datetime.ipynb

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  "metadata": {
    "colab": {
      "name": "datetime.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mgobeaalcoba/Full_Pandas/blob/main/datetime.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jW-PaDJ_vqNM",
        "outputId": "bcf56af3-ac73-491b-9449-688754255a26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D57twFIDvynx",
        "outputId": "26ec78c5-34b9-49c4-dbd5-e51434f0c26b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "%cd '/content/drive/My Drive/Colab Notebooks/db' # Me muevo hacia mi carpeta de databases\n",
        "!ls"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/My Drive/Colab Notebooks/db\n",
            "covid_19_data.csv\tpoblacion.csv  test_excel.xlsx\ttest.parquet\n",
            "london_merged.csv\ttest2.csv      test.hdf\t\ttest.pkl\n",
            "Meteorite_Landings.csv\ttest.csv       test_json.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El4Kv86KxU_K",
        "outputId": "0ce49578-b712-46fd-bd68-141f8a5f09c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import pandas as pd\n",
        "pd.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.1.5'"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqL9alcFzsk4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "outputId": "50a52751-186c-4214-bdd3-35af821bce8e"
      },
      "source": [
        "df = pd.read_csv('covid_19_data.csv')\n",
        "df.sample(10)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>390</th>\n",
              "      <td>391</td>\n",
              "      <td>01/30/2020</td>\n",
              "      <td>Shaanxi</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>1/30/20 16:00</td>\n",
              "      <td>63.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>902</th>\n",
              "      <td>903</td>\n",
              "      <td>02/06/2020</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-02-01T19:53:03</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2120</th>\n",
              "      <td>2121</td>\n",
              "      <td>02/23/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>South Korea</td>\n",
              "      <td>2020-02-23T23:33:07</td>\n",
              "      <td>602.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1135</th>\n",
              "      <td>1136</td>\n",
              "      <td>02/10/2020</td>\n",
              "      <td>Heilongjiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-10T07:33:05</td>\n",
              "      <td>331.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1581</th>\n",
              "      <td>1582</td>\n",
              "      <td>02/16/2020</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-16T12:03:06</td>\n",
              "      <td>287.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2703</th>\n",
              "      <td>2704</td>\n",
              "      <td>02/29/2020</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-29T09:13:10</td>\n",
              "      <td>1205.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1016.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>913</th>\n",
              "      <td>914</td>\n",
              "      <td>02/07/2020</td>\n",
              "      <td>Jiangxi</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-07T01:23:02</td>\n",
              "      <td>661.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>511</th>\n",
              "      <td>512</td>\n",
              "      <td>02/01/2020</td>\n",
              "      <td>Heilongjiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2/1/2020 10:33</td>\n",
              "      <td>80.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1725</th>\n",
              "      <td>1726</td>\n",
              "      <td>02/18/2020</td>\n",
              "      <td>Diamond Princess cruise ship</td>\n",
              "      <td>Others</td>\n",
              "      <td>2020-02-18T16:53:03</td>\n",
              "      <td>542.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3295</th>\n",
              "      <td>3296</td>\n",
              "      <td>03/04/2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>San Marino</td>\n",
              "      <td>2020-03-04T19:33:03</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SNo ObservationDate  ... Deaths Recovered\n",
              "390    391      01/30/2020  ...    0.0       0.0\n",
              "902    903      02/06/2020  ...    0.0       0.0\n",
              "2120  2121      02/23/2020  ...    6.0      18.0\n",
              "1135  1136      02/10/2020  ...    7.0      30.0\n",
              "1581  1582      02/16/2020  ...    0.0      82.0\n",
              "2703  2704      02/29/2020  ...    1.0    1016.0\n",
              "913    914      02/07/2020  ...    0.0      45.0\n",
              "511    512      02/01/2020  ...    2.0       2.0\n",
              "1725  1726      02/18/2020  ...    0.0       0.0\n",
              "3295  3296      03/04/2020  ...    1.0       0.0\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['ObservationDate'] = pd.to_datetime(df['ObservationDate'])"
      ],
      "metadata": {
        "id": "dsh1MkM8amDH"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "SvYyga3ba1zZ",
        "outputId": "ff12272e-5dc4-4684-9f0c-e2e88535b8fd"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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>3245</th>\n",
              "      <td>3246</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>Shandong</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-03-04T12:53:03</td>\n",
              "      <td>758.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>516.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>833</th>\n",
              "      <td>834</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>Orange, CA</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-02-01T19:53:03</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>502</td>\n",
              "      <td>2020-02-01</td>\n",
              "      <td>Sichuan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2/1/2020 1:52</td>\n",
              "      <td>207.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3021</th>\n",
              "      <td>3022</td>\n",
              "      <td>2020-03-02</td>\n",
              "      <td>Snohomish County, WA</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-03-02T20:53:02</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2488</th>\n",
              "      <td>2489</td>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>Shandong</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-27T14:13:06</td>\n",
              "      <td>756.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>387.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       SNo ObservationDate        Province/State  ... Confirmed Deaths  Recovered\n",
              "3245  3246      2020-03-04              Shandong  ...     758.0    6.0      516.0\n",
              "833    834      2020-02-05            Orange, CA  ...       1.0    0.0        0.0\n",
              "501    502      2020-02-01               Sichuan  ...     207.0    1.0        3.0\n",
              "3021  3022      2020-03-02  Snohomish County, WA  ...       4.0    1.0        0.0\n",
              "2488  2489      2020-02-27              Shandong  ...     756.0    6.0      387.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okNEHaKF0Wfm",
        "outputId": "4924249b-eaa4-4eb9-bf58-7b02900ec58c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "list(df) # las listo para copiar, pegar y luego eliminar aquellas que no voy a usar en mi análisis"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SNo',\n",
              " 'ObservationDate',\n",
              " 'Province/State',\n",
              " 'Country/Region',\n",
              " 'Last Update',\n",
              " 'Confirmed',\n",
              " 'Deaths',\n",
              " 'Recovered']"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EtNtzEz20h1n",
        "outputId": "71804d8c-868d-47d7-e9bf-94ba82baf467",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df = df[['ObservationDate',\n",
        " 'Country/Region',\n",
        " 'Confirmed',\n",
        " 'Deaths',\n",
        " 'Recovered']]\n",
        "df"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Country/Region</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>2020-01-22</td>\n",
              "      <td>Mainland China</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>2020-01-22</td>\n",
              "      <td>Mainland China</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>2020-01-22</td>\n",
              "      <td>Mainland China</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>2020-01-22</td>\n",
              "      <td>Mainland China</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>2020-01-22</td>\n",
              "      <td>Mainland China</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3390</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>Ukraine</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3391</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3392</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>US</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3393</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>US</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3394</th>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>US</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3395 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     ObservationDate  Country/Region  Confirmed  Deaths  Recovered\n",
              "0         2020-01-22  Mainland China        1.0     0.0        0.0\n",
              "1         2020-01-22  Mainland China       14.0     0.0        0.0\n",
              "2         2020-01-22  Mainland China        6.0     0.0        0.0\n",
              "3         2020-01-22  Mainland China        1.0     0.0        0.0\n",
              "4         2020-01-22  Mainland China        0.0     0.0        0.0\n",
              "...              ...             ...        ...     ...        ...\n",
              "3390      2020-03-04         Ukraine        1.0     0.0        0.0\n",
              "3391      2020-03-04       Australia        0.0     0.0        0.0\n",
              "3392      2020-03-04              US        0.0     0.0        0.0\n",
              "3393      2020-03-04              US        0.0     0.0        0.0\n",
              "3394      2020-03-04              US        0.0     0.0        0.0\n",
              "\n",
              "[3395 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4lE-7Ij0nJj",
        "outputId": "37d2815f-f9ec-45dc-cebe-378b923c79c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df_time = df.groupby('ObservationDate').sum() # Agrupo por la variable tipo tiempo\n",
        "df_time.head(5)\n",
        "# El tiempo ha quedado como el indice de mis datos"
      ],
      "execution_count": 26,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1438.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2118.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22           555.0    17.0       28.0\n",
              "2020-01-23           653.0    18.0       30.0\n",
              "2020-01-24           941.0    26.0       36.0\n",
              "2020-01-25          1438.0    42.0       39.0\n",
              "2020-01-26          2118.0    56.0       52.0"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4xfhF6I00zr",
        "outputId": "03c259a0-816b-4475-ac54-67f53df96359",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df1 = df_time['Confirmed'].iloc[10:15] # Selecciono entre los casos confirmado los que están entre la fila 10 y la fila 15 con .iloc\n",
        "df1"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObservationDate\n",
              "2020-02-01    12038.0\n",
              "2020-02-02    16787.0\n",
              "2020-02-03    19881.0\n",
              "2020-02-04    23892.0\n",
              "2020-02-05    27636.0\n",
              "Name: Confirmed, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KMpHMqTM1DDy",
        "outputId": "50228e78-327f-4c30-8970-db863ab34598",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df2 = df_time['Deaths'].iloc[12:17]\n",
        "df2"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObservationDate\n",
              "2020-02-03    426.0\n",
              "2020-02-04    492.0\n",
              "2020-02-05    564.0\n",
              "2020-02-06    634.0\n",
              "2020-02-07    719.0\n",
              "Name: Deaths, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3nU-XuK1EnB",
        "outputId": "1301f410-8081-4c9d-ed5b-3d7a0ae07739",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df1 - df2 # Resto confirmados menos muertos. Y donde no se comparten los indices de fechas me arroja variable nula"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ObservationDate\n",
              "2020-02-01        NaN\n",
              "2020-02-02        NaN\n",
              "2020-02-03    19455.0\n",
              "2020-02-04    23400.0\n",
              "2020-02-05    27072.0\n",
              "2020-02-06        NaN\n",
              "2020-02-07        NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfUNVCad1IDj",
        "outputId": "ba48d1f4-94e9-4715-f899-eeb226999637",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_time"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1438.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2118.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>2927.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>5578.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>6165.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>8235.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9925.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>12038.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>284.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>16787.0</td>\n",
              "      <td>362.0</td>\n",
              "      <td>472.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>19881.0</td>\n",
              "      <td>426.0</td>\n",
              "      <td>623.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-04</th>\n",
              "      <td>23892.0</td>\n",
              "      <td>492.0</td>\n",
              "      <td>852.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>27636.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>1124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>30818.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>1487.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>34392.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>2011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>37121.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>2616.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>40151.0</td>\n",
              "      <td>906.0</td>\n",
              "      <td>3244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>42763.0</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>3946.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-11</th>\n",
              "      <td>44803.0</td>\n",
              "      <td>1113.0</td>\n",
              "      <td>4683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>45222.0</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>5150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>60370.0</td>\n",
              "      <td>1371.0</td>\n",
              "      <td>6295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>66887.0</td>\n",
              "      <td>1523.0</td>\n",
              "      <td>8058.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-15</th>\n",
              "      <td>69032.0</td>\n",
              "      <td>1666.0</td>\n",
              "      <td>9395.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-16</th>\n",
              "      <td>71226.0</td>\n",
              "      <td>1770.0</td>\n",
              "      <td>10865.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-17</th>\n",
              "      <td>73260.0</td>\n",
              "      <td>1868.0</td>\n",
              "      <td>12583.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>75138.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>14352.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>75641.0</td>\n",
              "      <td>2122.0</td>\n",
              "      <td>16121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>76199.0</td>\n",
              "      <td>2247.0</td>\n",
              "      <td>18177.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>76843.0</td>\n",
              "      <td>2251.0</td>\n",
              "      <td>18890.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-22</th>\n",
              "      <td>78599.0</td>\n",
              "      <td>2458.0</td>\n",
              "      <td>22886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-23</th>\n",
              "      <td>78985.0</td>\n",
              "      <td>2469.0</td>\n",
              "      <td>23394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>79570.0</td>\n",
              "      <td>2629.0</td>\n",
              "      <td>25227.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>80415.0</td>\n",
              "      <td>2708.0</td>\n",
              "      <td>27905.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>81397.0</td>\n",
              "      <td>2770.0</td>\n",
              "      <td>30384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>82756.0</td>\n",
              "      <td>2814.0</td>\n",
              "      <td>33277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>84124.0</td>\n",
              "      <td>2872.0</td>\n",
              "      <td>36711.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>86013.0</td>\n",
              "      <td>2941.0</td>\n",
              "      <td>39782.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>88371.0</td>\n",
              "      <td>2996.0</td>\n",
              "      <td>42716.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>90309.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>45602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>92844.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>48229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>95124.0</td>\n",
              "      <td>3254.0</td>\n",
              "      <td>51171.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22           555.0    17.0       28.0\n",
              "2020-01-23           653.0    18.0       30.0\n",
              "2020-01-24           941.0    26.0       36.0\n",
              "2020-01-25          1438.0    42.0       39.0\n",
              "2020-01-26          2118.0    56.0       52.0\n",
              "2020-01-27          2927.0    82.0       61.0\n",
              "2020-01-28          5578.0   131.0      107.0\n",
              "2020-01-29          6165.0   133.0      126.0\n",
              "2020-01-30          8235.0   171.0      143.0\n",
              "2020-01-31          9925.0   213.0      222.0\n",
              "2020-02-01         12038.0   259.0      284.0\n",
              "2020-02-02         16787.0   362.0      472.0\n",
              "2020-02-03         19881.0   426.0      623.0\n",
              "2020-02-04         23892.0   492.0      852.0\n",
              "2020-02-05         27636.0   564.0     1124.0\n",
              "2020-02-06         30818.0   634.0     1487.0\n",
              "2020-02-07         34392.0   719.0     2011.0\n",
              "2020-02-08         37121.0   806.0     2616.0\n",
              "2020-02-09         40151.0   906.0     3244.0\n",
              "2020-02-10         42763.0  1013.0     3946.0\n",
              "2020-02-11         44803.0  1113.0     4683.0\n",
              "2020-02-12         45222.0  1118.0     5150.0\n",
              "2020-02-13         60370.0  1371.0     6295.0\n",
              "2020-02-14         66887.0  1523.0     8058.0\n",
              "2020-02-15         69032.0  1666.0     9395.0\n",
              "2020-02-16         71226.0  1770.0    10865.0\n",
              "2020-02-17         73260.0  1868.0    12583.0\n",
              "2020-02-18         75138.0  2007.0    14352.0\n",
              "2020-02-19         75641.0  2122.0    16121.0\n",
              "2020-02-20         76199.0  2247.0    18177.0\n",
              "2020-02-21         76843.0  2251.0    18890.0\n",
              "2020-02-22         78599.0  2458.0    22886.0\n",
              "2020-02-23         78985.0  2469.0    23394.0\n",
              "2020-02-24         79570.0  2629.0    25227.0\n",
              "2020-02-25         80415.0  2708.0    27905.0\n",
              "2020-02-26         81397.0  2770.0    30384.0\n",
              "2020-02-27         82756.0  2814.0    33277.0\n",
              "2020-02-28         84124.0  2872.0    36711.0\n",
              "2020-02-29         86013.0  2941.0    39782.0\n",
              "2020-03-01         88371.0  2996.0    42716.0\n",
              "2020-03-02         90309.0  3085.0    45602.0\n",
              "2020-03-03         92844.0  3160.0    48229.0\n",
              "2020-03-04         95124.0  3254.0    51171.0"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_time.diff() # Me arroja la diferencia entre un día y el dia previo en cada columna"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_bBLIAvKcSrY",
        "outputId": "a63fa110-add3-4b1e-bb0b-d205266fe3a2"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>98.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>288.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>497.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>680.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>809.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>2651.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>587.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>2070.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>1690.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>2113.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>4749.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>3094.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-04</th>\n",
              "      <td>4011.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>3744.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>3182.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>3574.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>524.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>2729.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>605.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>3030.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>628.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>2612.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>702.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-11</th>\n",
              "      <td>2040.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>737.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>419.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>467.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>15148.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>1145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>6517.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>1763.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-15</th>\n",
              "      <td>2145.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-16</th>\n",
              "      <td>2194.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1470.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-17</th>\n",
              "      <td>2034.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1718.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>1878.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>1769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>503.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>558.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>2056.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>644.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>713.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-22</th>\n",
              "      <td>1756.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>3996.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-23</th>\n",
              "      <td>386.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>508.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>585.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1833.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>845.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2678.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>982.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>2479.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>1359.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>2893.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>1368.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>3434.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>1889.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3071.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>2358.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>2934.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>1938.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>2886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>2535.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>2627.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>2280.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>2942.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22             NaN     NaN        NaN\n",
              "2020-01-23            98.0     1.0        2.0\n",
              "2020-01-24           288.0     8.0        6.0\n",
              "2020-01-25           497.0    16.0        3.0\n",
              "2020-01-26           680.0    14.0       13.0\n",
              "2020-01-27           809.0    26.0        9.0\n",
              "2020-01-28          2651.0    49.0       46.0\n",
              "2020-01-29           587.0     2.0       19.0\n",
              "2020-01-30          2070.0    38.0       17.0\n",
              "2020-01-31          1690.0    42.0       79.0\n",
              "2020-02-01          2113.0    46.0       62.0\n",
              "2020-02-02          4749.0   103.0      188.0\n",
              "2020-02-03          3094.0    64.0      151.0\n",
              "2020-02-04          4011.0    66.0      229.0\n",
              "2020-02-05          3744.0    72.0      272.0\n",
              "2020-02-06          3182.0    70.0      363.0\n",
              "2020-02-07          3574.0    85.0      524.0\n",
              "2020-02-08          2729.0    87.0      605.0\n",
              "2020-02-09          3030.0   100.0      628.0\n",
              "2020-02-10          2612.0   107.0      702.0\n",
              "2020-02-11          2040.0   100.0      737.0\n",
              "2020-02-12           419.0     5.0      467.0\n",
              "2020-02-13         15148.0   253.0     1145.0\n",
              "2020-02-14          6517.0   152.0     1763.0\n",
              "2020-02-15          2145.0   143.0     1337.0\n",
              "2020-02-16          2194.0   104.0     1470.0\n",
              "2020-02-17          2034.0    98.0     1718.0\n",
              "2020-02-18          1878.0   139.0     1769.0\n",
              "2020-02-19           503.0   115.0     1769.0\n",
              "2020-02-20           558.0   125.0     2056.0\n",
              "2020-02-21           644.0     4.0      713.0\n",
              "2020-02-22          1756.0   207.0     3996.0\n",
              "2020-02-23           386.0    11.0      508.0\n",
              "2020-02-24           585.0   160.0     1833.0\n",
              "2020-02-25           845.0    79.0     2678.0\n",
              "2020-02-26           982.0    62.0     2479.0\n",
              "2020-02-27          1359.0    44.0     2893.0\n",
              "2020-02-28          1368.0    58.0     3434.0\n",
              "2020-02-29          1889.0    69.0     3071.0\n",
              "2020-03-01          2358.0    55.0     2934.0\n",
              "2020-03-02          1938.0    89.0     2886.0\n",
              "2020-03-03          2535.0    75.0     2627.0\n",
              "2020-03-04          2280.0    94.0     2942.0"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_k-KbMo1OMC",
        "outputId": "89929474-a00e-4736-f850-11a743005368",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_time.diff().mean() # Aumento de casos, muertes y recuperados promedio"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Confirmed    2251.642857\n",
              "Deaths         77.071429\n",
              "Recovered    1217.690476\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOwe8Okq1VyY",
        "outputId": "90622fd0-7197-4048-940b-edf1309f3f20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_diff = df_time.diff()\n",
        "df_diff"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
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              "  <tbody>\n",
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              "      <th>2020-01-22</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>98.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>288.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <th>2020-01-25</th>\n",
              "      <td>497.0</td>\n",
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              "      <th>2020-01-26</th>\n",
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              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>809.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>2651.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>587.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>2070.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>1690.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>2113.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>4749.0</td>\n",
              "      <td>103.0</td>\n",
              "      <td>188.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>3094.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>151.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-04</th>\n",
              "      <td>4011.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>3744.0</td>\n",
              "      <td>72.0</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>3182.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>363.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>3574.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>524.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>2729.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>605.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>3030.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>628.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>2612.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>702.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-11</th>\n",
              "      <td>2040.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>737.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>419.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>467.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>15148.0</td>\n",
              "      <td>253.0</td>\n",
              "      <td>1145.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>6517.0</td>\n",
              "      <td>152.0</td>\n",
              "      <td>1763.0</td>\n",
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              "    <tr>\n",
              "      <th>2020-02-15</th>\n",
              "      <td>2145.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>1337.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-16</th>\n",
              "      <td>2194.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>1470.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-17</th>\n",
              "      <td>2034.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1718.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>1878.0</td>\n",
              "      <td>139.0</td>\n",
              "      <td>1769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>503.0</td>\n",
              "      <td>115.0</td>\n",
              "      <td>1769.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>558.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>2056.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>644.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>713.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-22</th>\n",
              "      <td>1756.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>3996.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-23</th>\n",
              "      <td>386.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>508.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>585.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>1833.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>845.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>2678.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>982.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>2479.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>1359.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>2893.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>1368.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>3434.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>1889.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>3071.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>2358.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>2934.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>1938.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>2886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>2535.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>2627.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>2280.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>2942.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22             NaN     NaN        NaN\n",
              "2020-01-23            98.0     1.0        2.0\n",
              "2020-01-24           288.0     8.0        6.0\n",
              "2020-01-25           497.0    16.0        3.0\n",
              "2020-01-26           680.0    14.0       13.0\n",
              "2020-01-27           809.0    26.0        9.0\n",
              "2020-01-28          2651.0    49.0       46.0\n",
              "2020-01-29           587.0     2.0       19.0\n",
              "2020-01-30          2070.0    38.0       17.0\n",
              "2020-01-31          1690.0    42.0       79.0\n",
              "2020-02-01          2113.0    46.0       62.0\n",
              "2020-02-02          4749.0   103.0      188.0\n",
              "2020-02-03          3094.0    64.0      151.0\n",
              "2020-02-04          4011.0    66.0      229.0\n",
              "2020-02-05          3744.0    72.0      272.0\n",
              "2020-02-06          3182.0    70.0      363.0\n",
              "2020-02-07          3574.0    85.0      524.0\n",
              "2020-02-08          2729.0    87.0      605.0\n",
              "2020-02-09          3030.0   100.0      628.0\n",
              "2020-02-10          2612.0   107.0      702.0\n",
              "2020-02-11          2040.0   100.0      737.0\n",
              "2020-02-12           419.0     5.0      467.0\n",
              "2020-02-13         15148.0   253.0     1145.0\n",
              "2020-02-14          6517.0   152.0     1763.0\n",
              "2020-02-15          2145.0   143.0     1337.0\n",
              "2020-02-16          2194.0   104.0     1470.0\n",
              "2020-02-17          2034.0    98.0     1718.0\n",
              "2020-02-18          1878.0   139.0     1769.0\n",
              "2020-02-19           503.0   115.0     1769.0\n",
              "2020-02-20           558.0   125.0     2056.0\n",
              "2020-02-21           644.0     4.0      713.0\n",
              "2020-02-22          1756.0   207.0     3996.0\n",
              "2020-02-23           386.0    11.0      508.0\n",
              "2020-02-24           585.0   160.0     1833.0\n",
              "2020-02-25           845.0    79.0     2678.0\n",
              "2020-02-26           982.0    62.0     2479.0\n",
              "2020-02-27          1359.0    44.0     2893.0\n",
              "2020-02-28          1368.0    58.0     3434.0\n",
              "2020-02-29          1889.0    69.0     3071.0\n",
              "2020-03-01          2358.0    55.0     2934.0\n",
              "2020-03-02          1938.0    89.0     2886.0\n",
              "2020-03-03          2535.0    75.0     2627.0\n",
              "2020-03-04          2280.0    94.0     2942.0"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iS4znkZ41l25",
        "outputId": "63546fa6-054e-4de6-ad07-2916b16c699e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df_time.head(1).to_dict()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Confirmed': {Timestamp('2020-01-22 00:00:00'): 555.0},\n",
              " 'Deaths': {Timestamp('2020-01-22 00:00:00'): 17.0},\n",
              " 'Recovered': {Timestamp('2020-01-22 00:00:00'): 28.0}}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FBXVgusl1dwJ"
      },
      "source": [
        "df_diff = df_diff.fillna({'Confirmed': 555.0,\n",
        " 'Deaths': 17.0,\n",
        " 'Recovered': 28.0})"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyfSlB6I5PIu",
        "outputId": "257cc981-4035-4389-93bc-94998b658577",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_diff.cumsum() # Me permite recuperar mi dataframe original al usar la suma acumulada"
      ],
      "execution_count": 42,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1438.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2118.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>2927.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>5578.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>6165.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>8235.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>143.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9925.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>12038.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>284.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>16787.0</td>\n",
              "      <td>362.0</td>\n",
              "      <td>472.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>19881.0</td>\n",
              "      <td>426.0</td>\n",
              "      <td>623.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-04</th>\n",
              "      <td>23892.0</td>\n",
              "      <td>492.0</td>\n",
              "      <td>852.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>27636.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>1124.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>30818.0</td>\n",
              "      <td>634.0</td>\n",
              "      <td>1487.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>34392.0</td>\n",
              "      <td>719.0</td>\n",
              "      <td>2011.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>37121.0</td>\n",
              "      <td>806.0</td>\n",
              "      <td>2616.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>40151.0</td>\n",
              "      <td>906.0</td>\n",
              "      <td>3244.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>42763.0</td>\n",
              "      <td>1013.0</td>\n",
              "      <td>3946.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-11</th>\n",
              "      <td>44803.0</td>\n",
              "      <td>1113.0</td>\n",
              "      <td>4683.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>45222.0</td>\n",
              "      <td>1118.0</td>\n",
              "      <td>5150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>60370.0</td>\n",
              "      <td>1371.0</td>\n",
              "      <td>6295.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>66887.0</td>\n",
              "      <td>1523.0</td>\n",
              "      <td>8058.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-15</th>\n",
              "      <td>69032.0</td>\n",
              "      <td>1666.0</td>\n",
              "      <td>9395.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-16</th>\n",
              "      <td>71226.0</td>\n",
              "      <td>1770.0</td>\n",
              "      <td>10865.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-17</th>\n",
              "      <td>73260.0</td>\n",
              "      <td>1868.0</td>\n",
              "      <td>12583.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>75138.0</td>\n",
              "      <td>2007.0</td>\n",
              "      <td>14352.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>75641.0</td>\n",
              "      <td>2122.0</td>\n",
              "      <td>16121.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>76199.0</td>\n",
              "      <td>2247.0</td>\n",
              "      <td>18177.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>76843.0</td>\n",
              "      <td>2251.0</td>\n",
              "      <td>18890.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-22</th>\n",
              "      <td>78599.0</td>\n",
              "      <td>2458.0</td>\n",
              "      <td>22886.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-23</th>\n",
              "      <td>78985.0</td>\n",
              "      <td>2469.0</td>\n",
              "      <td>23394.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>79570.0</td>\n",
              "      <td>2629.0</td>\n",
              "      <td>25227.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>80415.0</td>\n",
              "      <td>2708.0</td>\n",
              "      <td>27905.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>81397.0</td>\n",
              "      <td>2770.0</td>\n",
              "      <td>30384.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>82756.0</td>\n",
              "      <td>2814.0</td>\n",
              "      <td>33277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>84124.0</td>\n",
              "      <td>2872.0</td>\n",
              "      <td>36711.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>86013.0</td>\n",
              "      <td>2941.0</td>\n",
              "      <td>39782.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>88371.0</td>\n",
              "      <td>2996.0</td>\n",
              "      <td>42716.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>90309.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>45602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>92844.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>48229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>95124.0</td>\n",
              "      <td>3254.0</td>\n",
              "      <td>51171.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22           555.0    17.0       28.0\n",
              "2020-01-23           653.0    18.0       30.0\n",
              "2020-01-24           941.0    26.0       36.0\n",
              "2020-01-25          1438.0    42.0       39.0\n",
              "2020-01-26          2118.0    56.0       52.0\n",
              "2020-01-27          2927.0    82.0       61.0\n",
              "2020-01-28          5578.0   131.0      107.0\n",
              "2020-01-29          6165.0   133.0      126.0\n",
              "2020-01-30          8235.0   171.0      143.0\n",
              "2020-01-31          9925.0   213.0      222.0\n",
              "2020-02-01         12038.0   259.0      284.0\n",
              "2020-02-02         16787.0   362.0      472.0\n",
              "2020-02-03         19881.0   426.0      623.0\n",
              "2020-02-04         23892.0   492.0      852.0\n",
              "2020-02-05         27636.0   564.0     1124.0\n",
              "2020-02-06         30818.0   634.0     1487.0\n",
              "2020-02-07         34392.0   719.0     2011.0\n",
              "2020-02-08         37121.0   806.0     2616.0\n",
              "2020-02-09         40151.0   906.0     3244.0\n",
              "2020-02-10         42763.0  1013.0     3946.0\n",
              "2020-02-11         44803.0  1113.0     4683.0\n",
              "2020-02-12         45222.0  1118.0     5150.0\n",
              "2020-02-13         60370.0  1371.0     6295.0\n",
              "2020-02-14         66887.0  1523.0     8058.0\n",
              "2020-02-15         69032.0  1666.0     9395.0\n",
              "2020-02-16         71226.0  1770.0    10865.0\n",
              "2020-02-17         73260.0  1868.0    12583.0\n",
              "2020-02-18         75138.0  2007.0    14352.0\n",
              "2020-02-19         75641.0  2122.0    16121.0\n",
              "2020-02-20         76199.0  2247.0    18177.0\n",
              "2020-02-21         76843.0  2251.0    18890.0\n",
              "2020-02-22         78599.0  2458.0    22886.0\n",
              "2020-02-23         78985.0  2469.0    23394.0\n",
              "2020-02-24         79570.0  2629.0    25227.0\n",
              "2020-02-25         80415.0  2708.0    27905.0\n",
              "2020-02-26         81397.0  2770.0    30384.0\n",
              "2020-02-27         82756.0  2814.0    33277.0\n",
              "2020-02-28         84124.0  2872.0    36711.0\n",
              "2020-02-29         86013.0  2941.0    39782.0\n",
              "2020-03-01         88371.0  2996.0    42716.0\n",
              "2020-03-02         90309.0  3085.0    45602.0\n",
              "2020-03-03         92844.0  3160.0    48229.0\n",
              "2020-03-04         95124.0  3254.0    51171.0"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_diff.resample('7D').sum() # Agrupo las fechas cada 7 dias sumando los valores de esos dias. Es decir, consigo una vista semanal de mis datos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iJh8gs6adS84",
        "outputId": "6d126e57-54f5-4460-88ff-d0f5aac110b5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>5578.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>107.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>18314.0</td>\n",
              "      <td>361.0</td>\n",
              "      <td>745.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>20911.0</td>\n",
              "      <td>621.0</td>\n",
              "      <td>3831.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>30335.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>9669.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>5277.0</td>\n",
              "      <td>701.0</td>\n",
              "      <td>13553.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>12429.0</td>\n",
              "      <td>452.0</td>\n",
              "      <td>20324.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>2280.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>2942.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22          5578.0   131.0      107.0\n",
              "2020-01-29         18314.0   361.0      745.0\n",
              "2020-02-05         20911.0   621.0     3831.0\n",
              "2020-02-12         30335.0   894.0     9669.0\n",
              "2020-02-19          5277.0   701.0    13553.0\n",
              "2020-02-26         12429.0   452.0    20324.0\n",
              "2020-03-04          2280.0    94.0     2942.0"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2dKYASws5ZR7",
        "outputId": "2752cf91-91ae-41a7-df5f-3ba23d969d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_diff.resample('M').sum() # Agrupo las fechas por mes"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9925.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>76088.0</td>\n",
              "      <td>2728.0</td>\n",
              "      <td>39560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>9111.0</td>\n",
              "      <td>313.0</td>\n",
              "      <td>11389.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-31          9925.0   213.0      222.0\n",
              "2020-02-29         76088.0  2728.0    39560.0\n",
              "2020-03-31          9111.0   313.0    11389.0"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqJdiLPi5m6r",
        "outputId": "e2ca0b0b-13dd-4c58-c7da-d1397a81477a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_diff.resample('M').count()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-31              10      10         10\n",
              "2020-02-29              29      29         29\n",
              "2020-03-31               4       4          4"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxX5evhR517c",
        "outputId": "a00cd125-883f-491b-e632-3882700fe110",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_diff.resample('M').mean()"
      ],
      "execution_count": 47,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>992.500000</td>\n",
              "      <td>21.300000</td>\n",
              "      <td>22.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>2623.724138</td>\n",
              "      <td>94.068966</td>\n",
              "      <td>1364.137931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>2277.750000</td>\n",
              "      <td>78.250000</td>\n",
              "      <td>2847.250000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Confirmed     Deaths    Recovered\n",
              "ObservationDate                                     \n",
              "2020-01-31        992.500000  21.300000    22.200000\n",
              "2020-02-29       2623.724138  94.068966  1364.137931\n",
              "2020-03-31       2277.750000  78.250000  2847.250000"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5nTAmItL8PAm",
        "outputId": "cc2fca4c-12bb-48ee-bbbd-9a66f3d5143c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "df_cum = df_time.resample('12h').sum(min_count=1) # Le digo que para hacer la suma haya al menos un elemento\n",
        "df_cum"
      ],
      "execution_count": 49,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22 00:00:00</th>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22 12:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23 00:00:00</th>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23 12:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24 00:00:00</th>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02 00:00:00</th>\n",
              "      <td>90309.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>45602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02 12:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03 00:00:00</th>\n",
              "      <td>92844.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>48229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03 12:00:00</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04 00:00:00</th>\n",
              "      <td>95124.0</td>\n",
              "      <td>3254.0</td>\n",
              "      <td>51171.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Confirmed  Deaths  Recovered\n",
              "ObservationDate                                  \n",
              "2020-01-22 00:00:00      555.0    17.0       28.0\n",
              "2020-01-22 12:00:00        NaN     NaN        NaN\n",
              "2020-01-23 00:00:00      653.0    18.0       30.0\n",
              "2020-01-23 12:00:00        NaN     NaN        NaN\n",
              "2020-01-24 00:00:00      941.0    26.0       36.0\n",
              "...                        ...     ...        ...\n",
              "2020-03-02 00:00:00    90309.0  3085.0    45602.0\n",
              "2020-03-02 12:00:00        NaN     NaN        NaN\n",
              "2020-03-03 00:00:00    92844.0  3160.0    48229.0\n",
              "2020-03-03 12:00:00        NaN     NaN        NaN\n",
              "2020-03-04 00:00:00    95124.0  3254.0    51171.0\n",
              "\n",
              "[85 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ems3aWck-GkQ",
        "outputId": "32e2557a-6eb1-4a99-fc6b-98c2b9e888c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "df_cum = df_cum.interpolate() # Voy a tratar de completar todos mis valores nulos con .interpolate\n",
        "df_cum\n",
        "# Podría usar tmb: df_cum.bfill() o df_cum.ffill() o df_cum.fillna(-1000)\n",
        "# .interpolate pone los valores medios entre el registro anterior y el posterior"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22 00:00:00</th>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-22 12:00:00</th>\n",
              "      <td>604.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23 00:00:00</th>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23 12:00:00</th>\n",
              "      <td>797.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24 00:00:00</th>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02 00:00:00</th>\n",
              "      <td>90309.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>45602.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02 12:00:00</th>\n",
              "      <td>91576.5</td>\n",
              "      <td>3122.5</td>\n",
              "      <td>46915.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03 00:00:00</th>\n",
              "      <td>92844.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>48229.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03 12:00:00</th>\n",
              "      <td>93984.0</td>\n",
              "      <td>3207.0</td>\n",
              "      <td>49700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04 00:00:00</th>\n",
              "      <td>95124.0</td>\n",
              "      <td>3254.0</td>\n",
              "      <td>51171.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Confirmed  Deaths  Recovered\n",
              "ObservationDate                                  \n",
              "2020-01-22 00:00:00      555.0    17.0       28.0\n",
              "2020-01-22 12:00:00      604.0    17.5       29.0\n",
              "2020-01-23 00:00:00      653.0    18.0       30.0\n",
              "2020-01-23 12:00:00      797.0    22.0       33.0\n",
              "2020-01-24 00:00:00      941.0    26.0       36.0\n",
              "...                        ...     ...        ...\n",
              "2020-03-02 00:00:00    90309.0  3085.0    45602.0\n",
              "2020-03-02 12:00:00    91576.5  3122.5    46915.5\n",
              "2020-03-03 00:00:00    92844.0  3160.0    48229.0\n",
              "2020-03-03 12:00:00    93984.0  3207.0    49700.0\n",
              "2020-03-04 00:00:00    95124.0  3254.0    51171.0\n",
              "\n",
              "[85 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDOO8UdG_sLp"
      },
      "source": [
        "df_cum['rate'] = 1 - df_cum['Deaths']/df_cum['Confirmed'] # Creo la columna tasa de supervivencia"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRYLnHNc_t5G",
        "outputId": "6bab64da-4a22-4819-8c87-aa6d3857e3a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df_cum = df_cum.reset_index() # ¿Que pasa cuando mi variable tipo tiempo no pertenece a mi indice? \n",
        "df_cum"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>level_0</th>\n",
              "      <th>index</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020-01-22 00:00:00</td>\n",
              "      <td>555.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>0.969369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-22 12:00:00</td>\n",
              "      <td>604.0</td>\n",
              "      <td>17.5</td>\n",
              "      <td>29.0</td>\n",
              "      <td>0.971026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2020-01-23 00:00:00</td>\n",
              "      <td>653.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.972435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2020-01-23 12:00:00</td>\n",
              "      <td>797.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>0.972396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2020-01-24 00:00:00</td>\n",
              "      <td>941.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0.972370</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>80</th>\n",
              "      <td>80</td>\n",
              "      <td>80</td>\n",
              "      <td>2020-03-02 00:00:00</td>\n",
              "      <td>90309.0</td>\n",
              "      <td>3085.0</td>\n",
              "      <td>45602.0</td>\n",
              "      <td>0.965840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>81</td>\n",
              "      <td>81</td>\n",
              "      <td>2020-03-02 12:00:00</td>\n",
              "      <td>91576.5</td>\n",
              "      <td>3122.5</td>\n",
              "      <td>46915.5</td>\n",
              "      <td>0.965903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>82</td>\n",
              "      <td>82</td>\n",
              "      <td>2020-03-03 00:00:00</td>\n",
              "      <td>92844.0</td>\n",
              "      <td>3160.0</td>\n",
              "      <td>48229.0</td>\n",
              "      <td>0.965964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>83</td>\n",
              "      <td>83</td>\n",
              "      <td>2020-03-03 12:00:00</td>\n",
              "      <td>93984.0</td>\n",
              "      <td>3207.0</td>\n",
              "      <td>49700.0</td>\n",
              "      <td>0.965877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84</th>\n",
              "      <td>84</td>\n",
              "      <td>84</td>\n",
              "      <td>2020-03-04 00:00:00</td>\n",
              "      <td>95124.0</td>\n",
              "      <td>3254.0</td>\n",
              "      <td>51171.0</td>\n",
              "      <td>0.965792</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    level_0  index     ObservationDate  Confirmed  Deaths  Recovered      rate\n",
              "0         0      0 2020-01-22 00:00:00      555.0    17.0       28.0  0.969369\n",
              "1         1      1 2020-01-22 12:00:00      604.0    17.5       29.0  0.971026\n",
              "2         2      2 2020-01-23 00:00:00      653.0    18.0       30.0  0.972435\n",
              "3         3      3 2020-01-23 12:00:00      797.0    22.0       33.0  0.972396\n",
              "4         4      4 2020-01-24 00:00:00      941.0    26.0       36.0  0.972370\n",
              "..      ...    ...                 ...        ...     ...        ...       ...\n",
              "80       80     80 2020-03-02 00:00:00    90309.0  3085.0    45602.0  0.965840\n",
              "81       81     81 2020-03-02 12:00:00    91576.5  3122.5    46915.5  0.965903\n",
              "82       82     82 2020-03-03 00:00:00    92844.0  3160.0    48229.0  0.965964\n",
              "83       83     83 2020-03-03 12:00:00    93984.0  3207.0    49700.0  0.965877\n",
              "84       84     84 2020-03-04 00:00:00    95124.0  3254.0    51171.0  0.965792\n",
              "\n",
              "[85 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cruYKtvv_4FX",
        "outputId": "014055f4-80b3-489f-b22b-0da01fe8c733",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_cum.groupby(pd.Grouper(key='ObservationDate', freq = 'M'))[['rate']].mean() # hago un groupby con grouper para agrupar por mes"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>0.974594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>0.973562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>0.965920</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     rate\n",
              "ObservationDate          \n",
              "2020-01-31       0.974594\n",
              "2020-02-29       0.973562\n",
              "2020-03-31       0.965920"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8tbhpheAmRm",
        "outputId": "12f9b4ab-5c63-40dc-8830-b1629b6eedda",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
      },
      "source": [
        "sr = df_cum.groupby(pd.Grouper(key='ObservationDate', freq = '1D'))['rate'].mean() # Hago un groupby por dia para luego graficarlo con .plot\n",
        "sr.plot()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f180a57e8d0>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUCwKhWkBt40",
        "outputId": "b1987f7a-520b-466b-8166-fa2c18a09416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        }
      },
      "source": [
        "sr.plot()\n",
        "sr.rolling(window=7).mean().plot() # Grafico con datos con un intervalo de 7 dias\n",
        "sr.rolling(window=14).mean().plot() # Grafico con datos con un intervalo de 14 dias"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f180a3cdc10>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ee6ZmzxkCNaO"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZX-jGaMSB0SF",
        "outputId": "ec4e2e0c-0c0b-4358-fdb9-722aa3eb3df6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        }
      },
      "source": [
        "sr.rolling(window=14).apply(lambda x: np.std(x)).plot()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f180a2ca650>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xsUpV8B5CMun",
        "outputId": "5c04b4fa-9241-44d4-e78a-e8fc7dd23ede",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.groupby('Country/Region')['Confirmed'].max().sort_values(ascending=False)\n",
        "# Veo el max de casos por pais"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country/Region\n",
              "Mainland China    67332.0\n",
              "South Korea        5621.0\n",
              "Italy              3089.0\n",
              "Iran               2922.0\n",
              "Others              706.0\n",
              "                   ...   \n",
              "Ivory Coast           1.0\n",
              "Poland                1.0\n",
              "Nigeria               1.0\n",
              "Faroe Islands         1.0\n",
              "Colombia              0.0\n",
              "Name: Confirmed, Length: 90, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSmlZp8-Enkg",
        "outputId": "4934b7b8-49d3-4175-c628-a36784c13081",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "list(df)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ObservationDate', 'Country/Region', 'Confirmed', 'Deaths', 'Recovered']"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKj8mJWKDKYW",
        "outputId": "95ef1045-16a7-4ede-a290-71a719e90223",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "df_time = df.groupby(['Country/Region',\n",
        "            pd.Grouper(key='ObservationDate',freq='1D')]).sum()\n",
        "df_time\n",
        "# Agrupo por paises y luego por una variable de tipo tiempo agrupada por día"
      ],
      "execution_count": 73,
      "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></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Azerbaijan</th>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">Afghanistan</th>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Vietnam</th>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1462 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Confirmed  Deaths  Recovered\n",
              "Country/Region ObservationDate                              \n",
              " Azerbaijan    2020-02-28             1.0     0.0        0.0\n",
              "Afghanistan    2020-02-24             1.0     0.0        0.0\n",
              "               2020-02-25             1.0     0.0        0.0\n",
              "               2020-02-26             1.0     0.0        0.0\n",
              "               2020-02-27             1.0     0.0        0.0\n",
              "...                                   ...     ...        ...\n",
              "Vietnam        2020-02-29            16.0     0.0       16.0\n",
              "               2020-03-01            16.0     0.0       16.0\n",
              "               2020-03-02            16.0     0.0       16.0\n",
              "               2020-03-03            16.0     0.0       16.0\n",
              "               2020-03-04            16.0     0.0       16.0\n",
              "\n",
              "[1462 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQoiEONNEvOl"
      },
      "source": [
        "df_china = df_time.loc['Mainland China',:]\n",
        "# Quiero trabajar solo con China puesto que es el país donde mas observaciones han ocurrido. "
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oR_kVaI7Fcgf",
        "outputId": "98da3396-89b0-413d-c8fb-895eaf423385",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_china"
      ],
      "execution_count": 75,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-22</th>\n",
              "      <td>547.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-23</th>\n",
              "      <td>639.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-24</th>\n",
              "      <td>916.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>36.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-25</th>\n",
              "      <td>1399.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-26</th>\n",
              "      <td>2062.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>49.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-27</th>\n",
              "      <td>2863.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>58.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-28</th>\n",
              "      <td>5494.0</td>\n",
              "      <td>131.0</td>\n",
              "      <td>101.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-29</th>\n",
              "      <td>6070.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>120.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-30</th>\n",
              "      <td>8124.0</td>\n",
              "      <td>171.0</td>\n",
              "      <td>135.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9783.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>214.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>11871.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>275.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-02</th>\n",
              "      <td>16607.0</td>\n",
              "      <td>361.0</td>\n",
              "      <td>463.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-03</th>\n",
              "      <td>19693.0</td>\n",
              "      <td>425.0</td>\n",
              "      <td>614.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-04</th>\n",
              "      <td>23680.0</td>\n",
              "      <td>490.0</td>\n",
              "      <td>843.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-05</th>\n",
              "      <td>27409.0</td>\n",
              "      <td>562.0</td>\n",
              "      <td>1115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-06</th>\n",
              "      <td>30553.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>1476.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-07</th>\n",
              "      <td>34075.0</td>\n",
              "      <td>717.0</td>\n",
              "      <td>1998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-08</th>\n",
              "      <td>36778.0</td>\n",
              "      <td>804.0</td>\n",
              "      <td>2595.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-09</th>\n",
              "      <td>39790.0</td>\n",
              "      <td>904.0</td>\n",
              "      <td>3218.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-10</th>\n",
              "      <td>42306.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>3917.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-11</th>\n",
              "      <td>44327.0</td>\n",
              "      <td>1111.0</td>\n",
              "      <td>4635.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-12</th>\n",
              "      <td>44699.0</td>\n",
              "      <td>1116.0</td>\n",
              "      <td>5079.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-13</th>\n",
              "      <td>59832.0</td>\n",
              "      <td>1368.0</td>\n",
              "      <td>6213.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-14</th>\n",
              "      <td>66292.0</td>\n",
              "      <td>1520.0</td>\n",
              "      <td>7973.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-15</th>\n",
              "      <td>68347.0</td>\n",
              "      <td>1662.0</td>\n",
              "      <td>9294.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-16</th>\n",
              "      <td>70446.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>10748.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-17</th>\n",
              "      <td>72364.0</td>\n",
              "      <td>1863.0</td>\n",
              "      <td>12455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-18</th>\n",
              "      <td>74139.0</td>\n",
              "      <td>2002.0</td>\n",
              "      <td>14199.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-19</th>\n",
              "      <td>74546.0</td>\n",
              "      <td>2114.0</td>\n",
              "      <td>15952.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-20</th>\n",
              "      <td>74999.0</td>\n",
              "      <td>2236.0</td>\n",
              "      <td>18002.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-21</th>\n",
              "      <td>75472.0</td>\n",
              "      <td>2236.0</td>\n",
              "      <td>18693.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-22</th>\n",
              "      <td>76922.0</td>\n",
              "      <td>2441.0</td>\n",
              "      <td>22687.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-23</th>\n",
              "      <td>76938.0</td>\n",
              "      <td>2443.0</td>\n",
              "      <td>23170.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-24</th>\n",
              "      <td>77152.0</td>\n",
              "      <td>2593.0</td>\n",
              "      <td>24990.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-25</th>\n",
              "      <td>77660.0</td>\n",
              "      <td>2663.0</td>\n",
              "      <td>27650.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-26</th>\n",
              "      <td>78065.0</td>\n",
              "      <td>2715.0</td>\n",
              "      <td>30053.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-27</th>\n",
              "      <td>78498.0</td>\n",
              "      <td>2744.0</td>\n",
              "      <td>32898.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-28</th>\n",
              "      <td>78824.0</td>\n",
              "      <td>2788.0</td>\n",
              "      <td>36291.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>79251.0</td>\n",
              "      <td>2835.0</td>\n",
              "      <td>39279.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>79826.0</td>\n",
              "      <td>2870.0</td>\n",
              "      <td>42118.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-02</th>\n",
              "      <td>80026.0</td>\n",
              "      <td>2912.0</td>\n",
              "      <td>44810.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-03</th>\n",
              "      <td>80151.0</td>\n",
              "      <td>2945.0</td>\n",
              "      <td>47404.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-04</th>\n",
              "      <td>80271.0</td>\n",
              "      <td>2981.0</td>\n",
              "      <td>49955.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-22           547.0    17.0       28.0\n",
              "2020-01-23           639.0    18.0       30.0\n",
              "2020-01-24           916.0    26.0       36.0\n",
              "2020-01-25          1399.0    42.0       39.0\n",
              "2020-01-26          2062.0    56.0       49.0\n",
              "2020-01-27          2863.0    82.0       58.0\n",
              "2020-01-28          5494.0   131.0      101.0\n",
              "2020-01-29          6070.0   133.0      120.0\n",
              "2020-01-30          8124.0   171.0      135.0\n",
              "2020-01-31          9783.0   213.0      214.0\n",
              "2020-02-01         11871.0   259.0      275.0\n",
              "2020-02-02         16607.0   361.0      463.0\n",
              "2020-02-03         19693.0   425.0      614.0\n",
              "2020-02-04         23680.0   490.0      843.0\n",
              "2020-02-05         27409.0   562.0     1115.0\n",
              "2020-02-06         30553.0   632.0     1476.0\n",
              "2020-02-07         34075.0   717.0     1998.0\n",
              "2020-02-08         36778.0   804.0     2595.0\n",
              "2020-02-09         39790.0   904.0     3218.0\n",
              "2020-02-10         42306.0  1011.0     3917.0\n",
              "2020-02-11         44327.0  1111.0     4635.0\n",
              "2020-02-12         44699.0  1116.0     5079.0\n",
              "2020-02-13         59832.0  1368.0     6213.0\n",
              "2020-02-14         66292.0  1520.0     7973.0\n",
              "2020-02-15         68347.0  1662.0     9294.0\n",
              "2020-02-16         70446.0  1765.0    10748.0\n",
              "2020-02-17         72364.0  1863.0    12455.0\n",
              "2020-02-18         74139.0  2002.0    14199.0\n",
              "2020-02-19         74546.0  2114.0    15952.0\n",
              "2020-02-20         74999.0  2236.0    18002.0\n",
              "2020-02-21         75472.0  2236.0    18693.0\n",
              "2020-02-22         76922.0  2441.0    22687.0\n",
              "2020-02-23         76938.0  2443.0    23170.0\n",
              "2020-02-24         77152.0  2593.0    24990.0\n",
              "2020-02-25         77660.0  2663.0    27650.0\n",
              "2020-02-26         78065.0  2715.0    30053.0\n",
              "2020-02-27         78498.0  2744.0    32898.0\n",
              "2020-02-28         78824.0  2788.0    36291.0\n",
              "2020-02-29         79251.0  2835.0    39279.0\n",
              "2020-03-01         79826.0  2870.0    42118.0\n",
              "2020-03-02         80026.0  2912.0    44810.0\n",
              "2020-03-03         80151.0  2945.0    47404.0\n",
              "2020-03-04         80271.0  2981.0    49955.0"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_china.plot(figsize = (10,7), title = 'CODV-19')\n",
        "# con figsize defino el tamaño de mi grafica\n",
        "# con title le pongo un nombre a mi grafica"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "up3_97DpjCqX",
        "outputId": "fe2dc9db-f3d4-4a78-803c-34473e10a4c5"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18080ca990>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "v3lVAOCXj1Ka"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJwg70p5FdUO",
        "outputId": "a0eaffb0-aae1-41be-cd30-46281500e17d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "df_china.plot(figsize = (10,7), title = 'CODV-19') \n",
        "plt.xlabel('Date') # Le pongo nombre a mi eje X\n",
        "plt.ylabel('People') # Le pongo nombre a mi eje Y\n",
        "plt.show() # Le ordeno que muestre la grafica"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "APKkYSslFkIW",
        "outputId": "5cfe70bd-d7e7-4f56-df52-40c91e58efba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "ax = df_china.plot(figsize = (10,7), title = 'CODV-19',\n",
        "              legend = False, # le digo que cancele las legendas por defecto\n",
        "              style = ['r-','g--','b:*']) # Le especifico el stilo de mis series. Por ej \"r-\" es rojo continua\n",
        "ax.legend(['1','dos','3']) # Establezco las nuevas leyendas de mi grafico\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('People')\n",
        "plt.show()"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRmBtLOnGIMs",
        "outputId": "d6e15991-fc8c-431d-cecf-00ce288a2e6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_monthly = df_china.resample('M').max()\n",
        "df_monthly"
      ],
      "execution_count": 95,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9783.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>214.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>79251.0</td>\n",
              "      <td>2835.0</td>\n",
              "      <td>39279.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>80271.0</td>\n",
              "      <td>2981.0</td>\n",
              "      <td>49955.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered\n",
              "ObservationDate                              \n",
              "2020-01-31          9783.0   213.0      214.0\n",
              "2020-02-29         79251.0  2835.0    39279.0\n",
              "2020-03-31         80271.0  2981.0    49955.0"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o7Wz8kmyGR2j",
        "outputId": "fe7a3e76-e94b-4854-ea72-66377e7883ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        }
      },
      "source": [
        "df_monthly.plot(figsize = (10,7), kind='bar', stacked = True) # Con kind = \"bar\" le digo que mi grafico es de barras\n",
        "# con stacked = True le digo que mis barras son apiladas"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1807da0590>"
            ]
          },
          "metadata": {},
          "execution_count": 98
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E8I6hv8iGXeR",
        "outputId": "2a1e3727-b540-45a6-b7a3-f91a2260f20f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "df_monthly['Traitment'] = df_monthly['Confirmed'] - df_monthly['Deaths']- df_monthly['Recovered']\n",
        "df_monthly"
      ],
      "execution_count": 99,
      "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>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "      <th>Traitment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ObservationDate</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-01-31</th>\n",
              "      <td>9783.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>214.0</td>\n",
              "      <td>9356.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-29</th>\n",
              "      <td>79251.0</td>\n",
              "      <td>2835.0</td>\n",
              "      <td>39279.0</td>\n",
              "      <td>37137.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-31</th>\n",
              "      <td>80271.0</td>\n",
              "      <td>2981.0</td>\n",
              "      <td>49955.0</td>\n",
              "      <td>27335.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Confirmed  Deaths  Recovered  Traitment\n",
              "ObservationDate                                         \n",
              "2020-01-31          9783.0   213.0      214.0     9356.0\n",
              "2020-02-29         79251.0  2835.0    39279.0    37137.0\n",
              "2020-03-31         80271.0  2981.0    49955.0    27335.0"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMgKzJLDGuz5",
        "outputId": "9fb61109-fa2a-43f2-f45d-5176fe9ad35d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        "df_monthly[['Deaths', 'Recovered', 'Traitment']].T.plot(figsize = (10,7), kind = 'pie', subplots=True)\n",
        "# Con subplots = True y al trasponer mi dataframe la digo que me haga un pie por cada mes"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f1807b5e790>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1807f972d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f1807b5e3d0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 3 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-9SJ3ZyrG7aZ",
        "outputId": "7cf4aae2-738a-45b3-f094-1aeec0efb229",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "df_china['rate'] = 1- df_china['Deaths']/df_china['Confirmed']\n",
        "df_china['rate'].hist(figsize = (10,7), bins = 10)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1807ac0850>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rqQysbUHHQ6",
        "outputId": "05216b83-e6d2-4d14-ba2b-3d84dc5fa01f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        }
      },
      "source": [
        "df_china['rate'].plot(kind = 'kde', figsize = (10,7)) # Distribución parametrica sobre la tasa de supervivencia"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f17f98c9250>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}