26_ntb_Transformations_PySpark.ipynb

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      "authorship_tag": "ABX9TyNf8uHappnKYl4I33Y3LEke",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/luasampaio/data-engineering/blob/main/26_ntb_Transformations_PySpark.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Transformations with PySpark   - EM andamento\n",
        " - Luciana Sampaio\n",
        " - Analise Cartao Credito\n",
        "\n"
      ],
      "metadata": {
        "id": "Lt0ZZKHesgyr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql import SparkSession\n",
        "\n",
        "# Cria uma SparkSession\n",
        "spark = SparkSession.builder.appName(\"LoadCSVFromGoogleDrive\").getOrCreate()\n",
        "\n",
        "# Link direto para download do arquivo\n",
        "url = 'https://drive.google.com/uc?id=1rF29g_lhI6Yd9Kqcnj8-lHkJDRblQ4NI'\n",
        "\n",
        "# Baixar o arquivo localmente (opcional, caso o Spark não consiga carregar diretamente da URL)\n",
        "import requests\n",
        "\n",
        "response = requests.get(url)\n",
        "with open(\"data.csv\", \"wb\") as f:\n",
        "    f.write(response.content)\n",
        "\n",
        "# Carrega o arquivo CSV em um DataFrame do Spark\n",
        "df = spark.read.csv(\"data.csv\", header=True, inferSchema=True)\n",
        "\n",
        "# Mostra as primeiras linhas do DataFrame\n",
        "df.show()\n"
      ],
      "metadata": {
        "id": "6b2fva2sojRF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bf02c228-9dd5-48e3-d971-c9a03041e0f3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|checking_status|duration|      credit_history|            purpose|credit_amount|    savings_status|employment|installment_commitment|     personal_status|other_parties|residence_since| property_magnitude|age|other_payment_plans|   housing|existing_credits|                 job|num_dependents|own_telephone|foreign_worker|class|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|             <0|       6|'critical/other e...|           radio/tv|         1169|'no known savings'|       >=7|                     4|       'male single'|         none|              4|      'real estate'| 67|               none|       own|               2|             skilled|             1|          yes|           yes| good|\n",
            "|       0<=X<200|      48|     'existing paid'|           radio/tv|         5951|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              2|      'real estate'| 22|               none|       own|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|  'no checking'|      12|'critical/other e...|          education|         2096|              <100|    4<=X<7|                     2|       'male single'|         none|              3|      'real estate'| 49|               none|       own|               1|'unskilled resident'|             2|         none|           yes| good|\n",
            "|             <0|      42|     'existing paid'|furniture/equipment|         7882|              <100|    4<=X<7|                     2|       'male single'|    guarantor|              4|   'life insurance'| 45|               none|'for free'|               1|             skilled|             2|         none|           yes| good|\n",
            "|             <0|      24|'delayed previously'|          'new car'|         4870|              <100|    1<=X<4|                     3|       'male single'|         none|              4|'no known property'| 53|               none|'for free'|               2|             skilled|             2|         none|           yes|  bad|\n",
            "|  'no checking'|      36|     'existing paid'|          education|         9055|'no known savings'|    1<=X<4|                     2|       'male single'|         none|              4|'no known property'| 35|               none|'for free'|               1|'unskilled resident'|             2|          yes|           yes| good|\n",
            "|  'no checking'|      24|     'existing paid'|furniture/equipment|         2835|       500<=X<1000|       >=7|                     3|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               1|             skilled|             1|         none|           yes| good|\n",
            "|       0<=X<200|      36|     'existing paid'|         'used car'|         6948|              <100|    1<=X<4|                     2|       'male single'|         none|              2|                car| 35|               none|      rent|               1|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|  'no checking'|      12|     'existing paid'|           radio/tv|         3059|            >=1000|    4<=X<7|                     2|      'male div/sep'|         none|              4|      'real estate'| 61|               none|       own|               1|'unskilled resident'|             1|         none|           yes| good|\n",
            "|       0<=X<200|      30|'critical/other e...|          'new car'|         5234|              <100|unemployed|                     4|      'male mar/wid'|         none|              2|                car| 28|               none|       own|               2|'high qualif/self...|             1|         none|           yes|  bad|\n",
            "|       0<=X<200|      12|     'existing paid'|          'new car'|         1295|              <100|        <1|                     3|'female div/dep/mar'|         none|              1|                car| 25|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|             <0|      48|     'existing paid'|           business|         4308|              <100|        <1|                     3|'female div/dep/mar'|         none|              4|   'life insurance'| 24|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|       0<=X<200|      12|     'existing paid'|           radio/tv|         1567|              <100|    1<=X<4|                     1|'female div/dep/mar'|         none|              1|                car| 22|               none|       own|               1|             skilled|             1|          yes|           yes| good|\n",
            "|             <0|      24|'critical/other e...|          'new car'|         1199|              <100|       >=7|                     4|       'male single'|         none|              4|                car| 60|               none|       own|               2|'unskilled resident'|             1|         none|           yes|  bad|\n",
            "|             <0|      15|     'existing paid'|          'new car'|         1403|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              4|                car| 28|               none|      rent|               1|             skilled|             1|         none|           yes| good|\n",
            "|             <0|      24|     'existing paid'|           radio/tv|         1282|        100<=X<500|    1<=X<4|                     4|'female div/dep/mar'|         none|              2|                car| 32|               none|       own|               1|'unskilled resident'|             1|         none|           yes|  bad|\n",
            "|  'no checking'|      24|'critical/other e...|           radio/tv|         2424|'no known savings'|       >=7|                     4|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               2|             skilled|             1|         none|           yes| good|\n",
            "|             <0|      30|'no credits/all p...|           business|         8072|'no known savings'|        <1|                     2|       'male single'|         none|              3|                car| 25|               bank|       own|               3|             skilled|             1|         none|           yes| good|\n",
            "|       0<=X<200|      24|     'existing paid'|         'used car'|        12579|              <100|       >=7|                     4|'female div/dep/mar'|         none|              2|'no known property'| 44|               none|'for free'|               1|'high qualif/self...|             1|          yes|           yes|  bad|\n",
            "|  'no checking'|      24|     'existing paid'|           radio/tv|         3430|       500<=X<1000|       >=7|                     3|       'male single'|         none|              2|                car| 31|               none|       own|               1|             skilled|             2|          yes|           yes| good|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Pnd95QGookA",
        "outputId": "29d92ffd-39ba-4d83-dc61-c8b6629be2a2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- checking_status: string (nullable = true)\n",
            " |-- duration: integer (nullable = true)\n",
            " |-- credit_history: string (nullable = true)\n",
            " |-- purpose: string (nullable = true)\n",
            " |-- credit_amount: integer (nullable = true)\n",
            " |-- savings_status: string (nullable = true)\n",
            " |-- employment: string (nullable = true)\n",
            " |-- installment_commitment: integer (nullable = true)\n",
            " |-- personal_status: string (nullable = true)\n",
            " |-- other_parties: string (nullable = true)\n",
            " |-- residence_since: integer (nullable = true)\n",
            " |-- property_magnitude: string (nullable = true)\n",
            " |-- age: integer (nullable = true)\n",
            " |-- other_payment_plans: string (nullable = true)\n",
            " |-- housing: string (nullable = true)\n",
            " |-- existing_credits: integer (nullable = true)\n",
            " |-- job: string (nullable = true)\n",
            " |-- num_dependents: integer (nullable = true)\n",
            " |-- own_telephone: string (nullable = true)\n",
            " |-- foreign_worker: string (nullable = true)\n",
            " |-- class: string (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "source": [
        "\n",
        "df.orderBy(df['age'].desc()).show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKB65Bk1o2k7",
        "outputId": "28ded48b-7ff1-4bc9-f573-4646f099c716"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------+--------+--------------------+----------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|checking_status|duration|      credit_history|   purpose|credit_amount|    savings_status|employment|installment_commitment|     personal_status| other_parties|residence_since| property_magnitude|age|other_payment_plans|   housing|existing_credits|                 job|num_dependents|own_telephone|foreign_worker|class|\n",
            "+---------------+--------+--------------------+----------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|             <0|      24|'critical/other e...|'used car'|         6615|              <100|unemployed|                     2|       'male single'|          none|              4|'no known property'| 75|               none|'for free'|               2|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|             <0|       6|     'existing paid'| 'new car'|         1374|'no known savings'|unemployed|                     4|'female div/dep/mar'|          none|              3|   'life insurance'| 75|               none|       own|               1|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|       0<=X<200|       9|          'all paid'|'used car'|         5129|              <100|       >=7|                     2|'female div/dep/mar'|          none|              4|'no known property'| 74|               bank|'for free'|               1|'high qualif/self...|             2|          yes|           yes|  bad|\n",
            "|  'no checking'|       5|     'existing paid'|  business|         3448|              <100|    4<=X<7|                     1|       'male single'|          none|              4|      'real estate'| 74|               none|       own|               1|'unskilled resident'|             1|         none|           yes| good|\n",
            "|  'no checking'|      24|'critical/other e...|  business|         4526|              <100|    1<=X<4|                     3|       'male single'|          none|              2|      'real estate'| 74|               none|       own|               1|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|          >=200|       6|'critical/other e...| 'new car'|         1299|              <100|    1<=X<4|                     1|       'male single'|          none|              1|      'real estate'| 74|               none|       own|               3|'unemp/unskilled ...|             2|         none|            no| good|\n",
            "|       0<=X<200|      10|     'existing paid'| 'new car'|         7308|              <100|unemployed|                     2|       'male single'|          none|              4|'no known property'| 70|               bank|'for free'|               1|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|       0<=X<200|      16|'critical/other e...| 'new car'|         1175|              <100|unemployed|                     2|       'male single'|          none|              3|                car| 68|               none|'for free'|               3|'unemp/unskilled ...|             1|          yes|           yes| good|\n",
            "|  'no checking'|      18|     'existing paid'| 'new car'|         6761|'no known savings'|    1<=X<4|                     2|       'male single'|          none|              4|                car| 68|               none|      rent|               2|             skilled|             1|         none|           yes|  bad|\n",
            "|             <0|       6|     'existing paid'| 'new car'|        14896|              <100|       >=7|                     1|       'male single'|          none|              4|'no known property'| 68|               bank|       own|               1|'high qualif/self...|             1|          yes|           yes|  bad|\n",
            "|             <0|       6|'critical/other e...|  radio/tv|         1169|'no known savings'|       >=7|                     4|       'male single'|          none|              4|      'real estate'| 67|               none|       own|               2|             skilled|             1|          yes|           yes| good|\n",
            "|       0<=X<200|       9|     'existing paid'| education|         1199|              <100|    4<=X<7|                     4|'female div/dep/mar'|          none|              4|   'life insurance'| 67|               none|       own|               2|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|       0<=X<200|      18|     'existing paid'|   repairs|         3872|              <100|unemployed|                     2|'female div/dep/mar'|          none|              4|                car| 67|               none|       own|               1|             skilled|             1|          yes|           yes| good|\n",
            "|             <0|      12|'critical/other e...|'used car'|         1526|              <100|       >=7|                     4|       'male single'|          none|              4|'no known property'| 66|               none|'for free'|               2|'high qualif/self...|             1|         none|           yes| good|\n",
            "|       0<=X<200|      12|     'existing paid'|  radio/tv|          766|       500<=X<1000|    1<=X<4|                     4|       'male single'|          none|              3|      'real estate'| 66|               none|       own|               1|'unskilled resident'|             1|         none|           yes|  bad|\n",
            "|          >=200|      30|'delayed previously'|  business|         1908|              <100|       >=7|                     4|       'male single'|          none|              4|      'real estate'| 66|               none|       own|               1|'high qualif/self...|             1|          yes|           yes|  bad|\n",
            "|       0<=X<200|       9|     'existing paid'|  radio/tv|          790|       500<=X<1000|    1<=X<4|                     4|'female div/dep/mar'|          none|              3|      'real estate'| 66|               none|       own|               1|'unskilled resident'|             1|         none|           yes| good|\n",
            "|          >=200|      12|'critical/other e...| 'new car'|         1480|       500<=X<1000|unemployed|                     2|       'male single'|          none|              4|'no known property'| 66|               bank|'for free'|               3|'unemp/unskilled ...|             1|         none|           yes| good|\n",
            "|             <0|      21|'critical/other e...| 'new car'|          571|              <100|       >=7|                     4|       'male single'|          none|              4|      'real estate'| 65|               none|       own|               2|             skilled|             1|         none|           yes| good|\n",
            "|             <0|      42|'critical/other e...|   repairs|         3394|              <100|unemployed|                     4|       'male single'|'co applicant'|              4|                car| 65|               none|       own|               2|'unemp/unskilled ...|             1|         none|           yes| good|\n",
            "+---------------+--------+--------------------+----------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJ22IoqWo44e",
        "outputId": "5c674a18-4469-4bab-e9c7-e4b7e7a66f56"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('checking_status', 'string'), ('duration', 'int'), ('credit_history', 'string'), ('purpose', 'string'), ('credit_amount', 'int'), ('savings_status', 'string'), ('employment', 'string'), ('installment_commitment', 'int'), ('personal_status', 'string'), ('other_parties', 'string'), ('residence_since', 'int'), ('property_magnitude', 'string'), ('age', 'int'), ('other_payment_plans', 'string'), ('housing', 'string'), ('existing_credits', 'int'), ('job', 'string'), ('num_dependents', 'int'), ('own_telephone', 'string'), ('foreign_worker', 'string'), ('class', 'string')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Funcao para validar os dados nulos"
      ],
      "metadata": {
        "id": "85OoyhiySXQG"
      }
    },
    {
      "source": [
        "from pyspark.sql.functions import col, isnull, sum as _sum\n",
        "\n",
        "# verificando dados nulos\n",
        "for c in df.columns:  # Iterate over columns\n",
        "    null_count = df.select(_sum(isnull(col(c)).cast(\"int\")).alias(c)).collect()[0][0]\n",
        "    print(f\"Column {c}: {null_count} null values\")"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6iIwEvISN57",
        "outputId": "75977059-1eef-44dc-cf6f-f435b44e2ede"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column checking_status: 0 null values\n",
            "Column duration: 0 null values\n",
            "Column credit_history: 0 null values\n",
            "Column purpose: 0 null values\n",
            "Column credit_amount: 0 null values\n",
            "Column savings_status: 0 null values\n",
            "Column employment: 0 null values\n",
            "Column installment_commitment: 0 null values\n",
            "Column personal_status: 0 null values\n",
            "Column other_parties: 0 null values\n",
            "Column residence_since: 0 null values\n",
            "Column property_magnitude: 0 null values\n",
            "Column age: 0 null values\n",
            "Column other_payment_plans: 0 null values\n",
            "Column housing: 0 null values\n",
            "Column existing_credits: 0 null values\n",
            "Column job: 0 null values\n",
            "Column num_dependents: 0 null values\n",
            "Column own_telephone: 0 null values\n",
            "Column foreign_worker: 0 null values\n",
            "Column class: 0 null values\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import count\n",
        "\n",
        "\n",
        "result = df.select(count(\"class\")).collect()[0][0]\n",
        "\n",
        "print(f\"A soma da coluna 'class' é: {result}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzyiaSZ2SSOl",
        "outputId": "9f93f2b4-fa90-44e8-cecb-5d7a08e92e30"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A soma da coluna 'class' é: 1000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsnweUPtUX-m",
        "outputId": "bfb4650f-fb7a-45a2-f9e5-8e9b279f3e43"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Volumetria"
      ],
      "metadata": {
        "id": "cCl7OQFIVtug"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_rows = df.count()\n",
        "num_cols = len(df.columns)\n",
        "\n",
        "print(\"Numero de linhas na tabela\", num_rows)\n",
        "print(\"Numero de colunas na tabela\", num_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnDhs10UUasn",
        "outputId": "473ab634-66b5-4712-bdc2-f049c11e1940"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numero de linhas na tabela 1000\n",
            "Numero de colunas na tabela 21\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df2 = df.dropna()\n",
        "# Show the updated DataFrame\n",
        "df2.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgKEpgd_V0CH",
        "outputId": "70836687-5c9e-4972-dc35-dfd319851aa7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|checking_status|duration|      credit_history|            purpose|credit_amount|    savings_status|employment|installment_commitment|     personal_status|other_parties|residence_since| property_magnitude|age|other_payment_plans|   housing|existing_credits|                 job|num_dependents|own_telephone|foreign_worker|class|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "|             <0|       6|'critical/other e...|           radio/tv|         1169|'no known savings'|       >=7|                     4|       'male single'|         none|              4|      'real estate'| 67|               none|       own|               2|             skilled|             1|          yes|           yes| good|\n",
            "|       0<=X<200|      48|     'existing paid'|           radio/tv|         5951|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              2|      'real estate'| 22|               none|       own|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|  'no checking'|      12|'critical/other e...|          education|         2096|              <100|    4<=X<7|                     2|       'male single'|         none|              3|      'real estate'| 49|               none|       own|               1|'unskilled resident'|             2|         none|           yes| good|\n",
            "|             <0|      42|     'existing paid'|furniture/equipment|         7882|              <100|    4<=X<7|                     2|       'male single'|    guarantor|              4|   'life insurance'| 45|               none|'for free'|               1|             skilled|             2|         none|           yes| good|\n",
            "|             <0|      24|'delayed previously'|          'new car'|         4870|              <100|    1<=X<4|                     3|       'male single'|         none|              4|'no known property'| 53|               none|'for free'|               2|             skilled|             2|         none|           yes|  bad|\n",
            "|  'no checking'|      36|     'existing paid'|          education|         9055|'no known savings'|    1<=X<4|                     2|       'male single'|         none|              4|'no known property'| 35|               none|'for free'|               1|'unskilled resident'|             2|          yes|           yes| good|\n",
            "|  'no checking'|      24|     'existing paid'|furniture/equipment|         2835|       500<=X<1000|       >=7|                     3|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               1|             skilled|             1|         none|           yes| good|\n",
            "|       0<=X<200|      36|     'existing paid'|         'used car'|         6948|              <100|    1<=X<4|                     2|       'male single'|         none|              2|                car| 35|               none|      rent|               1|'high qualif/self...|             1|          yes|           yes| good|\n",
            "|  'no checking'|      12|     'existing paid'|           radio/tv|         3059|            >=1000|    4<=X<7|                     2|      'male div/sep'|         none|              4|      'real estate'| 61|               none|       own|               1|'unskilled resident'|             1|         none|           yes| good|\n",
            "|       0<=X<200|      30|'critical/other e...|          'new car'|         5234|              <100|unemployed|                     4|      'male mar/wid'|         none|              2|                car| 28|               none|       own|               2|'high qualif/self...|             1|         none|           yes|  bad|\n",
            "|       0<=X<200|      12|     'existing paid'|          'new car'|         1295|              <100|        <1|                     3|'female div/dep/mar'|         none|              1|                car| 25|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|             <0|      48|     'existing paid'|           business|         4308|              <100|        <1|                     3|'female div/dep/mar'|         none|              4|   'life insurance'| 24|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|\n",
            "|       0<=X<200|      12|     'existing paid'|           radio/tv|         1567|              <100|    1<=X<4|                     1|'female div/dep/mar'|         none|              1|                car| 22|               none|       own|               1|             skilled|             1|          yes|           yes| good|\n",
            "|             <0|      24|'critical/other e...|          'new car'|         1199|              <100|       >=7|                     4|       'male single'|         none|              4|                car| 60|               none|       own|               2|'unskilled resident'|             1|         none|           yes|  bad|\n",
            "|             <0|      15|     'existing paid'|          'new car'|         1403|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              4|                car| 28|               none|      rent|               1|             skilled|             1|         none|           yes| good|\n",
            "|             <0|      24|     'existing paid'|           radio/tv|         1282|        100<=X<500|    1<=X<4|                     4|'female div/dep/mar'|         none|              2|                car| 32|               none|       own|               1|'unskilled resident'|             1|         none|           yes|  bad|\n",
            "|  'no checking'|      24|'critical/other e...|           radio/tv|         2424|'no known savings'|       >=7|                     4|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               2|             skilled|             1|         none|           yes| good|\n",
            "|             <0|      30|'no credits/all p...|           business|         8072|'no known savings'|        <1|                     2|       'male single'|         none|              3|                car| 25|               bank|       own|               3|             skilled|             1|         none|           yes| good|\n",
            "|       0<=X<200|      24|     'existing paid'|         'used car'|        12579|              <100|       >=7|                     4|'female div/dep/mar'|         none|              2|'no known property'| 44|               none|'for free'|               1|'high qualif/self...|             1|          yes|           yes|  bad|\n",
            "|  'no checking'|      24|     'existing paid'|           radio/tv|         3430|       500<=X<1000|       >=7|                     3|       'male single'|         none|              2|                car| 31|               none|       own|               1|             skilled|             2|          yes|           yes| good|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CKiiv5_7WZtp",
        "outputId": "d71a146d-bd04-4cb1-feed-5d7ac61c167d"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Idade Média"
      ],
      "metadata": {
        "id": "QukkvT-HW9Iq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import avg, col, when\n",
        "avg_salary = df.select(avg(\"age\")).collect()[0][0]\n",
        "\n",
        "print(f\"A Idade Média é: {avg_salary}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KCddYzsWb45",
        "outputId": "92b3bb35-1e8a-4324-9f1c-755d3c9d5700"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A Idade Média é: 35.546\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Criando Categoria de Idade\n"
      ],
      "metadata": {
        "id": "tkF6AQDsXSxv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from pyspark.sql.functions import udf\n",
        "from pyspark.sql.types import StringType\n",
        "\n",
        "# Define a Python function\n",
        "def age_category(age):\n",
        "    if age < 18:\n",
        "        return \"Child\"\n",
        "    elif age < 65:\n",
        "        return \"Adult\"\n",
        "    else:\n",
        "        return \"Senior\"\n",
        "\n",
        "# Converte a função em uma UDF\n",
        "age_category_udf = udf(age_category, StringType())\n",
        "\n",
        "# Aplica a UDF ao DataFrame\n",
        "df = df.withColumn(\"age_category\", age_category_udf(df[\"age\"]))\n",
        "\n",
        "# Mostra o DataFrame atualizado\n",
        "df.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q_7CBTjXWzHt",
        "outputId": "a3b8f212-962a-4899-96de-7b817e11e52c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "|checking_status|duration|      credit_history|            purpose|credit_amount|    savings_status|employment|installment_commitment|     personal_status|other_parties|residence_since| property_magnitude|age|other_payment_plans|   housing|existing_credits|                 job|num_dependents|own_telephone|foreign_worker|class|age_category|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "|             <0|       6|'critical/other e...|           radio/tv|         1169|'no known savings'|       >=7|                     4|       'male single'|         none|              4|      'real estate'| 67|               none|       own|               2|             skilled|             1|          yes|           yes| good|      Senior|\n",
            "|       0<=X<200|      48|     'existing paid'|           radio/tv|         5951|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              2|      'real estate'| 22|               none|       own|               1|             skilled|             1|         none|           yes|  bad|       Adult|\n",
            "|  'no checking'|      12|'critical/other e...|          education|         2096|              <100|    4<=X<7|                     2|       'male single'|         none|              3|      'real estate'| 49|               none|       own|               1|'unskilled resident'|             2|         none|           yes| good|       Adult|\n",
            "|             <0|      42|     'existing paid'|furniture/equipment|         7882|              <100|    4<=X<7|                     2|       'male single'|    guarantor|              4|   'life insurance'| 45|               none|'for free'|               1|             skilled|             2|         none|           yes| good|       Adult|\n",
            "|             <0|      24|'delayed previously'|          'new car'|         4870|              <100|    1<=X<4|                     3|       'male single'|         none|              4|'no known property'| 53|               none|'for free'|               2|             skilled|             2|         none|           yes|  bad|       Adult|\n",
            "|  'no checking'|      36|     'existing paid'|          education|         9055|'no known savings'|    1<=X<4|                     2|       'male single'|         none|              4|'no known property'| 35|               none|'for free'|               1|'unskilled resident'|             2|          yes|           yes| good|       Adult|\n",
            "|  'no checking'|      24|     'existing paid'|furniture/equipment|         2835|       500<=X<1000|       >=7|                     3|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               1|             skilled|             1|         none|           yes| good|       Adult|\n",
            "|       0<=X<200|      36|     'existing paid'|         'used car'|         6948|              <100|    1<=X<4|                     2|       'male single'|         none|              2|                car| 35|               none|      rent|               1|'high qualif/self...|             1|          yes|           yes| good|       Adult|\n",
            "|  'no checking'|      12|     'existing paid'|           radio/tv|         3059|            >=1000|    4<=X<7|                     2|      'male div/sep'|         none|              4|      'real estate'| 61|               none|       own|               1|'unskilled resident'|             1|         none|           yes| good|       Adult|\n",
            "|       0<=X<200|      30|'critical/other e...|          'new car'|         5234|              <100|unemployed|                     4|      'male mar/wid'|         none|              2|                car| 28|               none|       own|               2|'high qualif/self...|             1|         none|           yes|  bad|       Adult|\n",
            "|       0<=X<200|      12|     'existing paid'|          'new car'|         1295|              <100|        <1|                     3|'female div/dep/mar'|         none|              1|                car| 25|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|       Adult|\n",
            "|             <0|      48|     'existing paid'|           business|         4308|              <100|        <1|                     3|'female div/dep/mar'|         none|              4|   'life insurance'| 24|               none|      rent|               1|             skilled|             1|         none|           yes|  bad|       Adult|\n",
            "|       0<=X<200|      12|     'existing paid'|           radio/tv|         1567|              <100|    1<=X<4|                     1|'female div/dep/mar'|         none|              1|                car| 22|               none|       own|               1|             skilled|             1|          yes|           yes| good|       Adult|\n",
            "|             <0|      24|'critical/other e...|          'new car'|         1199|              <100|       >=7|                     4|       'male single'|         none|              4|                car| 60|               none|       own|               2|'unskilled resident'|             1|         none|           yes|  bad|       Adult|\n",
            "|             <0|      15|     'existing paid'|          'new car'|         1403|              <100|    1<=X<4|                     2|'female div/dep/mar'|         none|              4|                car| 28|               none|      rent|               1|             skilled|             1|         none|           yes| good|       Adult|\n",
            "|             <0|      24|     'existing paid'|           radio/tv|         1282|        100<=X<500|    1<=X<4|                     4|'female div/dep/mar'|         none|              2|                car| 32|               none|       own|               1|'unskilled resident'|             1|         none|           yes|  bad|       Adult|\n",
            "|  'no checking'|      24|'critical/other e...|           radio/tv|         2424|'no known savings'|       >=7|                     4|       'male single'|         none|              4|   'life insurance'| 53|               none|       own|               2|             skilled|             1|         none|           yes| good|       Adult|\n",
            "|             <0|      30|'no credits/all p...|           business|         8072|'no known savings'|        <1|                     2|       'male single'|         none|              3|                car| 25|               bank|       own|               3|             skilled|             1|         none|           yes| good|       Adult|\n",
            "|       0<=X<200|      24|     'existing paid'|         'used car'|        12579|              <100|       >=7|                     4|'female div/dep/mar'|         none|              2|'no known property'| 44|               none|'for free'|               1|'high qualif/self...|             1|          yes|           yes|  bad|       Adult|\n",
            "|  'no checking'|      24|     'existing paid'|           radio/tv|         3430|       500<=X<1000|       >=7|                     3|       'male single'|         none|              2|                car| 31|               none|       own|               1|             skilled|             2|          yes|           yes| good|       Adult|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+-------------+---------------+-------------------+---+-------------------+----------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.cache()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_GPgDwWuXrVj",
        "outputId": "601445dc-5ed0-46d6-dad7-6016894cd061"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[checking_status: string, duration: int, credit_history: string, purpose: string, credit_amount: int, savings_status: string, employment: string, installment_commitment: int, personal_status: string, other_parties: string, residence_since: int, property_magnitude: string, age: int, other_payment_plans: string, housing: string, existing_credits: int, job: string, num_dependents: int, own_telephone: string, foreign_worker: string, class: string, age_category: string]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filtrando"
      ],
      "metadata": {
        "id": "HL5zIY8QnBho"
      }
    },
    {
      "source": [
        "foreign_worker_df = df[df[\"foreign_worker\"] == \"no\"]  # Filter the DataFrame\n",
        "foreign_worker_count = foreign_worker_df.count()      # contar a quantidade de dados\n",
        "print(f\"Number of foreign workers with 'no': {foreign_worker_count}\")\n",
        "\n",
        "# If you want to display the filtered DataFrame:\n",
        "foreign_worker_df.show()"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHG-b3u-k89P",
        "outputId": "c1aa8ce3-f32e-4bf0-d21b-708f1cb5a7c2"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of foreign workers with 'no': 37\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+-------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "|checking_status|duration|      credit_history|            purpose|credit_amount|    savings_status|employment|installment_commitment|     personal_status| other_parties|residence_since| property_magnitude|age|other_payment_plans|housing|existing_credits|                 job|num_dependents|own_telephone|foreign_worker|class|age_category|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+-------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "|             <0|      10|'critical/other e...|          'new car'|         2241|              <100|        <1|                     1|       'male single'|          none|              3|      'real estate'| 48|               none|   rent|               2|'unskilled resident'|             2|         none|            no| good|       Adult|\n",
            "|  'no checking'|      10|'critical/other e...|furniture/equipment|         2069|'no known savings'|    1<=X<4|                     2|      'male mar/wid'|          none|              1|                car| 26|               none|    own|               2|             skilled|             1|         none|            no| good|       Adult|\n",
            "|             <0|      24|     'existing paid'|furniture/equipment|         7721|'no known savings'|        <1|                     1|'female div/dep/mar'|          none|              2|   'life insurance'| 30|               none|    own|               1|             skilled|             1|          yes|            no| good|       Adult|\n",
            "|             <0|      10|'critical/other e...|furniture/equipment|         2132|'no known savings'|        <1|                     2|'female div/dep/mar'|'co applicant'|              3|      'real estate'| 27|               none|   rent|               2|             skilled|             1|         none|            no| good|       Adult|\n",
            "|          >=200|       6|     'existing paid'|          'new car'|          709|            >=1000|        <1|                     2|      'male mar/wid'|          none|              2|      'real estate'| 27|               none|    own|               1|'unemp/unskilled ...|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|      10|     'existing paid'|           radio/tv|         1924|              <100|    1<=X<4|                     1|       'male single'|          none|              4|   'life insurance'| 38|               none|    own|               1|             skilled|             1|          yes|            no| good|       Adult|\n",
            "|             <0|       9|'critical/other e...|            repairs|         1288|        100<=X<500|       >=7|                     3|       'male single'|     guarantor|              4|      'real estate'| 48|               none|    own|               2|             skilled|             2|         none|            no| good|       Adult|\n",
            "|       0<=X<200|       8|     'existing paid'|           radio/tv|         1414|              <100|    1<=X<4|                     4|       'male single'|     guarantor|              2|      'real estate'| 33|               none|    own|               1|             skilled|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|      12|     'existing paid'|         'used car'|         1413|            >=1000|    4<=X<7|                     3|       'male single'|          none|              2|   'life insurance'| 55|               none|    own|               1|             skilled|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|       6|'no credits/all p...|          'new car'|         1204|        100<=X<500|    1<=X<4|                     4|       'male single'|          none|              1|'no known property'| 35|               bank|   rent|               1|             skilled|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|      10|'critical/other e...|          'new car'|         1231|              <100|       >=7|                     3|       'male single'|          none|              4|      'real estate'| 32|               none|    own|               2|'unskilled resident'|             2|         none|            no| good|       Adult|\n",
            "|             <0|      14|     'existing paid'|          'new car'|         8978|              <100|       >=7|                     1|      'male div/sep'|          none|              4|   'life insurance'| 45|               none|    own|               1|'high qualif/self...|             1|          yes|            no|  bad|       Adult|\n",
            "|  'no checking'|      18|     'existing paid'|          'new car'|         2662|'no known savings'|    4<=X<7|                     4|       'male single'|          none|              3|   'life insurance'| 32|               none|    own|               1|             skilled|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|      12|     'existing paid'|           business|         1076|              <100|    1<=X<4|                     2|      'male mar/wid'|          none|              2|      'real estate'| 26|               none|    own|               1|             skilled|             1|          yes|            no| good|       Adult|\n",
            "|  'no checking'|      10|     'existing paid'|          'new car'|         1287|'no known savings'|       >=7|                     4|       'male single'|'co applicant'|              2|   'life insurance'| 45|               none|    own|               1|'unskilled resident'|             1|         none|            no| good|       Adult|\n",
            "|          >=200|       4|     'existing paid'|          'new car'|         1494|'no known savings'|        <1|                     1|       'male single'|          none|              2|      'real estate'| 29|               none|    own|               1|'unskilled resident'|             2|         none|            no| good|       Adult|\n",
            "|             <0|       8|'critical/other e...|          'new car'|         3398|              <100|    4<=X<7|                     1|       'male single'|          none|              4|      'real estate'| 39|               none|    own|               2|'unskilled resident'|             1|         none|            no| good|       Adult|\n",
            "|       0<=X<200|       9|     'existing paid'|furniture/equipment|          959|              <100|    1<=X<4|                     1|'female div/dep/mar'|          none|              2|                car| 29|               none|    own|               1|             skilled|             1|         none|            no|  bad|       Adult|\n",
            "|             <0|      15|     'existing paid'|           radio/tv|         1053|              <100|        <1|                     4|      'male mar/wid'|          none|              2|      'real estate'| 27|               none|    own|               1|             skilled|             1|         none|            no| good|       Adult|\n",
            "|  'no checking'|      10|     'existing paid'|          'new car'|         1597|       500<=X<1000|    1<=X<4|                     3|       'male single'|          none|              2|'no known property'| 40|               none|   rent|               1|'unskilled resident'|             2|         none|            no| good|       Adult|\n",
            "+---------------+--------+--------------------+-------------------+-------------+------------------+----------+----------------------+--------------------+--------------+---------------+-------------------+---+-------------------+-------+----------------+--------------------+--------------+-------------+--------------+-----+------------+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "foreign_worker_df.groupBy(\"foreign_worker\").count().show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNQcICcklGEH",
        "outputId": "fc617dfe-10c5-4b05-cea2-e9fdd37c0a02"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+-----+\n",
            "|foreign_worker|count|\n",
            "+--------------+-----+\n",
            "|            no|   37|\n",
            "+--------------+-----+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Convert the Spark DataFrame to a Pandas DataFrame for plotting\n",
        "pandas_df = foreign_worker_df.toPandas()\n",
        "\n",
        "# Example plot: Histogram of age\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.hist(pandas_df['age'], bins=20, edgecolor='black')\n",
        "plt.title('Distribuição Idade Funcionarios Estrangeiros')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ZYQ3dgNjnuPT",
        "outputId": "284cc433-d8fb-43ea-dc08-a3b037724741"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "foreign_worker_count = foreign_worker_df.count()\n",
        "print(f\"Quantidade funcionario estrangeiro é : {foreign_worker_count}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NruSV74dl3jb",
        "outputId": "1f2d2ead-2e43-4122-a7e1-6a938c4fda86"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Quantidade funcionario estrangeiro é : 37\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBWivClumuMi",
        "outputId": "f90e0517-49ab-472f-c0d5-c7892419679d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    }
  ]
}