aula02_minicurso_ds.ipynb

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    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO9tYCf891uLEtqMetktj62",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LuisOTSG/minicurso-cds/blob/main/aula02_minicurso_ds.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importação de bibliotecas e leitura de arquivos**"
      ],
      "metadata": {
        "id": "dNFWXKIOO0y8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "59zuD_wN_A0k"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "import numpy as np\n",
        "\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arq = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
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        "id": "xQ48kLnkDvl1",
        "outputId": "187c9649-ff88-4259-8a24-02fa9d4dfb65"
      },
      "execution_count": 2,
      "outputs": [
        {
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              "      Upload widget is only available when the cell has been executed in the\n",
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              "      <script>// Copyright 2017 Google LLC\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
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              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
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              "\n",
              "  for (const file of files) {\n",
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              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
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              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
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              "\n",
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              "      yield {\n",
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              "          data: base64,\n",
              "        },\n",
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              "\n",
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              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
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              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving AB_NYC_2019.csv to AB_NYC_2019.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(io.BytesIO(arq['AB_NYC_2019.csv']))"
      ],
      "metadata": {
        "id": "BMBPpWVkCAsG"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Inspeção do conjunto de dados**"
      ],
      "metadata": {
        "id": "4KcAGMwzOmo2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Retornar o tipo da variável\n",
        "type(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmEkdjy8EmsB",
        "outputId": "26bf8d5d-6fe0-4863-b3cd-0ba1e6c90ecd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retornar o tamanho do dataframe (linhas e colunas)\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJMxw3LLMalM",
        "outputId": "6176fc01-eb13-4daa-949e-a13b75b75c44"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48895, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mostrar as 5 primeiras linhas do dataframe\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "E7_eb1nJMjbJ",
        "outputId": "a31a7fe8-87e3-4f7e-9833-e9ad581ad9ed"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id                                              name  host_id  \\\n",
              "0  2539                Clean & quiet apt home by the park     2787   \n",
              "1  2595                             Skylit Midtown Castle     2845   \n",
              "2  3647               THE VILLAGE OF HARLEM....NEW YORK !     4632   \n",
              "3  3831                   Cozy Entire Floor of Brownstone     4869   \n",
              "4  5022  Entire Apt: Spacious Studio/Loft by central park     7192   \n",
              "\n",
              "     host_name neighbourhood_group neighbourhood  latitude  longitude  \\\n",
              "0         John            Brooklyn    Kensington  40.64749  -73.97237   \n",
              "1     Jennifer           Manhattan       Midtown  40.75362  -73.98377   \n",
              "2    Elisabeth           Manhattan        Harlem  40.80902  -73.94190   \n",
              "3  LisaRoxanne            Brooklyn  Clinton Hill  40.68514  -73.95976   \n",
              "4        Laura           Manhattan   East Harlem  40.79851  -73.94399   \n",
              "\n",
              "         room_type  price  minimum_nights  number_of_reviews last_review  \\\n",
              "0     Private room    149               1                  9  2018-10-19   \n",
              "1  Entire home/apt    225               1                 45  2019-05-21   \n",
              "2     Private room    150               3                  0         NaN   \n",
              "3  Entire home/apt     89               1                270  2019-07-05   \n",
              "4  Entire home/apt     80              10                  9  2018-11-19   \n",
              "\n",
              "   reviews_per_month  calculated_host_listings_count  availability_365  \n",
              "0               0.21                               6               365  \n",
              "1               0.38                               2               355  \n",
              "2                NaN                               1               365  \n",
              "3               4.64                               1               194  \n",
              "4               0.10                               1                 0  "
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              "      <td>2539</td>\n",
              "      <td>Clean &amp; quiet apt home by the park</td>\n",
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              "      <td>Private room</td>\n",
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              "      <td>Jennifer</td>\n",
              "      <td>Manhattan</td>\n",
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              "      <td>40.75362</td>\n",
              "      <td>-73.98377</td>\n",
              "      <td>Entire home/apt</td>\n",
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              "      <td>THE VILLAGE OF HARLEM....NEW YORK !</td>\n",
              "      <td>4632</td>\n",
              "      <td>Elisabeth</td>\n",
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              "      <td>-73.94190</td>\n",
              "      <td>Private room</td>\n",
              "      <td>150</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>365</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3831</td>\n",
              "      <td>Cozy Entire Floor of Brownstone</td>\n",
              "      <td>4869</td>\n",
              "      <td>LisaRoxanne</td>\n",
              "      <td>Brooklyn</td>\n",
              "      <td>Clinton Hill</td>\n",
              "      <td>40.68514</td>\n",
              "      <td>-73.95976</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>89</td>\n",
              "      <td>1</td>\n",
              "      <td>270</td>\n",
              "      <td>2019-07-05</td>\n",
              "      <td>4.64</td>\n",
              "      <td>1</td>\n",
              "      <td>194</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Entire Apt: Spacious Studio/Loft by central park</td>\n",
              "      <td>7192</td>\n",
              "      <td>Laura</td>\n",
              "      <td>Manhattan</td>\n",
              "      <td>East Harlem</td>\n",
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              "      <td>-73.94399</td>\n",
              "      <td>Entire home/apt</td>\n",
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              "          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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retornar o tipo de dado de cada coluna\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iIBx6HKuMn4_",
        "outputId": "04e592b2-7ca4-4640-a0e3-f29a5e0f6b5b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "name                               object\n",
              "host_id                             int64\n",
              "host_name                          object\n",
              "neighbourhood_group                object\n",
              "neighbourhood                      object\n",
              "latitude                          float64\n",
              "longitude                         float64\n",
              "room_type                          object\n",
              "price                               int64\n",
              "minimum_nights                      int64\n",
              "number_of_reviews                   int64\n",
              "last_review                        object\n",
              "reviews_per_month                 float64\n",
              "calculated_host_listings_count      int64\n",
              "availability_365                    int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar o número de dados faltantes em cada coluna\n",
        "df.isna().sum()\n",
        "# df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8l6QUHENG6G",
        "outputId": "cdbb075c-e93d-4c75-8400-8578db8e8436"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                    0\n",
              "name                                 16\n",
              "host_id                               0\n",
              "host_name                            21\n",
              "neighbourhood_group                   0\n",
              "neighbourhood                         0\n",
              "latitude                              0\n",
              "longitude                             0\n",
              "room_type                             0\n",
              "price                                 0\n",
              "minimum_nights                        0\n",
              "number_of_reviews                     0\n",
              "last_review                       10052\n",
              "reviews_per_month                 10052\n",
              "calculated_host_listings_count        0\n",
              "availability_365                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Retornar informações gerais do dataframe\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yleMT4HNJHC",
        "outputId": "9bbfbaba-0e94-4d03-bf09-0cd4f1fefa70"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 48895 entries, 0 to 48894\n",
            "Data columns (total 16 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              48895 non-null  int64  \n",
            " 1   name                            48879 non-null  object \n",
            " 2   host_id                         48895 non-null  int64  \n",
            " 3   host_name                       48874 non-null  object \n",
            " 4   neighbourhood_group             48895 non-null  object \n",
            " 5   neighbourhood                   48895 non-null  object \n",
            " 6   latitude                        48895 non-null  float64\n",
            " 7   longitude                       48895 non-null  float64\n",
            " 8   room_type                       48895 non-null  object \n",
            " 9   price                           48895 non-null  int64  \n",
            " 10  minimum_nights                  48895 non-null  int64  \n",
            " 11  number_of_reviews               48895 non-null  int64  \n",
            " 12  last_review                     38843 non-null  object \n",
            " 13  reviews_per_month               38843 non-null  float64\n",
            " 14  calculated_host_listings_count  48895 non-null  int64  \n",
            " 15  availability_365                48895 non-null  int64  \n",
            "dtypes: float64(3), int64(7), object(6)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Manipulação de dados**"
      ],
      "metadata": {
        "id": "HWZMjyErRMPO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecionar colunas desejadas no dataframe\n",
        "# df.loc[linhas, colunas] \n",
        "cols = ['host_name', 'price']\n",
        "# ':' é um comando que seleciona todas as linhas e as colunas estão em uma variável\n",
        "df1 = df.loc[:, cols]\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lTQ7lGRLPFCU",
        "outputId": "8ea7e8c1-b992-42a9-e6af-15a74a75cafa"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     host_name  price\n",
              "0         John    149\n",
              "1     Jennifer    225\n",
              "2    Elisabeth    150\n",
              "3  LisaRoxanne     89\n",
              "4        Laura     80"
            ],
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              "\n",
              "  <div id=\"df-1c9bf696-5e3b-47a7-9207-bafd07c35605\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <td>Laura</td>\n",
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              "                                                     [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",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Deletando as linhas que tenham ao menos uma coluna com valor faltante\n",
        "df1 = df.dropna()"
      ],
      "metadata": {
        "id": "yDp7QI6IQFgA"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTqhxiqXRwfl",
        "outputId": "195ddb2d-2f77-496f-e198-9a14c1211ebb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38821, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Respondendo à perguntas de negócio**"
      ],
      "metadata": {
        "id": "Z-QkTv2Ii44S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Qual o valor médio do aluguel na cidade de Nova York?**"
      ],
      "metadata": {
        "id": "vWPvBPAvSen_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['price']\n",
        "\n",
        "price = df1.loc[:, cols]\n",
        "\n",
        "np.mean(price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRSkGu47Si7I",
        "outputId": "5dd8088a-fa77-4ee0-d2e9-9dd34aa66f94"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price    142.332526\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Qual o nome das regiões?**"
      ],
      "metadata": {
        "id": "VL2zg4DzVrkZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['neighbourhood_group']\n",
        "\n",
        "regiao = df1.loc[:, cols]\n",
        "\n",
        "np.unique(regiao)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C3emyRdaVYrf",
        "outputId": "ca569fe5-f344-4650-9c08-5aaa8fa0ea52"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bronx', 'Brooklyn', 'Manhattan', 'Queens', 'Staten Island'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Qual o valor máximo do aluguel na cidade de Nova York?**"
      ],
      "metadata": {
        "id": "4mGNJq0KWEER"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cols = ['price']\n",
        "\n",
        "price = df1.loc[:, cols]\n",
        "\n",
        "np.max(price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bAO7ibLkV__W",
        "outputId": "d0b0e85b-4246-4c11-a283-fdf09ccc134d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "price    10000\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}