Programação para Ciência de Dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.1"
    },
    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "colab": {
      "name": "Avaliacao_PCD_MBA_USP.ipynb",
      "provenance": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "44CX0Zis3Be9",
        "colab_type": "text"
      },
      "source": [
        "# <span style=\"color:blue\"> MBA em Ciência de Dados</span>\n",
        "# <span style=\"color:blue\">Programação para Ciência de Dados</span>\n",
        "\n",
        "## <span style=\"color:blue\">Avaliação Final</span>\n",
        "**Material Produzido por Luis Gustavo Nonato**<br>\n",
        "**Cemeai - ICMC/USP São Carlos**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CJ_8LnnA3Be-",
        "colab_type": "text"
      },
      "source": [
        "A avaliação vale 10 pontos. As questões de 1 a 4, caso respondidas da forma correta, totalizam 10 pontos. A última questão é um bônus que, se respondida corretamente, pode ajudar na nota final, ou seja, caso tenha respondido incorretamente uma das questões de 1 a 4, mas tenha acertado a questão bônus, ainda poderá atingir 10 pontos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mUakr7R3Be_",
        "colab_type": "text"
      },
      "source": [
        "<font color='red'>**ATENÇÃO:** Quando terminar de exame, você deve fazer um \"upload\" do notebook no _moodle_</font>."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwGxIDU43Be_",
        "colab_type": "text"
      },
      "source": [
        "Para responder as questões abaixo será necessário fazer uso do arquivo <font style=\"font-family: monaco\">covid.csv\"</font> disponível para download no sistema _Moodle_. As colunas do conjunto de dados fornece informações sobre:\n",
        "- **'Province/State'**: estado ou província onde os casos de COVID19 foram registrados\n",
        "- **'Country/Region'**: país onde os casos de COVID19 foram registrados\n",
        "- **'Lat'** e **'Long'**: latitude e longitude de referência\n",
        "- **'Date'**: data dos registros\n",
        "- **'Value'**: quantidade de casos registrados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6Bs8Y-63BfA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQGbFntF3BfD",
        "colab_type": "text"
      },
      "source": [
        "### Questão 1 (2 pontos)\n",
        "Considerando os dados contidos no arquivo <font style=\"font-family: monaco\">covid.csv\"</font>:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeQIuTlp3BfF",
        "colab_type": "text"
      },
      "source": [
        "#### a) (0.5 pontos)\n",
        "Carregue o arquivo \"covid.csv\" e armazena em um pandas DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DA7gt23HKzif",
        "colab_type": "code",
        "outputId": "846a61e9-c743-4fb2-f175-87018d864725",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "#importando o dataset\n",
        "from google.colab import files\n",
        "up = files.upload()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-f631b110-fbaa-4aac-b8ba-d3579b48da32\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-f631b110-fbaa-4aac-b8ba-d3579b48da32\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving covid.csv to covid (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zf3iXMpqLvuF",
        "colab_type": "code",
        "outputId": "b3251730-a871-48f1-a335-3532482872a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#lendo o dataset como csv e nomeando como 'data'\n",
        "data = pd.read_csv('covid.csv')\n",
        "data.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>#adm1+name</td>\n",
              "      <td>#country+name</td>\n",
              "      <td>#geo+lat</td>\n",
              "      <td>#geo+lon</td>\n",
              "      <td>#date</td>\n",
              "      <td>#affected+infected+value+num</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region  ...        Date                         Value\n",
              "0     #adm1+name  #country+name  ...       #date  #affected+infected+value+num\n",
              "1            NaN    afghanistan  ...  2020-03-18                            22\n",
              "2            NaN    afghanistan  ...  2020-03-17                            22\n",
              "3            NaN    afghanistan  ...  2020-03-16                            21\n",
              "4            NaN    Afghanistan  ...  2020-03-15                            16\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tfftjSfq3BfJ",
        "colab_type": "text"
      },
      "source": [
        "#### b) (0.5 pontos)\n",
        "Elimine a primeira linha do DataFrame, a qual contém a informação:\n",
        "```python\n",
        "#adm1+name,#country+name,#geo+lat,#geo+lon,#date,#affected+infected+value+num\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BjFqSl3y3BfK",
        "colab_type": "code",
        "outputId": "dc5a3880-8fdd-4d26-f346-2cd6b043c96c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "#excluindo a primeira linha do dataset\n",
        "data = data.drop(0)\n",
        "data.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region Lat Long        Date Value\n",
              "1            NaN    afghanistan  33   65  2020-03-18    22\n",
              "2            NaN    afghanistan  33   65  2020-03-17    22\n",
              "3            NaN    afghanistan  33   65  2020-03-16    21\n",
              "4            NaN    Afghanistan  33   65  2020-03-15    16\n",
              "5            NaN    afghanistan  33   65  2020-03-14    11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gONaSZw3BfN",
        "colab_type": "text"
      },
      "source": [
        "#### c) (1 ponto)\n",
        "Verifique qual o tipo de dado da coluna \"Value\" e converta o tipo para inteiro (<font color='blue'>int</font>)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "273exMvLQotv",
        "colab_type": "code",
        "outputId": "4286328d-daf4-4fb0-ce95-990dbb63b46e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "#verificando o tipo de dados de cada coluna do dataset - antes da mudança\n",
        "data.dtypes"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Province/State    object\n",
              "Country/Region    object\n",
              "Lat               object\n",
              "Long              object\n",
              "Date              object\n",
              "Value             object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8-Q9PCUn3BfN",
        "colab_type": "code",
        "outputId": "0a4ffe5f-24e0-43e7-987b-d86e38dbd61d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#verificando o tipo de dados da coluna 'Value' - antes da mudança\n",
        "data['Value'].dtypes"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kItOImxfM_qA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#alterando o tipo de dado da coluna 'Value' para int\n",
        "data['Value'] = data['Value'].astype('int')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bbnVPOz_RBob",
        "colab_type": "code",
        "outputId": "df9bc79d-4c44-4e51-8855-e3ff82cdae6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "#verificando o tipo de dados de cada coluna do dataset - depois da mudança\n",
        "data.dtypes"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Province/State    object\n",
              "Country/Region    object\n",
              "Lat               object\n",
              "Long              object\n",
              "Date              object\n",
              "Value              int64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P05Rv1qtRUss",
        "colab_type": "code",
        "outputId": "28162692-d86a-4ad1-a5fd-991536bee286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#verificando o tipo de dados da coluna 'Value' - depois da mudança\n",
        "data['Value'].dtypes"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OUc4zXXz3BfR",
        "colab_type": "text"
      },
      "source": [
        "### Questão 2 (2 pontos)\n",
        "Devido a um erro de armazenamento de inteiros, alguns países apresentam mais de 8 bilhões de casos de COVID19 (valor da coluna \"Value\" maior ou igual a 8.000.000.000) \n",
        "\n",
        "Elimine as linhas onde o número de casos é maior ou igual a 8 bilhões."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VlZFsaQsXLy-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "26a1d732-589f-4db4-e108-7f661dc3ed41"
      },
      "source": [
        "#verificando o maior valor armazenado na coluna 'Value' - antes da mudança\n",
        "data['Value'].max()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8000009822"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmGo4jAuXU2E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#selecionando apenas as linhas que possuem valor menor do que 8bi\n",
        "data = data.query('Value < 8000000000')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhEm_Z2XX44V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b9d06ea1-d320-4e0f-e85e-28963c6c0cea"
      },
      "source": [
        "#verificando o maior valor armazenado na coluna 'Value' - depois da mudança\n",
        "data['Value'].max()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "67800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5ZTfNNe3BfW",
        "colab_type": "text"
      },
      "source": [
        "### Questão 3 (3 pontos)\n",
        "O nome de alguns países estão com a primeira letra em maíuscula e outros não.\n",
        "\n",
        "Transforme os nomes dos países na coluna \"Country/Region\" para que contenham apenas letras **minúsculas**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "frbFmuLc3BfX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "6ec78a3e-d5ae-419c-bfb7-93a180b53b09"
      },
      "source": [
        "#checando os tipos de valores armazenados na coluna 'Country/Region' - antes da mudança\n",
        "print(data['Country/Region'])"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1        afghanistan\n",
            "2        afghanistan\n",
            "3        afghanistan\n",
            "4        Afghanistan\n",
            "5        afghanistan\n",
            "            ...     \n",
            "26330         zambia\n",
            "26331         Zambia\n",
            "26332         Zambia\n",
            "26333         zambia\n",
            "26334         zambia\n",
            "Name: Country/Region, Length: 26315, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uapu5hzhYHhz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#transformando os valores da coluna para que todos contenham apenas letras minúsculas\n",
        "data['Country/Region'] = data['Country/Region'].apply(lambda x: x.lower())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qXwDJYCuZcwp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "outputId": "c71d6b04-1755-43c7-d5e7-1a94bb4a5520"
      },
      "source": [
        "#checando os tipos de valores armazenados na coluna 'Country/Region' - depois da mudança\n",
        "print(data['Country/Region'])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1        afghanistan\n",
            "2        afghanistan\n",
            "3        afghanistan\n",
            "4        afghanistan\n",
            "5        afghanistan\n",
            "            ...     \n",
            "26330         zambia\n",
            "26331         zambia\n",
            "26332         zambia\n",
            "26333         zambia\n",
            "26334         zambia\n",
            "Name: Country/Region, Length: 26315, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swlygqe-3Bfb",
        "colab_type": "text"
      },
      "source": [
        "### Questão 4 (3 pontos)\n",
        "Quais são os três países com a maior quantidade de infectados?\n",
        "\n",
        "Lembre de considerar apenas o maior valor de cada país.\n",
        "\n",
        "**DICA:** groupby + max"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8aDxKrCacHWB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "outputId": "b36bf2f4-0381-4e23-c178-0f7f1a0ff95c"
      },
      "source": [
        "#armazenando o maior valor da coluna 'Value' de cada país na variável 'total_infectados'\n",
        "total_infectados = data.groupby('Country/Region')['Value'].max()\n",
        "\n",
        "#exibindo os três países com maior número de infectados\n",
        "print(total_infectados.sort_values()[-3:])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Country/Region\n",
            "iran     17361\n",
            "italy    35713\n",
            "china    67800\n",
            "Name: Value, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xVzeoDJE3Bfe",
        "colab_type": "text"
      },
      "source": [
        "### Questão Bonus (2 pontos)\n",
        "Considerando que a taxa de crescimento diária do vírus é de 1.31, quantos dias até o Brasil atingir 10000 casos?\n",
        "\n",
        "OBS: A fórmula para a quantidade de pessoas infectada no dia $n$ a partir da quantidade atual é:\n",
        "\n",
        "$ quantidade\\_atual * (1+1.31^{n})$ (dentro do parênteses: 1 mais 1.31 elevado a $n$)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aenAUWJd9do3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "b82e005a-a1c5-41ab-bca2-b8e9d6add057"
      },
      "source": [
        "data.head()\n"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province/State</th>\n",
              "      <th>Country/Region</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province/State Country/Region Lat Long        Date  Value\n",
              "1            NaN    afghanistan  33   65  2020-03-18     22\n",
              "2            NaN    afghanistan  33   65  2020-03-17     22\n",
              "3            NaN    afghanistan  33   65  2020-03-16     21\n",
              "4            NaN    afghanistan  33   65  2020-03-15     16\n",
              "5            NaN    afghanistan  33   65  2020-03-14     11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kb4qwB_l9fQn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "2149361e-317e-4232-e607-fa7d0c66eb5c"
      },
      "source": [
        "#renomeando as colunas do DataFrame para melhor manipulá-las\n",
        "data.columns = ['Province', 'Country', 'Lat', 'Long', 'Date', 'Value']\n",
        "data.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province</th>\n",
              "      <th>Country</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>afghanistan</td>\n",
              "      <td>33</td>\n",
              "      <td>65</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Province      Country Lat Long        Date  Value\n",
              "1      NaN  afghanistan  33   65  2020-03-18     22\n",
              "2      NaN  afghanistan  33   65  2020-03-17     22\n",
              "3      NaN  afghanistan  33   65  2020-03-16     21\n",
              "4      NaN  afghanistan  33   65  2020-03-15     16\n",
              "5      NaN  afghanistan  33   65  2020-03-14     11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRJhSL7Q9xY-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "outputId": "5d604d5b-aee7-402b-ea7b-ea27d16942f2"
      },
      "source": [
        "#criando um novo DataFrame para armazenar apenas os dados do Brasil\n",
        "brazil = data.query('Country == \"brazil\"')\n",
        "brazil.head()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Province</th>\n",
              "      <th>Country</th>\n",
              "      <th>Lat</th>\n",
              "      <th>Long</th>\n",
              "      <th>Date</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1540</th>\n",
              "      <td>NaN</td>\n",
              "      <td>brazil</td>\n",
              "      <td>-14.235</td>\n",
              "      <td>-51.9253</td>\n",
              "      <td>2020-03-18</td>\n",
              "      <td>372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1541</th>\n",
              "      <td>NaN</td>\n",
              "      <td>brazil</td>\n",
              "      <td>-14.235</td>\n",
              "      <td>-51.9253</td>\n",
              "      <td>2020-03-17</td>\n",
              "      <td>321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1542</th>\n",
              "      <td>NaN</td>\n",
              "      <td>brazil</td>\n",
              "      <td>-14.235</td>\n",
              "      <td>-51.9253</td>\n",
              "      <td>2020-03-16</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1543</th>\n",
              "      <td>NaN</td>\n",
              "      <td>brazil</td>\n",
              "      <td>-14.235</td>\n",
              "      <td>-51.9253</td>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>162</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1544</th>\n",
              "      <td>NaN</td>\n",
              "      <td>brazil</td>\n",
              "      <td>-14.235</td>\n",
              "      <td>-51.9253</td>\n",
              "      <td>2020-03-14</td>\n",
              "      <td>151</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Province Country      Lat      Long        Date  Value\n",
              "1540      NaN  brazil  -14.235  -51.9253  2020-03-18    372\n",
              "1541      NaN  brazil  -14.235  -51.9253  2020-03-17    321\n",
              "1542      NaN  brazil  -14.235  -51.9253  2020-03-16    200\n",
              "1543      NaN  brazil  -14.235  -51.9253  2020-03-15    162\n",
              "1544      NaN  brazil  -14.235  -51.9253  2020-03-14    151"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EpCfQu8RAQox",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "55d50e60-a055-48e2-fd06-333d3fa15460"
      },
      "source": [
        "#Criando as variáveis e o loop para retornar a quantidade de dias em que o Brasil terá 1000 infectados a uma taxa de crescimento de 1.31/dia\n",
        "max_value = brazil['Value'].max()\n",
        "crescimento = 0.0131\n",
        "dia = 0\n",
        "\n",
        "while(max_value <= 1000):\n",
        "  max_value = max_value + (max_value * crescimento)\n",
        "  dia = dia + 1\n",
        "\n",
        "print('A previsão é de que em {} dias, o Brasil atingirá 1000 infectados.'.format(dia))"
      ],
      "execution_count": 161,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "A previsão é de que em 76 dias, o Brasil atingirá 1000 infectados.\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}