Data Quest/Data_Quest.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Data Quest.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNXmso9luelag9eqsjpltxU",
      "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/Mithun162001/Data-Quest/blob/main/Data_Quest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tNMXmx5rRKaT"
      },
      "source": [
        "\n",
        "Name:- Mithun G\n",
        "\n",
        "USN:- 19BTRCR006\n",
        " \n",
        "Branch:- Data Science\n",
        "\n",
        "Semester:- 3rd SEM\n",
        "\n",
        "Mail ID:- mithun162001@gmail.com\n",
        "\n",
        "College:- School of Engineering and technology, Jain University"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOq2JhH9SA1E"
      },
      "source": [
        "# importing libraries\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E_lHRNWVVHmh"
      },
      "source": [
        "Uploading the file into Colab directory to drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylqYFifrREWI",
        "outputId": "8c8b50e1-0e4d-4ea9-c022-073b23763d4b",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded= files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f5627c1f-3061-4e88-a8b7-3103ef80f305\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-f5627c1f-3061-4e88-a8b7-3103ef80f305\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving global_terror.csv to global_terror (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1fBVb9AWVNl8"
      },
      "source": [
        "Reading the csv file into a Pandas dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCjmjILuVRtT",
        "outputId": "fd2a2221-dd49-4a39-a714-8b61a97e4e20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "data= pd.read_csv('global_terror (1).csv')\n",
        "data.head()   # To display the first five observations of the dataframe"
      ],
      "execution_count": null,
      "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>iyear</th>\n",
              "      <th>imonth</th>\n",
              "      <th>iday</th>\n",
              "      <th>country</th>\n",
              "      <th>country_txt</th>\n",
              "      <th>region</th>\n",
              "      <th>region_txt</th>\n",
              "      <th>provstate</th>\n",
              "      <th>city</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>attacktype1</th>\n",
              "      <th>attacktype1_txt</th>\n",
              "      <th>targtype1</th>\n",
              "      <th>targtype1_txt</th>\n",
              "      <th>natlty1</th>\n",
              "      <th>natlty1_txt</th>\n",
              "      <th>natlty2</th>\n",
              "      <th>weaptype1</th>\n",
              "      <th>weaptype1_txt</th>\n",
              "      <th>nkill</th>\n",
              "      <th>propextent</th>\n",
              "      <th>propextent_txt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>2</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>National</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>18.456792</td>\n",
              "      <td>-69.951164</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>14</td>\n",
              "      <td>Private Citizens &amp; Property</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>19.371887</td>\n",
              "      <td>-99.086624</td>\n",
              "      <td>6</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>5</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>15.478598</td>\n",
              "      <td>120.599741</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>10</td>\n",
              "      <td>Journalists &amp; Media</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
              "      <td>8</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>37.997490</td>\n",
              "      <td>23.762728</td>\n",
              "      <td>3</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>Explosives</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.580412</td>\n",
              "      <td>130.396361</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   iyear  imonth  iday  country  ... weaptype1_txt  nkill propextent propextent_txt\n",
              "0   1970       7     2       58  ...       Unknown    1.0        NaN            NaN\n",
              "1   1970       0     0      130  ...       Unknown    0.0        NaN            NaN\n",
              "2   1970       1     0      160  ...       Unknown    1.0        NaN            NaN\n",
              "3   1970       1     0       78  ...    Explosives    NaN        NaN            NaN\n",
              "4   1970       1     0      101  ...    Incendiary    NaN        NaN            NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gKhu0oNFWNX5"
      },
      "source": [
        "To see the number of observations and the columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sbla0yWyVTxH",
        "outputId": "ba3fb4a1-9e9a-43ef-943b-0f61a30ab9ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.shape        # from the output it is clear that we have toal of 191464 observations and 23 columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(191464, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ngbNxNnWd_U"
      },
      "source": [
        "To see the what are the columns we are having in this dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kM-diIl_Wcjo",
        "outputId": "713a9026-5d60-4632-c0f9-a4c8844b1e5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['iyear', 'imonth', 'iday', 'country', 'country_txt', 'region',\n",
              "       'region_txt', 'provstate', 'city', 'latitude', 'longitude',\n",
              "       'attacktype1', 'attacktype1_txt', 'targtype1', 'targtype1_txt',\n",
              "       'natlty1', 'natlty1_txt', 'natlty2', 'weaptype1', 'weaptype1_txt',\n",
              "       'nkill', 'propextent', 'propextent_txt'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlIi9xEZW6zs"
      },
      "source": [
        "To check whether there are any missing values are present in the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtQyS-TPXGFn",
        "outputId": "e97a7e4c-dd66-4501-8cd2-d8d1db8ae793",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "null_data=data.isna().sum()\n",
        "null_data\n",
        "# from the output it is clear that we have some NA values in teh provstate, city, latitude, longitude, natlty1, natlty1_txt, natlty2, nkill, propextent, propextent_text columns\n",
        "# the numbers in the output are the total number of missing values present in that column"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               2\n",
              "city                  426\n",
              "latitude             4580\n",
              "longitude            4581\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "natlty2            179585\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGIrREfaXtAN"
      },
      "source": [
        "We calculate the percentage of NA values present in dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ra93vdygXzW4",
        "outputId": "e0511928-fb49-4352-f931-57edf252003f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "total= np.product(data.shape)     # this gives the total number of values present in the entire data frame\n",
        "missing= null_data.sum()          # this gives the number of missing values present in the entire dataset\n",
        "percentage= (missing/total)/100   # percentage is calculated\n",
        "print(\"Percentage of NA values present in dataset is \", percentage)\n",
        "\n",
        "# from the output it is clear that there are only small number of missing values present if we consider the entire dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of NA values present in dataset is  0.0010264229488481432\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3VpLCe0Yz8J"
      },
      "source": [
        "**Handling missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2s3odS1Y77w",
        "outputId": "8495fe72-172c-4140-8bfa-283a3b3e6b69",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Checking whether the columns having missing values are Numerical or Catergorical\n",
        "print(data['provstate'].dtype)        # Categorical\n",
        "print(data['city'].dtype)             # Categorical\n",
        "print(data['latitude'].dtype)         # Numerical\n",
        "print(data['longitude'].dtype)        # Numerical\n",
        "print(data['natlty1'].dtype)          # Numerical\n",
        "print(data['natlty1_txt'].dtype)      # Categorical\n",
        "print(data['natlty2'].dtype)          # Numerical\n",
        "print(data['nkill'].dtype)            # Numerical\n",
        "print(data['propextent'].dtype)       # Numerical\n",
        "print(data['propextent_txt'].dtype)   # Categorical"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "object\n",
            "object\n",
            "float64\n",
            "float64\n",
            "float64\n",
            "object\n",
            "float64\n",
            "float64\n",
            "float64\n",
            "object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7kbLg_garDQ",
        "outputId": "2716b9ba-1cfe-49d0-ba02-6882acb42391",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Since 'natlty2' has more NA values we will look into it\n",
        "print(data['natlty2'].unique())\n",
        "print(data['natlty2'].skew())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[  nan  217.  233.  161.  422.  216.   97.   30.  144.  142.   92.  218.\n",
            "  605.  130.   96.  185.   45.  145.  100.   98.   61.  110.   94.   59.\n",
            "  359.  201.  173.   83.   51.   21.  199.  209.   38.   95.   69.   60.\n",
            "  160.  231.  101.   75.  235.   11.   43.  159.  163.  999.  155.  153.\n",
            "  183.   15.  143.  113.  186.  162.   65.  603.  200.  122.  208.    4.\n",
            "  157.   47.  362.  213.   89.  203.   36.  112.  167.   14.  106.  104.\n",
            "  179.  166.    6.   78.  197.  156.  198.   87.   84.  123.  151.   42.\n",
            "  124.  168.   53.   19.  102.  205.   81.   93.  177.  222.   55.   74.\n",
            "   44.  228.   34.    8.  229.    5.  175. 1003.  219.  195.   28.   26.\n",
            "   35.  141.  139.  107.   50.   12.  147.  202.   22.   16.  121.  204.\n",
            "   67.   99.  108.  182.  137.  174.  184.  103.  118.   32.  138.   86.\n",
            "   88.  114.  223.  136.  146.  334.  207.   62.   63.   41.  214.   90.\n",
            "  119. 1004.   18.   54.  158.   37.  206.   56.  111.   33.  164.   76.\n",
            "  238.  215.    7.]\n",
            "4.3248560019563005\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMY6EeIdbpkP"
      },
      "source": [
        "Since there are only few values in the column 'natlty2' and it's not acting as any use to the dataset we can drop this column. Even mean or any central tendency tools cannot be applied on this column as there is a lot of skewness in this column...."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3tIxvU-hboPE",
        "outputId": "4f194499-13c5-495e-f910-dfb57f607be8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "# we will have a copy of original dataset into another dataframe\n",
        "data_copy= data\n",
        "data_copy.head()"
      ],
      "execution_count": null,
      "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>iyear</th>\n",
              "      <th>imonth</th>\n",
              "      <th>iday</th>\n",
              "      <th>country</th>\n",
              "      <th>country_txt</th>\n",
              "      <th>region</th>\n",
              "      <th>region_txt</th>\n",
              "      <th>provstate</th>\n",
              "      <th>city</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>attacktype1</th>\n",
              "      <th>attacktype1_txt</th>\n",
              "      <th>targtype1</th>\n",
              "      <th>targtype1_txt</th>\n",
              "      <th>natlty1</th>\n",
              "      <th>natlty1_txt</th>\n",
              "      <th>natlty2</th>\n",
              "      <th>weaptype1</th>\n",
              "      <th>weaptype1_txt</th>\n",
              "      <th>nkill</th>\n",
              "      <th>propextent</th>\n",
              "      <th>propextent_txt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>2</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>National</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>18.456792</td>\n",
              "      <td>-69.951164</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>14</td>\n",
              "      <td>Private Citizens &amp; Property</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>19.371887</td>\n",
              "      <td>-99.086624</td>\n",
              "      <td>6</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>5</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>15.478598</td>\n",
              "      <td>120.599741</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>10</td>\n",
              "      <td>Journalists &amp; Media</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
              "      <td>8</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>37.997490</td>\n",
              "      <td>23.762728</td>\n",
              "      <td>3</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6</td>\n",
              "      <td>Explosives</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.580412</td>\n",
              "      <td>130.396361</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   iyear  imonth  iday  country  ... weaptype1_txt  nkill propextent propextent_txt\n",
              "0   1970       7     2       58  ...       Unknown    1.0        NaN            NaN\n",
              "1   1970       0     0      130  ...       Unknown    0.0        NaN            NaN\n",
              "2   1970       1     0      160  ...       Unknown    1.0        NaN            NaN\n",
              "3   1970       1     0       78  ...    Explosives    NaN        NaN            NaN\n",
              "4   1970       1     0      101  ...    Incendiary    NaN        NaN            NaN\n",
              "\n",
              "[5 rows x 23 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27pv-KLTdeb7"
      },
      "source": [
        "We will be dropping the natlty2 column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i_ylRSG1eDL9",
        "outputId": "b8e88422-a57f-4653-e78c-0e185cb3d8b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "data_copy= data_copy.drop('natlty2', axis=1)\n",
        "data_copy.head()"
      ],
      "execution_count": null,
      "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>iyear</th>\n",
              "      <th>imonth</th>\n",
              "      <th>iday</th>\n",
              "      <th>country</th>\n",
              "      <th>country_txt</th>\n",
              "      <th>region</th>\n",
              "      <th>region_txt</th>\n",
              "      <th>provstate</th>\n",
              "      <th>city</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>attacktype1</th>\n",
              "      <th>attacktype1_txt</th>\n",
              "      <th>targtype1</th>\n",
              "      <th>targtype1_txt</th>\n",
              "      <th>natlty1</th>\n",
              "      <th>natlty1_txt</th>\n",
              "      <th>weaptype1</th>\n",
              "      <th>weaptype1_txt</th>\n",
              "      <th>nkill</th>\n",
              "      <th>propextent</th>\n",
              "      <th>propextent_txt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>2</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>National</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>18.456792</td>\n",
              "      <td>-69.951164</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>14</td>\n",
              "      <td>Private Citizens &amp; Property</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>19.371887</td>\n",
              "      <td>-99.086624</td>\n",
              "      <td>6</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>5</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>15.478598</td>\n",
              "      <td>120.599741</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>10</td>\n",
              "      <td>Journalists &amp; Media</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
              "      <td>8</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>37.997490</td>\n",
              "      <td>23.762728</td>\n",
              "      <td>3</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>6</td>\n",
              "      <td>Explosives</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.580412</td>\n",
              "      <td>130.396361</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>8</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   iyear  imonth  iday  country  ... weaptype1_txt  nkill propextent propextent_txt\n",
              "0   1970       7     2       58  ...       Unknown    1.0        NaN            NaN\n",
              "1   1970       0     0      130  ...       Unknown    0.0        NaN            NaN\n",
              "2   1970       1     0      160  ...       Unknown    1.0        NaN            NaN\n",
              "3   1970       1     0       78  ...    Explosives    NaN        NaN            NaN\n",
              "4   1970       1     0      101  ...    Incendiary    NaN        NaN            NaN\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD2Tr_Hzdlnv",
        "outputId": "3d1c16c7-1357-4223-a19d-bf7bde96e569",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               2\n",
              "city                  426\n",
              "latitude             4580\n",
              "longitude            4581\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2YnXDaOShRZj"
      },
      "source": [
        "Now we will check the provstate column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3szL2E6zhamS",
        "outputId": "649745fb-fdc1-434f-eee7-8f8bfa21114b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['provstate'].unique())\n",
        "print(data_copy['provstate'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['National' 'Federal' 'Tarlac' ... 'Cascades' 'Madaba' 'West']\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhS5vECzkK6Y",
        "outputId": "c3492a37-68d6-4f13-81ef-fa6f8dc4c593",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_mode= data_copy['provstate'].mode().values[0]\n",
        "data_mode"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Baghdad'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rC_Op4c9qihy"
      },
      "source": [
        "data_copy['provstate'].fillna(value= data_mode, inplace= True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lqqc7-gJqxWK",
        "outputId": "e2f392b7-8006-4c6e-befc-7ee12d380f12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy.isna().sum()          # we can see that provstate has now 0 NA values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                  426\n",
              "latitude             4580\n",
              "longitude            4581\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO16EUhNtO5H"
      },
      "source": [
        "Now we will check the city column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CthFPietWow",
        "outputId": "6a581e16-0a92-4cff-af9e-7deb4dba405e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['city'].unique())\n",
        "print(data_copy['city'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Santo Domingo' 'Mexico city' 'Unknown' ... 'Khar Tangi' 'Sorkhi Sang'\n",
            " 'Dik Sar']\n",
            "426\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FUf0ikditaQk",
        "outputId": "83abeb49-6acf-4f91-8187-96834da13560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_mode1= data_copy['city'].mode().values[0]\n",
        "data_mode1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Unknown'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnqQ8woBtj_D",
        "outputId": "98186bee-b561-4da1-ccb8-862ec4757622",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['city'].fillna(value= data_mode1, inplace= True)\n",
        "data_copy.isna().sum()      # now even the city column has 0 NA values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude             4580\n",
              "longitude            4581\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4whlYqZt1NC"
      },
      "source": [
        "Now we will check with the latitude column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjOnB76Btwdf",
        "outputId": "f7d4b56b-b70e-480f-97d8-54245637fdfb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['latitude'].unique())\n",
        "print(data_copy['latitude'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[18.456792 19.371887 15.478598 ... 36.057119 36.22929  31.639085]\n",
            "4580\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zm8NZHWWt6x0",
        "outputId": "5e1a71aa-bb9d-43dc-9f43-ebd6d362d73f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_mean= data_copy['latitude'].mean()\n",
        "data_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.46212053553783"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLZJa0xfuFEM",
        "outputId": "79a48ba8-e98e-486f-ee86-c0f937157748",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['latitude'].fillna(value= data_mean, inplace= True)\n",
        "data_copy.isna().sum()      # now even the latitude column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTpBloTMuMGK"
      },
      "source": [
        "Now we will check the longitude column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uOJwQ84nuOq4",
        "outputId": "ff59307d-44f8-4d5b-aa09-9dca1c02fd6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['longitude'].unique())\n",
        "print(data_copy['longitude'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-69.951164 -99.086624 120.599741 ...  64.845477  65.460288  64.243436]\n",
            "4581\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncMXEUMquUWK",
        "outputId": "8e7a8549-f164-4e9f-a177-a7c2e07f015e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_mean1= data_copy['longitude'].mean()\n",
        "data_mean1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.90212643654844"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d6yQwlB4uYfE",
        "outputId": "f9d37239-f7d6-4e16-9538-8ef897cef23d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['longitude'].fillna(value= data_mean1, inplace= True)\n",
        "data_copy.isna().sum()      # now even the longitude column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1              1722\n",
              "natlty1_txt          1722\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SRm5fSRVufaQ"
      },
      "source": [
        "Now let's see natlty1_txt column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rPid6iVujHV",
        "outputId": "b0ae7b9b-adf2-4d86-a445-10e16b7a2238",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['natlty1_txt'].unique())\n",
        "print(data_copy['natlty1_txt'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Dominican Republic' 'Belgium' 'United States' 'Uruguay' 'Germany'\n",
            " 'Great Britain' 'Venezuela' 'Puerto Rico' 'Israel' 'Austria'\n",
            " 'Switzerland' 'Jordan' 'Ethiopia' 'Spain' nan 'Japan' 'International'\n",
            " 'Egypt' 'Paraguay' 'Soviet Union' 'Ireland' 'Argentina' 'Philippines'\n",
            " 'China' 'West Bank and Gaza Strip' 'Multinational' 'Northern Ireland'\n",
            " 'Brazil' 'Haiti' 'South Africa' 'Portugal' 'Colombia' 'Greece'\n",
            " 'Nicaragua' 'Indonesia' 'Yugoslavia' 'Italy' 'Rhodesia' 'Ivory Coast'\n",
            " 'Iran' 'Canada' 'Poland' 'Sweden' 'Turkey' 'Costa Rica' 'Russia' 'Iraq'\n",
            " 'Cuba' 'France' 'Malawi' 'Democratic Republic of the Congo' 'Netherlands'\n",
            " 'Chile' 'Hong Kong' 'Yemen' 'Mexico' 'Syria' 'India' 'New Zealand'\n",
            " 'Senegal' 'Algeria' 'Peru' 'Czechoslovakia' 'Saudi Arabia' 'Denmark'\n",
            " 'West Germany (FRG)' 'Bahamas' 'Bolivia' 'El Salvador' 'Botswana'\n",
            " 'Guatemala' 'Lebanon' 'North Korea' 'Albania' 'South Korea'\n",
            " 'United Kingdom' 'Pakistan' 'Kenya' 'Cyprus' 'Panama' 'Sri Lanka'\n",
            " 'Ecuador' 'Malaysia' 'Morocco' 'Somalia' 'Nigeria' 'Namibia' 'Armenia'\n",
            " 'Barbados' 'Kuwait' 'Mozambique' 'Guyana' 'Taiwan' 'Tunisia' 'Myanmar'\n",
            " 'Trinidad and Tobago' 'Mauritania' 'Bulgaria' 'Thailand' 'Bahrain'\n",
            " 'Finland' 'Vietnam' 'United Arab Emirates' 'Malta' 'Australia' 'Zimbabwe'\n",
            " 'Zambia' 'Angola' 'Norway' 'Jamaica' 'Uganda' 'Croatia' 'Lesotho'\n",
            " 'Tanzania' 'Guinea' 'Fiji' 'Djibouti' 'Libya' 'Eritrea' 'Afghanistan'\n",
            " 'Honduras' 'Zaire' 'Mali' 'Bangladesh' 'Grenada' 'Vanuatu' 'Belize'\n",
            " 'North Yemen' 'Romania' 'Sudan' 'Chad' 'Central African Republic'\n",
            " 'Liberia' 'Seychelles' 'Dominica' \"People's Republic of the Congo\"\n",
            " 'Comoros' 'Suriname' 'Vatican City' 'Lithuania' 'Guadeloupe' 'Martinique'\n",
            " 'Western Sahara' 'Swaziland' 'Burkina Faso' 'Man, Isle of'\n",
            " 'New Caledonia' 'Cambodia' 'Singapore' 'Corsica' 'Luxembourg' 'Niger'\n",
            " 'Nepal' 'Hungary' 'Togo' 'Iceland' 'Sinhalese' 'Ghana' 'Maldives'\n",
            " 'Mauritius' 'Papua New Guinea' 'Qatar' 'Rwanda' 'Cameroon' 'Estonia'\n",
            " 'Sierra Leone' 'Latvia' 'Greenland' 'Bosnia-Herzegovina' 'Madagascar'\n",
            " 'Burundi' 'Azerbaijan' 'Kazakhstan' 'Georgia' 'Antigua and Barbuda'\n",
            " 'Commonwealth of Independent States' 'Ukraine' 'Moldova'\n",
            " 'Equatorial Guinea' 'Tajikistan' 'Laos' 'Brunei' 'Uzbekistan'\n",
            " 'Serbia-Montenegro' 'Republic of the Congo' 'Gabon' 'Slovak Republic'\n",
            " 'Macedonia' 'Mongolia' 'Benin' 'Belarus' 'Oman' 'Czech Republic' 'Gambia'\n",
            " 'Asian' 'Serbia' 'St. Kitts and Nevis' 'Kyrgyzstan' 'Macau' 'Slovenia'\n",
            " 'French Guiana' 'East Timor' 'Kosovo' 'Solomon Islands' 'St. Lucia'\n",
            " 'Guinea-Bissau' 'Montenegro' 'Bhutan' 'Bermuda' 'St. Martin'\n",
            " 'South Sudan' 'Turkmenistan' 'Saba (Netherlands Antilles)' 'Tuvalu'\n",
            " 'Virgin Islands (U.S.)' 'Marshall Islands']\n",
            "1722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8nSh67vuxGO",
        "outputId": "35a5a6c3-e0f2-4c03-887e-cba5845f7508",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_mode2= data_copy['natlty1_txt'].mode().values[0]\n",
        "data_mode2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Iraq'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKmU-e3Tu2gI",
        "outputId": "51e41cfc-4b9e-49b9-be68-025ac9f7fcde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['natlty1_txt'].fillna(value= data_mode2, inplace= True)\n",
        "data_copy.isna().sum()      # now even the natlty1_txt column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1                 0\n",
              "natlty1_txt             0\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5tYOxU5v249"
      },
      "source": [
        "Since the mode of ntalty_txt is Iraq then ntalty value also should be value labelled for Iraq"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GG_i-b4lv_a4",
        "outputId": "28b1b357-e17f-4052-a81e-8d3dd4d40e54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "selected= data[data['natlty1_txt'] == 'Iraq']\n",
        "selected['natlty1'].values[0]    # which means Iraq is labelled as 95.0 which is the mode of the ntalty_txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "95.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdJ9Uy2-wyn6",
        "outputId": "46cbea79-85ad-4f3b-a843-cad5fd958b23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['natlty1'].fillna(value= selected['natlty1'].values[0] , inplace= True)\n",
        "data_copy.isna().sum()      # now even the natlty1 column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1                 0\n",
              "natlty1_txt             0\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill               11029\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EtJ834DyF1-"
      },
      "source": [
        "Now we will look into nkill column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUeX7lJCyFRK",
        "outputId": "7485a568-6e76-4d0b-d1fc-a5d49a2dd6ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['nkill'].unique())\n",
        "print(data_copy['nkill'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.000e+00 0.000e+00       nan 7.000e+00 4.700e+01 2.000e+00 3.600e+01\n",
            " 5.000e+00 3.000e+00 4.000e+00 2.500e+01 1.500e+01 2.600e+01 8.000e+00\n",
            " 8.100e+01 6.000e+00 9.000e+00 1.600e+01 3.000e+01 3.100e+01 1.200e+01\n",
            " 2.100e+01 1.400e+01 8.800e+01 1.100e+01 1.000e+01 2.700e+01 1.800e+01\n",
            " 2.200e+01 1.900e+01 9.200e+01 1.300e+01 7.300e+01 1.000e+02 4.200e+01\n",
            " 4.300e+01 1.700e+01 9.800e+01 4.220e+02 4.800e+01 3.400e+01 5.400e+01\n",
            " 5.000e+01 3.500e+01 2.000e+01 4.100e+01 3.700e+01 2.800e+01 4.000e+01\n",
            " 3.200e+01 8.500e+01 2.300e+01 3.000e+02 6.000e+01 2.400e+01 5.800e+01\n",
            " 7.000e+01 8.700e+01 4.500e+01 3.800e+01 2.900e+01 7.400e+01 8.300e+01\n",
            " 9.000e+01 6.600e+01 8.000e+01 6.700e+01 5.100e+01 3.900e+01 1.140e+02\n",
            " 1.240e+02 7.600e+01 3.300e+01 7.500e+01 5.700e+01 6.200e+01 4.600e+01\n",
            " 5.600e+01 6.300e+01 1.200e+02 1.020e+02 7.800e+01 7.900e+01 5.200e+01\n",
            " 7.700e+01 2.000e+02 4.900e+01 1.110e+02 1.650e+02 4.400e+01 2.410e+02\n",
            " 6.400e+01 1.080e+02 6.500e+01 1.320e+02 2.700e+02 2.280e+02 1.100e+02\n",
            " 1.360e+02 1.800e+02 2.500e+02 9.300e+01 1.300e+02 5.900e+01 9.400e+01\n",
            " 5.300e+01 1.460e+02 3.290e+02 9.700e+01 7.100e+01 2.400e+02 2.270e+02\n",
            " 1.260e+02 1.060e+02 3.880e+02 6.800e+01 8.400e+01 8.200e+01 1.710e+02\n",
            " 1.070e+02 5.500e+01 1.120e+02 7.200e+01 9.600e+01 1.400e+02 6.100e+01\n",
            " 1.050e+02 1.500e+02 1.150e+02 8.900e+01 1.180e+03 1.700e+02 1.680e+02\n",
            " 1.210e+02 3.750e+02 9.100e+01 3.040e+02 1.230e+02 1.350e+02 2.560e+02\n",
            " 1.090e+02 2.710e+02 2.060e+02 1.040e+02 3.200e+02 2.750e+02 2.240e+02\n",
            " 1.180e+02 1.290e+02 9.500e+01 2.590e+02 1.385e+03 1.900e+02 1.190e+02\n",
            " 1.010e+02 1.160e+02 5.180e+02 3.440e+02 1.600e+02 1.880e+02 1.030e+02\n",
            " 2.050e+02 1.450e+02 1.530e+02 1.270e+02 6.900e+01 1.410e+02 1.340e+02\n",
            " 4.000e+02 8.600e+01 1.840e+02 2.100e+02 1.420e+02 2.120e+02 2.870e+02\n",
            " 3.150e+02 1.510e+02 6.700e+02 1.570e+03 3.100e+02 2.980e+02 9.530e+02\n",
            " 5.170e+02 2.010e+02 1.220e+02 1.580e+02 1.170e+02 1.440e+02 2.080e+02\n",
            " 1.520e+02 2.300e+02 2.800e+02 1.740e+02 1.430e+02 3.830e+02 2.830e+02\n",
            " 1.540e+02 2.840e+02 4.330e+02 2.660e+02 1.330e+02 1.630e+02 1.280e+02\n",
            " 5.880e+02 3.110e+02 3.300e+02 4.660e+02 3.390e+02]\n",
            "11029\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FL2zV-KEybpj",
        "outputId": "1aafb741-84c0-45e8-8beb-8726a2b6b5ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_mean3= data_copy['nkill'].mean()\n",
        "data_mean3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4107351677889546"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y4fEdPJMyiJA",
        "outputId": "a8310391-be64-407c-8822-06fd55be0b36",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['nkill'].fillna(value= data_mean3, inplace= True)\n",
        "data_copy.isna().sum()      # now even the nkill column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1                 0\n",
              "natlty1_txt             0\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill                   0\n",
              "propextent         124178\n",
              "propextent_txt     124178\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "397xTLT6yo6Y"
      },
      "source": [
        "Now we will look into propextent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcA0Mybnyojq",
        "outputId": "42cde817-4da3-48bc-fde9-cf2386ce33f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(data_copy['propextent_txt'].unique())\n",
        "print(data_copy['propextent_txt'].isna().sum())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nan 'Minor (likely < $1 million)'\n",
            " 'Major (likely >= $1 million but < $1 billion)' 'Unknown'\n",
            " 'Catastrophic (likely >= $1 billion)']\n",
            "124178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxejatSOy1Il",
        "outputId": "f1fae15c-d2df-447a-f40f-0d8646840e2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "data_mode3= data_copy['propextent_txt'].mode().values[0]\n",
        "data_mode3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Minor (likely < $1 million)'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raSgCp_3y-MT",
        "outputId": "a51e59aa-33d5-4af7-b454-cb206ff71069",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['propextent_txt'].fillna(value= data_mode3, inplace= True)\n",
        "data_copy.isna().sum()      # now even the propextent_txt column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear                   0\n",
              "imonth                  0\n",
              "iday                    0\n",
              "country                 0\n",
              "country_txt             0\n",
              "region                  0\n",
              "region_txt              0\n",
              "provstate               0\n",
              "city                    0\n",
              "latitude                0\n",
              "longitude               0\n",
              "attacktype1             0\n",
              "attacktype1_txt         0\n",
              "targtype1               0\n",
              "targtype1_txt           0\n",
              "natlty1                 0\n",
              "natlty1_txt             0\n",
              "weaptype1               0\n",
              "weaptype1_txt           0\n",
              "nkill                   0\n",
              "propextent         124178\n",
              "propextent_txt          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jKcJMoStzHLD"
      },
      "source": [
        "Now we will look into propextent"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDI9V42gzK0l",
        "outputId": "531dfaf1-13c1-4d32-ff6f-e39fdb8461bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "selected1= data[data['propextent_txt'] == 'Minor (likely < $1 million)']\n",
        "selected1['propextent'].values[0]    # which means Minor (likely < $1 million) is labelled as 3.0 which is the mode of the propextent_txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8KwPjzdOzeQl",
        "outputId": "ad0d8c8b-89ed-4cb4-ed58-6cb585bdc1be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data_copy['propextent'].fillna(value= selected['propextent'].values[0] , inplace= True)\n",
        "data_copy.isna().sum()      # now even the propextent column has 0 NA values by replacing with mean of the values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iyear              0\n",
              "imonth             0\n",
              "iday               0\n",
              "country            0\n",
              "country_txt        0\n",
              "region             0\n",
              "region_txt         0\n",
              "provstate          0\n",
              "city               0\n",
              "latitude           0\n",
              "longitude          0\n",
              "attacktype1        0\n",
              "attacktype1_txt    0\n",
              "targtype1          0\n",
              "targtype1_txt      0\n",
              "natlty1            0\n",
              "natlty1_txt        0\n",
              "weaptype1          0\n",
              "weaptype1_txt      0\n",
              "nkill              0\n",
              "propextent         0\n",
              "propextent_txt     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhpkwJeXzwXn"
      },
      "source": [
        "All Missing values are handled finally, there are no missing values present in the dataset now"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRllnARHz3sd",
        "outputId": "56dcd80c-bfbd-489f-f1fa-af10978cd10a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "data_copy.head()"
      ],
      "execution_count": null,
      "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>iyear</th>\n",
              "      <th>imonth</th>\n",
              "      <th>iday</th>\n",
              "      <th>country</th>\n",
              "      <th>country_txt</th>\n",
              "      <th>region</th>\n",
              "      <th>region_txt</th>\n",
              "      <th>provstate</th>\n",
              "      <th>city</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>attacktype1</th>\n",
              "      <th>attacktype1_txt</th>\n",
              "      <th>targtype1</th>\n",
              "      <th>targtype1_txt</th>\n",
              "      <th>natlty1</th>\n",
              "      <th>natlty1_txt</th>\n",
              "      <th>weaptype1</th>\n",
              "      <th>weaptype1_txt</th>\n",
              "      <th>nkill</th>\n",
              "      <th>propextent</th>\n",
              "      <th>propextent_txt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>58</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>2</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>National</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>18.4568</td>\n",
              "      <td>-69.9512</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>14</td>\n",
              "      <td>Private Citizens &amp; Property</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Minor (likely &lt; $1 million)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>19.3719</td>\n",
              "      <td>-99.0866</td>\n",
              "      <td>6</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>21.0</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Minor (likely &lt; $1 million)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>5</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>15.4786</td>\n",
              "      <td>120.6</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>10</td>\n",
              "      <td>Journalists &amp; Media</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>13</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Minor (likely &lt; $1 million)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
              "      <td>8</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>37.9975</td>\n",
              "      <td>23.7627</td>\n",
              "      <td>3</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>6</td>\n",
              "      <td>Explosives</td>\n",
              "      <td>2.410735</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Minor (likely &lt; $1 million)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.5804</td>\n",
              "      <td>130.396</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>217.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>8</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>2.410735</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Minor (likely &lt; $1 million)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   iyear  imonth  iday  ...     nkill propextent               propextent_txt\n",
              "0   1970       7     2  ...  1.000000        3.0  Minor (likely < $1 million)\n",
              "1   1970       0     0  ...  0.000000        3.0  Minor (likely < $1 million)\n",
              "2   1970       1     0  ...  1.000000        3.0  Minor (likely < $1 million)\n",
              "3   1970       1     0  ...  2.410735        3.0  Minor (likely < $1 million)\n",
              "4   1970       1     0  ...  2.410735        3.0  Minor (likely < $1 million)\n",
              "\n",
              "[5 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    }
  ]
}