Analise_Covid19_Brasil_DIO.ipynb

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      "name": "Analise_Covid19_Brasil_DIO_2.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V-aEDY62VWqw"
      },
      "source": [
        "# Análise COVID-19"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "szZfW2QsVWqz"
      },
      "source": [
        "## Bibliotecas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzH5NcAvVWq3"
      },
      "source": [
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "from datetime import datetime"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pvo_D_oxVWq4"
      },
      "source": [
        "## DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qu58Q0vbVWq5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "1a6433ff-400c-4044-db68-b05edb00a289"
      },
      "source": [
        "url = 'https://github.com/neylsoncrepalde/projeto_eda_covid/blob/master/covid_19_data.csv?raw=true'\n",
        "\n",
        "df = pd.read_csv(url, parse_dates=['ObservationDate', 'Last Update'])\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SNo</th>\n",
              "      <th>ObservationDate</th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Last Update</th>\n",
              "      <th>Confirmed</th>\n",
              "      <th>Deaths</th>\n",
              "      <th>Recovered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Anhui</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-01-22 17:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Beijing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-01-22 17:00:00</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Chongqing</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-01-22 17:00:00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Fujian</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-01-22 17:00:00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>Gansu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-01-22 17:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26708</th>\n",
              "      <td>26709</td>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>776.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26709</th>\n",
              "      <td>26710</td>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Xinjiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>76.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>73.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26710</th>\n",
              "      <td>26711</td>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Yukon</td>\n",
              "      <td>Canada</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26711</th>\n",
              "      <td>26712</td>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Yunnan</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>185.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>183.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26712</th>\n",
              "      <td>26713</td>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Zhejiang</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>1268.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1267.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26713 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         SNo ObservationDate Province/State  ... Confirmed Deaths  Recovered\n",
              "0          1      2020-01-22          Anhui  ...       1.0    0.0        0.0\n",
              "1          2      2020-01-22        Beijing  ...      14.0    0.0        0.0\n",
              "2          3      2020-01-22      Chongqing  ...       6.0    0.0        0.0\n",
              "3          4      2020-01-22         Fujian  ...       1.0    0.0        0.0\n",
              "4          5      2020-01-22          Gansu  ...       0.0    0.0        0.0\n",
              "...      ...             ...            ...  ...       ...    ...        ...\n",
              "26708  26709      2020-05-19        Wyoming  ...     776.0   10.0        0.0\n",
              "26709  26710      2020-05-19       Xinjiang  ...      76.0    3.0       73.0\n",
              "26710  26711      2020-05-19          Yukon  ...      11.0    0.0       11.0\n",
              "26711  26712      2020-05-19         Yunnan  ...     185.0    2.0      183.0\n",
              "26712  26713      2020-05-19       Zhejiang  ...    1268.0    1.0     1267.0\n",
              "\n",
              "[26713 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_fiTgUrVWq6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70ad94b2-2b0b-457b-e54a-0cdd509ef91f"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26713 entries, 0 to 26712\n",
            "Data columns (total 8 columns):\n",
            " #   Column           Non-Null Count  Dtype         \n",
            "---  ------           --------------  -----         \n",
            " 0   SNo              26713 non-null  int64         \n",
            " 1   ObservationDate  26713 non-null  datetime64[ns]\n",
            " 2   Province/State   12882 non-null  object        \n",
            " 3   Country/Region   26713 non-null  object        \n",
            " 4   Last Update      26713 non-null  datetime64[ns]\n",
            " 5   Confirmed        26713 non-null  float64       \n",
            " 6   Deaths           26713 non-null  float64       \n",
            " 7   Recovered        26713 non-null  float64       \n",
            "dtypes: datetime64[ns](2), float64(3), int64(1), object(2)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZEDbE70VWq7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a39f2a24-5d63-4eb5-8352-63a767161ed8"
      },
      "source": [
        "df.describe().drop(columns='SNo').T"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Confirmed</th>\n",
              "      <td>26713.0</td>\n",
              "      <td>5690.647550</td>\n",
              "      <td>23417.369124</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>192.0</td>\n",
              "      <td>1350.0</td>\n",
              "      <td>352845.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deaths</th>\n",
              "      <td>26713.0</td>\n",
              "      <td>373.353236</td>\n",
              "      <td>2214.074387</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>35341.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recovered</th>\n",
              "      <td>26713.0</td>\n",
              "      <td>1736.979224</td>\n",
              "      <td>10864.727709</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>187.0</td>\n",
              "      <td>289392.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count         mean           std  ...    50%     75%       max\n",
              "Confirmed  26713.0  5690.647550  23417.369124  ...  192.0  1350.0  352845.0\n",
              "Deaths     26713.0   373.353236   2214.074387  ...    3.0    28.0   35341.0\n",
              "Recovered  26713.0  1736.979224  10864.727709  ...    8.0   187.0  289392.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bQXMtKOzVWq9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "45e734d0-48da-4462-c7da-3b8101badfc1"
      },
      "source": [
        "df.describe(include='O').T"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Province/State</th>\n",
              "      <td>12882</td>\n",
              "      <td>354</td>\n",
              "      <td>Diamond Princess cruise ship</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country/Region</th>\n",
              "      <td>26713</td>\n",
              "      <td>223</td>\n",
              "      <td>US</td>\n",
              "      <td>4990</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                count unique                           top  freq\n",
              "Province/State  12882    354  Diamond Princess cruise ship   175\n",
              "Country/Region  26713    223                            US  4990"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dcY64IxcVWq9"
      },
      "source": [
        "#Alterar nomes das colunas:\n",
        "df.columns = [re.sub(r'[/ ]', '_', col).lower() for col in df.columns]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY7TmlGxVWq-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5eee650f-629a-482f-c7f5-a24a79b323ad"
      },
      "source": [
        "df.sample(4)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe thead th {\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>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>826</th>\n",
              "      <td>827</td>\n",
              "      <td>2020-02-05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Nepal</td>\n",
              "      <td>2020-01-31 08:15:53</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17356</th>\n",
              "      <td>17357</td>\n",
              "      <td>2020-04-21</td>\n",
              "      <td>Vermont</td>\n",
              "      <td>US</td>\n",
              "      <td>2020-04-21 23:39:09</td>\n",
              "      <td>818.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1904</th>\n",
              "      <td>1905</td>\n",
              "      <td>2020-02-20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Germany</td>\n",
              "      <td>2020-02-18 17:03:03</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1874</th>\n",
              "      <td>1875</td>\n",
              "      <td>2020-02-20</td>\n",
              "      <td>Jiangsu</td>\n",
              "      <td>Mainland China</td>\n",
              "      <td>2020-02-20 12:53:02</td>\n",
              "      <td>631.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>356.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         sno observationdate province_state  ... confirmed deaths  recovered\n",
              "826      827      2020-02-05            NaN  ...       1.0    0.0        0.0\n",
              "17356  17357      2020-04-21        Vermont  ...     818.0   40.0        0.0\n",
              "1904    1905      2020-02-20            NaN  ...      16.0    0.0       12.0\n",
              "1874    1875      2020-02-20        Jiangsu  ...     631.0    0.0      356.0\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef1KZ0n1VWq-"
      },
      "source": [
        "## Análise dos casos de Covid-19 no Brasil"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bs88BDALVWq_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ac2873f-d13e-4461-a17a-0db3e1ac34d0"
      },
      "source": [
        "#df.loc[df.country_region == 'Brazil', 'province_state'].notnull().sum()\n",
        "df.loc[df.country_region == 'Brazil'].notnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sno                85\n",
              "observationdate    85\n",
              "province_state      0\n",
              "country_region     85\n",
              "last_update        85\n",
              "confirmed          85\n",
              "deaths             85\n",
              "recovered          85\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2scNRUM4VWq_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "7ebe57fa-871e-4184-eeb7-f9e28e38e563"
      },
      "source": [
        "df_brasil = df.loc[df.country_region == 'Brazil'].drop(columns=['province_state','sno'])\n",
        "df_brasil"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>observationdate</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>82</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-23 17:00:00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2559</th>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2668</th>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2776</th>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-29 21:03:05</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24850</th>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-16 02:32:19</td>\n",
              "      <td>220291.0</td>\n",
              "      <td>14962.0</td>\n",
              "      <td>84970.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25227</th>\n",
              "      <td>2020-05-16</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-17 02:32:32</td>\n",
              "      <td>233511.0</td>\n",
              "      <td>15662.0</td>\n",
              "      <td>89672.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25604</th>\n",
              "      <td>2020-05-17</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-18 02:32:21</td>\n",
              "      <td>241080.0</td>\n",
              "      <td>16118.0</td>\n",
              "      <td>94122.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25981</th>\n",
              "      <td>2020-05-18</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-19 02:32:18</td>\n",
              "      <td>255368.0</td>\n",
              "      <td>16853.0</td>\n",
              "      <td>100459.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26358</th>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>271885.0</td>\n",
              "      <td>17983.0</td>\n",
              "      <td>106794.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>85 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      observationdate country_region  ...   deaths  recovered\n",
              "82         2020-01-23         Brazil  ...      0.0        0.0\n",
              "2455       2020-02-26         Brazil  ...      0.0        0.0\n",
              "2559       2020-02-27         Brazil  ...      0.0        0.0\n",
              "2668       2020-02-28         Brazil  ...      0.0        0.0\n",
              "2776       2020-02-29         Brazil  ...      0.0        0.0\n",
              "...               ...            ...  ...      ...        ...\n",
              "24850      2020-05-15         Brazil  ...  14962.0    84970.0\n",
              "25227      2020-05-16         Brazil  ...  15662.0    89672.0\n",
              "25604      2020-05-17         Brazil  ...  16118.0    94122.0\n",
              "25981      2020-05-18         Brazil  ...  16853.0   100459.0\n",
              "26358      2020-05-19         Brazil  ...  17983.0   106794.0\n",
              "\n",
              "[85 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K6JPYEY-VWrA"
      },
      "source": [
        "### Número de casos confirmados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Mmi9TBHVWrB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6fd2dfc-21e0-4b9f-c17d-5a3e2123e77d"
      },
      "source": [
        "df_brasil = df_brasil[df_brasil.confirmed > 0]\n",
        "df_brasil.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(84, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olnt5S9aVWrC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "5f261b3f-0da7-4f51-8559-62dae79841cf"
      },
      "source": [
        "fig = px.line(df_brasil, x='observationdate', y='confirmed',\n",
        "        labels={'observationdate':'Data', 'confirmed':'Número de casos confirmados'},\n",
        "        title='Casos confirmados no Brasil', width=1000, height=400)\n",
        "\n",
        "fig.update_layout(\n",
        "    margin=dict(l=30, r=20, t=60, b=5),\n",
        "    font=dict(size=15, color='black')\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": 11,
      "outputs": [
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hEeyuMDaVWrP"
      },
      "source": [
        "### Número de novos casos por dia"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgQCXIG9VWrQ"
      },
      "source": [
        "#Função para fazer a contagem de novos casos:\n",
        "#[Subtração entre o número de casos de um dia e o dia anterior]\n",
        "def dif(v):\n",
        "    J=[v[i+1]-v[i] for i in range(len(v)-1)]\n",
        "    J.insert(0, v[0])\n",
        "    return J"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G2mCqYKVWrQ"
      },
      "source": [
        "def dif2(v):\n",
        "    J=[v[0]]\n",
        "    for i in range(len(v)-1):\n",
        "        J.append(v[i+1]-v[i])\n",
        "    return np.array(J)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xBr7a8g9VWrR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "outputId": "b7f0b574-58ba-4017-e148-f2888ec15c25"
      },
      "source": [
        "df_brasil = df_brasil.assign( novoscasos=dif(df_brasil['confirmed'].values) )\n",
        "df_brasil"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>observationdate</th>\n",
              "      <th>country_region</th>\n",
              "      <th>last_update</th>\n",
              "      <th>confirmed</th>\n",
              "      <th>deaths</th>\n",
              "      <th>recovered</th>\n",
              "      <th>novoscasos</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2455</th>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2559</th>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2668</th>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-26 23:53:02</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2776</th>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-29 21:03:05</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2903</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-02-29 21:03:05</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24850</th>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-16 02:32:19</td>\n",
              "      <td>220291.0</td>\n",
              "      <td>14962.0</td>\n",
              "      <td>84970.0</td>\n",
              "      <td>17126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25227</th>\n",
              "      <td>2020-05-16</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-17 02:32:32</td>\n",
              "      <td>233511.0</td>\n",
              "      <td>15662.0</td>\n",
              "      <td>89672.0</td>\n",
              "      <td>13220.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25604</th>\n",
              "      <td>2020-05-17</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-18 02:32:21</td>\n",
              "      <td>241080.0</td>\n",
              "      <td>16118.0</td>\n",
              "      <td>94122.0</td>\n",
              "      <td>7569.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25981</th>\n",
              "      <td>2020-05-18</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-19 02:32:18</td>\n",
              "      <td>255368.0</td>\n",
              "      <td>16853.0</td>\n",
              "      <td>100459.0</td>\n",
              "      <td>14288.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26358</th>\n",
              "      <td>2020-05-19</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-20 02:32:19</td>\n",
              "      <td>271885.0</td>\n",
              "      <td>17983.0</td>\n",
              "      <td>106794.0</td>\n",
              "      <td>16517.0</td>\n",
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              "      observationdate country_region  ... recovered  novoscasos\n",
              "2455       2020-02-26         Brazil  ...       0.0         1.0\n",
              "2559       2020-02-27         Brazil  ...       0.0         0.0\n",
              "2668       2020-02-28         Brazil  ...       0.0         0.0\n",
              "2776       2020-02-29         Brazil  ...       0.0         1.0\n",
              "2903       2020-03-01         Brazil  ...       0.0         0.0\n",
              "...               ...            ...  ...       ...         ...\n",
              "24850      2020-05-15         Brazil  ...   84970.0     17126.0\n",
              "25227      2020-05-16         Brazil  ...   89672.0     13220.0\n",
              "25604      2020-05-17         Brazil  ...   94122.0      7569.0\n",
              "25981      2020-05-18         Brazil  ...  100459.0     14288.0\n",
              "26358      2020-05-19         Brazil  ...  106794.0     16517.0\n",
              "\n",
              "[84 rows x 7 columns]"
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          "metadata": {
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        },
        "outputId": "474316f3-0903-4d6b-f47b-95772a9270f8"
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      "source": [
        "#Visualizando\n",
        "fig = px.line(df_brasil, x='observationdate', y='novoscasos', title='Novos casos por dia',\n",
        "        labels={'observationdate':'Data', 'novoscasos':'Novos casos'}, width=1000, height=400)\n",
        "\n",
        "fig.update_layout(\n",
        "    margin=dict(l=20, r=20, t=60, b=0),\n",
        "    #font_family=\"Times New Roman\",\n",
        "    font=dict(\n",
        "      family=\"Times New Roman\", size=18\n",
        "    )\n",
        "    #paper_bgcolor=\"LightSteelBlue\"\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": 15,
      "outputs": [
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    {
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      "metadata": {
        "id": "UUElRddMVWrS"
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      "source": [
        "### Número de mortes"
      ]
    },
    {
      "cell_type": "code",
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          "height": 417
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        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(x=df_brasil.observationdate, y=df_brasil.deaths,\n",
        "               name='Mortes', mode='lines+markers', line=dict(color='red'))\n",
        ")\n",
        "\n",
        "fig.update_layout(title='Mortes por COVID-19 no Brasil',\n",
        "                  xaxis_title='Data', yaxis_title='Número de mortes',\n",
        "                  margin=dict(l=30, r=30, t=50, b=5),\n",
        "                  width=1000, height=400, font=dict(size=16))\n",
        "fig.show()"
      ],
      "execution_count": 16,
      "outputs": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "fmDgvXAbVWrT"
      },
      "source": [
        "### Taxa _média_ de crescimento (por dia)\n",
        "\n",
        "Vamos calcular a taxa de crescimento médio do COVID desde o primeiro caso."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2_rF3BXeVWrT"
      },
      "source": [
        "$$taxa~média~de~crescimento = \\sqrt[n]{x_1/x_0} - 1$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnfR0EsxVWrU"
      },
      "source": [
        "def taxa_crescimento_medio(dados, variable, data_inicio=None, data_fim=None):\n",
        "    #Se data_inicio for None, define como a primeira data disponível no dataset:\n",
        "    if data_inicio == None:\n",
        "        data_inicio = dados.observationdate.loc[dados[variable] > 0].min()\n",
        "    else:\n",
        "        data_inicio = pd.to_datetime(data_inicio)\n",
        "        \n",
        "    if data_fim == None:\n",
        "        data_fim = dados.observationdate.iloc[-1]\n",
        "    else:\n",
        "        data_fim = pd.to_datetime(data_fim)\n",
        "    \n",
        "    #Define os valores inicial e final:\n",
        "    ini = dados.loc[dados.observationdate == data_inicio, variable].values[0]\n",
        "    fim = dados.loc[dados.observationdate == data_fim, variable].values[0]\n",
        "    \n",
        "    #Define o número de pontos no tempo que vamos avaliar:\n",
        "    n = (data_fim - data_inicio).days\n",
        "    \n",
        "    #Calcula a taxa:\n",
        "    taxa = (fim/ini)**(1/n) - 1\n",
        "    print(f'Período de {n} dias.')\n",
        "\n",
        "    return taxa*100"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7SgEa7YVWrW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aaf6fb9d-f49e-42a4-9e85-01f5ecb57694"
      },
      "source": [
        "cresc_medio = taxa_crescimento_medio(df_brasil, 'confirmed', '2020-03-15')\n",
        "print(f'O crescimento médio dos casos confimados de COVID no Brasil no período avaliado foi de {cresc_medio.round(2)}%.')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Período de 65 dias.\n",
            "O crescimento médio dos casos confimados de COVID no Brasil no período avaliado foi de 12.1%.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IHG8ZFiVWrV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df5051d7-bc34-45b6-c764-5363f0afe63f"
      },
      "source": [
        "cresc_medio = taxa_crescimento_medio(df_brasil, 'confirmed')\n",
        "print(f'O crescimento médio dos casos confimados de COVID no Brasil no período avaliado foi de {cresc_medio.round(2)}%.')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Período de 83 dias.\n",
            "O crescimento médio dos casos confimados de COVID no Brasil no período avaliado foi de 16.27%.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wpjCNEd6VWrW"
      },
      "source": [
        "Agora, vamos observar o comportamento da **taxa de crescimento no tempo**. Para isso, vamos definir uma função para calcular a taxa de crescimento diária."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GgVauX7VWrW"
      },
      "source": [
        "$$taxa~de~crescimento = \\frac{x_1}{x_0} - 1$$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLgtabBdVWrX"
      },
      "source": [
        "def taxa_crescimento_diario(dados, variable, data_inicio=None):\n",
        "    if data_inicio == None:\n",
        "        data_inicio = dados.observationdate.loc[dados[variable] > 0].min()\n",
        "    else:\n",
        "        data_inicio = pd.to_datetime(data_inicio)\n",
        "    \n",
        "    data_fim = dados.observationdate.max()\n",
        "    n = (data_fim - data_inicio).days\n",
        "    taxas = list(map( lambda x: (dados[variable].iloc[x]/dados[variable].iloc[x-1]) - 1,\n",
        "                             range(0+len(dados[variable])-n,len(dados[variable])) ))\n",
        "    return np.array(taxas)#*100"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3vRlb_lgVWrb"
      },
      "source": [
        "def Grafic_taxa_crescimento(inicio):\n",
        "    taxa_dia = taxa_crescimento_diario(df_brasil, 'confirmed', inicio)\n",
        "\n",
        "    fig = go.Figure()\n",
        "    fig.add_trace(\n",
        "        go.Scatter(x=pd.date_range(inicio, df_brasil.observationdate.max())[1:], y=taxa_dia,\n",
        "                   name='Taxa', mode='lines+markers', line=dict(color='red'), marker={'color':'black'})\n",
        "    )\n",
        "    fig.update_layout(title='Taxa de crescimento de casos confirmados no Brasil',\n",
        "                      xaxis_title='Data', yaxis_title='Taxa de crescimento',\n",
        "                      margin=dict(l=50, r=30, t=50, b=5),\n",
        "                      width=1100, height=400, font=dict(size=16))\n",
        "    fig.show()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YGAdJQ_JVWrb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "10d8ea51-6515-43d6-e640-adcbaf1d8fa5"
      },
      "source": [
        "#Grafic_taxa_crescimento(df_brasil.observationdate.min())\n",
        "Grafic_taxa_crescimento('2020-03-14')"
      ],
      "execution_count": 22,
      "outputs": [
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          "metadata": {
            "tags": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "yuNFbRvRVWrc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35c71994-7d34-4818-985c-702ce1563b95"
      },
      "source": [
        "#taxa_dia = taxa_crescimento_diario(df_brasil, 'confirmed', '2020-03-15')\n",
        "#taxa_dia = taxa_crescimento_diario(df_brasil, 'confirmed', '2020-05-18')\n",
        "taxa_dia = taxa_crescimento_diario(df_brasil, 'confirmed')\n",
        "taxa_dia"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 1.        , 0.        , 0.        ,\n",
              "       0.        , 1.        , 0.        , 2.25      , 0.        ,\n",
              "       0.53846154, 0.25      , 0.24      , 0.22580645, 0.36842105,\n",
              "       1.90384615, 0.        , 0.07284768, 0.2345679 , 0.605     ,\n",
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              "       0.24450194, 0.16787942, 0.13662661, 0.16875489, 0.14472362,\n",
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              "       0.10705012, 0.0883558 , 0.05444923, 0.05404357, 0.057335  ,\n",
              "       0.06216486, 0.09351575, 0.08008234, 0.09771848, 0.06365046,\n",
              "       0.0688748 , 0.08583163, 0.08807264, 0.0941457 , 0.05752004,\n",
              "       0.05312249, 0.04867147, 0.06672166, 0.0629258 , 0.09662639,\n",
              "       0.07236338, 0.08190877, 0.06240554, 0.04253465, 0.04237887,\n",
              "       0.05082727, 0.06690271, 0.06851902, 0.08429602, 0.06001153,\n",
              "       0.03241389, 0.05926663, 0.06467921])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kB-UIML_VWrd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c4948e0-633c-4243-ed4c-55f14692a9d0"
      },
      "source": [
        "#Observe que as taxas diárias de crescimento aplicadas à população inicial\n",
        "#fornece exatamente a população final, como esperado.\n",
        "pop_inicial=1\n",
        "\n",
        "pop_final = pop_inicial\n",
        "for i in taxa_dia:\n",
        "    pop_final *= (1+i)\n",
        "\n",
        "print( f'{pop_final:.1f} == {df_brasil.confirmed.iloc[-1]}' )"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "271885.0 == 271885.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUtBEsLnVWre"
      },
      "source": [
        "### Predições de Séries Temporais\n",
        "\n",
        "Vamos construir um modelo de séries temporais para prever os novos casos. Antes analisaremos a série temporal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "U0ZywQAAVWri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d005b26f-8ab6-4148-f6af-5a310db324df"
      },
      "source": [
        "#Instalar o pacote pmdarima antes de importar a biblioteca statsmodels, senão tem que reiniciar o notebook.\n",
        "!pip install pmdarima"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pmdarima\n",
            "  Downloading pmdarima-1.8.2-cp37-cp37m-manylinux1_x86_64.whl (1.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5 MB 4.3 MB/s \n",
            "\u001b[?25hCollecting statsmodels!=0.12.0,>=0.11\n",
            "  Downloading statsmodels-0.12.2-cp37-cp37m-manylinux1_x86_64.whl (9.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.5 MB 33.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.29.24)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.1.5)\n",
            "Requirement already satisfied: numpy~=1.19.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.19.5)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.4.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.24.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (1.0.1)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima) (57.4.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima) (1.15.0)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima) (0.5.1)\n",
            "Installing collected packages: statsmodels, pmdarima\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed pmdarima-1.8.2 statsmodels-0.12.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sv4VyM6GVWre"
      },
      "source": [
        "from statsmodels.tsa.seasonal import seasonal_decompose"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HpNxFPMVWrf"
      },
      "source": [
        "def Grafic_decompose(serie):\n",
        "    res = seasonal_decompose(serie)\n",
        "\n",
        "    fig, (ax1,ax2,ax3,ax4) = plt.subplots(4,1,figsize=(8,8))\n",
        "    ax1.plot(res.observed)\n",
        "    ax1.set_title('Série original')\n",
        "\n",
        "    ax2.plot(res.trend)\n",
        "    ax2.set_title('Tendência', pad=5)\n",
        "\n",
        "    ax3.plot(res.seasonal)\n",
        "    ax3.set_title('Sazonalidade')\n",
        "\n",
        "    ax4.scatter(novoscasos.index, res.resid)\n",
        "    ax4.plot(novoscasos.index, res.resid)\n",
        "    ax4.axhline(0, linestyle='dashed', c='black')\n",
        "    ax4.set_title('Resíduos')\n",
        "\n",
        "    fig.tight_layout(pad=0.7)\n",
        "    plt.show()"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EE2AanEoVWrf"
      },
      "source": [
        "Decompondo a série de novos casos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0xGC9e0XVWrg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "fc0e835c-15e1-4140-9f32-56f9e791067f"
      },
      "source": [
        "novoscasos = df_brasil.novoscasos\n",
        "novoscasos.index = df_brasil.observationdate\n",
        "\n",
        "Grafic_decompose(novoscasos)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1m8Y-7MVWrg"
      },
      "source": [
        "Decompondo a série de casos confirmados\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNa0uayxVWrh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "d3ac58db-2d8e-4dad-8059-74817390afe6"
      },
      "source": [
        "confirmados = df_brasil.confirmed\n",
        "confirmados.index = df_brasil.observationdate\n",
        "\n",
        "Grafic_decompose(confirmados)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bj_hPAiZVWrh"
      },
      "source": [
        "#### Predizendo o número de casos confirmados com um AUTO-ARIMA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "px5H6JCmVWri"
      },
      "source": [
        "from pmdarima.arima import auto_arima"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dv4l5aSIVWri"
      },
      "source": [
        "modelo = auto_arima(confirmados)"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHTR4FKkVWrj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "97ca4bc5-6d29-40e9-b7e9-566d0f0ba4e5"
      },
      "source": [
        "fig = go.Figure(go.Scatter(\n",
        "    x=confirmados.index, y=confirmados, name='Observed', mode='lines+markers'\n",
        "))\n",
        "fig.add_trace(go.Scatter(x=confirmados.index, y = modelo.predict_in_sample(), name='Predicted'))\n",
        "\n",
        "data_inicial=pd.to_datetime('2020-05-20')\n",
        "data_final  =pd.to_datetime('2020-06-04')\n",
        "n = (data_final - data_inicial).days\n",
        "fig.add_trace(go.Scatter(x=pd.date_range(data_inicial, data_final), y=modelo.predict(n), name='Forecast'))\n",
        "\n",
        "fig.update_layout(title=f'Previsão de casos confirmados para os próximos {n} dias',\n",
        "                  yaxis_title='Casos confirmados', xaxis_title='Data',\n",
        "                  margin=dict(l=30, r=30, t=50, b=5),\n",
        "                  width=1100, height=400, font=dict(size=14))\n",
        "fig.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0ETjcjrVWrj"
      },
      "source": [
        "#### Forecasting com Prophet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BZSgA3Q8VWrk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a76a50c3-95d1-4a2b-fa35-68cc6c281efd"
      },
      "source": [
        "!pip3 install fbprophet"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: fbprophet in /usr/local/lib/python3.7/dist-packages (0.7.1)\n",
            "Requirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.29.24)\n",
            "Requirement already satisfied: cmdstanpy==0.9.5 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.9.5)\n",
            "Requirement already satisfied: pystan>=2.14 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.19.1.1)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.19.5)\n",
            "Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.1.5)\n",
            "Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (3.2.2)\n",
            "Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.0.9)\n",
            "Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.3.2)\n",
            "Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (0.10.5.2)\n",
            "Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (2.8.2)\n",
            "Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from fbprophet) (4.62.0)\n",
            "Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->fbprophet) (0.5.11)\n",
            "Requirement already satisfied: pytz>=2014.10 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->fbprophet) (2018.9)\n",
            "Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (2.1.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (1.15.0)\n",
            "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->fbprophet) (0.2.1)\n",
            "Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->fbprophet) (4.0.0.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->fbprophet) (2.4.7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vkS_ZUZtVWrk"
      },
      "source": [
        "from fbprophet import Prophet"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKiTLEmAP7ZJ"
      },
      "source": [
        "#preparando os dados:\n",
        "train = confirmados.reset_index()[:-5]\n",
        "test  = confirmados.reset_index()[-5:]\n",
        "\n",
        "#renomear colunas:\n",
        "train.rename(columns={'observationdate':'ds','confirmed':'y'}, inplace=True)\n",
        "test.rename( columns={'observationdate':'ds','confirmed':'y'}, inplace=True)\n",
        "test = test.set_index('ds')\n",
        "test = test['y']\n",
        "\n",
        "#Supondo que toda a população seja infectada:\n",
        "#https://www.ibge.gov.br/apps/populacao/projecao/box_popclock.php\n",
        "pop = 213481773\n",
        "#pop = 1000000\n",
        "train['cap'] = pop"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "r7SxX3QfPqmG",
        "outputId": "4736f6df-c171-4e24-d394-1b963fb5c7f0"
      },
      "source": [
        "train"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .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>ds</th>\n",
              "      <th>y</th>\n",
              "      <th>cap</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-02-26</td>\n",
              "      <td>1.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-02-27</td>\n",
              "      <td>1.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-02-28</td>\n",
              "      <td>1.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-02-29</td>\n",
              "      <td>2.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-01</td>\n",
              "      <td>2.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>2020-05-10</td>\n",
              "      <td>162699.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>169594.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>2020-05-12</td>\n",
              "      <td>178214.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>190137.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>2020-05-14</td>\n",
              "      <td>203165.0</td>\n",
              "      <td>213481773</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>79 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           ds         y        cap\n",
              "0  2020-02-26       1.0  213481773\n",
              "1  2020-02-27       1.0  213481773\n",
              "2  2020-02-28       1.0  213481773\n",
              "3  2020-02-29       2.0  213481773\n",
              "4  2020-03-01       2.0  213481773\n",
              "..        ...       ...        ...\n",
              "74 2020-05-10  162699.0  213481773\n",
              "75 2020-05-11  169594.0  213481773\n",
              "76 2020-05-12  178214.0  213481773\n",
              "77 2020-05-13  190137.0  213481773\n",
              "78 2020-05-14  203165.0  213481773\n",
              "\n",
              "[79 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7KiAd8aeVWrl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac4bee2d-3601-4df3-dede-f808ce161b3f"
      },
      "source": [
        "#Modelo:\n",
        "profeta = Prophet(growth=\"logistic\", changepoints=['2020-03-21', '2020-03-30', '2020-04-25', '2020-05-03', '2020-05-10'])\n",
        "\n",
        "#Treinar o modelo:\n",
        "profeta.fit(train)\n",
        "\n",
        "#Construindo previsões para o futuro:\n",
        "future_dates = profeta.make_future_dataframe(periods=200)\n",
        "future_dates['cap'] = pop\n",
        "forecast =  profeta.predict(future_dates)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Pz0tSK7VWrl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "4c0fd6e8-45d9-4004-fec0-131edf81b580"
      },
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(x=forecast.ds, y=forecast.yhat, name='Predição'))\n",
        "fig.add_trace(go.Scatter(x=test.index, y=test, name='Observados - Teste'))\n",
        "fig.add_trace(go.Scatter(x=train.ds, y=train.y, name='Observados - Treino'))\n",
        "fig.update_layout(title='Predições de casos confirmados no Brasil (supondo que toda a população seja infectada)',\n",
        "                  yaxis_title='Casos confirmados', xaxis_title='Data',\n",
        "                  margin=dict(l=30, r=30, t=80, b=5), width=1100, height=400, font=dict(size=14))\n",
        "fig.show()"
      ],
      "execution_count": 38,
      "outputs": [
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