Assignment_01_Data_Visualization_COVID19.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/usmankhawar22/Covid-19-Analysis/blob/master/Assignment_01_Data_Visualization_COVID19.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Assignment # 01: Data Preprocessing and Visualization\n",
        "\n",
        "#**MUHAMMAD USMAN KHAWAR\n",
        "#208775\n",
        "#BESE-8B **"
      ]
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      "source": [
        "# CS471: Machine Learning\n",
        "### BESE-8 AB\n",
        "### Instructors: Prof. Dr. Faisal Shafait, Dr. Muhammad Muneeb Ullah\n",
        "#### Due Date: March 18, 2020"
      ]
    },
    {
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      "metadata": {
        "id": "vxrmXF2fRx-D",
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      "source": [
        "### Introduction\n",
        "\n",
        "The Novel Coronavirus (COVID19) which causes respiratory illnesss was first detected in Wuhan, China. Since the disease is contagious, it has spread all over the world due to the high number of tourism and business visits to / from China. As you all are very well aware, COVID19 has been labelled as a global pandemic now by World Health Organization. Countries such as Italy, China, Qatar, USA, Saudi Arabia, etc, have cancelled most of the flights and a lockdown notice has been issued in Italy to avoid further wide-spread of the virus. \n",
        "Scientists all over the world are trying to drive insights from the datasets to come up with a possible prediction which will help with early diagnosis of COVID-19, estimating the spread, and the virus frequency and duration. \n",
        "Johns Hopkins University has made an excellent dashboard using the affected cases data. The dataset is made available at Kaggle and can be downloaded for the purpose of research.\n",
        "For this assignment, we will use the Kaggle COVID-19 dataset (Download here: <a href=\"https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset\" title=\"DATASET\">DATASET</a>) and will try to learn some insights using the power of Machine Learning.\n",
        "The Johns Hopkins University's web based dashboard can be accessed here:\n",
        "<a href=\"https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6\" title=\"Web Dashboard\">Web Dashboard</a>"
      ]
    },
    {
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        "id": "BlU8SEvkRx-J",
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      "source": [
        "#### Question # 01\n",
        "\n",
        "As you must have noticed, there are 6 files in the data you have downloaded from Kaggle.\n",
        "For this question, we will use the main file `covid_19_data.csv`.\n",
        "The steps to follow are given:\n",
        "1. Read the data file and examine the features\n",
        "2. Since the dataset is a bit sparse, clean the dataset by dropping rows which have missing points.\n",
        "3. Print the initial 10 rows of the data."
      ]
    },
    {
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      "metadata": {
        "id": "rpOEGf7uRx-O",
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      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Yl-T-vtq_X3",
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        "outputId": "21cc58e1-225b-444b-bde6-3229425bfc01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df = pd.read_csv(\"covid_19_data.csv\")\n",
        "df\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNo</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>01/22/2020</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/22/2020 17:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6717</th>\n",
              "      <td>6718</td>\n",
              "      <td>03/18/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Guernsey</td>\n",
              "      <td>2020-03-17T18:33:03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6718</th>\n",
              "      <td>6719</td>\n",
              "      <td>03/18/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jersey</td>\n",
              "      <td>2020-03-17T18:33:03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6719</th>\n",
              "      <td>6720</td>\n",
              "      <td>03/18/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>2020-03-17T16:13:14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6720</th>\n",
              "      <td>6721</td>\n",
              "      <td>03/18/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Republic of the Congo</td>\n",
              "      <td>2020-03-17T21:33:03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6721</th>\n",
              "      <td>6722</td>\n",
              "      <td>03/18/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Gambia</td>\n",
              "      <td>2020-03-18T14:13:56</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6722 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       SNo ObservationDate Province/State  ... Confirmed Deaths  Recovered\n",
              "0        1      01/22/2020          Anhui  ...       1.0    0.0        0.0\n",
              "1        2      01/22/2020        Beijing  ...      14.0    0.0        0.0\n",
              "2        3      01/22/2020      Chongqing  ...       6.0    0.0        0.0\n",
              "3        4      01/22/2020         Fujian  ...       1.0    0.0        0.0\n",
              "4        5      01/22/2020          Gansu  ...       0.0    0.0        0.0\n",
              "...    ...             ...            ...  ...       ...    ...        ...\n",
              "6717  6718      03/18/2020            NaN  ...       0.0    0.0        0.0\n",
              "6718  6719      03/18/2020            NaN  ...       0.0    0.0        0.0\n",
              "6719  6720      03/18/2020            NaN  ...       0.0    0.0        0.0\n",
              "6720  6721      03/18/2020            NaN  ...       0.0    0.0        0.0\n",
              "6721  6722      03/18/2020            NaN  ...       0.0    0.0        0.0\n",
              "\n",
              "[6722 rows x 8 columns]"
            ]
          },
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          "execution_count": 5
        }
      ]
    },
    {
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        "outputId": "1b5345e5-15f3-43f2-c4f7-520c72602b44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "missing_data = df.isnull()\n",
        "missing_data.head(5)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>SNo</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     SNo  ObservationDate  Province/State  ...  Confirmed  Deaths  Recovered\n",
              "0  False            False           False  ...      False   False      False\n",
              "1  False            False           False  ...      False   False      False\n",
              "2  False            False           False  ...      False   False      False\n",
              "3  False            False           False  ...      False   False      False\n",
              "4  False            False           False  ...      False   False      False\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzimBKXqsnKG",
        "colab_type": "code",
        "outputId": "9cd5029d-9b0a-415c-d0b4-58302b8de171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        }
      },
      "source": [
        "#data observation \n",
        "for column in missing_data.columns.values.tolist():\n",
        "    print(column)\n",
        "    print (missing_data[column].value_counts())\n",
        "    print(\"\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "SNo\n",
            "False    6722\n",
            "Name: SNo, dtype: int64\n",
            "\n",
            "ObservationDate\n",
            "False    6722\n",
            "Name: ObservationDate, dtype: int64\n",
            "\n",
            "Province/State\n",
            "False    3956\n",
            "True     2766\n",
            "Name: Province/State, dtype: int64\n",
            "\n",
            "Country/Region\n",
            "False    6722\n",
            "Name: Country/Region, dtype: int64\n",
            "\n",
            "Last Update\n",
            "False    6722\n",
            "Name: Last Update, dtype: int64\n",
            "\n",
            "Confirmed\n",
            "False    6722\n",
            "Name: Confirmed, dtype: int64\n",
            "\n",
            "Deaths\n",
            "False    6722\n",
            "Name: Deaths, dtype: int64\n",
            "\n",
            "Recovered\n",
            "False    6722\n",
            "Name: Recovered, dtype: int64\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYD3V3IPtkqP",
        "colab_type": "code",
        "outputId": "1af3eb24-2832-45aa-ae53-50edaa8272ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "#dropping sparse data\n",
        " df2=df          #we cant drop rows for iran in later question#3\n",
        " df1=df.dropna()   #for question 2\n",
        " df2['Country/Region'].unique()\n",
        " "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mainland China', 'Hong Kong', 'Macau', 'Taiwan', 'US', 'Japan',\n",
              "       'Thailand', 'South Korea', 'Singapore', 'Philippines', 'Malaysia',\n",
              "       'Vietnam', 'Australia', 'Mexico', 'Brazil', 'Colombia', 'France',\n",
              "       'Nepal', 'Canada', 'Cambodia', 'Sri Lanka', 'Ivory Coast',\n",
              "       'Germany', 'Finland', 'United Arab Emirates', 'India', 'Italy',\n",
              "       'UK', 'Russia', 'Sweden', 'Spain', 'Belgium', 'Others', 'Egypt',\n",
              "       'Iran', 'Israel', 'Lebanon', 'Iraq', 'Oman', 'Afghanistan',\n",
              "       'Bahrain', 'Kuwait', 'Austria', 'Algeria', 'Croatia',\n",
              "       'Switzerland', 'Pakistan', 'Georgia', 'Greece', 'North Macedonia',\n",
              "       'Norway', 'Romania', 'Denmark', 'Estonia', 'Netherlands',\n",
              "       'San Marino', ' Azerbaijan', 'Belarus', 'Iceland', 'Lithuania',\n",
              "       'New Zealand', 'Nigeria', 'North Ireland', 'Ireland', 'Luxembourg',\n",
              "       'Monaco', 'Qatar', 'Ecuador', 'Azerbaijan', 'Czech Republic',\n",
              "       'Armenia', 'Dominican Republic', 'Indonesia', 'Portugal',\n",
              "       'Andorra', 'Latvia', 'Morocco', 'Saudi Arabia', 'Senegal',\n",
              "       'Argentina', 'Chile', 'Jordan', 'Ukraine', 'Saint Barthelemy',\n",
              "       'Hungary', 'Faroe Islands', 'Gibraltar', 'Liechtenstein', 'Poland',\n",
              "       'Tunisia', 'Palestine', 'Bosnia and Herzegovina', 'Slovenia',\n",
              "       'South Africa', 'Bhutan', 'Cameroon', 'Costa Rica', 'Peru',\n",
              "       'Serbia', 'Slovakia', 'Togo', 'Vatican City', 'French Guiana',\n",
              "       'Malta', 'Martinique', 'Republic of Ireland', 'Bulgaria',\n",
              "       'Maldives', 'Bangladesh', 'Moldova', 'Paraguay', 'Albania',\n",
              "       'Cyprus', 'St. Martin', 'Brunei', 'occupied Palestinian territory',\n",
              "       \"('St. Martin',)\", 'Burkina Faso', 'Channel Islands', 'Holy See',\n",
              "       'Mongolia', 'Panama', 'Bolivia', 'Honduras', 'Congo (Kinshasa)',\n",
              "       'Jamaica', 'Reunion', 'Turkey', 'Cuba', 'Guyana', 'Kazakhstan',\n",
              "       'Cayman Islands', 'Guadeloupe', 'Ethiopia', 'Sudan', 'Guinea',\n",
              "       'Antigua and Barbuda', 'Aruba', 'Kenya', 'Uruguay', 'Ghana',\n",
              "       'Jersey', 'Namibia', 'Seychelles', 'Trinidad and Tobago',\n",
              "       'Venezuela', 'Curacao', 'Eswatini', 'Gabon', 'Guatemala',\n",
              "       'Guernsey', 'Mauritania', 'Rwanda', 'Saint Lucia',\n",
              "       'Saint Vincent and the Grenadines', 'Suriname', 'Kosovo',\n",
              "       'Central African Republic', 'Congo (Brazzaville)',\n",
              "       'Equatorial Guinea', 'Uzbekistan', 'Guam', 'Puerto Rico', 'Benin',\n",
              "       'Greenland', 'Liberia', 'Mayotte', 'Republic of the Congo',\n",
              "       'Somalia', 'Tanzania', 'The Bahamas', 'Barbados', 'Montenegro',\n",
              "       'The Gambia', 'Kyrgyzstan', 'Mauritius', 'Zambia', 'Djibouti',\n",
              "       'Gambia, The'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wExoKLDQRx-e",
        "colab_type": "text"
      },
      "source": [
        "#### Question # 02\n",
        "\n",
        "1. Rank the countries in the dataset in descending order according to the number of deaths.\n",
        "2. Figure out the most affected provinces/states in top 3 countries with most number of Deaths.\n",
        "3. Repeat the first two steps for Recovered and Confirmed cases as well.\n",
        "4. Plot a bar graph for top 10 states in China and US in accordance to the death cases, and recovered cases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rf0rnv0y0kHU",
        "colab_type": "code",
        "outputId": "6fb277b6-fc54-4457-9979-af4a6f4ea251",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "df1.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SNo                  int64\n",
              "ObservationDate     object\n",
              "Province/State      object\n",
              "Country/Region      object\n",
              "Last Update         object\n",
              "Confirmed          float64\n",
              "Deaths             float64\n",
              "Recovered          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3ib2ZsQ1ZrK",
        "colab_type": "code",
        "outputId": "cc307340-0e03-4bb4-90a6-406551c8976a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#getting all the countries\n",
        "couuntries=df1['Country/Region'].unique()\n",
        "couuntries"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Mainland China', 'Hong Kong', 'Macau', 'Taiwan', 'US', 'Canada',\n",
              "       'Australia', 'Germany', 'Others', 'Israel', 'Lebanon', 'Iraq',\n",
              "       'Austria', 'France', 'UK', 'Denmark', 'Netherlands'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReefCnjQ3x88",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "here I am group sorting by the highest number of deaths by countries and their provinces and latest value of dates\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWWxwXNc7Xhr",
        "colab_type": "code",
        "outputId": "6a38a436-e487-4e5c-cd09-e36c0f371601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "print(\"Countries ranked upon death rate\")\n",
        "x=df1.groupby(['Country/Region'])\n",
        "for c in couuntries:\n",
        "  \n",
        "  entity= x.get_group(c)\n",
        "  sum=entity['Deaths'].sum()\n",
        "  print(c, sum)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Countries ranked upon death rate\n",
            "Mainland China 103022.0\n",
            "Hong Kong 90.0\n",
            "Macau 0.0\n",
            "Taiwan 24.0\n",
            "US 691.0\n",
            "Canada 24.0\n",
            "Australia 51.0\n",
            "Germany 0.0\n",
            "Others 151.0\n",
            "Israel 0.0\n",
            "Lebanon 0.0\n",
            "Iraq 0.0\n",
            "Austria 0.0\n",
            "France 801.0\n",
            "UK 250.0\n",
            "Denmark 14.0\n",
            "Netherlands 125.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ponIYb5u8sTE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Prov(country, df,parameter):\n",
        "  df=df.groupby(['Country/Region']).get_group(country).groupby(['Province/State'])\n",
        "  #china=china.sort_values('Deaths', ascending=False)\n",
        "  #china=china.groupby(['Province/State'])\n",
        "  values=df.groups.keys()\n",
        "  prov=dict()\n",
        "  for x in values:\n",
        "    latest = df.get_group(x)['ObservationDate'].max()\n",
        "  \n",
        "    sum=df.get_group(x).groupby('ObservationDate').get_group(latest)[parameter].sum()\n",
        "    prov[x]=sum\n",
        "    #print(dea)\n",
        "  prov= (sorted(prov.items(), key=lambda x: x[1], reverse=True)[:10])\n",
        "  return(prov)\n",
        "\n",
        "\n",
        "def plot(coun):\n",
        "  coun=dict(coun)\n",
        "  import matplotlib.pyplot as plt;\n",
        "  plt.xlabel('Provinces')\n",
        "  plt.ylabel('Total')\n",
        "  plt.title('top 10 provinces')\n",
        "  plt.bar(range(len(coun)),list(coun.values()), align='center')\n",
        "  plt.xticks(range(len(coun)),list(coun.keys()))\n",
        "  plt.xticks(rotation=90)\n",
        "  plt.show()\n",
        "\n",
        "#for question 4\n",
        "# def plotline(coun):\n",
        "#   coun=dict(coun)\n",
        "#   import matplotlib.pyplot as plt;\n",
        "\n",
        "#   plt.plot(range(len(coun)),list(coun.values()), align='center')\n",
        "#   plt.xticks(range(len(coun)),list(coun.keys()))\n",
        "#   plt.xticks(rotation=90)\n",
        "#   plt.show()\n",
        "  \n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ri2ADi44E3Nq",
        "colab_type": "text"
      },
      "source": [
        "#creating functions of above for the repitition steps\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFPGnsEmGSMp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "def rank(dataframe,couuntries,parameter):\n",
        "  fram=dataframe.sort_values(parameter, ascending=False).drop_duplicates(['Country/Region','Province/State'])\n",
        "  x=fram.groupby(['Country/Region'])\n",
        "  records = dict()\n",
        "  print('Countries Ranked upon', parameter)\n",
        "  for c in couuntries:\n",
        "    entity= x.get_group(c)\n",
        "    sum=entity[parameter].sum()\n",
        "    records[c] = sum\n",
        "  sortedranks = sorted(records.items(), key=lambda x: x[1], reverse=True)\n",
        "  return sortedranks"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WszLuTJXI084",
        "colab_type": "code",
        "outputId": "ba8adf97-e448-43af-d3e6-4ca740b2c202",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "deathsC=rank(df1,couuntries,'Deaths')\n",
        "deathsC\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Countries Ranked upon Deaths\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mainland China', 3237.0),\n",
              " ('France', 148.0),\n",
              " ('US', 141.0),\n",
              " ('UK', 80.0),\n",
              " ('Netherlands', 58.0),\n",
              " ('Canada', 8.0),\n",
              " ('Others', 7.0),\n",
              " ('Australia', 6.0),\n",
              " ('Hong Kong', 4.0),\n",
              " ('Denmark', 4.0),\n",
              " ('Taiwan', 1.0),\n",
              " ('Macau', 0.0),\n",
              " ('Germany', 0.0),\n",
              " ('Israel', 0.0),\n",
              " ('Lebanon', 0.0),\n",
              " ('Iraq', 0.0),\n",
              " ('Austria', 0.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGinWnsHL5Ux",
        "colab_type": "code",
        "outputId": "2916afe0-478b-41fc-e4a6-7efc4644e960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "ConfirmedC=rank(df1,couuntries,'Confirmed')\n",
        "ConfirmedC"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Countries Ranked upon Confirmed\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mainland China', 80907.0),\n",
              " ('France', 9080.0),\n",
              " ('US', 8546.0),\n",
              " ('UK', 3098.0),\n",
              " ('Netherlands', 2056.0),\n",
              " ('Denmark', 1115.0),\n",
              " ('Others', 773.0),\n",
              " ('Canada', 694.0),\n",
              " ('Australia', 576.0),\n",
              " ('Hong Kong', 181.0),\n",
              " ('Taiwan', 47.0),\n",
              " ('Macau', 15.0),\n",
              " ('Germany', 5.0),\n",
              " ('Austria', 2.0),\n",
              " ('Israel', 1.0),\n",
              " ('Lebanon', 1.0),\n",
              " ('Iraq', 0.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGiY0QGjMTL-",
        "colab_type": "code",
        "outputId": "f6835533-1463-4b1e-cdfa-f71695f283c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "RecoveredC=rank(df1,couuntries,'Recovered')\n",
        "RecoveredC"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Countries Ranked upon Recovered\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('Mainland China', 69653.0),\n",
              " ('Others', 325.0),\n",
              " ('US', 137.0),\n",
              " ('Hong Kong', 92.0),\n",
              " ('UK', 85.0),\n",
              " ('Australia', 23.0),\n",
              " ('Taiwan', 17.0),\n",
              " ('Canada', 13.0),\n",
              " ('France', 12.0),\n",
              " ('Macau', 10.0),\n",
              " ('Netherlands', 2.0),\n",
              " ('Denmark', 1.0),\n",
              " ('Germany', 0.0),\n",
              " ('Israel', 0.0),\n",
              " ('Lebanon', 0.0),\n",
              " ('Iraq', 0.0),\n",
              " ('Austria', 0.0)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGEAu5sO4TVN",
        "colab_type": "text"
      },
      "source": [
        "# function for top province\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bLPgR6x0Ruba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def topProvince(dataframe,countries,parameters):\n",
        "  df2=df.sort_values(parameters, ascending=False).drop_duplicates(['Country/Region','Province/State'])\n",
        "  max=df2.groupby(['Country/Region'])\n",
        "  for c in countries:\n",
        "    top=max.get_group(c).iloc[0]\n",
        "    print(top['Country/Region'],top['Province/State'], top[parameters])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwXoowGBE2Hw",
        "colab_type": "text"
      },
      "source": [
        "#the most affected provinces/states in top 3 countries with most number of Deaths."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV-roneYc_Dy",
        "colab_type": "code",
        "outputId": "755a1d64-9749-4c99-be39-70566308065f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "topCountries = [c[0] for c in deathsC[:3]]\n",
        "#rankProvinces(topCountries, data, 'Deaths')\n",
        "#topCountries\n",
        "\n",
        "topProvince(df,topCountries,'Deaths')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mainland China Hubei 3122.0\n",
            "France France 148.0\n",
            "US Washington 55.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scGpq2hlj3HY",
        "colab_type": "text"
      },
      "source": [
        "#the most affected provinces/states in top 3 countries with most number of Recovery."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjhMM6MljeTX",
        "colab_type": "code",
        "outputId": "757810b5-29dd-4dc8-cf0b-9553889ba0a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "topCountries = [c[0] for c in RecoveredC[:3]]\n",
        "#rankProvinces(topCountries, data, 'Deaths')\n",
        "#topCountries\n",
        "\n",
        "topProvince(df,topCountries,'Recovered')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mainland China Hubei 56927.0\n",
            "Others Diamond Princess cruise ship 325.0\n",
            "US US 106.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-aO-3o7xkVdN",
        "colab_type": "text"
      },
      "source": [
        "#the most affected provinces/states in top 3 countries with most number of Recovery."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ocbTq-L4kU94",
        "colab_type": "code",
        "outputId": "a5a9f83e-d53f-45fc-e7f6-ccde7689327e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "topCountries = [c[0] for c in ConfirmedC[:3]]\n",
        "#rankProvinces(topCountries, data, 'Deaths')\n",
        "#topCountries\n",
        "\n",
        "topProvince(df,topCountries,'Confirmed')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mainland China Hubei 67800.0\n",
            "France France 9043.0\n",
            "US New York 2495.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTXsbBMT8iHy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df.get_group().groupby('ObservationDate')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtHKBG2A47E6",
        "colab_type": "text"
      },
      "source": [
        "#bar graph for top 10 states in China and US in accordance to the death cases, and recovered cases."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggnIHR_K5ByE",
        "colab_type": "text"
      },
      "source": [
        "#For Death"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2Itdr8BGD2o",
        "colab_type": "code",
        "outputId": "adf0b176-76c9-47d8-c4d6-6de4d8206ae2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 733
        }
      },
      "source": [
        "chin=Prov('Mainland China',df1,'Deaths')\n",
        "print('for China')\n",
        "plot(chin)\n",
        " \n",
        "\n",
        "US=Prov('US',df1,'Deaths')\n",
        "print('For US')\n",
        "plot(US)\n",
        "\n",
        "# #chin=dict(chin)\n",
        "\n",
        "# import matplotlib.pyplot as plt;\n",
        "# chin=dict(chin)\n",
        "# plt.plot(list(chin.keys()), list(chin.values()), marker='', color='red', linewidth=2,label=\"death cases\")\n",
        "# plt.xticks(rotation=90)\n",
        "\n",
        "\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "for China\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZaDxZUA2k9zRgTlk+gOq64A0txAbYBLif5TfGQXvNYJ8H9qV8dttXSHrmyG9p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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "For US\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RPZO1emKxyzpBERNbavwRET2TxB8R0TNJ/BERPZPEHxHRM0n8ERE9k8QfEdEzSfwRET3T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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-IicW0g5FvN",
        "colab_type": "text"
      },
      "source": [
        "#For Recovery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zMxUKyvLcrTV",
        "colab_type": "code",
        "outputId": "22a91132-d404-46e2-8f3d-530ead0ebf19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 762
        }
      },
      "source": [
        "china=Prov('Mainland China',df1,'Recovered')\n",
        "print('for China')\n",
        "plot(china)\n",
        " \n",
        "\n",
        "USR=Prov('US',df1,'Recovered')\n",
        "print('For US')\n",
        "plot(USR)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "for China\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZNbr898CrxjjWh4APDSk/m2oqloiI6MF43VmXlZ8PSdp2UCjpqcBDrdYqIiLWCON1Z6n8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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "For US\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pl7EHWbqPBtc+lH7dDwKfo3QhjbsRzTx0FmXnM1HGM9YE/kDZgKZpYtZuk8eZ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\nkhQiIqKWpBAREbUkhYiIqCUpRERE7f8DLYDN5bPWT/UAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rb8OL5ddRx-y",
        "colab_type": "text"
      },
      "source": [
        "#### Question # 03\n",
        "\n",
        "Since we have dates in the data, let's quantify out how the virus has spread over time and which areas are most affected by the virus. This will help us figure out if the situation is getting any better over time or not.\n",
        "\n",
        "1. Plot the confirmed, deaths and recovered cases as line graphs with dates for US, Iran and China each.\n",
        "2. Looking at the plots, explain which country has seen a surge in confirmed, and death cases?\n",
        "3. Work out the death rate due to COVID-19 in these three countries.\n",
        "4. By examining the plots, which country has been able to efficiently and swiftly recover the confirmed cases? Work out the recovery rate for each country (US, Iran and China) and then answer the question?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rpMhFJSY9C25",
        "colab_type": "text"
      },
      "source": [
        "we extract the date wise "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jk5LZCfp9qJR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def graphCases(dataframe, country, feature):\n",
        "  df=dataframe.get_group(country).groupby('ObservationDate')\n",
        "  #dateGrouped = data.get_group(country).groupby('ObservationDate')\n",
        "\n",
        "  dates=df.groups.keys()\n",
        "  values = dict()\n",
        "  for date in dates:\n",
        "    values[date] = df.get_group(date)[feature].sum()\n",
        "  return values\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XzQCKDXRwXQC",
        "colab_type": "text"
      },
      "source": [
        "#plotting confirmed\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WhIeYGEkwZ5Q",
        "colab_type": "code",
        "outputId": "eacf17e7-af5c-4e21-aa7d-fff95fa821f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 818
        }
      },
      "source": [
        "#df2=df2.groupby('Country/Region')\n",
        "ChinaC = graphCases(df2, 'Mainland China', 'Confirmed')\n",
        "USC = graphCases(df2, 'US', 'Confirmed')\n",
        "IranC = graphCases(df2, 'Iran', 'Confirmed')\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "fig= plt.figure(figsize=(16,13))\n",
        "plt.xticks(rotation=90)\n",
        "plt.plot(list(USC), list(USC.values()),  color='red')\n",
        "plt.plot(list(ChinaC), list(ChinaC.values()), color='blue')\n",
        "\n",
        "plt.plot(list(IranC), list(IranC.values()), color='green')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No handles with labels found to put in legend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DACNNiQVguSmxAMBIU2IBWG5KLAAw0pRYAJZbX1+y6qrNfWIBAEaCEgvAclt4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\nSZIkNUb/B3MBDXJuPWbgAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h-4lZm5ol8TC",
        "colab_type": "text"
      },
      "source": [
        "recovered cases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryZmOQFyl3Ks",
        "colab_type": "code",
        "outputId": "fd515c9f-7a22-455f-e24d-5d3e6bfda393",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        }
      },
      "source": [
        "ChinaR = graphCases(df2, 'Mainland China', 'Recovered')\n",
        "USR = graphCases(df2, 'US', 'Recovered')\n",
        "IranR = graphCases(df2, 'Iran', 'Recovered')\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig= plt.figure(figsize=(16,13))\n",
        "plt.plot(list(USR), list(USR.values()),  color='red', label='US')\n",
        "plt.plot(list(ChinaR), list(ChinaR.values()), color='blue',label='China')\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.plot(list(IranR), list(IranR.values()), color='green',label='Iran')\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VlpYlVpIkSVLJ++9/YY89svu/Dh8Oxx2XOpGWlSVWkiRJUkn75BPYdVd49lm45RY45JDU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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ilp1tCnFmx2s",
        "colab_type": "text"
      },
      "source": [
        "Deaths"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPkCN0dimw9F",
        "colab_type": "code",
        "outputId": "4cbb70bc-e9b7-4b90-b70c-b6af54bf07b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        }
      },
      "source": [
        "ChinaD = graphCases(df2, 'Mainland China', 'Deaths')\n",
        "USD = graphCases(df2, 'US', 'Deaths')\n",
        "IranD = graphCases(df2, 'Iran', 'Deaths')\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "fig= plt.figure(figsize=(16,13))\n",
        "plt.plot(list(USD), list(USD.values()),  color='red', label='US')\n",
        "plt.plot(list(ChinaD), list(ChinaD.values()), color='blue',label='China')\n",
        "\n",
        "plt.plot(list(IranD), list(IranD.values()), color='green',label='Iran')\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AttvGTiNJyhZlunVNCKE6aVEdnSTJQwBJkny8zuu3A+Mz3y4Edl7nx5tmjvE9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\nJClf/Pv9fzPilxE8fsTjNKrZKHQ4lZY3WJKUSg8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8VuiQVAlZrEo5Zvr0ZJqaTTeFfv1C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\ny2bNSp6vVg222AL23TcpUDt2TP5oVK9e2LglSdKaxXHMxDkTk2tIf/3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N7gNz8/0MsW+4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\nvsAPBITaAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQrcCwU3nubp",
        "colab_type": "text"
      },
      "source": [
        "#china has seen a surge in confirmed, while iran has seen it in death cases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_phtJCfoAhE",
        "colab_type": "text"
      },
      "source": [
        "**bold Work out the death rate due to COVID-19 in these three countries**\n",
        "# no of deaths can be interpreted in various days.here I will do with total deaths/ no of days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qjrzxKAgaWi",
        "colab_type": "code",
        "outputId": "9e61bbc8-ee4a-4065-fbf3-91eb83b37e1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "xf=df2.get_group('Mainland China').groupby('ObservationDate')\n",
        "#xf.head()\n",
        "xf['ObservationDate'].max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObservationDate\n",
              "01/22/2020    01/22/2020\n",
              "01/23/2020    01/23/2020\n",
              "01/24/2020    01/24/2020\n",
              "01/25/2020    01/25/2020\n",
              "01/26/2020    01/26/2020\n",
              "01/27/2020    01/27/2020\n",
              "01/28/2020    01/28/2020\n",
              "01/29/2020    01/29/2020\n",
              "01/30/2020    01/30/2020\n",
              "01/31/2020    01/31/2020\n",
              "02/01/2020    02/01/2020\n",
              "02/02/2020    02/02/2020\n",
              "02/03/2020    02/03/2020\n",
              "02/04/2020    02/04/2020\n",
              "02/05/2020    02/05/2020\n",
              "02/06/2020    02/06/2020\n",
              "02/07/2020    02/07/2020\n",
              "02/08/2020    02/08/2020\n",
              "02/09/2020    02/09/2020\n",
              "02/10/2020    02/10/2020\n",
              "02/11/2020    02/11/2020\n",
              "02/12/2020    02/12/2020\n",
              "02/13/2020    02/13/2020\n",
              "02/14/2020    02/14/2020\n",
              "02/15/2020    02/15/2020\n",
              "02/16/2020    02/16/2020\n",
              "02/17/2020    02/17/2020\n",
              "02/18/2020    02/18/2020\n",
              "02/19/2020    02/19/2020\n",
              "02/20/2020    02/20/2020\n",
              "02/21/2020    02/21/2020\n",
              "02/22/2020    02/22/2020\n",
              "02/23/2020    02/23/2020\n",
              "02/24/2020    02/24/2020\n",
              "02/25/2020    02/25/2020\n",
              "02/26/2020    02/26/2020\n",
              "02/27/2020    02/27/2020\n",
              "02/28/2020    02/28/2020\n",
              "02/29/2020    02/29/2020\n",
              "03/01/2020    03/01/2020\n",
              "03/02/2020    03/02/2020\n",
              "03/03/2020    03/03/2020\n",
              "03/04/2020    03/04/2020\n",
              "03/05/2020    03/05/2020\n",
              "03/06/2020    03/06/2020\n",
              "03/07/2020    03/07/2020\n",
              "03/08/2020    03/08/2020\n",
              "03/09/2020    03/09/2020\n",
              "03/10/2020    03/10/2020\n",
              "03/11/2020    03/11/2020\n",
              "03/12/2020    03/12/2020\n",
              "03/13/2020    03/13/2020\n",
              "03/14/2020    03/14/2020\n",
              "03/15/2020    03/15/2020\n",
              "03/16/2020    03/16/2020\n",
              "03/17/2020    03/17/2020\n",
              "03/18/2020    03/18/2020\n",
              "Name: ObservationDate, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iz1cN-s6n67Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Rate(dataframe, country, parameter):\n",
        "  countrySum=dataframe.get_group(country).groupby('ObservationDate')\n",
        "  totaldays=len(countrySum.groups.keys())\n",
        "  latestdays=dataframe.get_group(country)['ObservationDate'].max()\n",
        "  total=countrySum.get_group(latestdays)[parameter].sum()\n",
        "  \n",
        "  rate= total/totaldays\n",
        "  print(\"Rate of\",country,\"is\", rate)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uQsCZ2yyf4LU",
        "colab_type": "code",
        "outputId": "82770312-65d4-4f04-e1a9-6652eae5b03d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(\"Death RATES\")\n",
        "Rate(df2,\"Mainland China\",'Deaths')\n",
        "Rate(df2,\"Iran\",'Deaths')\n",
        "Rate(df2,\"US\",'Deaths')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Death RATES\n",
            "Death Rate of Mainland China is 56.78947368421053\n",
            "Death Rate of Iran is 39.13793103448276\n",
            "Death Rate of US is 2.0701754385964914\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ki9vnW84LEIa",
        "colab_type": "code",
        "outputId": "ae29c639-2bb6-4d71-e519-494e3670e217",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        }
      },
      "source": [
        "print(\"Recovery RATES\")\n",
        "Rate(df2,\"Mainland China\",'Recovered')\n",
        "Rate(df2,\"Iran\",'Recovered')\n",
        "Rate(df2,\"US\",'Recovered')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recovery RATES\n",
            "Rate of Mainland China is 1221.982456140351\n",
            "Rate of Iran is 185.82758620689654\n",
            "Rate of US is 1.8596491228070176\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcRc4_90Rx-_",
        "colab_type": "text"
      },
      "source": [
        "#### Question # 04\n",
        "\n",
        "In this question, we will look at another data file namely `time_series_covid_19_deaths.csv`.\n",
        "In this file, time series data of death occurred due to COVID-19 in countries/states/provinces is given.\n",
        "\n",
        "1. Read the file and examine the features.\n",
        "2. Clean the dataset by dropping missing rows with data points.\n",
        "3. Since you already have information for States/Provinces in US, and China with most deaths, you can use those States/Provinces and plot the number of deaths against each of the top 5 states in both China and US."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UD8m3QWzRx_B",
        "colab_type": "code",
        "outputId": "5143f0b0-d475-40a4-dffe-c05061ebe5a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "\n",
        "\n",
        "dataset = pd.read_csv('time_series_covid_19_deaths.csv')\n",
        "dataset.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
              "       '1/24/20', '1/25/20', '1/26/20', '1/27/20', '1/28/20', '1/29/20',\n",
              "       '1/30/20', '1/31/20', '2/1/20', '2/2/20', '2/3/20', '2/4/20', '2/5/20',\n",
              "       '2/6/20', '2/7/20', '2/8/20', '2/9/20', '2/10/20', '2/11/20', '2/12/20',\n",
              "       '2/13/20', '2/14/20', '2/15/20', '2/16/20', '2/17/20', '2/18/20',\n",
              "       '2/19/20', '2/20/20', '2/21/20', '2/22/20', '2/23/20', '2/24/20',\n",
              "       '2/25/20', '2/26/20', '2/27/20', '2/28/20', '2/29/20', '3/1/20',\n",
              "       '3/2/20', '3/3/20', '3/4/20', '3/5/20', '3/6/20', '3/7/20', '3/8/20',\n",
              "       '3/9/20', '3/10/20', '3/11/20', '3/12/20', '3/13/20', '3/14/20'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bbf8nt-pMxf3",
        "colab_type": "code",
        "outputId": "e3a22989-e77f-4975-d091-9fccb7c7e8f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "dataset\n"
      ],
      "execution_count": 0,
      "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>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>1/22/20</th>\n",
              "      <th>1/23/20</th>\n",
              "      <th>1/24/20</th>\n",
              "      <th>1/25/20</th>\n",
              "      <th>1/26/20</th>\n",
              "      <th>1/27/20</th>\n",
              "      <th>1/28/20</th>\n",
              "      <th>1/29/20</th>\n",
              "      <th>1/30/20</th>\n",
              "      <th>1/31/20</th>\n",
              "      <th>2/1/20</th>\n",
              "      <th>2/2/20</th>\n",
              "      <th>2/3/20</th>\n",
              "      <th>2/4/20</th>\n",
              "      <th>2/5/20</th>\n",
              "      <th>2/6/20</th>\n",
              "      <th>2/7/20</th>\n",
              "      <th>2/8/20</th>\n",
              "      <th>2/9/20</th>\n",
              "      <th>2/10/20</th>\n",
              "      <th>2/11/20</th>\n",
              "      <th>2/12/20</th>\n",
              "      <th>2/13/20</th>\n",
              "      <th>2/14/20</th>\n",
              "      <th>2/15/20</th>\n",
              "      <th>2/16/20</th>\n",
              "      <th>2/17/20</th>\n",
              "      <th>2/18/20</th>\n",
              "      <th>2/19/20</th>\n",
              "      <th>2/20/20</th>\n",
              "      <th>2/21/20</th>\n",
              "      <th>2/22/20</th>\n",
              "      <th>2/23/20</th>\n",
              "      <th>2/24/20</th>\n",
              "      <th>2/25/20</th>\n",
              "      <th>2/26/20</th>\n",
              "      <th>2/27/20</th>\n",
              "      <th>2/28/20</th>\n",
              "      <th>2/29/20</th>\n",
              "      <th>3/1/20</th>\n",
              "      <th>3/2/20</th>\n",
              "      <th>3/3/20</th>\n",
              "      <th>3/4/20</th>\n",
              "      <th>3/5/20</th>\n",
              "      <th>3/6/20</th>\n",
              "      <th>3/7/20</th>\n",
              "      <th>3/8/20</th>\n",
              "      <th>3/9/20</th>\n",
              "      <th>3/10/20</th>\n",
              "      <th>3/11/20</th>\n",
              "      <th>3/12/20</th>\n",
              "      <th>3/13/20</th>\n",
              "      <th>3/14/20</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>101.0000</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Japan</td>\n",
              "      <td>36.0000</td>\n",
              "      <td>138.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.2833</td>\n",
              "      <td>103.8333</td>\n",
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              "      <td>Nepal</td>\n",
              "      <td>28.1667</td>\n",
              "      <td>84.2500</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>2.5000</td>\n",
              "      <td>112.5000</td>\n",
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              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Saint Lucia</td>\n",
              "      <td>13.9094</td>\n",
              "      <td>-60.9789</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Saint Vincent and the Grenadines</td>\n",
              "      <td>12.9843</td>\n",
              "      <td>-61.2872</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Suriname</td>\n",
              "      <td>3.9193</td>\n",
              "      <td>-56.0278</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>440</th>\n",
              "      <td>Virgin Islands, U.S.</td>\n",
              "      <td>US</td>\n",
              "      <td>18.3358</td>\n",
              "      <td>-64.8963</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>441</th>\n",
              "      <td>NaN</td>\n",
              "      <td>occupied Palestinian territory</td>\n",
              "      <td>31.9522</td>\n",
              "      <td>35.2332</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>442 rows × 57 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Province/State                    Country/Region  ...  3/13/20  3/14/20\n",
              "0                     NaN                          Thailand  ...        1        1\n",
              "1                     NaN                             Japan  ...       19       22\n",
              "2                     NaN                         Singapore  ...        0        0\n",
              "3                     NaN                             Nepal  ...        0        0\n",
              "4                     NaN                          Malaysia  ...        0        0\n",
              "..                    ...                               ...  ...      ...      ...\n",
              "437                   NaN                       Saint Lucia  ...        0        0\n",
              "438                   NaN  Saint Vincent and the Grenadines  ...        0        0\n",
              "439                   NaN                          Suriname  ...        0        0\n",
              "440  Virgin Islands, U.S.                                US  ...        0        0\n",
              "441                   NaN    occupied Palestinian territory  ...        0        0\n",
              "\n",
              "[442 rows x 57 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6bXhOcGLm_r",
        "colab_type": "code",
        "outputId": "290a9bce-6944-4bc7-e5fc-7b44660f48c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "# replace all NA's with 0\n",
        "dataset.fillna(0)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Thailand</td>\n",
              "      <td>15.0000</td>\n",
              "      <td>101.0000</td>\n",
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              "      <td>Japan</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>Singapore</td>\n",
              "      <td>1.2833</td>\n",
              "      <td>103.8333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>28.1667</td>\n",
              "      <td>84.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Malaysia</td>\n",
              "      <td>2.5000</td>\n",
              "      <td>112.5000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>437</th>\n",
              "      <td>0</td>\n",
              "      <td>Saint Lucia</td>\n",
              "      <td>13.9094</td>\n",
              "      <td>-60.9789</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>438</th>\n",
              "      <td>0</td>\n",
              "      <td>Saint Vincent and the Grenadines</td>\n",
              "      <td>12.9843</td>\n",
              "      <td>-61.2872</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>0</td>\n",
              "      <td>Suriname</td>\n",
              "      <td>3.9193</td>\n",
              "      <td>-56.0278</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>440</th>\n",
              "      <td>Virgin Islands, U.S.</td>\n",
              "      <td>US</td>\n",
              "      <td>18.3358</td>\n",
              "      <td>-64.8963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>441</th>\n",
              "      <td>0</td>\n",
              "      <td>occupied Palestinian territory</td>\n",
              "      <td>31.9522</td>\n",
              "      <td>35.2332</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>442 rows × 57 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Province/State                    Country/Region  ...  3/13/20  3/14/20\n",
              "0                       0                          Thailand  ...        1        1\n",
              "1                       0                             Japan  ...       19       22\n",
              "2                       0                         Singapore  ...        0        0\n",
              "3                       0                             Nepal  ...        0        0\n",
              "4                       0                          Malaysia  ...        0        0\n",
              "..                    ...                               ...  ...      ...      ...\n",
              "437                     0                       Saint Lucia  ...        0        0\n",
              "438                     0  Saint Vincent and the Grenadines  ...        0        0\n",
              "439                     0                          Suriname  ...        0        0\n",
              "440  Virgin Islands, U.S.                                US  ...        0        0\n",
              "441                     0    occupied Palestinian territory  ...        0        0\n",
              "\n",
              "[442 rows x 57 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0y0nxlL1DBA",
        "colab_type": "text"
      },
      "source": [
        "#Plotting\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lbb-0zAEqmvf",
        "colab_type": "text"
      },
      "source": [
        "plot the number of deaths against each of the top 5 states in both China and US."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSB1kEmCy0QD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def pltTop(coun):\n",
        "\n",
        "  coun=dict(coun)\n",
        "  \n",
        "  plt.plot(list(coun.keys()), list(coun.values()), marker='', color='red', linewidth=2,label=\"death cases\")\n",
        "  plt.xticks(rotation=90)\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "\n",
        "  #chin=dict(chin)\n",
        "\n",
        "# import matplotlib.pyplot as plt;\n",
        "# chin=dict(chin)\n",
        "# plt.plot(list(chin.keys()), list(chin.values()), marker='', color='red', linewidth=2,label=\"death cases\")\n",
        "# \n",
        "\n",
        "\n",
        "# plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Jc1tz8DyYas",
        "colab_type": "code",
        "outputId": "9d600937-f976-4afd-a661-c04118b834a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        }
      },
      "source": [
        "print('For China')\n",
        "pltTop(chin)\n",
        "print('For US')\n",
        "pltTop(US)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "For China\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WLSS9suQ0QUkTACLiiVLr7ESSVgJzOuBAb7cCvxkRT+blfYHvRERbg26kkHRH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\n2Bk8dEAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "For US\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w5T2ZsFDRw4ShhPMIV+DrItVxNfAVYBe2vSM5r5r/GDpYz8wyh93do4x4yMpw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\nJKf+P0VIA8C9YvyAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5a4jFKjhQO8s",
        "colab_type": "text"
      },
      "source": [
        "#**note the above data dont make much of a sense, I am now plotting the growth of death curve wrt China and US**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3m3F_ScQ-tE9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def curve(provi, df):\n",
        "  df=df.groupby(['Province/State']).get_group(provi)\n",
        "  crv=df.groupby(['ObservationDate'])\n",
        "\n",
        "  values=crv.groups.keys()\n",
        "  prov=dict()\n",
        "  sum=0\n",
        "  for x in values:\n",
        "    \n",
        "  \n",
        "    sum=crv.get_group(x)['Deaths'].sum()\n",
        "    prov[x]=sum\n",
        "  return(prov)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3QgxQZSuQgMq",
        "colab_type": "text"
      },
      "source": [
        "#for China"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WXMaBkZPAEp-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# china=df.groupby(['Country/Region']).get_group('Mainland China')\n",
        "# # chinaProvinces = ['Hubei', 'Henan', 'Heilongjiang', 'Beijing', 'Guangdong']\n",
        "# dfx=china.groupby(['Province/State']).get_group('Hubei')\n",
        "\n",
        "# dfx=dfx.groupby(['ObservationDate'])\n",
        "# # dfx\n",
        "# values=dfx.groups.keys()\n",
        "# # values\n",
        "# prov=dict()\n",
        "# for x in values:\n",
        "#   sum=dfx.get_group(x)['Deaths'].sum()\n",
        "#   prov[x]=sum\n",
        "# # print( prov)\n",
        "# print(prov)\n",
        "# # chin=dict(prov)\n",
        "# # plt.plot(list(chin.keys()), list(chin.values()), marker='', color='red', linewidth=2)\n",
        "# # #plt.xticks(rotation=90)\n",
        "# # #   # prov= (sorted(prov.items(), key=lambda x: x[1], reverse=True)[:10])\n",
        "# # #   # return(prov)\n",
        "\n",
        "\n",
        "\n",
        "#curve('Hubei',china)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hADjVKxXIFZK",
        "colab_type": "code",
        "outputId": "9ae17582-32a8-40b2-c0dd-a08d33690da8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "\n",
        "chinaProvinces = ['Hubei', 'Henan', 'Heilongjiang', 'Beijing', 'Guangdong']\n",
        "for c in chinaProvinces:\n",
        "  provC=curve(c,china) \n",
        "  print('Death Curve for',c)\n",
        "  fig= plt.figure(figsize=(16,13))\n",
        "  plt.xticks(rotation=90)\n",
        "  plt.title('Death Curve for'+c)\n",
        "  plt.plot(list(provC.keys()), list(provC.values()),color='red', linewidth=2)\n",
        "  \n",
        "  \n",
        "\n",
        "  \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Death Curve for Hubei\n",
            "Death Curve for Henan\n",
            "Death Curve for Heilongjiang\n",
            "Death Curve for Beijing\n",
            "Death Curve for Guangdong\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h3/+G8ePTWJMmsMMOcOihsMsu0KJF1ohSYwkhjIkx9pzTawt0zaokSZKkhTB1Ktx5Z5pF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\nJEmqHJtVSZIkSVLl2KxKkiRJkirn/wMk5xG/CwTdzwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f+97aaQAG1mQmnkcm+fUkPy2l/NvY285McnGSr5dSPp7kkSS/1p2IAAAVLF6cXHxxuz73\nXFtlAaZgMrfa/kOSNf1N+67OxgEA6BM335zMnZvsv39y8sm10wAMtMm8jicAwGhZsuTl084N/MoE\nMBX+FgUAWNUXv5jMmZPst19yyim10wAMPMUTAGCiJUuSiy5q16adAB3hb1IAgIluuSV59NFk332T\n97+/dhqAoaB4AgCMW7o0ufDCdn3OOaadAB3ib1MAgHG33prMnp3svXdy6qm10wAMDcUTACBpp50X\nXNCuTTsBOsrfqAAASXLbbckjjyR77pl84AO10wAMFcUTAGDZspdPO6dNq5sHYMgongAA/+2/JQ8/\nnOyxR/LBD9ZOAzB0FE8AYLRNnHaefbZpJ0AXKJ4AwGi7/fbkwQeT3XZLTjutdhqAoaR4AgCja/ny\n5Pzz2/XZZyfTp9fNAzCkFE8AYHR95SvJAw8ku+6afOhDtdMADC3FEwAYTROnnWedZdoJ0EWKJwAw\nmr72tWTWrGTnnZOPfKR2GoChpngCAKNnxQpnOwF6SPEEAEbPn/1Zcu+9yZvfbNoJ0AOKJwAwWlas\nSD772XZ91lnJhhvWzQMwAhRPAGC0fOMbyT33JDNmJGecUTsNwEhQPAGA0bFy5UvTzjPPNO0E6BHF\nEwAYHd/8ZnL33cmOOyYf/WjtNAAjwxVuAPBKbrstmTmzdgo65RvfaB/PPDPZaKO6WQBGiOIJAGty\n332mYsNohx2Sj32sdgqAkaJ4AsCa3Hln+7jffsnpp9fNQmeUkrznPcnGG9dOAjBSFE8AWJOf/ax9\n/JVfST71qbpZAGCAuVwIANbk7rvbx733rpsDAAac4gkAazJePPfZp24OABhwiicArM6yZcm997br\nPfesmwUABpziCQCr88ADbfmcMSN59atrpwGAgaZ4AsDq2GYLAB2jeALA6ozfaOtiIQCYMsUTAFbH\njbYA0DGKJwCsjq22ANAxiicArGr58uSee9q1G20BYMoUTwBY1YMPJkuXJjvumGy+ee00ADDwFE8A\nWJVttgDQUYonAKzKjbYA0FGKJwCsyo22ANBRiicArGp84mmrLQB0hOIJABOtWPHSjbZ77VU3CwAM\nCcUTACZ66KFkyZJkhx2SLbaonQYAhoLiCQATuVgIADpO8QSAiVwsBAAdp3gCwERewxMAOk7xBICJ\nbLUFgI5TPAFg3IoVycyZ7dqNtgDQMYonAIx75JFk8eJk++2TLbesnQYAhobiCQDjbLMFgK5QPAFg\nnBttAaArFE8AGDc+8XSjLQB0lOIJAONMPAGgKxRPAEiSlStfutFW8QSAjlI8ASBpb7R94YVku+2S\nrbaqnQYAhoriCQCJbbYA0EWKJwAkLxVPFwsBQMcpngCQeA1PAOgixRMAElttAaCLFE8AaBrFEwC6\nSPEEgNmzk+efT7bdNnnd62qnAYCho3gCgGknAHSV4gkAbrQFgK5SPAHAjbYA0FWKJwDYagsAXaV4\nAjDaJt5oa6stAHSF4gnAaJszJ3nuuWSbbZKtt66dBgCGkuIJwGizzRYAuk7xBGC0jV8sZJstAHSN\n4gnAaDPxBICuUzwBGG2KJwB0neIJwOhqGlttAaAHFE8ARtfcucmzzyave117qy0A0BWKJwCja+Lr\nd5ZSNwsADDHFE4DRNb7N1vlOAOgqxROA0eViIQDoCcUTgNHlYiEA6AnFE4DR1DQmngDQI4onAKPp\nsceSp59Ottoq2Xbb2mkAYKgpngCMponbbN1oCwBdpXgCMJpsswWAnlE8ARhNiicA9IziCcBocqMt\nAPSM4gnA6Gmal4qniScAdJ3iCcDoeeKJ5Kmnkte8Jtluu9ppAGDoKZ4AjB432gJATymeAIweFwsB\nQE8pngCMnvHi6WIhAOgJxROA0eNiIQDoKcUTgNFjqy0A9JTiCcBoeeKJZMGCZIstku23r50GAEaC\n4gnAaJk47XSjLQD0hOIJwGixzRYAek7xBGC0THwNTwCgJxRPAEaLiScA9JziCcBoUTwBoOcUTwBG\nx4IF7a22m22W7LBD7TQAMDIUTwBGhxttAaAKxROA0WGbLQBUoXgCMDrcaAsAVSieAIwOE08AqELx\nBGB0jE88FU8A6CnFE4DR8OSTyeOPJ69+dbLjjrXTAMBIUTwBGA0zZ7aPe+2VbOA/fwDQS/7LC8Bo\ncLEQAFSjeAIwGlwsBADVKJ4AjAbFEwCqUTwBGA222gJANYonAMPvqaeSefOSTTZJdtqpdhoAGDmK\nJwDDz422AFCV//oCMPxsswWAqhRPAIafi4UAoCrFE4Dhp3gCQFWKJwDDz1ZbAKhK8QRguD3zTPLz\nnyevelUyY0btNAAwkhRPAIbb+DbbPfdMpk2rmwUARpTiCcBwGy+ettkCQDWKJwDDzcVCAFCd4gnA\ncHOxEABUp3gCMNxMPAGgOsUTgOH17LPJo48mG2+c7Lxz7TQAMLIUTwCG18yZ7aMbbQGgKsUTgOFl\nmy0A9AXFE4DhpXgCQF9QPAEYXm60BYC+oHgCMLxMPAGgLyieAAynRYuSRx5JNtoo2WWX2mkAYKQp\nngAMp/EbbffYI5k+vW4WABhxiicAw8k2WwDoG4onAMNp/GIhxRMAqlM8ARhO4xNPN9oCQHWKJwDD\nyVZbAOgbiicAw+f555OHHko23DDZddfaaQBg5CmeAAyfe+5pH3ffvS2fAEBViicAw8c2WwDoK4on\nAMPHjbYA0FcUTwCGjxttAaCvKJ4ADB9bbQGgr6y1eJZSvlhKeaKUcteEt326lPLzUsq/jf3zK92N\nCQCT9MILyYMPJtOnJ7vtVjsNAJDJTTxvSfLLq3n755qmOWDsn293NhYArKd7702api2dG21UOw0A\nkEkUz6ZpvpdkYQ+yAMDU2WYLAH1nKmc8f6+UcufYVtyt1vRBpZRPlFLuKKXcMX/+/Ck8HQBMwviN\nti4WAoC+sb7F8/okuyQ5IMm8JFes6QObprmxaZpDmqY5ZJtttlnPpwOASTLxBIC+s17Fs2max5um\nWdE0zcokNyU5rLOxAGA9KZ4A0HfWq3iWUrab8Mf3JblrTR8LAD2zeHHywAPJtGnJ7rvXTgMAjJm+\ntg8opXw1ybFJti6lzElyXpJjSykHJGmSPJzkt7uYEQAm5957k5Urkz32SDbeuHYaAGDMWotn0zSn\nr+bNN3chCwBMzfjFQrbZAkBfmcqttgDQX8bPd7rRFgD6iuIJwPBwsRAA9CXFE4DhYastAPQlxROA\n4bBkSXL//ckGG7SXCwEAfUPxBGA43Hdfe6PtLrskr3pV7TQAwASKJwDDwTZbAOhbiicAw8GNtgDQ\ntxRPAIaDG20BoG8pngAMB1ttAaBvKZ4ADL6lS5NZs5JSkj33rJ0GAFiF4gnA4Js1K1mxItl552ST\nTWqnAQBWoXgCMPhsswWAvqZ4AjD43GgLAH1N8QRg8LnRFgD6muIJwOAb32pr4gkAfUnxBGCwLVuW\n3HefG20BoI8pngAMtlmzkuXLkxkzkk03rZ0GAFgNxROAweZiIQDoe4onAIPNxUIA0PcUTwAGm9fw\nBIC+p3gCMNhstQWAvqd4AjC4li9P7r23XbvRFgD6luIJwOC6//725VR22inZbLPaaQCANVA8ARhc\nttkCwEBQPAEYXG60BYCBoHgCMLjcaAsAA0HxBGBw2WoLAANB8QRgME280XavvepmAQBekeIJwGB6\n8MFkyZJkhx2SzTevnQYAeAWKJwCDyTZbABgYiicAg8mNtgAwMBRPAAaTG20BYGAongAMJlttAWBg\nKJ4ADJ4VK5J77mnXbrQFgL6neAIweB56KFm8ONl+++Q1r6mdBgBYC8UTgMFjmy0ADBTFE4DB42Ih\nABgoiicAg8fEEwAGiuIJwODxGp4AMFAUTwAGy8qVycyZ7VrxBICBoHgCMFgefjh58cXkjW9Mttyy\ndhoAYBIUTwAGi222ADBwFE8ABosbbQFg4CieAAwWN9oCwMBRPAEYLLbaAsDAUTwBGBwrVyqeADCA\nFE8ABsfs2ckLLyRveEPy2tfWTgMATJLiCcDgMO0EgIGkeAIwONxoCwADSfEEYHC40RYABpLiCcDg\nMPEEgIE0vXYAAEbMCy8kf//3ydKl6/65zngCwEBSPAHorU9+MrnmmvX//G23TbbeunN5AICuUzwB\n6K1vf7t9/KVfSjbddN0///TTO5sHAOg6xROA3pkzJ3nggWSLLdoCOm1a7UQAQA+4XAiA3vnud9vH\no49WOgFghCieAPTOd77TPh5zTNUYAEBvKZ4A9M74xPPYY6vGAAB6S/EEoDfmzk1mzUo23zw58MDa\naQCAHlI8AeiN8WnnUUcl091tBwCjRPEEoDec7wSAkaV4AtAbzncCwMhSPAHovnnzknvvTV796uSg\ng2qnAQB6TPEEoPu+97328aijkg03rJsFAOg5xROA7nO+EwBGmuIJQPc53wkAI03xBKC7Hn88mTkz\n2XTT5JBDaqcBACpQPAHorvFp55FHOt8JACNK8QSgu2yzBYCRp3gC0F0uFgKAkad4AtA9TzyR3H13\nsskmyaGH1k4DAFSieALQPeOv3/m2tyUbbVQ3CwBQjeIJQPc43wkARPEEoJuc7wQAongC0C0LFiR3\n3ZW86lXJYYfVTgMAVKR4AtAd4+c7jzgi2XjjulkAgKoUTwC6w/lOAGCM4glAdzjfCQCMUTwB6LyF\nC5Of/rTdYvvWt9ZOAwBUpngC0Hnf+17SNMnhh7eXCwEAI03xBKDznO8EACZQPAHoPOc7AYAJFE8A\nOuupp5Kf/CTZaKN2qy0AMPIUTwA66/vff+l85yab1E4DAPQBxROAzrLNFgBYheIJQGe5WAgAWIXi\nCUDnPP108uMfJxtu6HwnAPDvFE8AOucf/qE93/nWtyabblo7DQDQJxRPADrH+U4AYDUUTwA6x/lO\nAGA1FE8AOuOZZ5If/SiZPj054ojaaQCAPqJ4AtAZP/hBsnJlcthhyatfXTsNANBHFE8AOsP5TgBg\nDRRPADrD+U4AYA0UTwCm7tlnkx/+MJk2LXnb22qnAQD6jOIJwNT94AfJihXJoYcmm21WOw0A0GcU\nTwCmbnybrfOdAMBqKJ4ATN34xULOdwIAq6F4AjA1ixYld9zRnu888sjaaQCAPqR4AjA14+c7Dz44\n2Xzz2mkAgD6keAIwNV5GBQBYC8UTgKkZP9/pYiEAYA0UTwDW3/PPJ//6r8kGGyRHHVU7DQDQpxRP\nANbfP/5jsnx5ctBByRZb1E4DAPQpxROA9ed8JwAwCYonAOvP+U4AYBIUTwDWzwsvJP/yL853AgBr\npXgCsH7+6Z+SZcuSAw5IttyydhoAoI8pngCsn/Ftts53AgBroXgCsH7GLxZyvhMAWAvFE4B19/zz\nyT//c1JKcvTRtdMAAH1O8QRg3f3pnyZLlyZHHJFstVXtNABAn1M8AVg3L7yQXHppu/7Up+pmAQAG\nguIJwLq58cbk8ceTgw9OTjyxdhoAYAAongBM3osvJpdc0q7PPbc94wkAsBaKJwCTd9NNyWOPJQce\nmJx0Uu00AMCAUDwBmJzFi007AYD1ongCMDlf+EIyd26y//7JySfXTgMADBDFE4C1W7Ikufjidn3e\neaadAMA6UTwBWLubb05+/vPkLW8x7QQA1pniCcArW7Ikueiidn3uuckG/tMBAKwbvz0A8Mq+9KVk\nzpxk332T972vdhoAYAApngCs2dKlpp0AwJT5DQKANbvllmT27GSffZJf/dXaaQCAAaV4ArB6S5cm\nF17Yrs85x7QTAFhvfosAYPVuvTV55JFkr72SU0+tnQYAGGCKJwC/aNmyl087p02rmwcAGGiKJwC/\n6LbbkocfTvbcM/m1X6udBgAYcIonAC+3bFlywQXt+uyzTTsBgClTPAF4uS9/OXnooWT33ZPTTqud\nBgAYAoonAC9Zvty0EwDoOMUTgJfcfnvywAPJbrslp59eOw0AMCQUTwBay5cn55/frs8+O5k+vW4e\nAGBoKJ4AtL761eT++5Ndd00+9KHaaQCAIaJ4ApCsWPHStPOss0w7AYCOUjwBSL72teS++5Kdd04+\n8pHaaQCAIaN4Aoy6FSuSz362XZt2AgBdsNbiWUr5YinliVLKXRPe9tpSyv8qpcwae9yquzEB6Jqv\nfz25997kzW9Ofv3Xa6cBAIbQZCaetyT55VXe9skkf9c0zW5J/m7szwAMmonTzjPPTDbcsG4eAGAo\nrbV4Nk3zvSQLV3nzyUluHS9gaMsAACAASURBVFvfmuSUDucCoBe++c1k5sxkp52SM86onQYAGFLr\ne8Zz26Zp5o2tH0uy7Zo+sJTyiVLKHaWUO+bPn7+eTwdAV9x+e/v4h3+YbLRR3SwAwNCa8uVCTdM0\nSZpXeP+NTdMc0jTNIdtss81Unw6ATlmxIvne99r1e95TNwsAMNTWt3g+XkrZLknGHp/oXCQAeuIn\nP0meeaa9VGjHHWunAQCG2PoWz28l+ejY+qNJ/rozcQDome9+t3085pi6OQCAoTeZl1P5apJ/SrJH\nKWVOKeXjSS5OclwpZVaSd4/9GYBB8p3vtI/HHlszBQAwAtb6KuFN05y+hne9q8NZAOiVlSuT73+/\nXZt4AgBdNuXLhQAYQHfemTz1VPsyKjNm1E4DAAw5xRNgFDnfCQD0kOIJMIqc7wQAekjxBBg1K1e+\n9PqdiicA0AOKJ8CoueuuZOHCZIcdnO8EAHpC8QQYNePnO489NimlahQAYDQongCjZvx8p4uFAIAe\nUTwBRonznQBABYonwCi5++5kwYJk++2TnXeunQYAGBGKJ8Aocb4TAKhA8QQYJc53AgAVKJ4Ao6Jp\nXj7xBADoEcUTYFTMnJnMn59st12y66610wAAI0TxBBgVzncCAJUongCjwvlOAKASxRNgFDjfCQBU\npHgCjIJ7700efzzZdttk991rpwEARoziCTAKnO8EACpSPAFGgfOdAEBFiifAsGual4qn850AQAWK\nJ8CwmzUreeyx5PWvT/bcs3YaAGAEKZ4Aw27iNlvnOwGAChRPgGE3frGQ850AQCWKJ8Awc74TAOgD\niifAMHvggWTu3GTrrZO9966dBgAYUYonwDBzvhMA6AOKJ8Awc74TAOgDiifAsHK+EwDoE4onwLB6\n6KFkzpzkta9N9tmndhoAYIQpngDDauL5zg38dQ8A1OM3EYBhNX6+0zZbAKAyxRNgWE2ceAIAVKR4\nAgyjhx9OZs9Ottoq2W+/2mkAgBGneAIMo/Fp59vf7nwnAFCd30YAhpHznQBAH1E8AYaR850AQB9R\nPAGGzSOPtGc8t9wyectbaqcBAFA8AYbO+Dbbo49Opk2rmwUAIIonwPBxvhMA6DOKJ8Cwcb4TAOgz\niifAMHn00eTBB5MttkgOOKB2GgCAJIonwHBxvhMA6EOKJ8Awcb4TAOhDiifAMHG+EwDoQ4onwLD4\n+c+T++9PNt88OfDA2mkAAP6d4gkwLMa32R51VDJ9et0sAAATKJ4Aw2J8m63znQBAn1E8AYbF+MTT\n+U4AoM8ongDDYN685L77ks02Sw46qHYaAICXUTwBhsH4tPPII5MNN6ybBQBgFYonwDBwvhMA6GOK\nJ8AwcL4TAOhjiifAoJs1K7nnnmTTTZNDDqmdBgDgFyieAIPuggvax9NPd74TAOhLiifAIHvggeTL\nX06mTUvOPLN2GgCA1VI8AQbZBRckK1YkZ5yR7Lxz7TQAAKuleAIMqgcfTG67rZ12nnVW7TQAAGuk\neAIMqgsvbKedH/lIsssutdMAAKyR4gkwiB5+OLn11mSDDUw7AYC+p3gCDKILL0yWL08+/OFkt91q\npwEAeEWKJ8CgeeSR5EtfaqedZ59dOw0AwFopngCD5qKL2mnn6acnu+9eOw0AwFopngCDZPbs5Itf\nTEox7QQABobiCTBILrooWbYsOe20ZM89a6cBAJgUxRNgUDz6aHLzze2085xzaqcBAJg0xRNgUFx8\ncTvt/OAHk732qp0GAGDSFE+AQTBnTvKFL5h2AgADSfEEGASXXJIsXZp84APJ3nvXTgMAsE4UT4B+\nN3ductNN7dq0EwAYQIonQL+75JJkyZLk1FOTffetnQYAYJ0pngD9bN685MYb2/W559bNAgCwnhRP\ngH526aXJ4sXJ+9+f7Ldf7TQAAOtF8QToV489ltxwQ7s27QQABpjiCdCvLrusnXaeckqy//610wAA\nrDfFE6AfPf54cv317dq0EwAYcIonQD+6/PLkxReT9743OfDA2mkAAKZE8QToN088kVx3Xbs27QQA\nhoDiCdBvxqedJ52UHHxw7TQAAFOmeAL0k/nzX5p2nnde3SwAAB2ieAL0kyuuSF54ITnxRNNOAGBo\nKJ4A/WLBguTaa9u1aScAMEQUT4B+ceWVyfPPJyeckBx6aO00AAAdo3gC9IMnn0yuuaZdu8kWABgy\niidAP/jc55JFi5Ljj08OP7x2GgCAjlI8AWpbuDC5+up27WwnADCEFE+A2j7/+eS555LjjkuOOKJ2\nGgCAjlM8AWp66qnkqqvatWknADCkFE+Amq66Knn22eRd70qOPLJ2GgCArlA8AWp5+ul2m21i2gkA\nDDXFE6CWq69Onnkmecc7kqOPrp0GAKBrFE+AGp55pn0JlcS0EwAYeoonQA3XXNNutT3mmPYfAIAh\npngC9NqzzyZXXtmuTTsBgBGgeAL02jXXtC+jcvTRybHH1k4DANB1iidALz333MunnaXUzQMA0AOK\nJ0AvXXttsnBh+5qd73xn7TQAAD2heAL0yqJFyRVXtGvTTgBghCieAL1y3XXJk08mRxyRvPvdtdMA\nAPSM4gnQC4sWJZdf3q4//WnTTgBgpCieAL1w/fXJggXJ4Ycnxx1XOw0AQE8pngDd9vzzyWWXtWtn\nOwGAEaR4AnTbDTck8+cnhx2WHH987TQAAD2neAJ00wsvJJde2q5NOwGAEaV4AnTTn/5p8sQTySGH\nJCecUDsNAEAViidAt7z4omknAEAUT4DuufHG5LHHkoMPTk48sXYaAIBqFE+Abli8OLnkknZ97rmm\nnQDASFM8AbrhppuSefOSAw9MTjqpdhoAgKoUT4BOW7w4ufjidm3aCQCgeAJ03Be+kMydm+y/f3Ly\nybXTAABUN712AIC+9Dd/k/ze7yVLlqz75z71VPto2gkAkETxBFi9665LZs9e/89/29uSU07pXB4A\ngAGmeAKsatmy5Ac/aNc//Wmy9dbr/jVe//pkA6cZAAASxRPgF/3oR8miRckeeyT77ls7DQDAwPN/\nxwOs6jvfaR+POaZqDACAYaF4Aqzqu99tH489tmoMAIBhoXgCTLR8efL977drE08AgI5QPAEm+vGP\n2/Odu+2WvPGNtdMAAAwFxRNgIuc7AQA6TvEEmMj5TgCAjlM8AcatWOF8JwBAFyieAOP+7d+SZ59N\ndtkledObaqcBABgaiifAOOc7AQC6QvEEGOd8JwBAVyieAEl7vvN732vXJp4AAB2leAIkyZ13Js88\nk7z5zcmOO9ZOAwAwVBRPgMT5TgCALlI8AZKXiqfznQAAHad4Aqxc6fU7AQC6SPEEuPPO5Kmnkp12\nSmbMqJ0GAGDoKJ4AXkYFAKCrFE8AFwsBAHSV4gmMtpUrX3r9ThNPAICuUDyB0XbXXcnChckOOzjf\nCQDQJYonMNomnu8spWoUAIBhpXgCo835TgCArlM8gdHlfCcAQE9Mn8onl1IeTvJckhVJljdNc0gn\nQgH0xN13JwsWJNtvn+y8c+00AABDa0rFc8w7mqZZ0IGvA9BbzncCAPSErbbA6HK+EwCgJ6ZaPJsk\n/7OU8sNSyidW9wGllE+UUu4opdwxf/78KT4dQIc0zcsnngAAdM1Ui+dRTdMclOSEJP+plPL2VT+g\naZobm6Y5pGmaQ7bZZpspPh1Ah8ycmcyfn2y3XbLrrrXTAAAMtSkVz6Zpfj72+ESSv0xyWCdCAXTd\n+DZb5zsBALpuvYtnKeXVpZTNx9dJfinJXZ0KBtBV49tsne8EAOi6qdxqu22SvyztpGB6kq80TfPf\nO5IKoJua5uUTTwAAumq9i2fTNA8m2b+DWQB64957kyeeSLbdNtl999ppAACGnpdTAUaP850AAD2l\neAKjx8uoAAD0lOIJjJaJ5ztdLAQA0BOKJzBaZs1KHnssef3rkz33rJ0GAGAkKJ7AaJk47XS+EwCg\nJxRPYLQ43wkA0HOKJzA6nO8EAKhC8QRGxwMPJHPnJltvney9d+00AAAjQ/EERofznQAAVSiewOgY\nL57OdwIA9JTiCYyGpnnpYiHnOwEAekrxBEbDgw8mc+Ykr3tdss8+tdMAAIwUxRMYDePTzre/PdnA\nX30AAL3kty9gNDjfCQBQjeIJDD/nOwEAqlI8geH38MPJ7NnJVlsl++1XOw0AwMhRPIHh53wnAEBV\nfgMDhp/znQAAVSmewPBzvhMAoCrFExhujzzSnvHccsvkLW+pnQYAYCQpnsBwm3i+c9q0ulkAAEaU\n4gkMt/HznbbZAgBUo3gCw83FQgAA1SmewPC6447koYeS17wm2X//2mkAAEaW4gkMr898pn38xCec\n7wQAqEjxBIbTD3+Y/O3fJptumvzBH9ROAwAw0hRPYDj98R+3j7/7u8nrX183CwDAiFM8geHz4x8n\n3/pWsskmyX/9r7XTAACMPMUTGD7j087f+Z1k223rZgEAQPEEhsxPfpL81V8lr3pV8od/WDsNAABR\nPIFhMz7t/O3fTt7whrpZAABIongCw+TOO5O/+Itk441NOwEA+ojiCQyPz362ffzEJ5I3vrFuFgAA\n/p3iCQyHu+5KvvnNZKONkj/6o9ppAACYQPEEhsP4tPM//Idk++3rZgEA4GUUT2Dw3X138o1vtNPO\nT36ydhoAAFaheAKD77OfTZom+fjHkze9qXYaAABWoXgCg23mzOTP/izZcMPkU5+qnQYAgNVQPIHB\ndv75L007d9ihdhoAAFZD8QQG1z33JF/9qmknAECfUzyBwTU+7fyN30h23LF2GgAA1kDxBAbTffe1\n087p0007AQD6nOIJDKbzz09Wrkw+9rFkxozaaQAAeAWKJzB4Zs1Kbr+9nXaeeWbtNAAArIXiCQye\nCy5op51nnJG8+c210wAAsBaKJzBYHngg+fKXk2nTkrPOqp0GAIBJUDyBwXLBBcmKFcmv/3qy8861\n0wAAMAmKJzA4Hnwwue02004AgAGjeAKD48IL22nnhz+c7Lpr7TQAAEyS4gkMhocfTm69Ndlgg+Ts\ns2unAQBgHSiewGC48MJk+fLkQx9KdtutdhoAANaB4gn0v0ceSb70JdNOAIABpXgC/e+ii9pp52mn\nJXvsUTsNAADrSPEE+tvs2ckXv5iUYtoJADCgFE+gv118cbJsWfLBDyZ77VU7DQAA60HxBPrXo48m\nN9/cTjvPOad2GgAA1pPiCfSviy9Oli5NPvCBZO+9a6cBAGA9KZ5Af5ozJ/nCF9q1aScAwEBTPIH+\ndMkl7bTz1FOTffetnQYAgClQPIH+M3ductNN7frcc+tmAQBgyhRPoP9cckmyZEnyq7+a7Ldf7TQA\nAEyR4gn0l3nzkhtvbNemnQAAQ0HxBPrLpZcmixcn73tf8pa31E4DAEAHKJ5A/3jsseSGG9q1aScA\nwNBQPIH+cdll7bTz5JOTAw6onQYAgA5RPIH+8PjjyfXXt2vTTgCAoaJ4Av3h8suTF19MTjopOeig\n2mkAAOggxROo74knkj/5k3Z93nl1swAA0HGKJ1DfFVckL7yQnHhicvDBtdMAANBhiidQ1/z5ybXX\ntmvTTgCAoaR4AnWNTztPOCE59NDaaQAA6ALFE6hnwQLTTgCAEaB4AvVceWXy/PPJ8ccnb31r7TQA\nAHSJ4gnU8eSTyTXXtGvTTgCAoaZ4AnV87nPJokXJccclRxxROw0AAF2keAK9t3BhcvXV7dq0EwBg\n6CmeQO99/vPJc88l7353cuSRtdMAANBliifQW089lVx1Vbs27QQAGAmKJ9BbV12VPPts8s53Jkcd\nVTsNAAA9oHgCvfP00+0228S0EwBghEyvHQBe0bJlyfz5tVPQKdddlzzzTHLsscnb3147DQAAPaJ4\n0r9efDF5y1uS+++vnYROM+0EABgpiif966ab2tK5ySbJllvWTkOn/MqvtBNPAABGhuJJf1q8OLn4\n4nb91a8mJ59cNw8AALDeXC5Ef7rppmTevOSAA5L3vrd2GgAAYAoUT/rPxGnneeclpdTNAwAATIni\nSf+5+eZk7txk//1tsQUAgCGgeNJflix5adp57rmmnQAAMAQUT/rLF7+YzJmT7LdfcsoptdMAAAAd\noHjSP5YsSS66qF2fe26ygf95AgDAMPCbPf3jlluSRx9N9t03ef/7a6cBAAA6RPGkPyxdmlx4Ybs+\n5xzTTgAAGCJ+u6c/3HprMnt2svfeyamn1k4DAAB0kOJJfcuWmXYCAMAQ8xs+9d12W/Lww8meeyb/\nf3t3Hi1HWed//P3NBrIkZEHCnrDpwAioLM5RjyCKUWcERxlwxw0U2VXgB7giKswZR5wZ+P0yKjOi\nEAR1yGAg7CJoCJCwCAkJgogogiyjjqCQfH9/PHVN29zu6vs8t/qp6v68zqmTvt33m8/z7a6u5XZ1\n1UEH5R6NiIiIiIiMM+14Sl7PPAOnnx5uf+ITMHFi3vGIiIiIiMi4046n5HXeeXD//fCCF8DBB+ce\njYiIiIiIVEA7npJP66edp56qTztFRERERAaUdjwln299C+67D3bcEQ45JPdoRERERESkItrxlDye\nfRY+97lw+9RTYdKkvOMREREREZHKaMdT8jj/fPjpT2GHHeDtb889GhERERERqZB2PKX/Wj/tPOUU\nfdopIiIiIjLgtOMp/bdgAaxeDdtvD+98Z+7RiIiIiIhIxbTjKf21Zo0+7RQRERERGTLa8ZT+uvBC\nuOcemDtXn3aKiIiIiAwJ7XhK/6xZA6edFm6fcgpMnpx3PCIiIiIi0hfa8ZT+uegiWLkS5syBd787\n92hERERERKRPtOMp/bF27bpPO08+WZ92ioiIiIgMEe14Sn9cfDHcfTdssw285z25RyMiIiIiIn2k\nHU+p3tq18NnPhtsnnwxTpuQdj4iIiIiI9JWuZTFsrrsO3v9+eOqp/mWuWQOPPAJbbw3vfW//ckVE\nREREpBa04zlM3OG44+C++/Lkn3aaPu0UERERERlC2vEcJgsXwm23weabw5IlMKmPL//668OMGf3L\nExERERGR2tCO57Bwh898Jtw+6aRwkh8REREREZE+0MmFhsWll8Ly5TB7Nnzwg7lHIyIiIiIiQ0Q7\nnsOg9dPOE0+E5z0v73hERERERGSoaMdzGCxaBLfeCpttBocfnns0IiIiIiIyZLTjOejc4dOfDrdP\nOEGfdoqIiIiISN9px3PQXXYZ3HILPP/58KEP5R6NiIiIiIgMIe14DrLW73Z+/OOwwQZ5xyMiIiIi\nIkNJO56DbPFiWLoUNt0UPvzh3KMREREREZEhpR3PQdX6aefHPgYbbph3PCIiIiIiMrS04zmorrwS\nliyBWbPgiCNyj0ZERERERIaYdjwHUeunnR/9KGy0Ud7xiIiIiIjIUNOO5yC6+mr40Y9g5kz4yEdy\nj0ZERERERIacdjwHTfunnRtvnHc8IiIiIiIy9LTjOWiuvRZuuAFmzIAjj8w9GhEREREREe14DpyR\nTzuPP16fdoqIiIiISC1ox3OQXHcdXH89TJ8ORx2VezQiIiIiIiKAdjwHy8innccdB1On5h2LiIiI\niIhIQTueg+L668MnnptsAkcfnXs0IiIiIiIif6Ydz0Ex8mnnscfCtGl5xyIiIiIiItJCO56D4IYb\n4Jprwg7nMcfkHo2IiIiIiMhf0I7nIBj5tPOYY8KhtiIiIiIiIjWiHc+mu/FGuOqqcDKhY4/NPRoR\nEREREZHn0I5n04182nn00eEyKiIiIiIiIjWjHc8m+/GP4corYeONwyVUREREREREaihpx9PM5pnZ\nPWZ2r5mdNF6Dkh61fto5Y0besYiIiIiIiHQQveNpZhOBfwNeD+wMvM3Mdh6vgUmJm26CxYtho430\naaeIiIiIiNTapITavYB73f0+ADNbABwA3D0eA+u7iy+Gb3wj9yh6d3fxNB91FMycmXcsIiIiIiIi\nXaTseG4JPNjy8y+Avdt/ycwOAw4D2GabbRLiKnbvvfDf/517FGMzdSocf3zuUYiIiIiIiHSVsuPZ\nE3efD8wH2GOPPbzqvGhveQvs3LAjhXfZBWbNyj0KERERERGRrlJ2PB8Ctm75eavivmbacccwiYiI\niIiIyLhKOavtzcCOZjbXzKYAhwALx2dYIiIiIiIiMiiiP/F092fN7EhgMTAR+Lq73zVuIxMRERER\nEZGBkPQdT3dfBCwap7GIiIiIiIjIAEo51FZERERERESklHY8RUREREREpFLa8RQREREREZFKacdT\nREREREREKqUdTxEREREREamUdjxFRERERESkUtrxFBERERERkUppx1NEREREREQqpR1PERERERER\nqZR2PEVERERERKRS2vEUERERERGRSmnHU0RERERERCqlHU8RERERERGplHY8RUREREREpFLa8RQR\nEREREZFKacdTREREREREKqUdTxEREREREamUdjxFRERERESkUtrxFBERERERkUppx1NEREREREQq\npR1PERERERERqZR2PEVERERERKRS2vEUERERERGRSmnHU0RERERERCqlHU8RERERERGplHY8RURE\nREREpFLm7v0LM3sUeKBvgXFmAb/pY92wZDZprMpUpjKVqUxlKlOZylTmoGX2y7buvulz7nV3TS0T\ncEs/64Yls0ljVaYylalMZSpTmcpUpjIHLTP3pENtRUREREREpFLa8RQREREREZFKacfzueb3uW5Y\nMps0VmUqU5nKVKYylalMZSpz0DKz6uvJhURERERERGT46BNPERERERERqZR2PEVERERERKRS2vEU\nERERERGRSmnHU0RERERERCo1KfcAcjKz1wEHAlsWdz0EXOLul/dQ+0LggLbahe6+ooq6lPEm9hmb\nmaPPacC8trrF7v5kD5k5Xs/YzJQ+o2oz9ZmS2aQ+m/b+7GufDXx+mpSpdWB1mU1bB8YuMw3Yq61u\nqfdw5srY2kx9pmQ2qU9t05RnNmabpo6G9hNPM/sycAzwA+DMYvoBcLSZnVVSeyKwADBgaTEZcIGZ\nnTTedSnjTewzNjNHn+8GlgH7ABsU077ArcVj3TJzvJ6xmSl9RtVm6jMls0l9Nu392dc+G/j8NClT\n68DBmodyrBv2B1YDnwbeUEyfAVYXj3XLjKrN1GdKZpP61DbNAG3T1Ja7D+UErOpwvwGry2qByaPc\nP6VbbWxdynhT+4zNzNDnPcAmo9w/vdP/mfv1jMxM6TOqNlOfKZmN6rPD/bV9f/azzyY+P03KTHlu\nM7yvNQ/Vb92wApgzyv1zgRUlmVG1mfpMyWxSn9qmqeeyL/o5quM0tJ94Ak+b2Z6j3L8n8HRJ7Vpg\ni1Hu37x4bLzrIH68KX3G1ubo04DRDltZWzzWTY7XM7Y2pc/Y2hx9pmQ2qc+mvT/73WfTnp8mZWod\nOFjzUI51wyTgF6Pc/xAwuSQztjZHnymZTepT2zSDtU1TS8P8Hc9DgXPMbGPWLRS2Bv6neKybY4Gr\nzWw18GBx3zbADsCRFdSljDe2LqU2R5+nA8vM7Iq2zNcCp5Vk5ng9Y2tT+oytzdFnSmaT+jyUZr0/\n+91nbF1KbY7lV9PWDVoH1u/1zLFu+Dpws5ktaKnbGjgE+FpJZmxtjj5TMpvUp7ZpBmubppas+Lh2\naJnZbFq+rOvuD/dYN4Hnfln8ZndfU0XdOIw3qi62NkefZjYdeB3P/fL1E1WNN6XPhMyUPqNqM/WZ\nktmYPovaxrw/c/TZpOenSZmJdVoHdq9p2jowdpm5M/CmtrqF7n53D5lRtZn6TMlsUp/apinPbMw2\nTR1px9Nssrs/03bfLHf/Ta4xdRM73pQ+czxHTXtdRFI07f0ZS8uv+mU2af4BzUN1fV1ERHoxtN/x\nNLN9zewXwK/M7Aozm9Py8BUltbua2RIze9DM5hd/iRh5bOl416WMN7HP2MwcfW5tZgvM7IdmdrKZ\nTW557L9KMnO8nrGZKX1G1WbqMyWzSX027f3Z1z4b+Pw0KTPludU6sH6vZ451w1Qz+4KZnWdmb2t7\n7OySzKjaTH2mZDapT23TDNA2TW3lPrtRrgm4GdiluP1WwumuX1b8vLyk9gbC9XQ2AT4G3AVsX1Yb\nW5cy3sQ+YzNz9Hkl8CFgd+BfgB8BM2v8esZmpvQZVZupz5TMJvXZtPdnX/ts4PPTpEytAwfr9cyx\nbvgO8EXCNUcXFj+vVzy2rCQzqjZTnymZTepT2zT1XPZFP0d1nLIPIFvjcHvbz7sQTll8YLeFQYfa\nfSlWDiULkqi6lPGOc5+xmf3o87a2n9858sZuyOvZa2ZKn1G1mfpMyWxyn017f1ba5wA8P03K1Dqw\n2a9njnVDe90pwI3AzIjMnmpr0mdKZp371DZNPZd90c9RHafsA8jWONwCzG67byvgNuB3JbW3A9Pa\n7tu1mIkeG++6lPEm9hmbmaPPu4D12+57DXAv8Ksavp6xmSl9RtVm6jMls0l9Nu392dc+G/j8NClT\n68DBmodyrBtWABPa7ju0+P8eKMmMqs3UZ0pmk/rUNs0AbdPUdco+gGyNhxdtt1Hu3wQ4paT27RSH\nvrTdvw3w7+NdlzLexD5jM3P0eRzwqlHufzFwZQ1fz9jMlD6jajP1mZLZpD6b9v7sa58NfH6alKl1\n4GDNQznWDWcCrxnl/nnA6pLMqNpMfaZkNqlPbdMM0DZNXaehP6stgJnNAHD3x3OPpRex403pM8dz\n1LTXRSRF096fsbT8ql9mk+Yf0DwkItJYufd8c02EvzIsAB4lfNR9L/BIcd+cktpphC+LrwQeBx4j\nHE7xRWCT8a5LGW9in7GZOfqcBBwOXA7cUUyXEb6QPbmGr2dsZkqfUbWZ+kzJbFKfTXt/9rXPBj4/\nTcrUOnCw5qG+rxuK2tcB5xBOnLOwuD2vW01KbY4+UzIb1qe2aQZom6auU/YBZGscfgwcDExsuW8i\ncAiwpKR2MXAiLd/DAGYX910x3nUp403sMzYzR58XEBbmLyN8H2ar4vY5wIU1fD1jM1P6jKrN1GdK\nZpP6bNr7s699NvD5vCwtWQAAFvxJREFUaVKm1oGDNQ/lWDd8GVhUjO0VxXRIcd9ZJZlRtZn6TMls\nUp/aphmgbZq6TtkHkK3x7sfWl3034Z5+PpYy3sQ+Y//fHH2uinks4+sZ+/+m9BlVm6nPlMwm9dm0\n92df+2zg89OkTK0DB2seyrFuGPUxwHp4bqNq69RnSmZN+9Q2zQBt09R1msDwutXMzjazvc1si2La\nu7ig7/KS2gfM7AQz22zkDjPbzMxOBB6soC5lvCl9xtbm6PNxMzvIzP48T5vZBDM7GHiiJDPH6xlb\nm9JnbG2OPlMym9Rn096f/e6zac9PkzK1DhyseSjHuuFpM9tzlPv3BJ4uyYytzdFnSmaT+syxbhiW\nPlMyU56j+sm955trAqYAHyYcM31nMV0GHEFxcd8utdOBM1h3rPbjhGO1zwBmjHddyngT+4zNzNHn\nHOBCwvdiVhXTI8V9c2v4esZmpvQZVZupz5TMJvXZtPdnX/ts4PPTpEytAwdrHppD/9cNLwFuAu4G\nriimFcAS4KUlmVG1mfpMyWxSn9qmGaBtmrpOOqutDBwzmwng7o/lHkuVUvrUcyQiMpj6vW4ws9nA\nlsWPD7n7w32q7fs6MKGuUX3mMCx9phiEPof5UFvM7HVm9n4z27bt/vf1UPtCM9vPzDZsu39eFXUp\n403sMzYzR59TzWx7d3+s9U1pZrv2kJnj9YzNTOkzqjZTnymZTeqzae/PvvbZwOenSZlaB1aX2bR1\nYHQt8Ji731pMDxd1s3qoi6rNtA5MeX6gOX1qm6Y8szHbNLWU+yPXXBPweeB6whnHfgoc1fLYspLa\no4F7gP8CfgYc0EttbF3KeBP7jM3M0ec/AL8EbgPuAvas+esZm5nSZ1Rtpj5TMpvUZ9Pen33ts4HP\nT5MytQ4crNczx7phX+AXwG8Ih5DOGUNmVG2mPlMym9SntmnqueyLfo7qOGUfQLbGw/cmJhW3NyGc\n2vqfi5+X91C7UXF7DnALcExZbWxdynjHoc/YzH73eRuweXF7L8Jx9G+u+esZk5nSZ1Rtpj5TMpvW\nZ9Pen33rs6HPT5MytQ4cnNczx7rhZmCX4vZbCdcdfVmPmVG1mfpMyWxSn9qmqeeyL/o5quM0ieE1\nyd2fBXD3J83s74D5ZnYR4cv93Uxw998XtT8zs32Ai4vDYqyCupTxpvQZW5ujz4nu/quibqmZ7Qtc\namZbA16SmeP1jK1N6TO2NkefKZlN6rNp789+99m056dJmVoHDtY8lGPdMMXd7yrqLjazFcB3LZyp\nsywztjZHnymZTepT2zSDtU1TT7F7rE2fgEuBV41y/+eAtSW11wC7t903CfgGsGa861LGm9hnbGaO\nPn8EbN9238bA1cAfa/h6xmam9BlVm6nPlMwm9dm092df+2zg89OkTK0DB2seyrFuuAWY3XbfVoRP\naH5XkhlVm6nPlMwm9altmgHapqnrlH0A2RoPL9r6HR7bsqR2S2CzDo+9fLzrUsab2GdsZo4+dwN2\nGOX+ycA7avh6xmam9BlVm6nPlMwm9dm092df+2zg89OkTK0DB2seyrFueA2w2yj3TwNOKcmMqs3U\nZ0pmk/rUNs0AbdPUdRray6mY2S2EL3xfDlzu7j8bQ+2twA2Ea2td5+5lF0pOqksZb2KfsZk5+lxc\n1Fzm7it7zUsZb2KfsZkpfUbVZuozJbNJfTbt/dnXPhv4/DQpU+vA6jKbtg6MXWbOJ/R4lbv/boyZ\nUbWZ+kzJbFKf2qapqDbHNk1dDe2OJ4CZzQHmFdOWrJspfuDuf+xSNwl4RVG3L/AYsJgwU6wa77px\nGG9UXWxtjj4tXCNrpGYnwgWbLycs7P+3JK/vr2dCZkqfUbWZ+kzJbEyfRe0cGvL+zNFnk56fJmUm\n1mkdWLPXM9O6YW/g9cB+wJ8IZ2293N1v76HHqNpMfaZkNqlPbdMM0DZNXQ31jmcrM5sMvJLwwu4D\nPOrub+yxdgvWzRQ7AEvc/Ygx1m0P3NRLXcp4E/uMzYx6fmIzzWwC0Lqwfwq4wt3P7DFzPF7PsfY5\n5tqUPhNrU+bbvr5XatLnWMbbtPdnX/ts4PMzHplNWDdoHdi9rjHrwNhaCxe237+o2xVYRtjB+nYP\nma21LwKW91Kbqc+UzKjnqCbrem3TlNfWepumNrwGx/vWcaLkux9d6iZQcrx22+8/P6YuZbzAzNQ+\nY2v72WdLzSwij4OPHW9KnwmZKX1uGlObqc+UzKjnKEefRW3t35/jsQzT8qtemYl1WgfW7PVMXDfE\nLjNfSsn3PMe7NlOfKZlN6lPbNNX12fdtmpxT9gFkaxymAl8AzgPe3vbY2SW1s4FzgH8DZgKfBu4A\nvk1xrZ0OdTPappmEC8lOB2aUZM5rub0J8LUi83w6fGG5+N0vArOK23sA9wH3Ag8wypnz2mqXAafS\ndjatHp7bPYBrgW8CWwNXAk8Srme1e0ntRsBnCRfJ/R/gUWAJ8J6SuknA4YTDD+4opsuK+yYnzCfz\nq6gDJhZjO619gQOc2qVuA+AE4OPA+sChwELgDIprRI1xnKt6+J1dW25PLuaJhYQLnW9QUntky/y3\nA+HC6E8QDhX56y513wXeGdnTdsDXCWeB3Aj4d+AnwEW0XLx7lLoJwPuA7wO3F/P/grL3SVE7rXiv\nrSz6ewxYUdy3SeQ8dFnJ41HLMCKXX0Vt1DIMLb/e0+/5p2weip1/Uuah2PlH81D5PFTyf36yh995\nHfB+2paRwPu61Bjh4vYHFbf3A74CfJhw+YixjvOaHn5nVtvP7ywyP0hxFF+X2jePzGfA8wlnFL0T\nuBDYqkvdl4jfMZgBfBL4QPEcnUw4c/E/AtNLavcF/hW4hLBO/GIv8yIVbA+hbZqR32/MNk1dp6E9\n1NbMvkO4kO8SwsbmM4SV7x/NbJm7v6RL7eWEjdMNgbcD3yKs/A4EXuPuB3SoW0tY2bXainAiAXf3\n7bpk/nlMZvZV4GHCzPf3hJXngR3q7nT3FxW3rwVOcPebzWwn4Hx336NL5v3AdwgrloeBC4AL3f2X\nnWqKuqXApwgbB2cCx3m4ftV+wOfc/W+61F4CfA+4qsjdkLDxfyrwkLuf3KHuAsKK/T8JzyeE5/Y9\nhBXNwV0yZ3R6CLjd3bcaz7qi9quEBe5S4F2E7+4cXzzWcf4zs28DDwLPA15A2Di9EHgT4ZTt7+qS\n+TvWXfNp5LpRGwB/IMx/UzvUtc57/0TYWDyXML/PdPd3d8m8y913KW5/H/iqu3/PwjWsTnf3l3eo\newj4MfBqwrxwAfB9d/9Tp6yW2uuL359GWNCfS9gg3p/wl8FXd6g7l/D+vIpwoe/fAj8ETgQucfd/\n6ZK5mHC69P9094eL+2YTVqKvdvf9O9R1Ws4YcKm7b94lM2oZFrv8KmqjlmFafpUuv6Lmn+L3ouYh\nrQMHax4qGc/P3X2bLo9/nvD9s2XA3wFfHlnelSxLzibswE0hLC/XI2zAvxH4tbsf0yXzjva7CN9f\nuwfA3XftUNc6H5xKOAz5fOBvgV+4+3FdMu92952L2xcS5v2LCGeefYe7v7ZD3aOE+XZTwvr2Andf\n3imnrXYRYed2KvBXxe1vA68lnO2203vlC4Q/8FxNeF/dD6wCjgA+7+4XdcmM2h7SNs1gbdPUVu49\n31wTcFvbz6cANxJmwGUltctbbv+82//b9thHCX+BelHLfff3ON5lnTJKMlcQLkgN4djz1sfuHEPm\nK4GzCSvfa4HDIp+f5SWZt7f9fHPx7wRgZZe6jn/l6vZY8fgawl/B72+ZRn7+03jXFbV3tNyeBMwn\n/EVsvW7P0chrTVjIPgx//uORtf6fHWq/QvgL72Yt95XOf22v520UfzHtMfOe9tdytOegUyZhZf0u\nYBHhL//nAvuPYbw9z3/t4xl5vxSvyYpe+xzjY2sIOxzXjjI9VZIZtQwreX46LkuKx6OWYWj5Vbb8\nipp/Uuah2PknZR6KnX80D/U0D/22w/Q74NmSzDtbnqNNCMvbfy4b78jzR/jU6DFgSvHzJMrXDQsJ\nnwi/ENgWmEPYAdkW2LbH53YZsGHLGMpez9b10a1jmIdG1kc7AZ8gfBq9kvDHhZ1KMlvX2Q+NIfPO\nltuTgBuL29OBn5RkRm0PoW2agdqmqes0geG1XvElXQDc/XTCX0+vJ6x4u2l93r7R5bG/4O7/RDjc\n4pNm9iUz25h1f60p83wzO97MPgpMNTNreazb63g2sMjMXg1cbmZnmdmrzOwzhDdcT9z9hx6++Lwl\n4RCIjn+xBZ42s/3N7CDAzexAADN7FWHB1s3/mtkrit9/E/B4kb+WdX/RGs3jZnZQ62tqZhPM7GDC\nYRDd3Afs4+5zW6bt3H0u8OsK6iD8hZiit2fd/TDC63EN4VCKrjwscRYV/4783HVecvejgbOAC8zs\n6OK56mX+m2ZmbzaztwDrufszvWYCF5vZf5jZdsD3zOxYM9vWzN4L/LzbcIuM37r7ee7+BsIGyk3A\nSSWZa81sJzPbE9jAzPYAMLMdCIcDdfKMmW1f/O5LCGcgxMNZJMv6fMDMTjCzzUbuMLPNzOxEwsZU\nJyuAw9193/YJ+E1JZuwyLGr5VWTELsO0/Oq+/IqdfyB+HtI6cLDmoSeBHd19atu0MfCrksxJ7v5s\nkfMk4VPPqWZ2ES3rqlGM1DxD2AgfWWY+C6ztFujubyJ8mjyf8Mnfz4Bn3P0Bd2//VLzV88zsxWb2\nUmCiF2f1LMZQ9txeZ2afNbPnFbffDGBm+xIOa+443CJjlbuf5uETr38gHBq6qCRzgplNJxxyvZGF\nMxaPnGio23O7tuUTyC0o1l3u/gTd5wOI3x7SNs1gbdPUU+4931wT4fCX14xy/zxgdUntZxnlOG3C\n8d4X95j/JsJhHg/3+Pufaps2Le6fDXyjpHYfwqELywl/2VwEHEbJsf7AgsjndjeK00QT3lhnEVaK\nd1F+odxdCYdqPEk4hfxOxf2bAkd3qZtT9Pgo4XCU1cXtC4G5JZkfYZQLPBePHTXedcXj36TlO0st\n93+AsPLtVPfVDvPe9sANPb4+E4CjCYeR/rKH3z+3bdqsZd67uof6QwkL198Q/vp+N+G7FNO61Fwf\nM+8VtfsRDtdaQTh87DuE73Q9AhzQpe7VhBXHvYS/8O7dMu+dWZI5nbAxupKwofh4kX8G3b/7+Fbg\nBR0eO7AkM2oZxjgsv4rf73kZxvAtv55gbMuvqPknZR6KnX/Gax4ay/wzpPPQWNeBnwP26vDYGSWZ\nlzLK912L/3Ntl7rLOswHs4GlPT5XGxK+Q3kJ4VDZst+/tm3avLh/JnBLSe1kwveRf15MawnrpPOB\nbbrURX+qBLyNsNP2a+AthMMsrwIeovun5gcTDu+9shjrG1vmg/NLMufwl9tDqwjrv67bQ2ibZqC2\naeo6De13POug+Kvb9u7+k9xjGSTFXxJx98dyj6WfzMx8DG9oM9sceLG7l/3FtvHMbBbwhLt3/Yt4\n8SnKTHcv+7RR0DJM0mj+qYfidcDdnxrlsS3d/aEx/n8bEg6BfWQMNbsBf+Pu/3csWS31EwmfXv2h\nx9+fRvikt3Q7wcw2cvffx4yrZWzm7s9auJ7j7oTDbrt+El184rkdcK+HT6Jjshu7PaRtms563aap\no6He8TSzFxIOm7mpdaFiZvPc/fIqapVZaeZehKMlbjaznQl/uV/h7pd1y+tSu7JsAVaM9YBivBD+\nirnQ3Vf0kBlVq8zozEvcfWVVmV3+z/e6+7n9qhuWzCaNtZfaluXeEm+5KPgALqeVWV3mNMK6q3X5\ntbiXnZbYWmXWM7PL//lad7+yX3XKrGdmTkP7HU8zO5pwaMdRwE/MrPXMYp+volaZlWZ+ivBF83Ms\nnA3uXwmH8PwfMzulJLNT7Undai18/2oB4fsWS4vJCN836HrMfmytMpMyF1SVWeIzfa4blswmjbVr\nbdty764BXk4rs7rMdxNOtrMP4cyeGxAux3Fr8Vi3zKhaZdYzs8TX+lynzHpmZjO0n3ia2Z2Ewzp+\nb+HL3hcD57n7WWa23N1fPN61yqw8c3fCGdQeJlyT67cWDiG6yTucmj2l1sxWAbt48cX0lvunAHe5\n+45dMqNqlVnbzPZLA/z5IcJ3tNYbz7phyWzSWBMzh2k5rcxqMu8hfC/9ybb7pxPWYzt1yYyqVWZt\nMxd2eohweaYNx7NOmfXMrKtJuQeQ0QQvDmFx959ZuAbPxWa2LZSeMSy2VpnVZT7r4Vj3P5jZT939\nt8X/8ZSFa8d1E1u7lnC2ufYz8G1OyRn9EmqVWc/MzQgXYG8/Y6ABP6qgblgymzTWlNphWU4rs7pM\nY/QzcpadDTelVpn1zHwl4XqP7d9LNWCvCuqUWc/MWhrmHc9fm9nu7n4bQPHXxb8Fvg68qKJaZVaX\n+Scz28DDiQVeOnKnhe9IlO00xNYeC1xtZqtZd8mDbQhndjyyJDO2Vpn1zLyUcGa+51yewcyuq6Bu\nWDKbNNaU2mFZTiuzuszTgWVmdgV/ufx6LXBaSWZsrTLrmbkE+IO7/6D9geKT1PGuU2Y9M2tpmA+1\n3ZLwSddzrk1kZi939xvHu1aZlWZO8eIaYm33zyKcbv3OLpkptRMIf3Fq/fL/zd7DmcZia5VZz0yR\nWEO0nFZmRZnF49MJn7i3n4ym7FrW0bXKrGemSF0N847nrYRrZF0GXOfuT1ddq0xlKlOZyhzMsSpT\nmZkzFwOXA5d5ydm7x6tWmcpUZn0z62qYdzwnES7EOo9wprDHKC747O6rqqhVpjKVqUxlDuZYlanM\nzJmzi5p5wE6EC9xfDlzlLZfnGc9aZSpTmfXNrKuh3fFsZ2ZbsO6F3YFwLbUjqqxVpjKVqUxlDuZY\nlanMXJkWvjKwN/B6YD/gKeAKdz+zh7yoWmUqU5n1zawVd9fUNhGub/ryftYqU5nKVKYyB3OsylRm\n5sxZwDsiM6NqlalMZdY3M+eUfQB1nID5/a5VZvMygYnA4YQzzL287bFTS/7fqFplKnOYMps0VmUq\nM3PmBsAJwMeB9YFDgYXAGYQzLXfLjKpVpjKVWd/Muk4TGFJmNqPDNBN4QxW1yhysTOD/Aa8ifAfn\nK2b2pZbH/r5bZkKtMpU5TJlNGqsylZkz8z8I15GdC3wf2AP4R8KnpOeUZMbWKlOZyqxvZj3l3vPN\nNQFrgPuA+1umkZ//VEWtMgcu846W25OA+cB3gfWA5SWZUbXKVOYwZTZprMpUZubM24p/DXiYdefw\nsNb/czxrlalMZdY3s67T0H7iSdix2Mfd57ZM27n7XOA5188ap1plDlbmlJEb7v6sux8G3A5cA2xU\nkhlbq0xlDlNmk8aqTGXmzBypcWBR8e/Iz15Wl1KrTGUqs76ZteM12PvNMQEfAXbr8NhRVdQqc+Ay\nvwnMG+X+DwDPlGRG1SpTmcOU2aSxKlOZmTO/yijf9wK2B24oyYyqVaYylVnfzLpOQ305FTN7IXAA\nsGVx10PAQndfUVWtMpWpTGUqczDHqkxl1jETWOklG3uxtcpUpjLrm1lHQ3uorZmdACwgHCO9tJgM\nuMDMTqqiVpnKVKYylTmYY1WmMuuaCZwYm9mtVpnKVGZ9M2sr90euuSZgFTB5lPunAKurqFWmMpWp\nTGUO5liVqUxlKlOZyqxLZl2nof3EE1gLbDHK/ZsXj1VRq0xlKlOZyqy+TpnKVKYylanMYc6spUm5\nB5DRscDVZrYaeLC4bxtgB+DIimqVqUxlKlOZgzlWZSpTmcpUpjLrkllLw35yoQnAXvzll3Vvdvc1\nVdUqU5nKVKYyB3OsylSmMpWpTGXWJbOOhnrHU0RERERERKo3zN/xFBERERERkT7QjqeIiIiIiIhU\nSjueIiIiIiIiUinteIqIiIiIiEiltOMpIiIiIiIilfr/B/jIJRtkZx8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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ddyRLliRXXdX2NAAAfSc8Aebbxo3N8aqrkqVL250FAGAeCE+A+WabLQAwZoQn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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ji32glHJvKeX+Usr9x44dW/1kAADT4pFHmuvdd3c7B0DHhg7MUsr6JN+c5BcX+3it9b5a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\nQQeYQgghhBBCCCEa4f8DkeofbiMnm/IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OAADj5IAfZojABACAcbL/khkiMAEAYJwEJjNEYAIAwLgcO5bs29c83rGj3VlgAgQmAACM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\nQgghhKgFTTCFEEIIIYQQQtTC/wcf13t0bv4FiwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpynjvAuQ0QU",
        "colab_type": "text"
      },
      "source": [
        "For US"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhdXDHfxQzeE",
        "colab_type": "code",
        "outputId": "335f3128-ab9f-4c55-bcb7-8adbee4d4cc6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "UsP = ['Washington','California', 'Florida', 'New York']\n",
        "US=df.groupby(['Country/Region']).get_group('US')\n",
        "for c in UsP:\n",
        "  provC=curve(c,US) \n",
        "  print('Death Curve for',c)\n",
        "  fig= plt.figure(figsize=(16,13))\n",
        "  plt.xticks(rotation=90)\n",
        "  plt.title('Death Curve for'+c)\n",
        "  plt.plot(list(provC.keys()), list(provC.values()),color='red', linewidth=2)\n",
        "  \n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Death Curve for Washington\n",
            "Death Curve for California\n",
            "Death Curve for Florida\n",
            "Death Curve for New York\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AAFhad95ZF81LLqmPSjnuuGS//RyVAi2KJwAALI1LLqmPSpk2rT4q5dvfTt70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\nUvVamAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Qcu21yUYbJeefn+yzT9NTkVVYaltK+c9Syn2llBuW+d4/l1JuKqX8qpRyfillw/aOCQAA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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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f/113ImhKZjABAOCFPPVUtanPo48mn/hEdfYl8JwUTAAAeD5lmey+e/LHPyab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\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KBpXiCQAArfS1ryXf/GYycmRywQXJFls0nQgGneIJAACt8v3v10OEkuSMM5Kd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\nJEmSJLXV/wf3fagPDk9IuQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsks4XbyRx_P",
        "colab_type": "text"
      },
      "source": [
        "#### Question # 05\n",
        "\n",
        "This aspect is interesting since we would get to work with Geographical Plots. You have the latitude and longitude data from the `time_series_covid_19_deaths.csv` data file. You will use that for this question.\n",
        "\n",
        "We will use BaseMap library to plot the latitude and longitude points on the world map.\n",
        "You can download BaseMap by running the following command in your terminal/command line prompt: \n",
        "```\n",
        "pip3 install https://github.com/matplotlib/basemap/archive/v1.1.0.tar.gz\n",
        "```\n",
        "\n",
        "1. Once you have it installed, load up a basic world map and plot the values.\n",
        "2. By visually examining, answer in which countries, the points scatter the most?\n",
        "\n",
        "For reference and working directions with Basemap, you may take help from this video tutorial: <a href=\"https://www.youtube.com/watch?time_continue=114&v=XiZbrii49pI&feature=emb_title\" BASEMAP >BASEMAP TUTORIAL</a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nk-C8dfuRx_S",
        "colab_type": "code",
        "outputId": "f64726c6-3f18-4d41-c33a-903f5eed965d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        }
      },
      "source": [
        "!apt-get install libgeos-3.5.0\n",
        "!apt-get install libgeos-dev\n",
        "!pip install https://github.com/matplotlib/basemap/archive/master.zip\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\rReading package lists... 0%\r\rReading package lists... 0%\r\rReading package lists... 0%\r\rReading package lists... 7%\r\rReading package lists... 7%\r\rReading package lists... 7%\r\rReading package lists... 7%\r\rReading package lists... 58%\r\rReading package lists... 64%\r\rReading package lists... 64%\r\rReading package lists... 65%\r\rReading package lists... 65%\r\rReading package lists... 72%\r\rReading package lists... 72%\r\rReading package lists... 72%\r\rReading package lists... 72%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 81%\r\rReading package lists... 87%\r\rReading package lists... 87%\r\rReading package lists... 87%\r\rReading package lists... 87%\r\rReading package lists... 93%\r\rReading package lists... 93%\r\rReading package lists... 93%\r\rReading package lists... 93%\r\rReading package lists... 93%\r\rReading package lists... 93%\r\rReading package lists... 94%\r\rReading package lists... 94%\r\rReading package lists... 95%\r\rReading package lists... 95%\r\rReading package lists... 98%\r\rReading package lists... 98%\r\rReading package lists... 98%\r\rReading package lists... 98%\r\rReading package lists... Done\r\n",
            "\rBuilding dependency tree... 0%\r\rBuilding dependency tree... 0%\r\rBuilding dependency tree... 50%\r\rBuilding dependency tree... 50%\r\rBuilding dependency tree... 97%\r\rBuilding dependency tree       \r\n",
            "\rReading state information... 0%\r\rReading state information... 0%\r\rReading state information... Done\r\n",
            "E: Unable to locate package libgeos-3.5.0\n",
            "E: Couldn't find any package by glob 'libgeos-3.5.0'\n",
            "E: Couldn't find any package by regex 'libgeos-3.5.0'\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "Suggested packages:\n",
            "  libgdal-doc\n",
            "The following NEW packages will be installed:\n",
            "  libgeos-dev\n",
            "0 upgraded, 1 newly installed, 0 to remove and 25 not upgraded.\n",
            "Need to get 73.1 kB of archives.\n",
            "After this operation, 486 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libgeos-dev amd64 3.6.2-1build2 [73.1 kB]\n",
            "Fetched 73.1 kB in 1s (143 kB/s)\n",
            "Selecting previously unselected package libgeos-dev.\n",
            "(Reading database ... 133872 files and directories currently installed.)\n",
            "Preparing to unpack .../libgeos-dev_3.6.2-1build2_amd64.deb ...\n",
            "Unpacking libgeos-dev (3.6.2-1build2) ...\n",
            "Setting up libgeos-dev (3.6.2-1build2) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Collecting https://github.com/matplotlib/basemap/archive/master.zip\n",
            "\u001b[?25l  Downloading https://github.com/matplotlib/basemap/archive/master.zip\n",
            "\u001b[K     | 207.6MB 452kB/s\n",
            "\u001b[?25hRequirement already satisfied: matplotlib!=3.0.1,>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (3.2.0)\n",
            "Requirement already satisfied: numpy>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (1.18.2)\n",
            "Collecting pyproj>=1.9.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/37/705ee471f71130d4ceee41bbcb06f3b52175cb89273cbb5755ed5e6374e0/pyproj-2.6.0-cp36-cp36m-manylinux2010_x86_64.whl (10.4MB)\n",
            "\u001b[K     |████████████████████████████████| 10.4MB 2.7MB/s \n",
            "\u001b[?25hCollecting pyshp>=1.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/27/16/3bf15aa864fb77845fab8007eda22c2bd67bd6c1fd13496df452c8c43621/pyshp-2.1.0.tar.gz (215kB)\n",
            "\u001b[K     |████████████████████████████████| 225kB 50.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from basemap==1.2.1) (1.12.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.4.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (2.8.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (1.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.1,>=1.0.0->basemap==1.2.1) (46.0.0)\n",
            "Building wheels for collected packages: basemap, pyshp\n",
            "  Building wheel for basemap (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for basemap: filename=basemap-1.2.1-cp36-cp36m-linux_x86_64.whl size=121756010 sha256=967bb2c572ab0efc2f5ce5ac2c94aa25ecaa541951fb1d4c57ada130b95872d3\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-tg96r2yf/wheels/98/4a/fc/ce719b75d97e646645c225f3332b1b217536100314922e9572\n",
            "  Building wheel for pyshp (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyshp: filename=pyshp-2.1.0-cp36-none-any.whl size=32609 sha256=749ff3c3da69922fed81695811481ce62623077b4ed0119a485959323e04cf7b\n",
            "  Stored in directory: /root/.cache/pip/wheels/a6/0c/de/321b5192ad416b328975a2f0385f72c64db4656501eba7cc1a\n",
            "Successfully built basemap pyshp\n",
            "Installing collected packages: pyproj, pyshp, basemap\n",
            "Successfully installed basemap-1.2.1 pyproj-2.6.0 pyshp-2.1.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "mpl_toolkits"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHdTGnvSEcq-",
        "colab_type": "code",
        "outputId": "21fe74a1-2efc-4323-99b0-f72f385bcf81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        }
      },
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from mpl_toolkits.basemap import Basemap\n",
        "\n",
        "dataset = pd.read_csv('time_series_covid_19_deaths.csv')\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(12,9))\n",
        "\n",
        "m = Basemap(projection='mill',\n",
        "           llcrnrlat = -90,\n",
        "           urcrnrlat = 90,\n",
        "           llcrnrlon = -180,\n",
        "           urcrnrlon = 180,\n",
        "           resolution = 'c')\n",
        "\n",
        "m.drawcoastlines()\n",
        "m.drawparallels(np.arange(-90,90,10),labels=[True,False,False,False])\n",
        "m.drawmeridians(np.arange(-180,180,30),labels=[0,0,0,1])\n",
        "\n",
        "latitude = dataset['Lat'].tolist()\n",
        "longitude = dataset['Long'].tolist()\n",
        "\n",
        "m.scatter(longitude,latitude,latlon=True, s=500, c='blue', marker='x', alpha=1)\n",
        "\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpUpcs2ZNsZ9pUUMtbYqroQ4NDaW5uTkdHBw4duxYHj9+vMh5HXK5XKk07tSp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goKCnH58mVs2LABPj4+aNiwITgcDvr374927RaA6DqaNh0JMzMz2Nr6gcgJlpY+sLQcAKItUFfn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q1bOHbsGPr06WMR8Ri7nT9/PurUqYOGDRtaRDzGbpcvXx6lVCoX650554BeNdQKhcIGQCGS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\nUZR88P/t199Pv3Q+7gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xE2pwj0CRx_d",
        "colab_type": "text"
      },
      "source": [
        "#### Question # 06\n",
        "\n",
        "For this question, we will use the `COVID19_open_line_list.csv` file. \n",
        "\n",
        "1. Read the data file and examine the features\n",
        "2. We will be working with these feature columns: `age, sex, symptoms, city, province, lives_in_Wuhan, and travel_history_location`.\n",
        "3. Since these are textual values, you need to encode them to integers (maintain an encoding dictionary as well).\n",
        "    * For example in `symptoms` column, you might assign fever -> 1, pneumonia -> 2, headache -> 3, etc.\n",
        "    * There are several multiple symptoms as well for a single row, for those you can just sum the assigned integers. For example a row might list these symptons: fever, headache, since fever -> 1 and headache -> 3, you can replace fever, headache with 1+3 = 4 and so on. Start the integer encoding from 1 so that when summing the numbers, you donot get the same number back (e.g. 0+3 = 3).\n",
        "    * You can do the same for other columns, just to avoid ambiguity, you can encode each column differently. \n",
        "    * For example, for the `symptoms` column, you starting assigning numbers from 1, let's say there were 4 symptoms, so the numbers would range from 1 - 4. For the next column, you can use numbers from 5 and so on. You are free to encode as you want. \n",
        "    * For ease, you can also use sklearn's LabelEncoder.\n",
        "4. Once you have integer coded the entire dataset (7 columns), you can compute the correlation between city (let city column be `y` for this case) and every other column.\n",
        "5. Pick the most 2 correlated fields (using the absolute value of correlations) and make `X`.\n",
        "6. Use MinMaxScaler to scale X. Note that this will output a `np.array`. Make it a DataFrame again and rename the columns appropriately.\n",
        "7. Print the head (5 rows) and tail (5 rows) of this dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WswP7HAhRx_g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_csv('COVID19_open_line_list.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNkFo8Y_3qIY",
        "colab_type": "code",
        "outputId": "64e82c16-ee3d-4dd7-edf2-6eaa4ecd9d48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 0,
      "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>ID</th>\n",
              "      <th>wuhan(0)_not_wuhan(1)</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>chronic_disease_binary</th>\n",
              "      <th>Unnamed: 33</th>\n",
              "      <th>Unnamed: 34</th>\n",
              "      <th>Unnamed: 35</th>\n",
              "      <th>Unnamed: 36</th>\n",
              "      <th>Unnamed: 37</th>\n",
              "      <th>Unnamed: 38</th>\n",
              "      <th>Unnamed: 39</th>\n",
              "      <th>Unnamed: 40</th>\n",
              "      <th>Unnamed: 41</th>\n",
              "      <th>Unnamed: 42</th>\n",
              "      <th>Unnamed: 43</th>\n",
              "      <th>Unnamed: 44</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13173.000000</td>\n",
              "      <td>13170.0</td>\n",
              "      <td>13147.000000</td>\n",
              "      <td>13147.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6769.250436</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.994650</td>\n",
              "      <td>110.495353</td>\n",
              "      <td>0.722222</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3920.886020</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.536335</td>\n",
              "      <td>27.985144</td>\n",
              "      <td>0.460889</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-37.813600</td>\n",
              "      <td>-124.953070</td>\n",
              "      <td>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3351.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.905380</td>\n",
              "      <td>111.729600</td>\n",
              "      <td>0.250000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6865.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>31.759410</td>\n",
              "      <td>115.441800</td>\n",
              "      <td>1.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10180.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35.781340</td>\n",
              "      <td>120.440400</td>\n",
              "      <td>1.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>13479.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>69.649468</td>\n",
              "      <td>153.400000</td>\n",
              "      <td>1.000000</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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 ID  wuhan(0)_not_wuhan(1)  ...  Unnamed: 43  Unnamed: 44\n",
              "count  13173.000000                13170.0  ...          0.0          0.0\n",
              "mean    6769.250436                    1.0  ...          NaN          NaN\n",
              "std     3920.886020                    0.0  ...          NaN          NaN\n",
              "min        1.000000                    1.0  ...          NaN          NaN\n",
              "25%     3351.000000                    1.0  ...          NaN          NaN\n",
              "50%     6865.000000                    1.0  ...          NaN          NaN\n",
              "75%    10180.000000                    1.0  ...          NaN          NaN\n",
              "max    13479.000000                    1.0  ...          NaN          NaN\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iyaq44be32j6",
        "colab_type": "code",
        "outputId": "eabf1c40-bfa8-457b-d3fc-c02a115aa434",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'age', 'sex', 'city', 'province', 'country',\n",
              "       'wuhan(0)_not_wuhan(1)', 'latitude', 'longitude', 'geo_resolution',\n",
              "       'date_onset_symptoms', 'date_admission_hospital', 'date_confirmation',\n",
              "       'symptoms', 'lives_in_Wuhan', 'travel_history_dates',\n",
              "       'travel_history_location', 'reported_market_exposure',\n",
              "       'additional_information', 'chronic_disease_binary', 'chronic_disease',\n",
              "       'source', 'sequence_available', 'outcome', 'date_death_or_discharge',\n",
              "       'notes_for_discussion', 'location', 'admin3', 'admin2', 'admin1',\n",
              "       'country_new', 'admin_id', 'data_moderator_initials', 'Unnamed: 33',\n",
              "       'Unnamed: 34', 'Unnamed: 35', 'Unnamed: 36', 'Unnamed: 37',\n",
              "       'Unnamed: 38', 'Unnamed: 39', 'Unnamed: 40', 'Unnamed: 41',\n",
              "       'Unnamed: 42', 'Unnamed: 43', 'Unnamed: 44'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AB8j80dY5cbz",
        "colab_type": "code",
        "outputId": "0330ae23-b69c-434a-cfc1-3b8e62860bba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "df=df[['age', 'sex', 'symptoms', 'city', 'province', 'lives_in_Wuhan','travel_history_location']]\n",
        "df"
      ],
      "execution_count": 0,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>symptoms</th>\n",
              "      <th>city</th>\n",
              "      <th>province</th>\n",
              "      <th>lives_in_Wuhan</th>\n",
              "      <th>travel_history_location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>30</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Chaohu City, Hefei City</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>yes</td>\n",
              "      <td>Wuhan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Baohe District, Hefei City</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>no</td>\n",
              "      <td>Luzhou Hunan, via Wuhan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>49</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>High-Tech Zone, Hefei City</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>no</td>\n",
              "      <td>Yinzhou Hunan, via Wuhan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>47</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>High-Tech Zone, Hefei City</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>no</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Feidong County, Hefei City</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>no</td>\n",
              "      <td>Wuhan</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14121</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14122</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14123</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14124</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14125</th>\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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14126 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age     sex symptoms  ... province lives_in_Wuhan   travel_history_location\n",
              "0       30    male      NaN  ...    Anhui            yes                     Wuhan\n",
              "1       47    male      NaN  ...    Anhui             no   Luzhou Hunan, via Wuhan\n",
              "2       49    male      NaN  ...    Anhui             no  Yinzhou Hunan, via Wuhan\n",
              "3       47  female      NaN  ...    Anhui             no                       NaN\n",
              "4       50  female      NaN  ...    Anhui             no                     Wuhan\n",
              "...    ...     ...      ...  ...      ...            ...                       ...\n",
              "14121  NaN     NaN      NaN  ...      NaN            NaN                       NaN\n",
              "14122  NaN     NaN      NaN  ...      NaN            NaN                       NaN\n",
              "14123  NaN     NaN      NaN  ...      NaN            NaN                       NaN\n",
              "14124  NaN     NaN      NaN  ...      NaN            NaN                       NaN\n",
              "14125  NaN     NaN      NaN  ...      NaN            NaN                       NaN\n",
              "\n",
              "[14126 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXyio0Q49mbj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=df.fillna('NA')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x94uVz2B9s-0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "for column in df.columns:\n",
        "  le.fit(df[column])\n",
        "  df[column] = le.transform(df[column])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6L37sdtv-esB",
        "colab_type": "code",
        "outputId": "cdb04fe8-f208-4cf6-823d-7f7ba2be1bda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "le.inverse_transform"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method LabelEncoder.inverse_transform of LabelEncoder()>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7sO4nDR-pR2",
        "colab_type": "code",
        "outputId": "f78c78be-43bb-4278-be3b-39b1cbb10966",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "source": [
        "for col in list(df.columns):\n",
        "  print('Absolute value of city with', col, abs(df['city'].corr(df[col])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Absolute value of correlatioin between city and age 0.06010611985495587\n",
            "Absolute value of correlatioin between city and sex 0.08383836348201464\n",
            "Absolute value of correlatioin between city and symptoms 0.047428758803770614\n",
            "Absolute value of correlatioin between city and city 1.0\n",
            "Absolute value of correlatioin between city and province 0.14296158283723115\n",
            "Absolute value of correlatioin between city and lives_in_Wuhan 0.05541514153675362\n",
            "Absolute value of correlatioin between city and travel_history_location 0.01997107127656418\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sW5r1pex_x_Y",
        "colab_type": "code",
        "outputId": "dcc5c247-6ca5-49ac-c5e9-d82148dff609",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        }
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 0,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>symptoms</th>\n",
              "      <th>city</th>\n",
              "      <th>province</th>\n",
              "      <th>lives_in_Wuhan</th>\n",
              "      <th>travel_history_location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.741257</td>\n",
              "      <td>-0.420462</td>\n",
              "      <td>0.060106</td>\n",
              "      <td>-0.011341</td>\n",
              "      <td>-0.405191</td>\n",
              "      <td>-0.256712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>-0.741257</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.483601</td>\n",
              "      <td>-0.083838</td>\n",
              "      <td>0.022234</td>\n",
              "      <td>0.453334</td>\n",
              "      <td>0.264579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>symptoms</th>\n",
              "      <td>-0.420462</td>\n",
              "      <td>0.483601</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.047429</td>\n",
              "      <td>0.011460</td>\n",
              "      <td>0.358518</td>\n",
              "      <td>0.210328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>city</th>\n",
              "      <td>0.060106</td>\n",
              "      <td>-0.083838</td>\n",
              "      <td>-0.047429</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.142962</td>\n",
              "      <td>-0.055415</td>\n",
              "      <td>-0.019971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>province</th>\n",
              "      <td>-0.011341</td>\n",
              "      <td>0.022234</td>\n",
              "      <td>0.011460</td>\n",
              "      <td>0.142962</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.015961</td>\n",
              "      <td>0.027648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lives_in_Wuhan</th>\n",
              "      <td>-0.405191</td>\n",
              "      <td>0.453334</td>\n",
              "      <td>0.358518</td>\n",
              "      <td>-0.055415</td>\n",
              "      <td>0.015961</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.411611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>travel_history_location</th>\n",
              "      <td>-0.256712</td>\n",
              "      <td>0.264579</td>\n",
              "      <td>0.210328</td>\n",
              "      <td>-0.019971</td>\n",
              "      <td>0.027648</td>\n",
              "      <td>0.411611</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              age  ...  travel_history_location\n",
              "age                      1.000000  ...                -0.256712\n",
              "sex                     -0.741257  ...                 0.264579\n",
              "symptoms                -0.420462  ...                 0.210328\n",
              "city                     0.060106  ...                -0.019971\n",
              "province                -0.011341  ...                 0.027648\n",
              "lives_in_Wuhan          -0.405191  ...                 0.411611\n",
              "travel_history_location -0.256712  ...                 1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BKvV3-bOBe9W",
        "colab_type": "text"
      },
      "source": [
        "age and sex are related by a factor of 0.74"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQtcAd4rBP8-",
        "colab_type": "code",
        "outputId": "d4d084c6-af33-431f-962b-fe6d27472ea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "X = df[['age','sex']]\n",
        "X"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>14122</th>\n",
              "      <td>116</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14123</th>\n",
              "      <td>116</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14124</th>\n",
              "      <td>116</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>14125</th>\n",
              "      <td>116</td>\n",
              "      <td>3</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>14126 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       age  sex\n",
              "0       39    5\n",
              "1       62    5\n",
              "2       64    5\n",
              "3       62    4\n",
              "4       66    4\n",
              "...    ...  ...\n",
              "14121  116    3\n",
              "14122  116    3\n",
              "14123  116    3\n",
              "14124  116    3\n",
              "14125  116    3\n",
              "\n",
              "[14126 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMsmKlJ4CKCw",
        "colab_type": "code",
        "outputId": "3061bc71-ab11-470c-d108-069f0d6922f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(X)\n",
        "#scaler.transform(X)\n",
        "X=scaler.transform(X)\n",
        "X=pd.DataFrame(X)\n",
        "X.columns=['age','sex']\n",
        "X"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<p>14126 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            age  sex\n",
              "0      0.336207  1.0\n",
              "1      0.534483  1.0\n",
              "2      0.551724  1.0\n",
              "3      0.534483  0.8\n",
              "4      0.568966  0.8\n",
              "...         ...  ...\n",
              "14121  1.000000  0.6\n",
              "14122  1.000000  0.6\n",
              "14123  1.000000  0.6\n",
              "14124  1.000000  0.6\n",
              "14125  1.000000  0.6\n",
              "\n",
              "[14126 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "X.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        age  sex\n",
              "0  0.336207  1.0\n",
              "1  0.534483  1.0\n",
              "2  0.551724  1.0\n",
              "3  0.534483  0.8\n",
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            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
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    {
      "cell_type": "code",
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        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "X.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       age  sex\n",
              "14121  1.0  0.6\n",
              "14122  1.0  0.6\n",
              "14123  1.0  0.6\n",
              "14124  1.0  0.6\n",
              "14125  1.0  0.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    }
  ]
}