33_ntb_Tratando_Dados_Nulos.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNApUtRmNjNir/bFy9iaUlT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luasampaio/CienciaDados/blob/main/33_ntb_Tratando_Dados_Nulos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Observações\n",
        "\n",
        "- how='all' significa que uma linha será removida somente se todos os seus valores forem nulos.\n",
        "- how='any' significa que uma linha será removida se pelo menos um dos seus valores for nulo."
      ],
      "metadata": {
        "id": "Si9UWPvGIdMo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fZXRhMByHmgz"
      },
      "outputs": [],
      "source": [
        "from pyspark.sql.functions import col\n",
        "import json\n",
        "import glob\n",
        "import re\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import SparkSession\n",
        "\n",
        "# Criando uma SparkSession\n",
        "spark = SparkSession.builder.appName(\"app\").getOrCreate()\n",
        "\n",
        "# Link corrigido para o arquivo CSV (usando raw.githubusercontent.com)\n",
        "csv_url = \"https://raw.githubusercontent.com/luasampaio/data-engineering-projects/main/_datasets/nyc_uber_2014.csv\"\n",
        "\n",
        "# Baixando o arquivo CSV para um DataFrame Pandas\n",
        "import pandas as pd\n",
        "import requests\n",
        "from io import StringIO\n",
        "\n",
        "response = requests.get(csv_url)\n",
        "csv_data = StringIO(response.text)  # Converte o texto recebido para um objeto legível\n"
      ],
      "metadata": {
        "id": "xP_y8CGqDOEX"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Baixando o arquivo CSV para um DataFrame Pandas\n",
        "import pandas as pd\n",
        "import requests\n",
        "from io import StringIO\n",
        "\n",
        "response = requests.get(csv_url)\n",
        "csv_data = StringIO(response.text)  # Converte o texto recebido para um objeto legível"
      ],
      "metadata": {
        "id": "LGIEtK4FEZVL"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# print(csv_data.getvalue())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "43V8vZDvEfpF",
        "outputId": "2d66ea5a-f36a-475d-eb5e-077da6ea97ed"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "27,6/1/2014 0:39:00,40.7349,-73.985,B02512\n",
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            "29,6/1/2014 0:40:00,40.7273,-73.9936,B02512\n",
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            "31,6/1/2014 0:44:00,40.7401,-73.9901,B02512\n",
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            "35,6/1/2014 0:53:00,40.7428,-73.9806,B02512\n",
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            "37,6/1/2014 0:59:00,40.7053,-73.9394,B02512\n",
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            "39,6/1/2014 1:00:00,40.7431,-73.9848,B02512\n",
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            "85,6/1/2014 4:56:00,40.645,-73.782,B02512\n",
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            "87,6/1/2014 5:10:00,40.7372,-73.9967,B02512\n",
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            "89,6/1/2014 5:48:00,40.7228,-74.0019,B02512\n",
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            "95,6/1/2014 6:35:00,40.7543,-73.9817,B02512\n",
            "96,6/1/2014 6:37:00,40.7751,-73.9633,B02512\n",
            "97,6/1/2014 6:46:00,40.6952,-74.1784,B02512\n",
            "98,6/1/2014 6:51:00,40.7621,-73.9817,B02512\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(csv_data)"
      ],
      "metadata": {
        "id": "BRRQhH6hEqU-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6wAk9QRLEyKv",
        "outputId": "cdb60ad5-3af9-44b7-f6b8-8ab535622a08"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "     Unnamed: 0         Date/Time      Lat      Lon    Base\n",
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              "2             2  4/1/2014 0:21:00  40.7316 -73.9873  B02512\n",
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              "4             4  4/1/2014 0:33:00  40.7594 -73.9722  B02512\n",
              "..          ...               ...      ...      ...     ...\n",
              "292          94  6/1/2014 6:27:00  40.7554 -73.9738  B02512\n",
              "293          95  6/1/2014 6:35:00  40.7543 -73.9817  B02512\n",
              "294          96  6/1/2014 6:37:00  40.7751 -73.9633  B02512\n",
              "295          97  6/1/2014 6:46:00  40.6952 -74.1784  B02512\n",
              "296          98  6/1/2014 6:51:00  40.7621 -73.9817  B02512\n",
              "\n",
              "[297 rows x 5 columns]"
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f9343cec-4af6-4e92-a8a1-454af360eba2\">\n",
              "    <style>\n",
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              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f9343cec-4af6-4e92-a8a1-454af360eba2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 297,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 0,\n        \"max\": 98,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          62,\n          40,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Date/Time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 250,\n        \"samples\": [\n          \"5/1/2014 5:17:00\",\n          \"4/1/2014 0:45:00\",\n          \"5/1/2014 1:20:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05870128101771192,\n        \"min\": 40.299,\n        \"max\": 41.0169,\n        \"num_unique_values\": 250,\n        \"samples\": [\n          40.6954,\n          40.7223,\n          40.7252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0995091534610338,\n        \"min\": -74.661,\n        \"max\": -73.462,\n        \"num_unique_values\": 242,\n        \"samples\": [\n          -74.1578,\n          -73.9887,\n          -73.9772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Base\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"B02512\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhwLIbLvE5TL",
        "outputId": "e8503ed7-28ad-4d09-8f81-a31c02c8d3ee"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 297 entries, 0 to 296\n",
            "Data columns (total 5 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   Unnamed: 0  297 non-null    int64  \n",
            " 1   Date/Time   297 non-null    object \n",
            " 2   Lat         297 non-null    float64\n",
            " 3   Lon         297 non-null    float64\n",
            " 4   Base        297 non-null    object \n",
            "dtypes: float64(2), int64(1), object(2)\n",
            "memory usage: 11.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxl80qi3E8pQ",
        "outputId": "c8aa6ca0-d377-4ba4-8e5f-14babdc951bb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gkfyO1sZFEYA",
        "outputId": "66d6f221-dc4d-4457-e0be-2dbfd4905b37"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Date/Time', 'Lat', 'Lon', 'Base'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adicionando a colunas em Maiusculo"
      ],
      "metadata": {
        "id": "3Sv3pN9aFbFy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns = [col.lower() for col in df.columns]"
      ],
      "metadata": {
        "id": "f_O0kyW0FT2K"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Alternativa para ver o nome da coluna"
      ],
      "metadata": {
        "id": "Cb7W7I49FmKA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for Loop in df.columns:\n",
        "    print(Loop)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TeSkNQhFFknZ",
        "outputId": "35f7880e-651f-44b0-8f38-99d885596c60"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "unnamed: 0\n",
            "date/time\n",
            "lat\n",
            "lon\n",
            "base\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- how='all' significa que uma linha será removida somente se todos os seus valores forem nulos.\n",
        "- how='any' significa que uma linha será removida se pelo menos um dos seus valores for nulo."
      ],
      "metadata": {
        "id": "uklOcGO6Ip0s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display(df.dropna(how='all'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Y9dGfuoAIAxx",
        "outputId": "dd70d2ab-3b3f-46af-80d8-f1caa5a7a397"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     unnamed: 0         date/time      lat      lon    base\n",
              "0             0  4/1/2014 0:11:00  40.7690 -73.9549  B02512\n",
              "1             1  4/1/2014 0:17:00  40.7267 -74.0345  B02512\n",
              "2             2  4/1/2014 0:21:00  40.7316 -73.9873  B02512\n",
              "3             3  4/1/2014 0:28:00  40.7588 -73.9776  B02512\n",
              "4             4  4/1/2014 0:33:00  40.7594 -73.9722  B02512\n",
              "..          ...               ...      ...      ...     ...\n",
              "292          94  6/1/2014 6:27:00  40.7554 -73.9738  B02512\n",
              "293          95  6/1/2014 6:35:00  40.7543 -73.9817  B02512\n",
              "294          96  6/1/2014 6:37:00  40.7751 -73.9633  B02512\n",
              "295          97  6/1/2014 6:46:00  40.6952 -74.1784  B02512\n",
              "296          98  6/1/2014 6:51:00  40.7621 -73.9817  B02512\n",
              "\n",
              "[297 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b98c24b4-fe74-4335-b356-1f6ebeb10ef3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>unnamed: 0</th>\n",
              "      <th>date/time</th>\n",
              "      <th>lat</th>\n",
              "      <th>lon</th>\n",
              "      <th>base</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>4/1/2014 0:11:00</td>\n",
              "      <td>40.7690</td>\n",
              "      <td>-73.9549</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>4/1/2014 0:17:00</td>\n",
              "      <td>40.7267</td>\n",
              "      <td>-74.0345</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>4/1/2014 0:21:00</td>\n",
              "      <td>40.7316</td>\n",
              "      <td>-73.9873</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>4/1/2014 0:28:00</td>\n",
              "      <td>40.7588</td>\n",
              "      <td>-73.9776</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4/1/2014 0:33:00</td>\n",
              "      <td>40.7594</td>\n",
              "      <td>-73.9722</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>292</th>\n",
              "      <td>94</td>\n",
              "      <td>6/1/2014 6:27:00</td>\n",
              "      <td>40.7554</td>\n",
              "      <td>-73.9738</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>293</th>\n",
              "      <td>95</td>\n",
              "      <td>6/1/2014 6:35:00</td>\n",
              "      <td>40.7543</td>\n",
              "      <td>-73.9817</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>294</th>\n",
              "      <td>96</td>\n",
              "      <td>6/1/2014 6:37:00</td>\n",
              "      <td>40.7751</td>\n",
              "      <td>-73.9633</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>295</th>\n",
              "      <td>97</td>\n",
              "      <td>6/1/2014 6:46:00</td>\n",
              "      <td>40.6952</td>\n",
              "      <td>-74.1784</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>98</td>\n",
              "      <td>6/1/2014 6:51:00</td>\n",
              "      <td>40.7621</td>\n",
              "      <td>-73.9817</td>\n",
              "      <td>B02512</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>297 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b98c24b4-fe74-4335-b356-1f6ebeb10ef3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "        document.querySelector('#df-b98c24b4-fe74-4335-b356-1f6ebeb10ef3 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-b98c24b4-fe74-4335-b356-1f6ebeb10ef3');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-928f3d3f-e225-46d8-b118-49ba27956c7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df\",\n  \"rows\": 297,\n  \"fields\": [\n    {\n      \"column\": \"unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 0,\n        \"max\": 98,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          62,\n          40,\n          95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date/time\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 250,\n        \"samples\": [\n          \"5/1/2014 5:17:00\",\n          \"4/1/2014 0:45:00\",\n          \"5/1/2014 1:20:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05870128101771192,\n        \"min\": 40.299,\n        \"max\": 41.0169,\n        \"num_unique_values\": 250,\n        \"samples\": [\n          40.6954,\n          40.7223,\n          40.7252\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lon\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0995091534610338,\n        \"min\": -74.661,\n        \"max\": -73.462,\n        \"num_unique_values\": 242,\n        \"samples\": [\n          -74.1578,\n          -73.9887,\n          -73.9772\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"base\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"B02512\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    }
  ]
}