "      <th>gender_of_respondent</th>\n",
              "      <th>The relathip with head</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>Level of Educuation</th>\n",
              "      <th>Type of Job</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>3.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Spouse</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>Secondary education</td>\n",
              "      <td>Self employed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>No</td>\n",
              "      <td>5.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>No formal education</td>\n",
              "      <td>Government Dependent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_3</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Other relative</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Vocational/Specialised training</td>\n",
              "      <td>Self employed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_4</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Formally employed Private</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_5</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>No</td>\n",
              "      <td>8.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Informally employed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  country  year  ...              Level of Educuation                Type of Job\n",
              "0   Kenya  2018  ...              Secondary education              Self employed\n",
              "1   Kenya  2018  ...              No formal education       Government Dependent\n",
              "2   Kenya  2018  ...  Vocational/Specialised training              Self employed\n",
              "3   Kenya  2018  ...                Primary education  Formally employed Private\n",
              "4   Kenya  2018  ...                Primary education        Informally employed\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJOnqof34bvm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "outputId": "d856c150-cb3a-4c44-a5f8-b9f5a598f162"
      },
      "source": [
        "#check more info\n",
        "myDataSet.tail(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>country</th>\n",
              "      <th>year</th>\n",
              "      <th>uniqueid</th>\n",
              "      <th>Has a Bank account</th>\n",
              "      <th>Type of Location</th>\n",
              "      <th>Cell Phone Access</th>\n",
              "      <th>household_size</th>\n",
              "      <th>Respondent Age</th>\n",
              "      <th>gender_of_respondent</th>\n",
              "      <th>The relathip with head</th>\n",
              "      <th>marital_status</th>\n",
              "      <th>Level of Educuation</th>\n",
              "      <th>Type of Job</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23514</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2107</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Spouse</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Self employed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23515</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2108</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>No</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Parent</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Other Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23516</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2109</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Married/Living together</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Self employed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23517</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2110</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Parent</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Other Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23518</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2111</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>No Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23519</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2113</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>4.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Divorced/Seperated</td>\n",
              "      <td>No formal education</td>\n",
              "      <td>Other Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23520</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2114</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Head of Household</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Secondary education</td>\n",
              "      <td>Other Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23521</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2115</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>5.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Parent</td>\n",
              "      <td>Widowed</td>\n",
              "      <td>Primary education</td>\n",
              "      <td>Other Income</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23522</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2116</td>\n",
              "      <td>No</td>\n",
              "      <td>Urban</td>\n",
              "      <td>Yes</td>\n",
              "      <td>7.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>Female</td>\n",
              "      <td>Parent</td>\n",
              "      <td>Divorced/Seperated</td>\n",
              "      <td>Secondary education</td>\n",
              "      <td>Self employed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23523</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>2018</td>\n",
              "      <td>uniqueid_2117</td>\n",
              "      <td>No</td>\n",
              "      <td>Rural</td>\n",
              "      <td>Yes</td>\n",
              "      <td>10.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Male</td>\n",
              "      <td>Child</td>\n",
              "      <td>Single/Never Married</td>\n",
              "      <td>Secondary education</td>\n",
              "      <td>No Income</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      country  year  ...  Level of Educuation    Type of Job\n",
              "23514  Uganda  2018  ...    Primary education  Self employed\n",
              "23515  Uganda  2018  ...    Primary education   Other Income\n",
              "23516  Uganda  2018  ...    Primary education  Self employed\n",
              "23517  Uganda  2018  ...    Primary education   Other Income\n",
              "23518  Uganda  2018  ...    Primary education      No Income\n",
              "23519  Uganda  2018  ...  No formal education   Other Income\n",
              "23520  Uganda  2018  ...  Secondary education   Other Income\n",
              "23521  Uganda  2018  ...    Primary education   Other Income\n",
              "23522  Uganda  2018  ...  Secondary education  Self employed\n",
              "23523  Uganda  2018  ...  Secondary education      No Income\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXpPKP0J6Cuo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "063ec47a-cd3d-4cdc-a5df-abbf8de5c7ba"
      },
      "source": [
        "myDataSet.info()\n",
        "#information on datamy"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23524 entries, 0 to 23523\n",
            "Data columns (total 13 columns):\n",
            "country                   23510 non-null object\n",
            "year                      23524 non-null int64\n",
            "uniqueid                  23524 non-null object\n",
            "Has a Bank account        23488 non-null object\n",
            "Type of Location          23509 non-null object\n",
            "Cell Phone Access         23513 non-null object\n",
            "household_size            23496 non-null float64\n",
            "Respondent Age            23490 non-null float64\n",
            "gender_of_respondent      23490 non-null object\n",
            "The relathip with head    23520 non-null object\n",
            "marital_status            23492 non-null object\n",
            "Level of Educuation       23495 non-null object\n",
            "Type of Job               23494 non-null object\n",
            "dtypes: float64(2), int64(1), object(10)\n",
            "memory usage: 2.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yU4sREfs6VXt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "71982afa-3c12-4a2c-fda2-343ca02ddada"
      },
      "source": [
        "myDataSet.describe()\n",
        "#summary of statistics"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>year</th>\n",
              "      <th>household_size</th>\n",
              "      <th>Respondent Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>23524.000000</td>\n",
              "      <td>23496.000000</td>\n",
              "      <td>23490.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2016.979000</td>\n",
              "      <td>3.681818</td>\n",
              "      <td>38.804300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.899669</td>\n",
              "      <td>2.279933</td>\n",
              "      <td>16.519996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2016.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>26.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2017.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>35.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2018.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>49.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2056.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               year  household_size  Respondent Age\n",
              "count  23524.000000    23496.000000    23490.000000\n",
              "mean    2016.979000        3.681818       38.804300\n",
              "std        0.899669        2.279933       16.519996\n",
              "min     2016.000000        0.000000       16.000000\n",
              "25%     2016.000000        2.000000       26.000000\n",
              "50%     2017.000000        3.000000       35.000000\n",
              "75%     2018.000000        5.000000       49.000000\n",
              "max     2056.000000       21.000000      100.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fboUzwNA6b_q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6b5f6ca7-13af-4499-9e18-bb42a85cfdce"
      },
      "source": [
        "myDataSet.shape\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23524, 13)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SMIGWDPn8d9E",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "1c64b98b-0c92-4792-dddc-b9f05ea55c89"
      },
      "source": [
        "myDataSet.columns\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'year', 'uniqueid', 'Has a Bank account', 'Type of Location',\n",
              "       'Cell Phone Access', 'household_size', 'Respondent Age',\n",
              "       'gender_of_respondent', 'The relathip with head', 'marital_status',\n",
              "       'Level of Educuation', 'Type of Job'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9Fuwqko8i1I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c9c445cb-517b-4b39-f5eb-12bae358fd89"
      },
      "source": [
        "myDataSet.year.unique()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2018, 2029, 2056, 2016, 2039, 2017])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e2oLO8OC83wg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "d5e06dfc-5280-4812-9528-272e00ca251b"
      },
      "source": [
        "myDataSet.marital_status.unique()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Married/Living together', 'Widowed', 'Single/Never Married',\n",
              "       'Divorced/Seperated', 'Dont know', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3INcbez9F3y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "38a68fa3-f787-4372-aca7-95fec82a7d69"
      },
      "source": [
        "myDataSet[\"Cell Phone Access\"].unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Yes', 'No', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kRTNVWLY9RdX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "13e399e7-c593-4552-fd19-e8830dec38fc"
      },
      "source": [
        "myDataSet[\"Type of Job\"].unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Self employed', 'Government Dependent',\n",
              "       'Formally employed Private', 'Informally employed',\n",
              "       'Formally employed Government', 'Farming and Fishing',\n",
              "       'Remittance Dependent', 'Other Income',\n",
              "       'Dont Know/Refuse to answer', nan, 'No Income'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMvcNxLU9ti6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8633d1b5-2e59-454b-f180-ac76aa4ed789"
      },
      "source": [
        "myDataSet.gender_of_respondent.unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Female', 'Male', nan], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ghXFnGGd98wQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "553d03cf-836e-4efb-db2f-72f9586ba366"
      },
      "source": [
        "myDataSet.columns"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['country', 'year', 'uniqueid', 'Has a Bank account', 'Type of Location',\n",
              "       'Cell Phone Access', 'household_size', 'Respondent Age',\n",
              "       'gender_of_respondent', 'The relathip with head', 'marital_status',\n",
              "       'Level of Educuation', 'Type of Job'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITexMGJ_-Bpz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3a4a01bd-39b3-43d2-87fd-158ee0961edc"
      },
      "source": [
        "#viewing unique values\n",
        "myDataSet.uniqueid.unique()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['uniqueid_1', 'uniqueid_2', 'uniqueid_3', ..., 'uniqueid_8757',\n",
              "       'uniqueid_8758', 'uniqueid_8759'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YiYPsbBn-I87",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "4cc54a21-ea97-4c37-e150-879ff43ec724"
      },
      "source": [
        "myDataSet.dtypes\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "country                    object\n",
              "year                        int64\n",
              "uniqueid                   object\n",
              "Has a Bank account         object\n",
              "Type of Location           object\n",
              "Cell Phone Access          object\n",
              "household_size            float64\n",
              "Respondent Age            float64\n",
              "gender_of_respondent       object\n",
              "The relathip with head     object\n",
              "marital_status             object\n",
              "Level of Educuation        object\n",
              "Type of Job                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u43zTU5U-ycS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "95126235-ed2c-4d65-824d-227be2feb4ea"
      },
      "source": [
        "myDataSet[\"Level of Educuation\"].unique()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Secondary education', 'No formal education',\n",
              "       'Vocational/Specialised training', 'Primary education',\n",
              "       'Tertiary education', 'Other/Dont know/RTA', '6', nan],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOkNTFQVBrd9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import libraries for graphs\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "trLj4pPqDOfY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myDataSet.dropna(inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAgEWnAiCU9M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "33f2a986-a552-446b-9355-2856bbe97c27"
      },
      "source": [
        "#plotting a histogram using matplotlib and seaborn\n",
        "#create a figure and give dimensions\n",
        "plt.figure(figsize = (8, 5))\n",
        "sns.distplot(myDataSet['Respondent Age'], color='green')\n",
        "#title to the graph\n",
        "plt.title('Distribution for Respondent Age')\n",
        "plt.xlabel('Respondent')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgEAAAFNCAYAAACZlLzrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpAJcypkEJlo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4cd89f3d-51ea-40cf-e4ef-5dd343e00054"
      },
      "source": [
        "myDataSet['Respondent Age'].mean()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38.80550914621085"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xllLfnfWHyw3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "0037e351-9c61-473a-8249-40b25de3d508"
      },
      "source": [
        "#another one lol\n",
        "#create afigure and give dimensions\n",
        "plt.figure(figsize = (12, 8))\n",
        "sns.countplot(myDataSet['country'])\n",
        "plt.title('Country Chart', color = 'green', )\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAHwCAYAAAB67dOHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UH_lCQ7aIPjP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-MiWATkjI97-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "a5218785-adff-4c5e-af55-db593852443d"
      },
      "source": [
        "#another one\n",
        "plt.figure(figsize = (12, 8))\n",
        "sns.countplot(myDataSet['marital_status'])"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd6f29a5d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAAHkCAYAAACUkCGBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nraE2AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JhPwbeLLJ9j5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}