Estatistica.ipynb

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      "source": [
        "# Média, Variância, Ordem, Posição e Correlação\n",
        "Caderno de Desenvolvimento <br>\n",
        "Curadoria de [Rafael Barbosa](https://www.linkedin.com/in/barbosa89/)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzRDceCvkFj2"
      },
      "source": [
        "## 1\\. Tráfego de São Paulo"
      ]
    },
    {
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      "metadata": {
        "id": "WCQi-s0Hpd5V"
      },
      "source": [
        "Neste exercício, vamos continuar a analisar os dados de mobilidade urbana da cidade de São Paulo. A base de dados está neste [link](https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/develop/dataset/traffic.csv) e é uma copia do dado original, presente neste [link](https://archive.ics.uci.edu/ml/datasets/Behavior+of+the+urban+traffic+of+the+city+of+Sao+Paulo+in+Brazil). A base de dados contem a quantidade de acidentes ocorridos na cidade entre 14/12/09 e 18/12/09, das 07:00h ás 20:00h, agregados em intervalos de 30 minutos."
      ]
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      "execution_count": 1,
      "metadata": {
        "id": "lblH8kIXzPZZ"
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      "outputs": [],
      "source": [
        "!wget -q \"https://raw.githubusercontent.com/andre-marcos-perez/ebac-course-utils/develop/dataset/traffic.csv\" -O traffic.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "cuuqGTo_ffKI"
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      "source": [
        "import pandas as pd"
      ]
    },
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      "execution_count": 3,
      "metadata": {
        "id": "VR3UdrrQgJ4K"
      },
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      "source": [
        "df = pd.read_csv('traffic.csv', sep=';')"
      ]
    },
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            "text/plain": [
              "   hour  bus_stoped  broken_truck  vehicle_excess  accident_victim  \\\n",
              "0     1           0             0               0                0   \n",
              "1     2           0             0               0                0   \n",
              "2     3           0             0               0                0   \n",
              "3     4           0             0               0                0   \n",
              "4     5           0             0               0                0   \n",
              "\n",
              "   running_over  fire_vehicles  occurrence_freight  \\\n",
              "0             0              0                   0   \n",
              "1             0              0                   0   \n",
              "2             0              0                   0   \n",
              "3             0              0                   0   \n",
              "4             0              0                   0   \n",
              "\n",
              "   incident__dangerous_freight  lack_electricity  fire  flooding  \\\n",
              "0                            0                 0     0         0   \n",
              "1                            0                 0     0         0   \n",
              "2                            0                 0     0         0   \n",
              "3                            0                 0     0         0   \n",
              "4                            0                 0     0         0   \n",
              "\n",
              "   manifestations  defect_trolleybuses  tree_road  semaphore_off  \\\n",
              "0               0                    0          0              0   \n",
              "1               0                    0          0              0   \n",
              "2               0                    0          0              0   \n",
              "3               0                    0          0              0   \n",
              "4               0                    0          0              0   \n",
              "\n",
              "   intermittent_emaphore slowness_traffic_%  \n",
              "0                      0                4,1  \n",
              "1                      0                6,6  \n",
              "2                      0                8,7  \n",
              "3                      0                9,2  \n",
              "4                      0               11,1  "
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        "df.head()"
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      "source": [
        "- **Exemplo**: (para relembrar)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WGcei39Pwc9u"
      },
      "source": [
        "O código abaixo extrai a 13ª linha do arquivo que representa a 14ª meia hora do dia 14/12/09, contadas a partir das 07:00h, ou seja, todos os incidentes ocorridos na cidade no dia em questão entre as 13:30h e 14:00h. Vemos, por exemplo, que dois ônibus e dois caminhões ficaram paradados nas ruas da cidade, atrapalhando o tráfego."
      ]
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4,9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc8ec1d1-2c17-4424-baab-63028b1ef46e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bc8ec1d1-2c17-4424-baab-63028b1ef46e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bc8ec1d1-2c17-4424-baab-63028b1ef46e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "    hour  bus_stoped  broken_truck  vehicle_excess  accident_victim  \\\n",
              "13    14           2             2               0                1   \n",
              "\n",
              "    running_over  fire_vehicles  occurrence_freight  \\\n",
              "13             0              0                   0   \n",
              "\n",
              "    incident__dangerous_freight  lack_electricity  fire  flooding  \\\n",
              "13                            0                 0     0         0   \n",
              "\n",
              "    manifestations  defect_trolleybuses  tree_road  semaphore_off  \\\n",
              "13               0                    0          0              0   \n",
              "\n",
              "    intermittent_emaphore slowness_traffic_%  \n",
              "13                      0                4,9  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.iloc[[13]]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrTLgE0tki6P"
      },
      "source": [
        "### **1.1. Agregação**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tIjNOs8q6aZ"
      },
      "source": [
        "Neste primeira atividade, você deve gerar um array NumPy por dia. Para cada array você deve somar todos os incidentes que aconteceram naquela meia hora. Sendo assim, cada array deve ter 27 posições, cada qual com a soma dos incidentes daquela meira hora."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fKzfMXTO280n"
      },
      "source": [
        "**Dica**: Você deve remover a primeira e a última coluna."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEUTG1OIs0VN"
      },
      "source": [
        "**Dica**: Os arrays devem ter os seguintes valores:\n",
        "\n",
        "```python\n",
        "14: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 3]\n",
        "15: [3, 0, 0, 0, 2, 4, 2, 1, 3, 0, 2, 0, 2, 3, 2, 2, 4, 7, 6, 3, 0, 1, 1, 5, 4, 6, 0]\n",
        "16: [1, 1, 2, 4, 4, 5, 3, 2, 4, 2, 1, 1, 3, 6, 2, 0, 2, 8, 4, 6, 4, 1, 2, 7, 15, 4, 0]\n",
        "17: [4, 1, 1, 0, 0, 0, 2, 3, 2, 7, 2, 1, 7, 3, 6, 4, 3, 2, 2, 3, 2, 5, 2, 0, 1, 4, 15]\n",
        "18: [10, 1, 1, 5, 5, 7, 3, 4, 3, 1, 2, 3, 2, 2, 6, 2, 2, 4, 2, 3, 0, 3, 3, 2, 3, 6, 0]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYPHIQfKIRyt",
        "outputId": "8243aafa-cb40-40d6-96e4-4473a2436faa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "14: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 5, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 2, 2, 3]\n",
            "15: [3, 0, 0, 0, 2, 4, 2, 1, 3, 0, 2, 0, 2, 3, 2, 2, 4, 7, 6, 3, 0, 1, 1, 5, 4, 6, 0]\n",
            "16: [1, 1, 2, 4, 4, 5, 3, 2, 4, 2, 1, 1, 3, 6, 2, 0, 2, 8, 4, 6, 4, 1, 2, 7, 15, 4, 0]\n",
            "17: [4, 1, 1, 0, 0, 0, 2, 3, 2, 7, 2, 1, 7, 3, 6, 4, 3, 2, 2, 3, 2, 5, 2, 0, 1, 4, 15]\n",
            "18: [10, 1, 1, 5, 5, 7, 3, 4, 3, 1, 2, 3, 2, 2, 6, 2, 2, 4, 2, 3, 0, 3, 3, 2, 3, 6, 0]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "# Remova as colunas desnecessárias\n",
        "df = df.drop(['hour', 'slowness_traffic_%'], axis=1)\n",
        "\n",
        "# Cria um array NumPy para cada dia\n",
        "arrays_por_dia = []\n",
        "# divide e itera o DataFrame em grupos de 27 linhas\n",
        "for i in range(int(len(df) / 27)): # cada grupo representa um dia\n",
        "    dia = df.iloc[i*27:(i+1)*27] # seleciona as linhas do dia para iteração\n",
        "    array_dia = np.sum(dia, axis=1).values # soma os valores de cada linha do dia e armazena no array_dia\n",
        "    arrays_por_dia.append(array_dia) # adiciona o array_dia à lista arrays_por_dia\n",
        "\n",
        "# Exibir os arrays por dia iterando a lista arrays_por_dia\n",
        "for i, array_dia in enumerate(arrays_por_dia):\n",
        "    print(f\"{i+14}:\",list(array_dia))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTh7w8rgkznY"
      },
      "source": [
        "### **1.2. Métricas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clBerimQs2PY"
      },
      "source": [
        "Para cada array você deve calcular as seguintes métricas:\n",
        "\n",
        " - **média**;\n",
        " - **desvio padrão**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fohdVuLzkdKP",
        "outputId": "adc80d1e-ae6d-4f67-9ab9-2ea34cc5ee83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Métricas para o dia 14:\n",
            "Média: 0.5925925925925926\n",
            "Desvio padrão: 1.1944085553034889\n",
            "\n",
            "\n",
            "Métricas para o dia 15:\n",
            "Média: 2.3333333333333335\n",
            "Desvio padrão: 2.0184335693983275\n",
            "\n",
            "\n",
            "Métricas para o dia 16:\n",
            "Média: 3.4814814814814814\n",
            "Desvio padrão: 3.047407767514698\n",
            "\n",
            "\n",
            "Métricas para o dia 17:\n",
            "Média: 3.037037037037037\n",
            "Desvio padrão: 3.0487578707046925\n",
            "\n",
            "\n",
            "Métricas para o dia 18:\n",
            "Média: 3.1481481481481484\n",
            "Desvio padrão: 2.1892617342417258\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# resposta da questão 1.2\n",
        "\n",
        "#iterando a lista arrays_por_dia para calcular as métricas\n",
        "for i, array_dia in enumerate(arrays_por_dia):\n",
        "    media = np.mean(array_dia)\n",
        "    desvio_padrao = np.std(array_dia)\n",
        "    print(f\"Métricas para o dia {i+14}:\")\n",
        "    print(f\"Média: {media}\")\n",
        "    print(f\"Desvio padrão: {desvio_padrao}\")\n",
        "    print(\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xMnUokVV4Pcw"
      },
      "source": [
        "### **1.3. Interpretação**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxAhj3O14rhZ"
      },
      "source": [
        "Baseado nos resultados da questão 1.2, responda:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VO4jLKYq4Pc6"
      },
      "source": [
        "> Qual dia apresenta a maior média de acidentes por meia hora?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oj3YJnQu4byN"
      },
      "source": [
        "**Resposta:** O dia 16, com média de 3,48 acidentes a cada meia hora."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3awOYi0G4fIP"
      },
      "source": [
        "> Qual dia apresenta a menor variação de acidentes por meia hora?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umQMblRh4fIP"
      },
      "source": [
        "**Resposta:** O dia 14, com desvio padrão de 1,19 em torno da média."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "waxn4B2UDHyl"
      },
      "source": [
        "---"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}