24_ntb_Analytics_Products.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/luasampaio/data-engineering/blob/main/24_ntb_Analytics_Products.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Configurações Iniciais e Importações**\n",
        "\n"
      ],
      "metadata": {
        "id": "MIfC1eBzBE4F"
      }
    },
    {
      "source": [
        "# Import necessary libraries\n",
        "!pip install pyspark\n",
        "from pyspark.sql import SparkSession\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.impute import SimpleImputer\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Ensure inline plotting\n",
        "%matplotlib inline"
      ],
      "cell_type": "code",
      "metadata": {
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        "id": "MgpW5WkklQ_2",
        "outputId": "79d14632-1b6f-4efb-abba-1931ad3fa2d6"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pyspark in /usr/local/lib/python3.11/dist-packages (3.5.4)\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.11/dist-packages (from pyspark) (0.10.9.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-  Lendo a URL\n"
      ],
      "metadata": {
        "id": "ZNFv396BAyJQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a SparkSession\n",
        "spark = SparkSession.builder.appName(\"LucianaSampaioApp\").getOrCreate()\n",
        "\n",
        "# URL para o conteúdo bruto do arquivo CSV\n",
        "url = 'https://raw.githubusercontent.com/luasampaio/datasets/main/products.csv'"
      ],
      "metadata": {
        "id": "FjKjatb5lXtv"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Convert para pandas"
      ],
      "metadata": {
        "id": "A4vTBPoAfCuE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lê o arquivo CSV diretamente do URL using pandas\n",
        "pandas_df = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "USq_X0Fn9HK9"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a Spark DataFrame from the pandas DataFrame\n",
        "df = spark.createDataFrame(pandas_df)"
      ],
      "metadata": {
        "id": "zd8axB-G9JbJ"
      },
      "execution_count": 9,
      "outputs": []
    },
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      "source": [
        "pandas_df"
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        "id": "ux3IClgafqeX",
        "outputId": "d20aa883-2b07-4406-e906-05978c765ba9"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     product_id                          product_name  brand_id  category_id  \\\n",
              "0             1                       Trek 820 - 2016         9            6   \n",
              "1             2    Ritchey Timberwolf Frameset - 2016         5            6   \n",
              "2             3       Surly Wednesday Frameset - 2016         8            6   \n",
              "3             4              Trek Fuel EX 8 29 - 2016         9            6   \n",
              "4             5          Heller Shagamaw Frame - 2016         3            6   \n",
              "..          ...                                   ...       ...          ...   \n",
              "316         317          Trek Checkpoint ALR 5 - 2019         9            7   \n",
              "317         318  Trek Checkpoint ALR 5 Women's - 2019         9            7   \n",
              "318         319   Trek Checkpoint SL 5 Women's - 2019         9            7   \n",
              "319         320           Trek Checkpoint SL 6 - 2019         9            7   \n",
              "320         321   Trek Checkpoint ALR Frameset - 2019         9            7   \n",
              "\n",
              "     model_year  list_price  \n",
              "0          2016      379.99  \n",
              "1          2016      749.99  \n",
              "2          2016      999.99  \n",
              "3          2016     2899.99  \n",
              "4          2016     1320.99  \n",
              "..          ...         ...  \n",
              "316        2019     1999.99  \n",
              "317        2019     1999.99  \n",
              "318        2019     2799.99  \n",
              "319        2019     3799.99  \n",
              "320        2019     3199.99  \n",
              "\n",
              "[321 rows x 6 columns]"
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pandas_df",
              "summary": "{\n  \"name\": \"pandas_df\",\n  \"rows\": 321,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92,\n        \"min\": 1,\n        \"max\": 321,\n        \"num_unique_values\": 321,\n        \"samples\": [\n          174,\n          133,\n          198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 291,\n        \"samples\": [\n          \"Trek Precaliber 16 Boys - 2017\",\n          \"Trek Precaliber 20 Boy's - 2018\",\n          \"Trek Silque SLR 7 Women's - 2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 9,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          2,\n          5,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 7,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          5,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2016,\n        \"max\": 2019,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2017,\n          2019,\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"list_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1612.1527653545163,\n        \"min\": 89.99,\n        \"max\": 11999.99,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          229.99,\n          1680.99,\n          1320.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sNBQ_9kOfuFw",
        "outputId": "4af33f61-8c7a-461a-f444-1ba12f6aeec2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 321 entries, 0 to 320\n",
            "Data columns (total 6 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   product_id    321 non-null    int64  \n",
            " 1   product_name  321 non-null    object \n",
            " 2   brand_id      321 non-null    int64  \n",
            " 3   category_id   321 non-null    int64  \n",
            " 4   model_year    321 non-null    int64  \n",
            " 5   list_price    321 non-null    float64\n",
            "dtypes: float64(1), int64(4), object(1)\n",
            "memory usage: 15.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "miRQXtYMf2oJ",
        "outputId": "2fad27b7-def8-4133-fa62-8ed57a4797fe"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       product_id    brand_id  category_id   model_year    list_price\n",
              "count  321.000000  321.000000   321.000000   321.000000    321.000000\n",
              "mean   161.000000    5.476636     4.028037  2017.591900   1520.591402\n",
              "std     92.808944    3.708131     2.188569     0.665063   1612.152765\n",
              "min      1.000000    1.000000     1.000000  2016.000000     89.990000\n",
              "25%     81.000000    1.000000     2.000000  2017.000000    439.990000\n",
              "50%    161.000000    7.000000     3.000000  2018.000000    761.990000\n",
              "75%    241.000000    9.000000     6.000000  2018.000000   2299.990000\n",
              "max    321.000000    9.000000     7.000000  2019.000000  11999.990000"
            ],
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              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>brand_id</th>\n",
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              "      <th>model_year</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>5.476636</td>\n",
              "      <td>4.028037</td>\n",
              "      <td>2017.591900</td>\n",
              "      <td>1520.591402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>92.808944</td>\n",
              "      <td>3.708131</td>\n",
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              "      <td>0.665063</td>\n",
              "      <td>1612.152765</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>89.990000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>2.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>439.990000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>761.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>241.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>2299.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "\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",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pandas_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115.38532335350929,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          321.0,\n          161.0,\n          241.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.70760564590327,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          321.0,\n          5.4766355140186915,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.2355765862584,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.02803738317757,\n          3.0,\n          321.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 863.7694023923713,\n        \"min\": 0.6650632977628129,\n        \"max\": 2019.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          321.0,\n          2017.5919003115264,\n          2018.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"list_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3959.509342291423,\n        \"min\": 89.99,\n        \"max\": 11999.99,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1520.5914018691587,\n          761.99,\n          321.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando valores faltantes\n",
        "\n",
        "pandas_df.isnull()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CaEuy-4nf6qI",
        "outputId": "ea3ae722-bf1d-473f-e1b1-c00409cf1feb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     product_id  product_name  brand_id  category_id  model_year  list_price\n",
              "0         False         False     False        False       False       False\n",
              "1         False         False     False        False       False       False\n",
              "2         False         False     False        False       False       False\n",
              "3         False         False     False        False       False       False\n",
              "4         False         False     False        False       False       False\n",
              "..          ...           ...       ...          ...         ...         ...\n",
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              "\n",
              "[321 rows x 6 columns]"
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              "      <th>3</th>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>316</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>318</th>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"pandas_df\",\n  \"rows\": 321,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_id\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category_id\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"list_price\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificar valores nulos\n",
        "pandas_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "id": "ageYpjWegKz3",
        "outputId": "8ae1043c-79cb-4da0-f897-6246cf47198f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_id      0\n",
              "product_name    0\n",
              "brand_id        0\n",
              "category_id     0\n",
              "model_year      0\n",
              "list_price      0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "source": [
        "# Contagem de valores por Ano\n",
        "sns.countplot(x='model_year', data=pandas_df)\n",
        "plt.title(\"Ano\")\n",
        "plt.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "7yLJX3M5gelz",
        "outputId": "89d95bf8-bc44-4288-98e2-87dba1545659"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Remoção de Linhas com Valores Nulos"
      ],
      "metadata": {
        "id": "tNRXr7uvgqKa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Removendo linhas com valores nulos\n",
        "pandas_df.dropna(inplace=True)\n",
        "\n",
        "# Verificando se ainda existem valores nulos\n",
        "print(pandas_df.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dy2hh2sBgjoC",
        "outputId": "0421f3ba-2055-46db-de1c-a868860d116c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "product_id      0\n",
            "product_name    0\n",
            "brand_id        0\n",
            "category_id     0\n",
            "model_year      0\n",
            "list_price      0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Quando tiver dúvida do tipo do dataframe.\n",
        "\n",
        "print(type(pandas_df))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zd-pgM6KGI1X",
        "outputId": "9ad0df86-8211-470f-bad1-595d9575e5e9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t17OlguLGiZG",
        "outputId": "b7afab6e-2d3a-4d06-9852-49e50ea5ef4d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- product_id: long (nullable = true)\n",
            " |-- product_name: string (nullable = true)\n",
            " |-- brand_id: long (nullable = true)\n",
            " |-- category_id: long (nullable = true)\n",
            " |-- model_year: long (nullable = true)\n",
            " |-- list_price: double (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Concatenando em uma nova coluna\n"
      ],
      "metadata": {
        "id": "BeEMtt9vHVbF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Estatísticas descritivas para todas as colunas (inclusive categóricas)\n",
        "pandas_df.describe(include='all')\n"
      ],
      "metadata": {
        "id": "xGYHlD0JGv8J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "1f8f61e2-672f-48cc-b2e5-6d12fc25a485"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        product_id                       product_name    brand_id  \\\n",
              "count   321.000000                                321  321.000000   \n",
              "unique         NaN                                291         NaN   \n",
              "top            NaN  Electra Townie Go! 8i - 2017/2018         NaN   \n",
              "freq           NaN                                  3         NaN   \n",
              "mean    161.000000                                NaN    5.476636   \n",
              "std      92.808944                                NaN    3.708131   \n",
              "min       1.000000                                NaN    1.000000   \n",
              "25%      81.000000                                NaN    1.000000   \n",
              "50%     161.000000                                NaN    7.000000   \n",
              "75%     241.000000                                NaN    9.000000   \n",
              "max     321.000000                                NaN    9.000000   \n",
              "\n",
              "        category_id   model_year    list_price  \n",
              "count    321.000000   321.000000    321.000000  \n",
              "unique          NaN          NaN           NaN  \n",
              "top             NaN          NaN           NaN  \n",
              "freq            NaN          NaN           NaN  \n",
              "mean       4.028037  2017.591900   1520.591402  \n",
              "std        2.188569     0.665063   1612.152765  \n",
              "min        1.000000  2016.000000     89.990000  \n",
              "25%        2.000000  2017.000000    439.990000  \n",
              "50%        3.000000  2018.000000    761.990000  \n",
              "75%        6.000000  2018.000000   2299.990000  \n",
              "max        7.000000  2019.000000  11999.990000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f3e30b97-9307-4a6f-ac3a-fd2d8ea129e7\" class=\"colab-df-container\">\n",
              "    <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>product_id</th>\n",
              "      <th>product_name</th>\n",
              "      <th>brand_id</th>\n",
              "      <th>category_id</th>\n",
              "      <th>model_year</th>\n",
              "      <th>list_price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>321.000000</td>\n",
              "      <td>321</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>321.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>291</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Electra Townie Go! 8i - 2017/2018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.476636</td>\n",
              "      <td>4.028037</td>\n",
              "      <td>2017.591900</td>\n",
              "      <td>1520.591402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>92.808944</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.708131</td>\n",
              "      <td>2.188569</td>\n",
              "      <td>0.665063</td>\n",
              "      <td>1612.152765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>89.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>81.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>439.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>161.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>761.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>241.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>2299.990000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>321.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2019.000000</td>\n",
              "      <td>11999.990000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f3e30b97-9307-4a6f-ac3a-fd2d8ea129e7')\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-f3e30b97-9307-4a6f-ac3a-fd2d8ea129e7 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-f3e30b97-9307-4a6f-ac3a-fd2d8ea129e7');\n",
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              "          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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1fa81d88-2eba-417e-961c-1f8f87843628')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\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",
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              "  }\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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1fa81d88-2eba-417e-961c-1f8f87843628 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\": \"pandas_df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115.38532335350929,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          321.0,\n          161.0,\n          241.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          291,\n          \"3\",\n          \"321\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111.70760564590327,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          321.0,\n          5.4766355140186915,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112.2355765862584,\n        \"min\": 1.0,\n        \"max\": 321.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.02803738317757,\n          3.0,\n          321.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 863.7694023923713,\n        \"min\": 0.6650632977628129,\n        \"max\": 2019.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          321.0,\n          2017.5919003115264,\n          2018.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"list_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3959.509342291423,\n        \"min\": 89.99,\n        \"max\": 11999.99,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1520.5914018691587,\n          761.99,\n          321.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(pandas_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "s7lLJPPXHA5R",
        "outputId": "27a968b5-be70-4aa4-d6d7-a216cdfa6980"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   product_id                        product_name  brand_id  category_id  \\\n",
              "0           1                     Trek 820 - 2016         9            6   \n",
              "1           2  Ritchey Timberwolf Frameset - 2016         5            6   \n",
              "2           3     Surly Wednesday Frameset - 2016         8            6   \n",
              "3           4            Trek Fuel EX 8 29 - 2016         9            6   \n",
              "4           5        Heller Shagamaw Frame - 2016         3            6   \n",
              "\n",
              "   model_year  list_price  \n",
              "0        2016      379.99  \n",
              "1        2016      749.99  \n",
              "2        2016      999.99  \n",
              "3        2016     2899.99  \n",
              "4        2016     1320.99  "
            ],
            "text/html": [
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              "      <th></th>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Ritchey Timberwolf Frameset - 2016</td>\n",
              "      <td>5</td>\n",
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              "      <td>749.99</td>\n",
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              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Heller Shagamaw Frame - 2016</td>\n",
              "      <td>3</td>\n",
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              "      <td>2016</td>\n",
              "      <td>1320.99</td>\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0308512-e42f-4707-ada6-47fb1d305c9a');\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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --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-4c2b030d-a690-440f-a93f-772f47359713 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(pandas_df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Ritchey Timberwolf Frameset - 2016\",\n          \"Heller Shagamaw Frame - 2016\",\n          \"Surly Wednesday Frameset - 2016\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"brand_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          3,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 6,\n        \"max\": 6,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model_year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2016,\n        \"max\": 2016,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2016\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"list_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 974.0252563460558,\n        \"min\": 379.99,\n        \"max\": 2899.99,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          749.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DataFrame no console (pode ser grande para grandes conjuntos de dados)\n"
      ],
      "metadata": {
        "id": "3awZiPfcIKIl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(pandas_df.to_string())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wftnLPSkixQ5",
        "outputId": "fd8b05d8-1f76-4794-842b-ae698bdd9018"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     product_id                                           product_name  brand_id  category_id  model_year  list_price\n",
            "0             1                                        Trek 820 - 2016         9            6        2016      379.99\n",
            "1             2                     Ritchey Timberwolf Frameset - 2016         5            6        2016      749.99\n",
            "2             3                        Surly Wednesday Frameset - 2016         8            6        2016      999.99\n",
            "3             4                               Trek Fuel EX 8 29 - 2016         9            6        2016     2899.99\n",
            "4             5                           Heller Shagamaw Frame - 2016         3            6        2016     1320.99\n",
            "5             6                  Surly Ice Cream Truck Frameset - 2016         8            6        2016      469.99\n",
            "6             7                               Trek Slash 8 27.5 - 2016         9            6        2016     3999.99\n",
            "7             8                  Trek Remedy 29 Carbon Frameset - 2016         9            6        2016     1799.99\n",
            "8             9                                   Trek Conduit+ - 2016         9            5        2016     2999.99\n",
            "9            10                                 Surly Straggler - 2016         8            4        2016     1549.00\n",
            "10           11                            Surly Straggler 650b - 2016         8            4        2016     1680.99\n",
            "11           12                     Electra Townie Original 21D - 2016         1            3        2016      549.99\n",
            "12           13                     Electra Cruiser 1 (24-Inch) - 2016         1            3        2016      269.99\n",
            "13           14          Electra Girl's Hawaii 1 (16-inch) - 2015/2016         1            3        2016      269.99\n",
            "14           15                                  Electra Moto 1 - 2016         1            3        2016      529.99\n",
            "15           16                   Electra Townie Original 7D EQ - 2016         1            3        2016      599.99\n",
            "16           17                        Pure Cycles Vine 8-Speed - 2016         4            3        2016      429.00\n",
            "17           18      Pure Cycles Western 3-Speed - Women's - 2015/2016         4            3        2016      449.00\n",
            "18           19                     Pure Cycles William 3-Speed - 2016         4            3        2016      449.00\n",
            "19           20         Electra Townie Original 7D EQ - Women's - 2016         1            3        2016      599.99\n",
            "20           21                     Electra Cruiser 1 (24-Inch) - 2016         1            1        2016      269.99\n",
            "21           22          Electra Girl's Hawaii 1 (16-inch) - 2015/2016         1            1        2016      269.99\n",
            "22           23          Electra Girl's Hawaii 1 (20-inch) - 2015/2016         1            1        2016      299.99\n",
            "23           24                     Electra Townie Original 21D - 2016         1            2        2016      549.99\n",
            "24           25                 Electra Townie Original 7D - 2015/2016         1            2        2016      499.99\n",
            "25           26                   Electra Townie Original 7D EQ - 2016         1            2        2016      599.99\n",
            "26           27                        Surly Big Dummy Frameset - 2017         8            6        2017      999.99\n",
            "27           28              Surly Karate Monkey 27.5+ Frameset - 2017         8            6        2017     2499.99\n",
            "28           29                                Trek X-Caliber 8 - 2017         9            6        2017      999.99\n",
            "29           30                  Surly Ice Cream Truck Frameset - 2017         8            6        2017      999.99\n",
            "30           31                                 Surly Wednesday - 2017         8            6        2017     1632.99\n",
            "31           32                      Trek Farley Alloy Frameset - 2017         9            6        2017      469.99\n",
            "32           33                        Surly Wednesday Frameset - 2017         8            6        2017      469.99\n",
            "33           34            Trek Session DH 27.5 Carbon Frameset - 2017         9            6        2017      469.99\n",
            "34           35                          Sun Bicycles Spider 3i - 2017         7            6        2017      832.99\n",
            "35           36                            Surly Troll Frameset - 2017         8            6        2017      832.99\n",
            "36           37                          Haro Flightline One ST - 2017         2            6        2017      379.99\n",
            "37           38                     Haro Flightline Two 26 Plus - 2017         2            6        2017      549.99\n",
            "38           39                                   Trek Stache 5 - 2017         9            6        2017     1499.99\n",
            "39           40                             Trek Fuel EX 9.8 29 - 2017         9            6        2017     4999.99\n",
            "40           41                                   Haro Shift R3 - 2017         2            6        2017     1469.99\n",
            "41           42                        Trek Fuel EX 5 27.5 Plus - 2017         9            6        2017     2299.99\n",
            "42           43                      Trek Fuel EX 9.8 27.5 Plus - 2017         9            6        2017     5299.99\n",
            "43           44                                     Haro SR 1.1 - 2017         2            6        2017      539.99\n",
            "44           45                                     Haro SR 1.2 - 2017         2            6        2017      869.99\n",
            "45           46                                     Haro SR 1.3 - 2017         2            6        2017     1409.99\n",
            "46           47                                 Trek Remedy 9.8 - 2017         9            6        2017     5299.99\n",
            "47           48                                 Trek Emonda S 4 - 2017         9            7        2017     1499.99\n",
            "48           49                                Trek Domane SL 6 - 2017         9            7        2017     3499.99\n",
            "49           50                       Trek Silque SLR 7 Women's - 2017         9            7        2017     5999.99\n",
            "50           51                       Trek Silque SLR 8 Women's - 2017         9            7        2017     6499.99\n",
            "51           52                               Surly Steamroller - 2017         8            7        2017      875.99\n",
            "52           53                             Surly Ogre Frameset - 2017         8            7        2017      749.99\n",
            "53           54                    Trek Domane SL Disc Frameset - 2017         9            7        2017     3199.99\n",
            "54           55                                 Trek Domane S 6 - 2017         9            7        2017     2699.99\n",
            "55           56                          Trek Domane SLR 6 Disc - 2017         9            7        2017     5499.99\n",
            "56           57                                 Trek Emonda S 5 - 2017         9            7        2017     1999.99\n",
            "57           58                                 Trek Madone 9.2 - 2017         9            7        2017     4999.99\n",
            "58           59                            Trek Domane S 5 Disc - 2017         9            7        2017     2599.99\n",
            "59           60                        Sun Bicycles ElectroLite - 2017         7            5        2017     1559.99\n",
            "60           61                         Trek Powerfly 8 FS Plus - 2017         9            5        2017     4999.99\n",
            "61           62                                    Trek Boone 7 - 2017         9            4        2017     3499.99\n",
            "62           63                    Trek Boone Race Shop Limited - 2017         9            4        2017     3499.99\n",
            "63           64                      Electra Townie Original 7D - 2017         1            3        2017      489.99\n",
            "64           65                    Sun Bicycles Lil Bolt Type-R - 2017         7            3        2017      346.99\n",
            "65           66                     Sun Bicycles Revolutions 24 - 2017         7            3        2017      250.99\n",
            "66           67            Sun Bicycles Revolutions 24 - Girl's - 2017         7            3        2017      250.99\n",
            "67           68                             Sun Bicycles Cruz 3 - 2017         7            3        2017      449.99\n",
            "68           69                             Sun Bicycles Cruz 7 - 2017         7            3        2017      416.99\n",
            "69           70              Electra Amsterdam Original 3i - 2015/2017         1            3        2017      659.99\n",
            "70           71                       Sun Bicycles Atlas X-Type - 2017         7            3        2017      416.99\n",
            "71           72                  Sun Bicycles Biscayne Tandem 7 - 2017         7            3        2017      619.99\n",
            "72           73                  Sun Bicycles Brickell Tandem 7 - 2017         7            3        2017      749.99\n",
            "73           74                           Electra Cruiser Lux 1 - 2017         1            3        2017      439.99\n",
            "74           75           Electra Cruiser Lux Fat Tire 1 Ladies - 2017         1            3        2017      599.99\n",
            "75           76                     Electra Girl's Hawaii 1 16\" - 2017         1            3        2017      299.99\n",
            "76           77                    Electra Glam Punk 3i Ladies' - 2017         1            3        2017      799.99\n",
            "77           78                 Sun Bicycles Biscayne Tandem CB - 2017         7            3        2017      647.99\n",
            "78           79         Sun Bicycles Boardwalk (24-inch Wheels) - 2017         7            3        2017      402.99\n",
            "79           80                 Sun Bicycles Brickell Tandem CB - 2017         7            3        2017      761.99\n",
            "80           81            Electra Amsterdam Fashion 7i Ladies' - 2017         1            3        2017     1099.99\n",
            "81           82           Electra Amsterdam Original 3i Ladies' - 2017         1            3        2017      659.99\n",
            "82           83                        Trek Boy's Kickster - 2015/2017         9            1        2017      149.99\n",
            "83           84                         Sun Bicycles Lil Kitt'n - 2017         7            1        2017      109.99\n",
            "84           85                                Haro Downtown 16 - 2017         2            1        2017      329.99\n",
            "85           86                            Trek Girl's Kickster - 2017         9            1        2017      149.99\n",
            "86           87                         Trek Precaliber 12 Boys - 2017         9            1        2017      189.99\n",
            "87           88                        Trek Precaliber 12 Girls - 2017         9            1        2017      189.99\n",
            "88           89                         Trek Precaliber 16 Boys - 2017         9            1        2017      209.99\n",
            "89           90                        Trek Precaliber 16 Girls - 2017         9            1        2017      209.99\n",
            "90           91           Trek Precaliber 24 (21-Speed) - Girls - 2017         9            1        2017      349.99\n",
            "91           92                                Haro Shredder 20 - 2017         2            1        2017      209.99\n",
            "92           93                          Haro Shredder 20 Girls - 2017         2            1        2017      209.99\n",
            "93           94                            Haro Shredder Pro 20 - 2017         2            1        2017      249.99\n",
            "94           95                     Electra Girl's Hawaii 1 16\" - 2017         1            1        2017      299.99\n",
            "95           96               Electra Moto 3i (20-inch) - Boy's - 2017         1            1        2017      349.99\n",
            "96           97          Electra Savannah 3i (20-inch) - Girl's - 2017         1            1        2017      349.99\n",
            "97           98         Electra Straight 8 3i (20-inch) - Boy's - 2017         1            1        2017      489.99\n",
            "98           99       Electra Sugar Skulls 1 (20-inch) - Girl's - 2017         1            1        2017      299.99\n",
            "99          100          Electra Townie 3i EQ (20-inch) - Boys' - 2017         1            1        2017      489.99\n",
            "100         101             Electra Townie 7D (20-inch) - Boys' - 2017         1            1        2017      339.99\n",
            "101         102                      Electra Townie Original 7D - 2017         1            2        2017      489.99\n",
            "102         103                        Sun Bicycles Streamway 3 - 2017         7            2        2017      551.99\n",
            "103         104                          Sun Bicycles Streamway - 2017         7            2        2017      481.99\n",
            "104         105                        Sun Bicycles Streamway 7 - 2017         7            2        2017      533.99\n",
            "105         106                             Sun Bicycles Cruz 3 - 2017         7            2        2017      449.99\n",
            "106         107                             Sun Bicycles Cruz 7 - 2017         7            2        2017      416.99\n",
            "107         108                   Sun Bicycles Cruz 3 - Women's - 2017         7            2        2017      449.99\n",
            "108         109                   Sun Bicycles Cruz 7 - Women's - 2017         7            2        2017      416.99\n",
            "109         110                          Sun Bicycles Drifter 7 - 2017         7            2        2017      470.99\n",
            "110         111                Sun Bicycles Drifter 7 - Women's - 2017         7            2        2017      470.99\n",
            "111         112                                        Trek 820 - 2018         9            6        2018      379.99\n",
            "112         113                                   Trek Marlin 5 - 2018         9            6        2018      489.99\n",
            "113         114                                   Trek Marlin 6 - 2018         9            6        2018      579.99\n",
            "114         115                               Trek Fuel EX 8 29 - 2018         9            6        2018     3199.99\n",
            "115         116                              Trek Marlin 7 - 2017/2018         9            6        2018      749.99\n",
            "116         117                             Trek Ticket S Frame - 2018         9            6        2018     1469.99\n",
            "117         118                                Trek X-Caliber 8 - 2018         9            6        2018      999.99\n",
            "118         119                                 Trek Kids' Neko - 2018         9            6        2018      469.99\n",
            "119         120                               Trek Fuel EX 7 29 - 2018         9            6        2018     2499.99\n",
            "120         121                          Surly Krampus Frameset - 2018         8            6        2018     2499.99\n",
            "121         122                            Surly Troll Frameset - 2018         8            6        2018     2499.99\n",
            "122         123                     Trek Farley Carbon Frameset - 2018         9            6        2018      999.99\n",
            "123         124                                   Surly Krampus - 2018         8            6        2018     1499.00\n",
            "124         125                           Trek Kids' Dual Sport - 2018         9            6        2018      469.99\n",
            "125         126                    Surly Big Fat Dummy Frameset - 2018         8            6        2018      469.99\n",
            "126         127                         Surly Pack Rat Frameset - 2018         8            6        2018      469.99\n",
            "127         128                                  Surly ECR 27.5 - 2018         8            6        2018     1899.00\n",
            "128         129                                Trek X-Caliber 7 - 2018         9            6        2018      919.99\n",
            "129         130                     Trek Stache Carbon Frameset - 2018         9            6        2018      919.99\n",
            "130         131                         Heller Bloodhound Trail - 2018         3            6        2018     2599.00\n",
            "131         132                         Trek Procal AL Frameset - 2018         9            6        2018     1499.99\n",
            "132         133                        Trek Procaliber Frameset - 2018         9            6        2018     1499.99\n",
            "133         134                     Trek Remedy 27.5 C Frameset - 2018         9            6        2018     1499.99\n",
            "134         135                         Trek X-Caliber Frameset - 2018         9            6        2018     1499.99\n",
            "135         136                               Trek Procaliber 6 - 2018         9            6        2018     1799.99\n",
            "136         137                             Heller Shagamaw GX1 - 2018         3            6        2018     2599.00\n",
            "137         138                             Trek Fuel EX 5 Plus - 2018         9            6        2018     2249.99\n",
            "138         139                              Trek Remedy 7 27.5 - 2018         9            6        2018     2999.99\n",
            "139         140                            Trek Remedy 9.8 27.5 - 2018         9            6        2018     4999.99\n",
            "140         141                                   Trek Stache 5 - 2018         9            6        2018     1599.99\n",
            "141         142                            Trek Fuel EX 8 29 XT - 2018         9            6        2018     3199.99\n",
            "142         143                               Trek Domane ALR 3 - 2018         9            7        2018     1099.99\n",
            "143         144                          Trek Domane ALR 4 Disc - 2018         9            7        2018     1549.99\n",
            "144         145                          Trek Domane ALR 5 Disc - 2018         9            7        2018     1799.99\n",
            "145         146                               Trek Domane SLR 6 - 2018         9            7        2018     4999.99\n",
            "146         147                        Trek Domane ALR 5 Gravel - 2018         9            7        2018     1799.99\n",
            "147         148                           Trek Domane SL 8 Disc - 2018         9            7        2018     5499.99\n",
            "148         149                          Trek Domane SLR 8 Disc - 2018         9            7        2018     7499.99\n",
            "149         150                                Trek Emonda SL 7 - 2018         9            7        2018     4499.99\n",
            "150         151                  Trek Domane ALR 4 Disc Women's - 2018         9            7        2018     1549.99\n",
            "151         152                   Trek Domane SL 5 Disc Women's - 2018         9            7        2018     2499.99\n",
            "152         153                        Trek Domane SL 7 Women's - 2018         9            7        2018     4999.99\n",
            "153         154                  Trek Domane SLR 6 Disc Women's - 2018         9            7        2018     5499.99\n",
            "154         155                          Trek Domane SLR 9 Disc - 2018         9            7        2018    11999.99\n",
            "155         156                         Trek Domane SL Frameset - 2018         9            7        2018     6499.99\n",
            "156         157                 Trek Domane SL Frameset Women's - 2018         9            7        2018     6499.99\n",
            "157         158                                 Trek CrossRip 1 - 2018         9            7        2018      959.99\n",
            "158         159                               Trek Emonda ALR 6 - 2018         9            7        2018     2299.99\n",
            "159         160                               Trek Emonda SLR 6 - 2018         9            7        2018     4499.99\n",
            "160         161                                       Surly ECR - 2018         8            7        2018     1899.00\n",
            "161         162                           Trek Emonda SL 6 Disc - 2018         9            7        2018     2999.99\n",
            "162         163                                  Surly Pack Rat - 2018         8            7        2018     1349.00\n",
            "163         164                            Surly Straggler 650b - 2018         8            7        2018     1549.00\n",
            "164         165                                       Trek 1120 - 2018         9            7        2018     2499.99\n",
            "165         166                        Trek Domane AL 2 Women's - 2018         9            7        2018      749.99\n",
            "166         167                              Surly ECR Frameset - 2018         8            7        2018      749.99\n",
            "167         168                                 Surly Straggler - 2018         8            7        2018     1549.00\n",
            "168         169                               Trek Emonda SLR 8 - 2018         9            7        2018     6499.99\n",
            "169         170                                 Trek CrossRip 2 - 2018         9            7        2018     1299.99\n",
            "170         171                                Trek Domane SL 6 - 2018         9            7        2018     3199.99\n",
            "171         172                   Trek Domane ALR Disc Frameset - 2018         9            7        2018     3199.99\n",
            "172         173                        Trek Domane ALR Frameset - 2018         9            7        2018     3199.99\n",
            "173         174                   Trek Domane SLR Disc Frameset - 2018         9            7        2018     3199.99\n",
            "174         175                        Trek Domane SLR Frameset - 2018         9            7        2018     3199.99\n",
            "175         176                          Trek Madone 9 Frameset - 2018         9            7        2018     3199.99\n",
            "176         177                          Trek Domane SLR 6 Disc - 2018         9            7        2018     5499.99\n",
            "177         178                                Trek Domane AL 2 - 2018         9            7        2018      749.99\n",
            "178         179                                Trek Domane AL 3 - 2018         9            7        2018      919.99\n",
            "179         180                        Trek Domane AL 3 Women's - 2018         9            7        2018      919.99\n",
            "180         181                                Trek Domane SL 5 - 2018         9            7        2018     2199.99\n",
            "181         182                           Trek Domane SL 5 Disc - 2018         9            7        2018     2499.99\n",
            "182         183                        Trek Domane SL 5 Women's - 2018         9            7        2018     2199.99\n",
            "183         184                           Trek Domane SL 6 Disc - 2018         9            7        2018     3499.99\n",
            "184         185                                   Trek Conduit+ - 2018         9            5        2018     2799.99\n",
            "185         186                                  Trek CrossRip+ - 2018         9            5        2018     4499.99\n",
            "186         187                                      Trek Neko+ - 2018         9            5        2018     2799.99\n",
            "187         188                             Trek XM700+ Lowstep - 2018         9            5        2018     3499.99\n",
            "188         189                              Trek Lift+ Lowstep - 2018         9            5        2018     2799.99\n",
            "189         190                                Trek Dual Sport+ - 2018         9            5        2018     2799.99\n",
            "190         191                             Electra Loft Go! 8i - 2018         1            5        2018     2799.99\n",
            "191         192                      Electra Townie Go! 8i - 2017/2018         1            5        2018     2599.99\n",
            "192         193                                      Trek Lift+ - 2018         9            5        2018     2799.99\n",
            "193         194                                     Trek XM700+ - 2018         9            5        2018     3499.99\n",
            "194         195                   Electra Townie Go! 8i Ladies' - 2018         1            5        2018     2599.99\n",
            "195         196                                     Trek Verve+ - 2018         9            5        2018     2299.99\n",
            "196         197                             Trek Verve+ Lowstep - 2018         9            5        2018     2299.99\n",
            "197         198                      Electra Townie Commute Go! - 2018         1            5        2018     2999.99\n",
            "198         199              Electra Townie Commute Go! Ladies' - 2018         1            5        2018     2999.99\n",
            "199         200                                 Trek Powerfly 5 - 2018         9            5        2018     3499.99\n",
            "200         201                              Trek Powerfly 5 FS - 2018         9            5        2018     4499.99\n",
            "201         202                         Trek Powerfly 5 Women's - 2018         9            5        2018     3499.99\n",
            "202         203                              Trek Powerfly 7 FS - 2018         9            5        2018     4999.99\n",
            "203         204                          Trek Super Commuter+ 7 - 2018         9            5        2018     3599.99\n",
            "204         205                         Trek Super Commuter+ 8S - 2018         9            5        2018     4999.99\n",
            "205         206                               Trek Boone 5 Disc - 2018         9            4        2018     3299.99\n",
            "206         207                               Trek Boone 7 Disc - 2018         9            4        2018     3999.99\n",
            "207         208                            Trek Crockett 5 Disc - 2018         9            4        2018     1799.99\n",
            "208         209                            Trek Crockett 7 Disc - 2018         9            4        2018     2999.99\n",
            "209         210                                 Surly Straggler - 2018         8            4        2018     1549.00\n",
            "210         211                            Surly Straggler 650b - 2018         8            4        2018     1549.00\n",
            "211         212                     Electra Townie Original 21D - 2018         1            3        2018      559.99\n",
            "212         213                     Electra Cruiser 1 - 2016/2017/2018         1            3        2018      269.99\n",
            "213         214                          Electra Tiger Shark 3i - 2018         1            3        2018      899.99\n",
            "214         215                      Electra Queen of Hearts 3i - 2018         1            3        2018      749.99\n",
            "215         216                           Electra Super Moto 8i - 2018         1            3        2018      899.99\n",
            "216         217                           Electra Straight 8 3i - 2018         1            3        2018      909.99\n",
            "217         218                    Electra Cruiser 7D - 2016/2017/2018         1            3        2018      319.99\n",
            "218         219                                 Electra Moto 3i - 2018         1            3        2018      639.99\n",
            "219         220                       Electra Cruiser 1 Ladies' - 2018         1            3        2018      269.99\n",
            "220         221                 Electra Cruiser 7D Ladies' - 2016/2018         1            3        2018      319.99\n",
            "221         222                     Electra Cruiser 1 Tall - 2016/2018         1            3        2018      269.99\n",
            "222         223                          Electra Cruiser Lux 3i - 2018         1            3        2018      529.99\n",
            "223         224                          Electra Cruiser Lux 7D - 2018         1            3        2018      479.99\n",
            "224         225                   Electra Delivery 3i - 2016/2017/2018         1            3        2018      959.99\n",
            "225         226             Electra Townie Original 21D EQ - 2017/2018         1            3        2018      679.99\n",
            "226         227       Electra Cruiser 7D (24-Inch) Ladies' - 2016/2018         1            3        2018      319.99\n",
            "227         228                    Electra Cruiser 7D Tall - 2016/2018         1            3        2018      319.99\n",
            "228         229                      Electra Cruiser Lux 1 - 2016/2018         1            3        2018      429.99\n",
            "229         230                   Electra Cruiser Lux 1 Ladies' - 2018         1            3        2018      429.99\n",
            "230         231                  Electra Cruiser Lux 3i Ladies' - 2018         1            3        2018      529.99\n",
            "231         232                  Electra Cruiser Lux 7D Ladies' - 2018         1            3        2018      479.99\n",
            "232         233                 Electra Cruiser Lux Fat Tire 7D - 2018         1            3        2018      639.99\n",
            "233         234                   Electra Daydreamer 3i Ladies' - 2018         1            3        2018      899.99\n",
            "234         235                          Electra Koa 3i Ladies' - 2018         1            3        2018      899.99\n",
            "235         236                  Electra Morningstar 3i Ladies' - 2018         1            3        2018      749.99\n",
            "236         237                                Electra Relic 3i - 2018         1            3        2018      849.99\n",
            "237         238          Electra Townie Balloon 8D EQ - 2016/2017/2018         1            3        2018      749.99\n",
            "238         239  Electra Townie Balloon 8D EQ Ladies' - 2016/2017/2018         1            3        2018      749.99\n",
            "239         240               Electra Townie Commute 27D Ladies - 2018         1            3        2018      899.99\n",
            "240         241                       Electra Townie Commute 8D - 2018         1            3        2018      749.99\n",
            "241         242               Electra Townie Commute 8D Ladies' - 2018         1            3        2018      699.99\n",
            "242         243          Electra Townie Original 21D EQ Ladies' - 2018         1            3        2018      679.99\n",
            "243         244             Electra Townie Original 21D Ladies' - 2018         1            3        2018      559.99\n",
            "244         245              Electra Townie Original 3i EQ - 2017/2018         1            3        2018      659.99\n",
            "245         246           Electra Townie Original 3i EQ Ladies' - 2018         1            3        2018      639.99\n",
            "246         247                   Electra Townie Original 7D EQ - 2018         1            3        2018      599.99\n",
            "247         248      Electra Townie Original 7D EQ Ladies' - 2017/2018         1            3        2018      599.99\n",
            "248         249                          Electra White Water 3i - 2018         1            3        2018      749.99\n",
            "249         250                      Electra Townie Go! 8i - 2017/2018         1            3        2018     2599.99\n",
            "250         251                      Electra Townie Commute Go! - 2018         1            3        2018     2999.99\n",
            "251         252              Electra Townie Commute Go! Ladies' - 2018         1            3        2018     2999.99\n",
            "252         253                   Electra Townie Go! 8i Ladies' - 2018         1            3        2018     2599.99\n",
            "253         254               Electra Townie Balloon 3i EQ - 2017/2018         1            3        2018      749.99\n",
            "254         255       Electra Townie Balloon 7i EQ Ladies' - 2017/2018         1            3        2018      899.99\n",
            "255         256                      Electra Townie Commute 27D - 2018         1            3        2018      899.99\n",
            "256         257       Electra Amsterdam Fashion 3i Ladies' - 2017/2018         1            3        2018      899.99\n",
            "257         258                 Electra Amsterdam Royal 8i - 2017/2018         1            3        2018     1259.90\n",
            "258         259               Electra Amsterdam Royal 8i Ladies - 2018         1            3        2018     1199.99\n",
            "259         260            Electra Townie Balloon 3i EQ Ladies' - 2018         1            3        2018      799.99\n",
            "260         261                    Electra Townie Balloon 7i EQ - 2018         1            3        2018      899.99\n",
            "261         262                                     Trek MT 201 - 2018         9            1        2018      249.99\n",
            "262         263                 Strider Classic 12 Balance Bike - 2018         6            1        2018       89.99\n",
            "263         264                                Strider Sport 16 - 2018         6            1        2018      249.99\n",
            "264         265                        Strider Strider 20 Sport - 2018         6            1        2018      289.99\n",
            "265         266                                Trek Superfly 20 - 2018         9            1        2018      399.99\n",
            "266         267                       Trek Precaliber 12 Girl's - 2018         9            1        2018      199.99\n",
            "267         268                                   Trek Kickster - 2018         9            1        2018      159.99\n",
            "268         269                        Trek Precaliber 12 Boy's - 2018         9            1        2018      199.99\n",
            "269         270                        Trek Precaliber 16 Boy's - 2018         9            1        2018      209.99\n",
            "270         271                       Trek Precaliber 16 Girl's - 2018         9            1        2018      209.99\n",
            "271         272                Trek Precaliber 20 6-speed Boy's - 2018         9            1        2018      289.99\n",
            "272         273               Trek Precaliber 20 6-speed Girl's - 2018         9            1        2018      289.99\n",
            "273         274                        Trek Precaliber 20 Boy's - 2018         9            1        2018      229.99\n",
            "274         275                       Trek Precaliber 20 Girl's - 2018         9            1        2018      229.99\n",
            "275         276             Trek Precaliber 24 (7-Speed) - Boys - 2018         9            1        2018      319.99\n",
            "276         277               Trek Precaliber 24 21-speed Boy's - 2018         9            1        2018      369.99\n",
            "277         278              Trek Precaliber 24 21-speed Girl's - 2018         9            1        2018      369.99\n",
            "278         279               Trek Precaliber 24 7-speed Girl's - 2018         9            1        2018      319.99\n",
            "279         280                           Trek Superfly 24 - 2017/2018         9            1        2018      489.99\n",
            "280         281       Electra Cruiser 7D (24-Inch) Ladies' - 2016/2018         1            1        2018      319.99\n",
            "281         282         Electra Cyclosaurus 1 (16-inch) - Boy's - 2018         1            1        2018      279.99\n",
            "282         283          Electra Heartchya 1 (20-inch) - Girl's - 2018         1            1        2018      319.99\n",
            "283         284           Electra Savannah 1 (20-inch) - Girl's - 2018         1            1        2018      319.99\n",
            "284         285         Electra Soft Serve 1 (16-inch) - Girl's - 2018         1            1        2018      279.99\n",
            "285         286                          Electra Starship 1 16\" - 2018         1            1        2018      279.99\n",
            "286         287          Electra Straight 8 1 (16-inch) - Boy's - 2018         1            1        2018      279.99\n",
            "287         288          Electra Straight 8 1 (20-inch) - Boy's - 2018         1            1        2018      389.99\n",
            "288         289                         Electra Superbolt 1 20\" - 2018         1            1        2018      319.99\n",
            "289         290                        Electra Superbolt 3i 20\" - 2018         1            1        2018      369.99\n",
            "290         291         Electra Sweet Ride 1 (20-inch) - Girl's - 2018         1            1        2018      319.99\n",
            "291         292        Electra Sweet Ride 3i (20-inch) - Girls' - 2018         1            1        2018      369.99\n",
            "292         293         Electra Tiger Shark 1 (20-inch) - Boys' - 2018         1            1        2018      319.99\n",
            "293         294        Electra Tiger Shark 3i (20-inch) - Boys' - 2018         1            1        2018      369.99\n",
            "294         295                          Electra Treasure 1 20\" - 2018         1            1        2018      319.99\n",
            "295         296                         Electra Treasure 3i 20\" - 2018         1            1        2018      369.99\n",
            "296         297                     Electra Under-The-Sea 1 16\" - 2018         1            1        2018      279.99\n",
            "297         298         Electra Water Lily 1 (16-inch) - Girl's - 2018         1            1        2018      279.99\n",
            "298         299                     Electra Townie Original 21D - 2018         1            2        2018      559.99\n",
            "299         300            Electra Townie Balloon 3i EQ Ladies' - 2018         1            2        2018      799.99\n",
            "300         301                    Electra Townie Balloon 7i EQ - 2018         1            2        2018      899.99\n",
            "301         302                       Electra Townie Original 1 - 2018         1            2        2018      449.99\n",
            "302         303                      Electra Townie Go! 8i - 2017/2018         1            2        2018     2599.99\n",
            "303         304             Electra Townie Original 21D EQ - 2017/2018         1            2        2018      679.99\n",
            "304         305               Electra Townie Balloon 3i EQ - 2017/2018         1            2        2018      749.99\n",
            "305         306       Electra Townie Balloon 7i EQ Ladies' - 2017/2018         1            2        2018      899.99\n",
            "306         307          Electra Townie Balloon 8D EQ - 2016/2017/2018         1            2        2018      749.99\n",
            "307         308  Electra Townie Balloon 8D EQ Ladies' - 2016/2017/2018         1            2        2018      749.99\n",
            "308         309                      Electra Townie Commute 27D - 2018         1            2        2018      899.99\n",
            "309         310               Electra Townie Commute 27D Ladies - 2018         1            2        2018      899.99\n",
            "310         311                       Electra Townie Commute 8D - 2018         1            2        2018      749.99\n",
            "311         312               Electra Townie Commute 8D Ladies' - 2018         1            2        2018      699.99\n",
            "312         313               Electra Townie Original 1 Ladies' - 2018         1            2        2018      449.99\n",
            "313         314          Electra Townie Original 21D EQ Ladies' - 2018         1            2        2018      679.99\n",
            "314         315             Electra Townie Original 21D Ladies' - 2018         1            2        2018      559.99\n",
            "315         316                   Trek Checkpoint ALR 4 Women's - 2019         9            7        2019     1699.99\n",
            "316         317                           Trek Checkpoint ALR 5 - 2019         9            7        2019     1999.99\n",
            "317         318                   Trek Checkpoint ALR 5 Women's - 2019         9            7        2019     1999.99\n",
            "318         319                    Trek Checkpoint SL 5 Women's - 2019         9            7        2019     2799.99\n",
            "319         320                            Trek Checkpoint SL 6 - 2019         9            7        2019     3799.99\n",
            "320         321                    Trek Checkpoint ALR Frameset - 2019         9            7        2019     3199.99\n"
          ]
        }
      ]
    },
    {
      "source": [
        "# Ver os nomes das colunas\n",
        "pandas_df.columns"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XDjphCIIjMu",
        "outputId": "a0ebff28-ca66-4961-f7c0-443b0c9661f5"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['product_id', 'product_name', 'brand_id', 'category_id', 'model_year',\n",
              "       'list_price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pandas_df.dtypes"
      ],
      "metadata": {
        "id": "8J74LGzPI6XN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "c4f6d249-a23b-41c4-b5dd-16bc4e843379"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_id        int64\n",
              "product_name     object\n",
              "brand_id          int64\n",
              "category_id       int64\n",
              "model_year        int64\n",
              "list_price      float64\n",
              "dtype: object"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>product_id</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_name</th>\n",
              "      <td>object</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brand_id</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>category_id</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>model_year</th>\n",
              "      <td>int64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>list_price</th>\n",
              "      <td>float64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Observação:\n",
        "- Para contar apenas o número total de ocorrências de cada produto, é melhor usar o método .size() ou contar uma coluna específica."
      ],
      "metadata": {
        "id": "biIeCbNGkNUV"
      }
    },
    {
      "source": [
        "TotalAno = pandas_df.groupby('model_year').size()\n",
        "print(f' Total Por Ano é: \\n{TotalAno}')"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrgdZ340kveU",
        "outputId": "1a52211d-8907-4417-f8c6-ef326d156c06"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Total Por Ano é: \n",
            "model_year\n",
            "2016     26\n",
            "2017     85\n",
            "2018    204\n",
            "2019      6\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  Usando .value_counts() (mais direto para contagens simples)"
      ],
      "metadata": {
        "id": "Aw_N_VZ9lNxL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "counts = pandas_df['model_year'].value_counts()\n",
        "# Exibir o resultado\n",
        "print(counts)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ppl2zaAFk8Ud",
        "outputId": "d6cbc065-02be-4112-f904-d78828039e4a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "model_year\n",
            "2018    204\n",
            "2017     85\n",
            "2016     26\n",
            "2019      6\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Criando um gráfico de barras\n",
        "plt.figure(figsize=(8, 5))\n",
        "counts.plot(kind='bar')\n",
        "plt.title(\"Quantidade Ano\")\n",
        "plt.xlabel(\"Ano\")\n",
        "plt.ylabel(\"Quantidade\")\n",
        "plt.xticks(rotation=45)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "ISTBlqTqlXDH",
        "outputId": "197ae644-4276-478a-94ae-767e1709cab2"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Personalizando as cores das barras\n",
        "counts.plot(kind='bar', color='skyblue', edgecolor='black')\n",
        "\n",
        "# Adicionando título e rótulos\n",
        "plt.title(\"Quantidade por Ano\", fontsize=16, fontweight='bold')\n",
        "plt.xlabel(\"Ano\", fontsize=12)\n",
        "plt.ylabel(\"Quantidade\", fontsize=12)\n",
        "\n",
        "# Melhorando a legibilidade dos rótulos no eixo X\n",
        "plt.xticks(rotation=45, fontsize=10)\n",
        "\n",
        "# Exibindo valores no topo de cada barra\n",
        "for i, v in enumerate(counts):\n",
        "    plt.text(i, v + 0.5, str(v), ha='center', fontsize=10, color='black')\n",
        "\n",
        "# Adicionando uma grade mais estilizada\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "\n",
        "# Exibindo o gráfico\n",
        "plt.tight_layout()  # Ajusta o layout para evitar sobreposição\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "6Pzi2hC3mGN-",
        "outputId": "cf397ee1-6bdf-4d44-8a2a-80a2bd9b234e"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}