Exercícios do Workshop Pandas - Militão.ipynb

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      "name": "Exercícios Workshop Pandas - Thiago Militão.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/residenthiago2011/Studying/blob/master/Exerc%C3%ADcios%20do%20Workshop%20Pandas%20-%20Milit%C3%A3o.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0xmvM7kES7h"
      },
      "source": [
        "# **Atividade: Workshop Excel ao Pandas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-k9lJvd5E6PN"
      },
      "source": [
        "O notebook a seguir é referente aos exercícios a serem realizados após o workshop de Pandas. \n",
        "\n",
        "**Antes de realizar a sua atividade, clique File > Save a Copy in Drive (Salvar uma Cópia no Drive)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fqgAj889E6Tr"
      },
      "source": [
        "Você faz parte de uma consultoria de negócios e foi contratado pela *Bikes S.A* para realizar uma breve análise dos negócios e responder a algumas questões, desenvolvendo a estratégia de negócios para a próxima temporada "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g773-XerHcOX"
      },
      "source": [
        "### **Exercício 1**\n",
        "Vamos carregar nosso dataset e entender um pouco sobre ele. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SBZ3_KpzHZpk"
      },
      "source": [
        "# Importe o pacote Pandas para a sessão (Lembre-se que, caso precise instalar, use o comando !pip install nome-do-pacote)\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
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              "status": 200,
              "status_text": ""
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          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "7f4c7ef0-6f3b-4d35-e1c3-65330f26ed1a"
      },
      "source": [
        "# Carregue o dataset\n",
        "\n",
        "# Importar o arquivo do seu computador para o Google Colab\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ffe80709-fe7e-41d1-afc9-8cf97c1dd110\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ffe80709-fe7e-41d1-afc9-8cf97c1dd110\">\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 SeoulBikeData.csv to SeoulBikeData.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BYiroNfEH9wI"
      },
      "source": [
        "# Importar o arquivo do Google Colab para o notebook\n",
        "import io\n",
        "df = pd.read_csv('SeoulBikeData.csv', encoding='unicode_escape')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "1sSicMX0IDpo",
        "outputId": "9d6f1604-5b7b-43ca-8224-18d7c3fa0b3a"
      },
      "source": [
        "# Verifique as primeiras 5 linhas com o método head\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Rented Bike Count</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Temperature(°C)</th>\n",
              "      <th>Humidity(%)</th>\n",
              "      <th>Wind speed (m/s)</th>\n",
              "      <th>Visibility (10m)</th>\n",
              "      <th>Dew point temperature(°C)</th>\n",
              "      <th>Solar Radiation (MJ/m2)</th>\n",
              "      <th>Rainfall(mm)</th>\n",
              "      <th>Snowfall (cm)</th>\n",
              "      <th>Seasons</th>\n",
              "      <th>Holiday</th>\n",
              "      <th>Functioning Day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>254</td>\n",
              "      <td>0</td>\n",
              "      <td>-5.2</td>\n",
              "      <td>37</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>204</td>\n",
              "      <td>1</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>173</td>\n",
              "      <td>2</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>39</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>107</td>\n",
              "      <td>3</td>\n",
              "      <td>-6.2</td>\n",
              "      <td>40</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01/12/2017</td>\n",
              "      <td>78</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>36</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2000</td>\n",
              "      <td>-18.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date  Rented Bike Count  Hour  ...  Seasons     Holiday  Functioning Day\n",
              "0  01/12/2017                254     0  ...   Winter  No Holiday              Yes\n",
              "1  01/12/2017                204     1  ...   Winter  No Holiday              Yes\n",
              "2  01/12/2017                173     2  ...   Winter  No Holiday              Yes\n",
              "3  01/12/2017                107     3  ...   Winter  No Holiday              Yes\n",
              "4  01/12/2017                 78     4  ...   Winter  No Holiday              Yes\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nFV2V6UyIKbs",
        "outputId": "7b4971f1-2e31-414f-b316-5f1c1582b7d9"
      },
      "source": [
        "# Verifique o tipo de arquivo importado\n",
        "type(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9SsbP_GI27v",
        "outputId": "3c7812af-32f1-42f3-fa5e-2c54bd104060"
      },
      "source": [
        "# Verifique as informações (info) do arquivo importado\n",
        "df.info()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8760 entries, 0 to 8759\n",
            "Data columns (total 14 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Date                       8760 non-null   object \n",
            " 1   Rented Bike Count          8760 non-null   int64  \n",
            " 2   Hour                       8760 non-null   int64  \n",
            " 3   Temperature(°C)            8760 non-null   float64\n",
            " 4   Humidity(%)                8760 non-null   int64  \n",
            " 5   Wind speed (m/s)           8760 non-null   float64\n",
            " 6   Visibility (10m)           8760 non-null   int64  \n",
            " 7   Dew point temperature(°C)  8760 non-null   float64\n",
            " 8   Solar Radiation (MJ/m2)    8760 non-null   float64\n",
            " 9   Rainfall(mm)               8760 non-null   float64\n",
            " 10  Snowfall (cm)              8760 non-null   float64\n",
            " 11  Seasons                    8760 non-null   object \n",
            " 12  Holiday                    8760 non-null   object \n",
            " 13  Functioning Day            8760 non-null   object \n",
            "dtypes: float64(6), int64(4), object(4)\n",
            "memory usage: 958.2+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGXBKWWHOsJK"
      },
      "source": [
        "**Execute a célula abaixo antes de seguir para o próximo exercício**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-3bRSDTJD6g"
      },
      "source": [
        "# Apenas execute esta célula para criar duas novas colunas com mês e ano.\n",
        "df['Date'] = pd.to_datetime(df.Date)\n",
        "df['Year'] = df.Date.dt.year\n",
        "df['Month'] = df.Date.dt.month"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DzPEX37CJa1y"
      },
      "source": [
        "### **Exercício 2**\n",
        "Agora que sabemos um pouco mais sobre nosso dataset, vamos extrair alguns insights dos dados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "p6yR1w1qMdl_",
        "outputId": "d7cac880-6145-4fc1-cb37-dd52b71fe22c"
      },
      "source": [
        "# Visualizando a tabela\n",
        "df.head(2)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Rented Bike Count</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Temperature(°C)</th>\n",
              "      <th>Humidity(%)</th>\n",
              "      <th>Wind speed (m/s)</th>\n",
              "      <th>Visibility (10m)</th>\n",
              "      <th>Dew point temperature(°C)</th>\n",
              "      <th>Solar Radiation (MJ/m2)</th>\n",
              "      <th>Rainfall(mm)</th>\n",
              "      <th>Snowfall (cm)</th>\n",
              "      <th>Seasons</th>\n",
              "      <th>Holiday</th>\n",
              "      <th>Functioning Day</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-01-12</td>\n",
              "      <td>254</td>\n",
              "      <td>0</td>\n",
              "      <td>-5.2</td>\n",
              "      <td>37</td>\n",
              "      <td>2.2</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-01-12</td>\n",
              "      <td>204</td>\n",
              "      <td>1</td>\n",
              "      <td>-5.5</td>\n",
              "      <td>38</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2000</td>\n",
              "      <td>-17.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Winter</td>\n",
              "      <td>No Holiday</td>\n",
              "      <td>Yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  Rented Bike Count  Hour  ...  Functioning Day  Year  Month\n",
              "0 2017-01-12                254     0  ...              Yes  2017      1\n",
              "1 2017-01-12                204     1  ...              Yes  2017      1\n",
              "\n",
              "[2 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GHkjjo0yJTde",
        "outputId": "083bd680-d269-4a84-9f9e-a1d23cd5a8c1"
      },
      "source": [
        "# Agrupe a tabela por ano e faça a soma das vendas por ano\n",
        "df.groupby('Year')[\"Rented Bike Count\"].sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year\n",
              "2017     185330\n",
              "2018    5986984\n",
              "Name: Rented Bike Count, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FRH4AZ_LT0S",
        "outputId": "99808f86-4103-4865-9b1c-7f2ca4cea309"
      },
      "source": [
        "# Agrupe a tabela por Estação (Seasons) e faça a soma das vendas por estação\n",
        "df.groupby(\"Seasons\")[\"Rented Bike Count\"].sum()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Seasons\n",
              "Autumn    1790002\n",
              "Spring    1611909\n",
              "Summer    2283234\n",
              "Winter     487169\n",
              "Name: Rented Bike Count, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mo7gdbYkNC0Z",
        "outputId": "d5a06126-279d-4354-b38c-ce8c555eb718"
      },
      "source": [
        "# Agrupe a tabela por Mês e tire a média (mean) das vendas por mês\n",
        "df.groupby(\"Month\")[\"Rented Bike Count\"].mean()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month\n",
              "1     386.080645\n",
              "2     393.023810\n",
              "3     611.608871\n",
              "4     772.526389\n",
              "5     895.091398\n",
              "6     981.566667\n",
              "7     929.219086\n",
              "8     825.524194\n",
              "9     693.508333\n",
              "10    842.725806\n",
              "11    685.294444\n",
              "12    419.047043\n",
              "Name: Rented Bike Count, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZglHMS8Ny9o"
      },
      "source": [
        "### **Comente quais foram os insights que você pôde perceber nesses números?**\n",
        "* Estamos em tendência de alta ou baixa?\n",
        "\n",
        "*Resposta:*\n",
        "**Os Meses 4,5,6,7 Foram os que Geraram Maior Receita, Pois o Aluguel de Bicicletas Nesse Periodo do Ano foi Melhor Que os Meses Consecutivos.**\n",
        "* Qual é a melhor estação?\n",
        "\n",
        "*Resposta:*\n",
        "**A Melhor Estação foi o Verão!**\n",
        "* Qual mês, em média, é melhor?\n",
        "\n",
        "*Resposta:*\n",
        "**O Mês de Junho é o Melhor Periodo, Com 981.56.**\n",
        "\n",
        "*Duplo clique nesta célula para editar a resposta*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MF8hcXtdPGd1"
      },
      "source": [
        "### **Exercício 3**\n",
        "Vamos criar algumas visualizações gráficas com o Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "SQF2EA0BNbug",
        "outputId": "ea50b188-3fa3-4447-89a9-78c176a63234"
      },
      "source": [
        "# Faça um gráfico (plot) em linha do agrupamento de Bikes alugadas por mês usando a média (mean) como função de agregação\n",
        "df.groupby(\"Month\")[\"Rented Bike Count\"].mean().plot(title='Média de Aluguéis de Bikes por Mês',\n",
        "                                                  xlabel='Mês', \n",
        "                                                  ylabel='Média Bikes Alugadas',\n",
        "                                                  figsize=(14,6));"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "f8wGl6BqPn9f",
        "outputId": "d7366772-0a15-4a16-d83c-f5de6b692797"
      },
      "source": [
        "# Faça um gráfico (plot) em barras do agrupamento de Bikes alugadas por Season usando a Soma como função de agregação\n",
        "\n",
        "# Criando uma variável com o agrupamento e organizando os dados do maior para o menor (Caso queira ver o resultado do agrupamento, execute agg_by_season em uma célula separada)\n",
        "agg_by_season = df.groupby('Seasons')[\"Rented Bike Count\"].sum().sort_values(ascending=False)\n",
        "\n",
        "# Criar o gráfico de barras\n",
        "agg_by_season.plot.bar(title='Total de Aluguéis de Bikes por Estação',\n",
        "                                                  xlabel='Estação', \n",
        "                                                  ylabel='Bikes Alugadas',\n",
        "                                                  figsize=(14,6),\n",
        "                                                  color='blue');"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "V1HuQ5cPukHQ",
        "outputId": "5368b13f-b9f8-4822-b567-3b1d2d65af45"
      },
      "source": [
        "# Gráfico (plot) em Barras do Agrupamento de Bikes Alugadas por Mês Usando a Soma Como Função de Agregação\n",
        "\n",
        "agg_by_month = df.groupby(\"Month\")[\"Rented Bike Count\"].sum().sort_values(ascending=True)\n",
        "\n",
        "agg_by_month.plot.bar(title=\"Total de Bikes Alugadas por Mês\",\n",
        "                      xlabel=\"Mês\",\n",
        "                      ylabel=\"Bikes Alugadas\",\n",
        "                      figsize=(14,6),\n",
        "                      color=\"blue\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa31ca820d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9U_L8p7TU0S"
      },
      "source": [
        "### **Exercício 4**\n",
        "Em nosso último exercício, vou propor um desafio."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtG3tzHsTg2D"
      },
      "source": [
        "A **correlação** é uma medida estatística que consegue medir qual é a força da relação entre duas variáveis numéricas. Deste modo, é possível medir quais são as variáveis do nosso dataset que mais influenciam o aluguel de Bikes.\n",
        "\n",
        "[A *correlação* é um número que varia entre 1 e -1](https://pt.wikipedia.org/wiki/Correla%C3%A7%C3%A3o). Sempre que este número se aproxima dos extremos 1 ou -1, dizemos que a relação é mais forte entre as variáveis, sendo 1 uma correlação positiva onde a variável A aumenta, B também aumenta e -1 é uma correlação negativa, quando A aumenta e B diminui.\n",
        "\n",
        "As correlações são mais fracas quanto mais o número se aproxima de zero."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBX2lPr6UrQ1"
      },
      "source": [
        "Tendo a documentação do método [df.corr( )](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html), calcule a correlação entre as variáveis e determine quais podem afetar as vendas do nosso cliente."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCISgxtPTfYF",
        "outputId": "671e85d5-808c-48ca-a27e-8ca1a1791da9"
      },
      "source": [
        "correlacao = df.corr(method='pearson', min_periods=1)\n",
        "correlacao.loc['Rented Bike Count',:]"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Rented Bike Count            1.000000\n",
              "Hour                         0.410257\n",
              "Temperature(°C)              0.538558\n",
              "Humidity(%)                 -0.199780\n",
              "Wind speed (m/s)             0.121108\n",
              "Visibility (10m)             0.199280\n",
              "Dew point temperature(°C)    0.379788\n",
              "Solar Radiation (MJ/m2)      0.261837\n",
              "Rainfall(mm)                -0.123074\n",
              "Snowfall (cm)               -0.141804\n",
              "Year                         0.215162\n",
              "Month                        0.070861\n",
              "Name: Rented Bike Count, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "i9DRRaXnRuH8",
        "outputId": "9ed71c9d-c021-47b5-eaf0-4c417bbbb61a"
      },
      "source": [
        "# Para visualizar o mapa de calor, basta executar o código abaixo\n",
        "import seaborn as sns\n",
        "sns.heatmap(correlacao);"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbnCmVXXWoQ2"
      },
      "source": [
        "## **Qual seria a estratégia que você apresentaria ao cliente?**\n",
        "* Em quais meses ele deve investir mais ou menos?\n",
        "\n",
        "*Resposta:*\n",
        "**O Maior Investimento Deve Ser Feito Durante o Meio do Ano, Mês de Junho. Assim Conseguese Captar Maior Numero de Clientes.**\n",
        "\n",
        "* Quais os melhores meses para criar promoções?\n",
        "\n",
        "*Resposta:*\n",
        "**O Mês no Qual Deve-se Fazer Alguma Promoção Para Alvancar as Vendas é Durante o Mês de Fevereiro, Nesse Periodo do Ano as Vendas Ficaram Muito Baixas.**\n",
        "* Que variáveis podem afetar mais as vendas do cliente?\n",
        "\n",
        "*Resposta:*\n",
        "**O Clima é a Variavel que Mais Afeta no Alugel das Bicicletas: Rainfall(-0.12), Snowfall(-0.14) e a Humidade(-0.19).**\n",
        "\n",
        "Escreva a sua resposta em um parágrafo explicando as suas ações embasadas nos dados coletados das respostas ou de suas próprias análises."
      ]
    }
  ]
}