Hacklytics.ipynb

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  "metadata": {
    "colab": {
      "name": "Hacklytics.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hlYC68MXt2w"
      },
      "source": [
        "# Hacklytics 2021\r\n",
        "\r\n",
        "All the best!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKS_PHXrYW_s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6b645c4-928c-4d91-b6da-d1d9d101b1bc"
      },
      "source": [
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "boNz3gsmZ3vG"
      },
      "source": [
        "path = '/content/drive/Shareddrives/Hacklytics_2021/Datasets/'"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eO9GO-fyY48F"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import seaborn as sns\r\n",
        "import datetime\r\n",
        "import matplotlib\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "df_wcc = pd.read_csv(path + 'world covid case.csv')\r\n",
        "df_cld = pd.read_csv(path + \"US State Level Data.csv\") "
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0L36O1CV_nk"
      },
      "source": [
        "# US Country Level Data\r\n",
        "\r\n",
        "Format:  \r\n",
        "\r\n",
        "date/county/state/fips/cases/deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jh_cVffRJmzJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128218e0-b7f4-4ef9-bf2b-17b8547e9119"
      },
      "source": [
        "df_cld.keys()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'county', 'state', 'fips', 'cases', 'deaths'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWzs76p9c0DH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "52647b7a-1e39-4212-c84c-80e207374e0f"
      },
      "source": [
        "df_cld.head()"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>date</th>\n",
              "      <th>county</th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-21</td>\n",
              "      <td>Snohomish</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53061.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Snohomish</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53061.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>Snohomish</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53061.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>Cook</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>17031.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>Snohomish</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53061.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date     county       state     fips  cases  deaths\n",
              "0  2020-01-21  Snohomish  Washington  53061.0      1     0.0\n",
              "1  2020-01-22  Snohomish  Washington  53061.0      1     0.0\n",
              "2  2020-01-23  Snohomish  Washington  53061.0      1     0.0\n",
              "3  2020-01-24       Cook    Illinois  17031.0      1     0.0\n",
              "4  2020-01-24  Snohomish  Washington  53061.0      1     0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "by3vfQlzEbjf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d04d34b1-5147-4952-e0f0-fc59c8306a81"
      },
      "source": [
        "df_cld.shape"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(998409, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QgRnA3lvdIgg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e241f31-e74b-405e-910e-a25989e1749e"
      },
      "source": [
        "df_cld.info()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 998409 entries, 0 to 998408\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   date    998409 non-null  object \n",
            " 1   county  998409 non-null  object \n",
            " 2   state   998409 non-null  object \n",
            " 3   fips    989140 non-null  float64\n",
            " 4   cases   998409 non-null  int64  \n",
            " 5   deaths  976918 non-null  float64\n",
            "dtypes: float64(2), int64(1), object(3)\n",
            "memory usage: 45.7+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1-KN1Lzdfxw"
      },
      "source": [
        "df_cld[\"date\"]= pd.to_datetime(df_cld[\"date\"]) "
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBOyPyyMhyN9"
      },
      "source": [
        "## Data Cleaning\r\n",
        "1. check for null\r\n",
        "2. check for unkown county / state\r\n",
        "3. check for duplicates( no duplicate row is found)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Q5WYGC2nnSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "087237e0-6b8e-4b66-b179-fc7016508d8b"
      },
      "source": [
        "#check for duplicates\r\n",
        "df_cld.duplicated().value_counts()"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    998409\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI1WwrnGn2IZ"
      },
      "source": [
        "#check for missing data\r\n",
        "#filter = df_cld['fips'].isnull()\r\n",
        "df_cld['fips'] = df_cld['fips'].fillna(\"unknown\")"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uv5K-ennh6qp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "51eb3df2-6077-4001-ad8e-eca4ae3d39c9"
      },
      "source": [
        "# Check for unique county\r\n",
        "# 8381 of the county is unknown\r\n",
        "df_cld['county'].value_counts()"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Washington              9862\n",
              "Unknown                 8381\n",
              "Jefferson               8253\n",
              "Franklin                7882\n",
              "Jackson                 7455\n",
              "                        ... \n",
              "Petroleum                123\n",
              "Skagway Municipality     112\n",
              "Esmeralda                 84\n",
              "Loving                    80\n",
              "Kalawao                   57\n",
              "Name: county, Length: 1930, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pTjtzRd1k_zM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee523c56-52e4-430f-bd82-432d23d9df6b"
      },
      "source": [
        "# fips == county\r\n",
        "df_cld['fips'].value_counts()"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "unknown    9269\n",
              "53061.0     381\n",
              "17031.0     378\n",
              "6059.0      377\n",
              "4013.0      376\n",
              "           ... \n",
              "48269.0     115\n",
              "2230.0      112\n",
              "32009.0      84\n",
              "48301.0      80\n",
              "15005.0      57\n",
              "Name: fips, Length: 3219, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKhdaT4ijzwe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "846d4481-9e3a-4736-e207-68722f26ce8c"
      },
      "source": [
        "# 9269 rows \r\n",
        "df_cld['fips'].isnull().sum()"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXZdn94vmEKr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9a3dd70-0a16-4610-a174-8966dcac1a8d"
      },
      "source": [
        "df_cld[['fips', 'county']].value_counts()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "fips     county              \n",
              "unknown  Unknown                 8381\n",
              "53061.0  Snohomish                381\n",
              "17031.0  Cook                     378\n",
              "6059.0   Orange                   377\n",
              "6037.0   Los Angeles              376\n",
              "                                 ... \n",
              "48269.0  King                     115\n",
              "2230.0   Skagway Municipality     112\n",
              "32009.0  Esmeralda                 84\n",
              "48301.0  Loving                    80\n",
              "15005.0  Kalawao                   57\n",
              "Length: 3222, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMkx9MDKEOe_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "858df739-efa6-4a48-8527-994e8bf21c10"
      },
      "source": [
        "dfcld_incomplete_rows = df_cld[df_cld.isnull().any(axis=1)] \r\n",
        "dfcld_incomplete_rows"
      ],
      "execution_count": 121,
      "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",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>county</th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>117486</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>Adjuntas</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72001</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117487</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>Aguada</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72003</td>\n",
              "      <td>7</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117488</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>Aguadilla</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72005</td>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117489</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>Aguas Buenas</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72007</td>\n",
              "      <td>22</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117490</th>\n",
              "      <td>2020-05-05</td>\n",
              "      <td>Aibonito</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72009</td>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997566</th>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>Vega Baja</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72145</td>\n",
              "      <td>2593</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997567</th>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>Vieques</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72147</td>\n",
              "      <td>195</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997568</th>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>Villalba</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72149</td>\n",
              "      <td>728</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997569</th>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>Yabucoa</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72151</td>\n",
              "      <td>941</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997570</th>\n",
              "      <td>2021-02-04</td>\n",
              "      <td>Yauco</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>72153</td>\n",
              "      <td>979</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21491 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             date        county        state   fips  cases  deaths\n",
              "117486 2020-05-05      Adjuntas  Puerto Rico  72001      3     NaN\n",
              "117487 2020-05-05        Aguada  Puerto Rico  72003      7     NaN\n",
              "117488 2020-05-05     Aguadilla  Puerto Rico  72005     11     NaN\n",
              "117489 2020-05-05  Aguas Buenas  Puerto Rico  72007     22     NaN\n",
              "117490 2020-05-05      Aibonito  Puerto Rico  72009     13     NaN\n",
              "...           ...           ...          ...    ...    ...     ...\n",
              "997566 2021-02-04     Vega Baja  Puerto Rico  72145   2593     NaN\n",
              "997567 2021-02-04       Vieques  Puerto Rico  72147    195     NaN\n",
              "997568 2021-02-04      Villalba  Puerto Rico  72149    728     NaN\n",
              "997569 2021-02-04       Yabucoa  Puerto Rico  72151    941     NaN\n",
              "997570 2021-02-04         Yauco  Puerto Rico  72153    979     NaN\n",
              "\n",
              "[21491 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRXXUM77ji5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8c7eb6e-c567-4218-d8e6-9dfcdb29b609"
      },
      "source": [
        "df_cld['state'].value_counts()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Texas                       75892\n",
              "Georgia                     50809\n",
              "Virginia                    41541\n",
              "Kentucky                    37012\n",
              "Missouri                    35428\n",
              "Illinois                    31813\n",
              "North Carolina              31509\n",
              "Iowa                        30770\n",
              "Kansas                      30571\n",
              "Tennessee                   30271\n",
              "Indiana                     29290\n",
              "Ohio                        27922\n",
              "Minnesota                   27139\n",
              "Michigan                    26310\n",
              "Mississippi                 26124\n",
              "Nebraska                    25863\n",
              "Arkansas                    23844\n",
              "Oklahoma                    23810\n",
              "Wisconsin                   22655\n",
              "Florida                     21878\n",
              "Puerto Rico                 21820\n",
              "Pennsylvania                21420\n",
              "Alabama                     21291\n",
              "Louisiana                   20808\n",
              "Colorado                    19895\n",
              "South Dakota                18994\n",
              "New York                    18838\n",
              "California                  18831\n",
              "West Virginia               16771\n",
              "North Dakota                15497\n",
              "South Carolina              14793\n",
              "Montana                     14728\n",
              "Washington                  12826\n",
              "Idaho                       12810\n",
              "Oregon                      11110\n",
              "New Mexico                  10049\n",
              "Utah                         9029\n",
              "Maryland                     8070\n",
              "Alaska                       7661\n",
              "New Jersey                   7231\n",
              "Wyoming                      7166\n",
              "Maine                        5360\n",
              "Massachusetts                4969\n",
              "Nevada                       4936\n",
              "Arizona                      4925\n",
              "Vermont                      4813\n",
              "New Hampshire                3519\n",
              "Connecticut                  2924\n",
              "Rhode Island                 1926\n",
              "Hawaii                       1403\n",
              "Delaware                     1279\n",
              "Virgin Islands               1046\n",
              "Northern Mariana Islands      558\n",
              "District of Columbia          335\n",
              "Guam                          327\n",
              "Name: state, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "us1-v_rNhhUh"
      },
      "source": [
        "## Cumulative Total\r\n",
        "1. total_us_cases = cumulative sum of cases\r\n",
        "2. total_us_deaths = cumulative sum of deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eri7iJ2Cf5bW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "4ad07ce4-eac7-402e-d658-cd13c8eed627"
      },
      "source": [
        "# Sum of cases on each invidual day\r\n",
        "total_us_cases = df_cld[['date', 'cases']].groupby(['date']).sum()\r\n",
        "total_us_cases.tail()"
      ],
      "execution_count": 123,
      "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>cases</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-31</th>\n",
              "      <td>26219020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-01</th>\n",
              "      <td>26358607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-02</th>\n",
              "      <td>26476526</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>26595540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>26722382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               cases\n",
              "date                \n",
              "2021-01-31  26219020\n",
              "2021-02-01  26358607\n",
              "2021-02-02  26476526\n",
              "2021-02-03  26595540\n",
              "2021-02-04  26722382"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "intUs7thnvOu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "3cf4db72-8b97-436e-c653-0156383d8df3"
      },
      "source": [
        "total_us_deaths = df_cld[['date', 'deaths']].groupby(['date']).sum()\r\n",
        "total_us_deaths.tail()"
      ],
      "execution_count": 124,
      "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>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-01-31</th>\n",
              "      <td>441285.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-01</th>\n",
              "      <td>443235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-02</th>\n",
              "      <td>446846.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-03</th>\n",
              "      <td>450689.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-04</th>\n",
              "      <td>455805.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              deaths\n",
              "date                \n",
              "2021-01-31  441285.0\n",
              "2021-02-01  443235.0\n",
              "2021-02-02  446846.0\n",
              "2021-02-03  450689.0\n",
              "2021-02-04  455805.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "flXzTm4HglxT"
      },
      "source": [
        "# set the index to date\r\n",
        "total_us_cases.reset_index(inplace=True)\r\n",
        "total_us_deaths.reset_index(inplace=True)"
      ],
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpdr9OhXhXDX"
      },
      "source": [
        "total_us_cases = total_us_cases.sort_values('date', ascending=True)\r\n",
        "total_us_deaths = total_us_deaths.sort_values('date', ascending=True)"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7pCPzgwtT0G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "31d4d779-71c1-4fbb-828d-7e53e78fa5ea"
      },
      "source": [
        "total_us_cases.value_counts()\r\n",
        "#total_us_deaths.value_counts()"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date        cases   \n",
              "2021-02-04  26722382    1\n",
              "2020-05-29  1755271     1\n",
              "2020-05-20  1559640     1\n",
              "2020-05-21  1585373     1\n",
              "2020-05-22  1609172     1\n",
              "                       ..\n",
              "2020-10-02  7362732     1\n",
              "2020-10-03  7410511     1\n",
              "2020-10-04  7445574     1\n",
              "2020-10-05  7507986     1\n",
              "2020-01-21  1           1\n",
              "Length: 381, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HULL7_Z9h_Wj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "e8c2931e-8f7c-48c1-f29e-cdab27dd8a86"
      },
      "source": [
        "\r\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\r\n",
        "# x_values = [datetime.datetime.strptime(d,\"%m/%d/%Y\").date() for d in total_cases['date']]\r\n",
        "# dates = matplotlib.dates.date2num(total_cases['date'])\r\n",
        "# matplotlib.pyplot.plot_date(dates, values)\r\n",
        "# Add x-axis and y-axis\r\n",
        "ax.plot(total_us_cases['date'],\r\n",
        "        total_us_cases['cases'],\r\n",
        "        color='purple')\r\n",
        "\r\n",
        "# Set title and labels for axes\r\n",
        "ax.set(xlabel=\"Date\",\r\n",
        "       ylabel=\"Cases\",\r\n",
        "       title=\"Daily Total Cases of Covid 19 in US\")\r\n",
        "\r\n",
        "plt.show()\r\n"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tE1c0qKZsIzL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "2fb1baaa-5455-4471-d3d5-7d9d450d5954"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\r\n",
        "# x_values = [datetime.datetime.strptime(d,\"%m/%d/%Y\").date() for d in total_cases['date']]\r\n",
        "# dates = matplotlib.dates.date2num(total_cases['date'])\r\n",
        "# matplotlib.pyplot.plot_date(dates, values)\r\n",
        "# Add x-axis and y-axis\r\n",
        "\r\n",
        "ax.plot(total_us_deaths['date'],\r\n",
        "        total_us_deaths['deaths'],\r\n",
        "        color='red')\r\n",
        "# Set title and labels for axes\r\n",
        "ax.set(xlabel=\"Date\",\r\n",
        "       ylabel=\"Deaths\",\r\n",
        "       title=\"Daily Total Deaths of Covid 19 in US\")\r\n",
        "\r\n",
        "plt.show()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RFmyd3dbrOGb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "ebbe2ea5-6158-45a6-e8a6-8082dcfc4bfe"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\r\n",
        "# x_values = [datetime.datetime.strptime(d,\"%m/%d/%Y\").date() for d in total_cases['date']]\r\n",
        "# dates = matplotlib.dates.date2num(total_cases['date'])\r\n",
        "# matplotlib.pyplot.plot_date(dates, values)\r\n",
        "# Add x-axis and y-axis\r\n",
        "\r\n",
        "ax.plot(total_us_deaths['date'],\r\n",
        "        total_us_deaths['deaths'],\r\n",
        "        color='red',\r\n",
        "        label = \"Death cases\")\r\n",
        "\r\n",
        "ax.plot(total_us_cases['date'],\r\n",
        "        total_us_cases['cases'],\r\n",
        "        color='purple',\r\n",
        "        label = \"Confirmed cases\")\r\n",
        "\r\n",
        "\r\n",
        "# Set title and labels for axes\r\n",
        "ax.set(xlabel=\"Date\",\r\n",
        "       ylabel=\"Activity\",\r\n",
        "       title=\"Daily Total Activity of Covid 19 in US\")\r\n",
        "\r\n",
        "plt.grid(True)\r\n",
        "plt.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "plneq3Fxym43"
      },
      "source": [
        "total_county_state = df_cld[['county','state','cases','deaths']].groupby(['county','state']).sum()"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaZ-fOqrzB0O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "4395eeb6-f00d-49f8-b2e8-b9f59c05d3a2"
      },
      "source": [
        "total_county_state.sort_values(by=['cases', 'deaths'], ascending=False, inplace=True)\r\n",
        "total_county_state.reset_index(inplace=True)\r\n",
        "total_county_state.head()"
      ],
      "execution_count": 132,
      "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>county</th>\n",
              "      <th>state</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>94861901</td>\n",
              "      <td>1821109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>84710461</td>\n",
              "      <td>6889874.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cook</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>54491615</td>\n",
              "      <td>1583260.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Miami-Dade</td>\n",
              "      <td>Florida</td>\n",
              "      <td>45062269</td>\n",
              "      <td>741800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Maricopa</td>\n",
              "      <td>Arizona</td>\n",
              "      <td>44756677</td>\n",
              "      <td>835389.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          county       state     cases     deaths\n",
              "0    Los Angeles  California  94861901  1821109.0\n",
              "1  New York City    New York  84710461  6889874.0\n",
              "2           Cook    Illinois  54491615  1583260.0\n",
              "3     Miami-Dade     Florida  45062269   741800.0\n",
              "4       Maricopa     Arizona  44756677   835389.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lnj8hOER0Usf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "outputId": "98827986-115d-4ced-86fb-51e1cf66341d"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 10))\r\n",
        "\r\n",
        "width= 0.35\r\n",
        "labels = total_county_state.state.head(10)\r\n",
        "x = np.arange(len(labels))\r\n",
        "ax.bar(x - width/2, total_county_state.cases.head(10), width, label='Confirmed Cases')\r\n",
        "ax.bar(x + width/2, total_county_state.deaths.head(10), width, label='Deaths')\r\n",
        "\r\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\r\n",
        "ax.set_ylabel('Numbers')\r\n",
        "ax.set_title('Top 10 Case & Deaths by State')\r\n",
        "ax.set_xticks(x)\r\n",
        "ax.set_xticklabels(labels)\r\n",
        "ax.legend()"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5eb8fc6b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqG0rpx0rBG6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "9ddf9c41-ffc1-462e-dd4d-0b93f3b5d132"
      },
      "source": [
        "total_us_cases.head()"
      ],
      "execution_count": 134,
      "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>date</th>\n",
              "      <th>cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-21</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        date  cases\n",
              "0 2020-01-21      1\n",
              "1 2020-01-22      1\n",
              "2 2020-01-23      1\n",
              "3 2020-01-24      2\n",
              "4 2020-01-25      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZSY48DnzFMwH"
      },
      "source": [
        "##Average COVID-19 Cases of Each State in Year 2020"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4GxnFlhxzbue",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "outputId": "6bcca40f-463d-408b-e9d7-126748749a77"
      },
      "source": [
        "#average cases of each state in Year 2020\r\n",
        "#Group by State\r\n",
        "\r\n",
        "matplotlib.style.use('seaborn-deep')\r\n",
        "\r\n",
        "year = (df_cld['date'] >= '2020-1-1') & (df_cld['date'] <= '2020-12-31')\r\n",
        "avg_state = df_cld[year].groupby('state').mean()\r\n",
        "\r\n",
        "avg_state.sort_values('state',ascending=True).plot(kind='bar', title=\"Average COVID-19 Cases of Each State in Year 2020\",ylabel=('Numbers'),figsize=(20,10),color=('turquoise','red'))\r\n",
        "plt.grid(True)\r\n",
        "plt.legend(['Confirmed Cases','Deaths'])\r\n",
        "plt.show()"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J5WYW7ztE1r-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "outputId": "c9e93d36-ef45-445e-95e7-335de8cb1852"
      },
      "source": [
        "#average cases of each state in Year 2020\r\n",
        "#Group by State\r\n",
        "matplotlib.style.use('seaborn-deep')\r\n",
        "\r\n",
        "year = (df_cld['date'] >= '2020-1-1') & (df_cld['date'] <= '2020-12-31')\r\n",
        "avg_state = df_cld[year].groupby('state').mean()\r\n",
        "\r\n",
        "avg_state.sort_values('state',ascending=True).plot(title=\"Average COVID-19 Cases Based on States in Year 2020\",ylabel=('Numbers'),figsize=(20,10),color=('deepskyblue','red'))\r\n",
        "plt.grid(True)\r\n",
        "plt.legend(['Confirmed Cases','Deaths'])\r\n",
        "plt.show()"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2vUq8Gq7zAU"
      },
      "source": [
        "## Correlation\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ddZTnrRR7ypH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "outputId": "d0271f76-6194-49b0-be40-47ad1528fd1e"
      },
      "source": [
        "df_cld.describe()"
      ],
      "execution_count": 137,
      "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>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9.984090e+05</td>\n",
              "      <td>976918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.567480e+03</td>\n",
              "      <td>61.853465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.369838e+04</td>\n",
              "      <td>476.314798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.700000e+01</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.110000e+02</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.332000e+03</td>\n",
              "      <td>28.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.134820e+06</td>\n",
              "      <td>27428.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              cases         deaths\n",
              "count  9.984090e+05  976918.000000\n",
              "mean   2.567480e+03      61.853465\n",
              "std    1.369838e+04     476.314798\n",
              "min    0.000000e+00       0.000000\n",
              "25%    4.700000e+01       1.000000\n",
              "50%    3.110000e+02       5.000000\n",
              "75%    1.332000e+03      28.000000\n",
              "max    1.134820e+06   27428.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lteC2XqL8lTs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "feca0323-25b3-4189-e343-5b78bde25d3f"
      },
      "source": [
        "sns.heatmap(df_cld.corr())"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5eb8d9be80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMoryTqiLOEQ"
      },
      "source": [
        "## **Prediction**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQUgSxVELUno"
      },
      "source": [
        "1.   Store the confirmed cases in US in a list\r\n",
        "2.   Count the days and store in list \r\n",
        "3.   Count the deaths and store in list \r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZEwdX5rL9R1"
      },
      "source": [
        "US_cases = np.array(total_us_cases.cases)\r\n",
        "US_deaths = np.array(total_us_deaths.deaths).reshape(-1,1)\r\n",
        "US_states = np.array(total_county_state.state).tolist()\r\n",
        "days_since_1_21 = np.array([i for i in range(len(total_us_cases.date))]).reshape(-1,1)\r\n",
        "dates = (df_cld.date.unique()).reshape(-1,1)"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TsuYUkSMwsl"
      },
      "source": [
        "days_in_future = 10\r\n",
        "future_forecast = np.array([i for i in range(len(days_since_1_21) + days_in_future)]).reshape(-1,1)"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zWrOPBCOOHl"
      },
      "source": [
        "start_date = datetime.datetime.strptime('1/21/2020', '%m/%d/%Y')\r\n",
        "future_forecast_dates = []\r\n",
        "for i in range(len(future_forecast)):\r\n",
        "  future_forecast_dates.append((start_date + datetime.timedelta(days=i)).strftime('%m/%d/%Y'))"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANCoRzwA8vqq"
      },
      "source": [
        "df_train = pd.DataFrame({\"Dates\":future_forecast_dates[:-10], \"Cases\": US_cases})\r\n",
        "df_train['Dates'] = pd.to_datetime(df_train['Dates'])\r\n",
        "df_train['Dates']=df_train['Dates'].map(datetime.datetime.toordinal)"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Zn3vMfm_A03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        },
        "outputId": "96a7310d-b7de-4276-da24-af99be68c42d"
      },
      "source": [
        "df_train.head()"
      ],
      "execution_count": 143,
      "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>Dates</th>\n",
              "      <th>Cases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>737445</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>737446</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>737447</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>737448</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>737449</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Dates  Cases\n",
              "0  737445      1\n",
              "1  737446      1\n",
              "2  737447      1\n",
              "3  737448      2\n",
              "4  737449      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xL50l3GyeVcS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "baf1eaf0-508a-4931-fdb4-c50606524d3e"
      },
      "source": [
        " from sklearn.utils import shuffle\r\n",
        " from sklearn.linear_model import LinearRegression\r\n",
        " from sklearn.model_selection import train_test_split\r\n",
        " X_train, X_test, y_train, y_test = train_test_split(df_train['Dates'].values.reshape(-1,1), df_train['Cases'].values.reshape(-1,1), test_size=0.30, random_state=0)\r\n",
        " model = LinearRegression() #create linear regression object\r\n",
        " model.fit(X_train, y_train) #train model on   train data\r\n",
        " model.score(X_train, y_train) #check score"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8253063158848017"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naS6uCBa-LEW"
      },
      "source": [
        "#### Predict the cases for the next ten days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a-1z-PlB-YT2"
      },
      "source": [
        "future_ten_days = [datetime.datetime.strptime(date, '%m/%d/%Y').date() for date in future_forecast_dates[-10:]]\r\n",
        "future_ten_days = np.array([datetime.datetime.toordinal(date) for date in future_ten_days]).reshape(-1,1)\r\n",
        "yhat_predict = model.predict(future_ten_days)\r\n",
        "# model.predict(np.array(future_ten_days).reshape(-1,1))"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jhU7GnL4FbsL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5be29812-3999-4d06-d7ad-584ca276f536"
      },
      "source": [
        "type(yhat_predict)\r\n",
        "yhat_predict.reshape(1,-1).tolist()[0]"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[18658804.020469666,\n",
              " 18721219.85472107,\n",
              " 18783635.688964844,\n",
              " 18846051.523216248,\n",
              " 18908467.35746765,\n",
              " 18970883.191719055,\n",
              " 19033299.02596283,\n",
              " 19095714.860214233,\n",
              " 19158130.694465637,\n",
              " 19220546.52870941]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1eXr-bZB-tb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "01c2b09b-352b-4bdc-ca9c-2ab817535795"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(20, 7))\r\n",
        "width= 0.35\r\n",
        "labels = future_forecast_dates[-10:]\r\n",
        "x = np.arange(len(labels))\r\n",
        "ax.bar(x - width/2, yhat_predict.reshape(1,-1).tolist()[0], width, label='Predicting Cases')\r\n",
        "\r\n",
        "for i, v in enumerate(yhat_predict):\r\n",
        "    plt.text(x[i]- 0.4, v + 300000, str(int(v)))\r\n",
        "# Add some text for labels, title and custom x-axis tick labels, etc.\r\n",
        "ax.set_ylabel('Numbers')\r\n",
        "ax.set_title('Corona Cases Prediction for The Next Ten Days')\r\n",
        "ax.set_xticks(x- width/2)\r\n",
        "ax.set_xticklabels(labels)\r\n",
        "ax.legend()\r\n",
        "plt.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KRBO_M8aNttU"
      },
      "source": [
        "import pickle\r\n",
        "\r\n",
        "with open('model.pkl', 'wb') as handle:\r\n",
        "    pickle.dump(model, handle, pickle.HIGHEST_PROTOCOL)"
      ],
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpuCpBB5voN5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d8dc226-c693-4f59-d106-4ff25a51bf34"
      },
      "source": [
        "with open('model.pkl', 'rb') as handle:\r\n",
        "    loadedModel = pickle.load(handle)  \r\n",
        "loadedModel.predict(future_ten_days)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18658804.02046967],\n",
              "       [18721219.85472107],\n",
              "       [18783635.68896484],\n",
              "       [18846051.52321625],\n",
              "       [18908467.35746765],\n",
              "       [18970883.19171906],\n",
              "       [19033299.02596283],\n",
              "       [19095714.86021423],\n",
              "       [19158130.69446564],\n",
              "       [19220546.52870941]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFzA4XyTvuZn"
      },
      "source": [
        ""
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCht2ynV-5Uu"
      },
      "source": [
        "# World Covid Case\r\n",
        "\r\n",
        "Format:\r\n",
        "\r\n",
        "iso_code /continent /location /date \r\n",
        "\r\n",
        "/total_cases /new_cases /new_cases_smoothed \r\n",
        "\r\n",
        "/total_deaths\t/new_deaths\t/new_deaths_smoothed\t\r\n",
        "\r\n",
        "/total_cases_per_million /new_cases_per_million \r\n",
        "/new_cases_smoothed_per_million\t\r\n",
        "\r\n",
        "/total_deaths_per_million\t/new_deaths_per_million\t/new_deaths_smoothed_per_million\r\n",
        "\r\n",
        "/reproduction_rate\t\r\n",
        "\r\n",
        "/icu_patients\t/icu_patients_per_million\t\r\n",
        "\r\n",
        "/hosp_patients\t/hosp_patients_per_million\r\n",
        "\r\n",
        "/weekly_icu_admissions\t/weekly_icu_admissions_per_million\t\r\n",
        "\r\n",
        "/weekly_hosp_admissions /weekly_hosp_admissions_per_million\t\r\n",
        "\r\n",
        "/new_tests\ttotal_tests\t/total_tests_per_thousand\t/new_tests_per_thousand\t/new_tests_smoothed\t/new_tests_smoothed_per_thousand\t/positive_rate\t/tests_per_case\t/tests_units\t/total_vaccinations\t/people_vaccinated\t/people_fully_vaccinated\t/new_vaccinations\t/new_vaccinations_smoothed\t/total_vaccinations_per_hundred\t/people_vaccinated_per_hundred\t/people_fully_vaccinated_per_hundred\t/new_vaccinations_smoothed_per_million\r\n",
        "\r\n",
        "/stringency_index\t/population\t/population_density\t/median_age\t/aged_65_older\t/aged_70_older\t/gdp_per_capita\t/extreme_poverty\t/cardiovasc_death_rate\t/diabetes_prevalence\t/female_smokers\t/male_smokers\t/handwashing_facilities\t/hospital_beds_per_thousand\t/life_expectancy\t/human_development_index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLQRfkgBCRys",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e6cebdb-bc21-4c77-c303-dd6e1d7c95b3"
      },
      "source": [
        "df_wcc.shape"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(35898, 59)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bt2tLDLU_CA3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "7573d45e-7180-43ba-b44b-bec119cc98e3"
      },
      "source": [
        "df_wcc.head(10)"
      ],
      "execution_count": 151,
      "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>iso_code</th>\n",
              "      <th>continent</th>\n",
              "      <th>location</th>\n",
              "      <th>date</th>\n",
              "      <th>total_cases</th>\n",
              "      <th>new_cases</th>\n",
              "      <th>new_cases_smoothed</th>\n",
              "      <th>total_deaths</th>\n",
              "      <th>new_deaths</th>\n",
              "      <th>new_deaths_smoothed</th>\n",
              "      <th>total_cases_per_million</th>\n",
              "      <th>new_cases_per_million</th>\n",
              "      <th>new_cases_smoothed_per_million</th>\n",
              "      <th>total_deaths_per_million</th>\n",
              "      <th>new_deaths_per_million</th>\n",
              "      <th>new_deaths_smoothed_per_million</th>\n",
              "      <th>reproduction_rate</th>\n",
              "      <th>icu_patients</th>\n",
              "      <th>icu_patients_per_million</th>\n",
              "      <th>hosp_patients</th>\n",
              "      <th>hosp_patients_per_million</th>\n",
              "      <th>weekly_icu_admissions</th>\n",
              "      <th>weekly_icu_admissions_per_million</th>\n",
              "      <th>weekly_hosp_admissions</th>\n",
              "      <th>weekly_hosp_admissions_per_million</th>\n",
              "      <th>new_tests</th>\n",
              "      <th>total_tests</th>\n",
              "      <th>total_tests_per_thousand</th>\n",
              "      <th>new_tests_per_thousand</th>\n",
              "      <th>new_tests_smoothed</th>\n",
              "      <th>new_tests_smoothed_per_thousand</th>\n",
              "      <th>positive_rate</th>\n",
              "      <th>tests_per_case</th>\n",
              "      <th>tests_units</th>\n",
              "      <th>total_vaccinations</th>\n",
              "      <th>people_vaccinated</th>\n",
              "      <th>people_fully_vaccinated</th>\n",
              "      <th>new_vaccinations</th>\n",
              "      <th>new_vaccinations_smoothed</th>\n",
              "      <th>total_vaccinations_per_hundred</th>\n",
              "      <th>people_vaccinated_per_hundred</th>\n",
              "      <th>people_fully_vaccinated_per_hundred</th>\n",
              "      <th>new_vaccinations_smoothed_per_million</th>\n",
              "      <th>stringency_index</th>\n",
              "      <th>population</th>\n",
              "      <th>population_density</th>\n",
              "      <th>median_age</th>\n",
              "      <th>aged_65_older</th>\n",
              "      <th>aged_70_older</th>\n",
              "      <th>gdp_per_capita</th>\n",
              "      <th>extreme_poverty</th>\n",
              "      <th>cardiovasc_death_rate</th>\n",
              "      <th>diabetes_prevalence</th>\n",
              "      <th>female_smokers</th>\n",
              "      <th>male_smokers</th>\n",
              "      <th>handwashing_facilities</th>\n",
              "      <th>hospital_beds_per_thousand</th>\n",
              "      <th>life_expectancy</th>\n",
              "      <th>human_development_index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-24</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.026</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.33</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.78</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.78</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.143</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.026</td>\n",
              "      <td>0.004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.78</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>AFG</td>\n",
              "      <td>Asia</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.429</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.103</td>\n",
              "      <td>0.051</td>\n",
              "      <td>0.011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.78</td>\n",
              "      <td>38928341.0</td>\n",
              "      <td>54.422</td>\n",
              "      <td>18.6</td>\n",
              "      <td>2.581</td>\n",
              "      <td>1.337</td>\n",
              "      <td>1803.987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>597.029</td>\n",
              "      <td>9.59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>37.746</td>\n",
              "      <td>0.5</td>\n",
              "      <td>64.83</td>\n",
              "      <td>0.498</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  iso_code continent  ... life_expectancy human_development_index\n",
              "0      AFG      Asia  ...           64.83                   0.498\n",
              "1      AFG      Asia  ...           64.83                   0.498\n",
              "2      AFG      Asia  ...           64.83                   0.498\n",
              "3      AFG      Asia  ...           64.83                   0.498\n",
              "4      AFG      Asia  ...           64.83                   0.498\n",
              "5      AFG      Asia  ...           64.83                   0.498\n",
              "6      AFG      Asia  ...           64.83                   0.498\n",
              "7      AFG      Asia  ...           64.83                   0.498\n",
              "8      AFG      Asia  ...           64.83                   0.498\n",
              "9      AFG      Asia  ...           64.83                   0.498\n",
              "\n",
              "[10 rows x 59 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1QUCa0joTDj"
      },
      "source": [
        "##Data Cleaning\r\n",
        "1. check for null\r\n",
        "2. check for duplicates\r\n",
        "3. check for outliers\r\n",
        "4. check for structural errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umsh2gscBdkB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d87526e-d5ac-4e99-9d05-1884dd0fbc0f"
      },
      "source": [
        "# to check whether ALL values are null in series of column\r\n",
        "df_wcc.isnull().all()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iso_code                                 False\n",
              "continent                                False\n",
              "location                                 False\n",
              "date                                     False\n",
              "total_cases                              False\n",
              "new_cases                                False\n",
              "new_cases_smoothed                       False\n",
              "total_deaths                             False\n",
              "new_deaths                               False\n",
              "new_deaths_smoothed                      False\n",
              "total_cases_per_million                  False\n",
              "new_cases_per_million                    False\n",
              "new_cases_smoothed_per_million           False\n",
              "total_deaths_per_million                 False\n",
              "new_deaths_per_million                   False\n",
              "new_deaths_smoothed_per_million          False\n",
              "reproduction_rate                        False\n",
              "icu_patients                             False\n",
              "icu_patients_per_million                 False\n",
              "hosp_patients                            False\n",
              "hosp_patients_per_million                False\n",
              "weekly_icu_admissions                    False\n",
              "weekly_icu_admissions_per_million        False\n",
              "weekly_hosp_admissions                   False\n",
              "weekly_hosp_admissions_per_million       False\n",
              "new_tests                                False\n",
              "total_tests                              False\n",
              "total_tests_per_thousand                 False\n",
              "new_tests_per_thousand                   False\n",
              "new_tests_smoothed                       False\n",
              "new_tests_smoothed_per_thousand          False\n",
              "positive_rate                            False\n",
              "tests_per_case                           False\n",
              "tests_units                              False\n",
              "total_vaccinations                       False\n",
              "people_vaccinated                        False\n",
              "people_fully_vaccinated                  False\n",
              "new_vaccinations                         False\n",
              "new_vaccinations_smoothed                False\n",
              "total_vaccinations_per_hundred           False\n",
              "people_vaccinated_per_hundred            False\n",
              "people_fully_vaccinated_per_hundred      False\n",
              "new_vaccinations_smoothed_per_million    False\n",
              "stringency_index                         False\n",
              "population                               False\n",
              "population_density                       False\n",
              "median_age                               False\n",
              "aged_65_older                            False\n",
              "aged_70_older                            False\n",
              "gdp_per_capita                           False\n",
              "extreme_poverty                          False\n",
              "cardiovasc_death_rate                    False\n",
              "diabetes_prevalence                      False\n",
              "female_smokers                           False\n",
              "male_smokers                             False\n",
              "handwashing_facilities                   False\n",
              "hospital_beds_per_thousand               False\n",
              "life_expectancy                          False\n",
              "human_development_index                  False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nc9JztbWBdc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b47f1d4-8a44-45d8-f5f0-ebb1855b0002"
      },
      "source": [
        "df_wcc.isnull().sum()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iso_code                                  1860\n",
              "continent                                 1860\n",
              "location                                     0\n",
              "date                                         0\n",
              "total_cases                                353\n",
              "new_cases                                  352\n",
              "new_cases_smoothed                         881\n",
              "total_deaths                              4269\n",
              "new_deaths                                4200\n",
              "new_deaths_smoothed                        881\n",
              "total_cases_per_million                   2210\n",
              "new_cases_per_million                     2212\n",
              "new_cases_smoothed_per_million            2712\n",
              "total_deaths_per_million                  6046\n",
              "new_deaths_per_million                    6047\n",
              "new_deaths_smoothed_per_million           2712\n",
              "reproduction_rate                         8275\n",
              "icu_patients                             31116\n",
              "icu_patients_per_million                 31116\n",
              "hosp_patients                            30236\n",
              "hosp_patients_per_million                30236\n",
              "weekly_icu_admissions                    35559\n",
              "weekly_icu_admissions_per_million        35559\n",
              "weekly_hosp_admissions                   35266\n",
              "weekly_hosp_admissions_per_million       35266\n",
              "new_tests                                20748\n",
              "total_tests                              21276\n",
              "total_tests_per_thousand                 21276\n",
              "new_tests_per_thousand                   20748\n",
              "new_tests_smoothed                       19125\n",
              "new_tests_smoothed_per_thousand          19125\n",
              "positive_rate                            20286\n",
              "tests_per_case                           20493\n",
              "tests_units                              18348\n",
              "total_vaccinations                       35062\n",
              "people_vaccinated                        35235\n",
              "people_fully_vaccinated                  35561\n",
              "new_vaccinations                         35190\n",
              "new_vaccinations_smoothed                34750\n",
              "total_vaccinations_per_hundred           35062\n",
              "people_vaccinated_per_hundred            35235\n",
              "people_fully_vaccinated_per_hundred      35561\n",
              "new_vaccinations_smoothed_per_million    34750\n",
              "stringency_index                          4617\n",
              "population                                1860\n",
              "population_density                        1860\n",
              "median_age                                3186\n",
              "aged_65_older                             3186\n",
              "aged_70_older                             3186\n",
              "gdp_per_capita                            2868\n",
              "extreme_poverty                          13122\n",
              "cardiovasc_death_rate                     2616\n",
              "diabetes_prevalence                       2188\n",
              "female_smokers                           10208\n",
              "male_smokers                             10539\n",
              "handwashing_facilities                   19338\n",
              "hospital_beds_per_thousand                4837\n",
              "life_expectancy                           2189\n",
              "human_development_index                   2189\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMSlalKvBdBI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ba34e30-dc2d-4b0b-9378-b44e4411001e"
      },
      "source": [
        "#check whether there are any duplicate rows\r\n",
        "df_wcc.duplicated().value_counts()"
      ],
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    35898\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMNO3lJjpqUu"
      },
      "source": [
        ""
      ],
      "execution_count": 154,
      "outputs": []
    }
  ]
}