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Pyspark baseline data quality checks with example to test

Allie Ubisse answered on January 5, 2024 Popularity 1/10 Helpfulness 1/10

answer Pyspark baseline data quality checks with example to test

Pyspark baseline data quality checks with example to test

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from pyspark.sql import SparkSession
from pyspark.sql import Row
from pyspark.sql.types import StructType, StructField, StringType, IntegerType
from pyspark.sql import functions as F
from pyspark.sql.window import Window

def perform_data_quality_checks(input_df):
    """
    Perform basic data quality checks on a PySpark DataFrame.

    Args:
        input_df (pyspark.sql.DataFrame): Input DataFrame for data quality checks.

    Returns:
        pyspark.sql.DataFrame: DataFrame with additional columns indicating data quality test results.
    """
    # Create a copy of the input DataFrame to store data quality test results
    output_df = input_df

    # Uniqueness Check
    for column in input_df.columns:
        output_df = output_df.withColumn(f"{column}_is_unique", F.count(column).over(Window.partitionBy(column)).alias(f"{column}_count") == 1)

    # Completeness Check
    for column in input_df.columns:
        output_df = output_df.withColumn(f"{column}_is_complete", F.col(column).isNotNull())

    # Data Coverage Check
    for column in input_df.columns:
        total_records = input_df.count()
        output_df = output_df.withColumn(f"{column}_data_coverage", F.count(column).over(Window.partitionBy(column)).alias(f"{column}_count") / total_records)

    # Constraint Checks (Example: Checking if a numerical column is greater than 0)
    constraint_checks = [
        (F.col("age") > 0).alias("age_positive"),
        # Add more constraint checks as needed
    ]
    output_df = output_df.withColumn("constraint_checks", F.struct(*constraint_checks))

    return output_df

if __name__ == "__main__":
    # Initialize Spark session
    spark = SparkSession.builder.master("local").appName("DataQualityExample").getOrCreate()

    # Create an example DataFrame
    data = [
        Row(user_id=1, email="john@example.com", age=25),
        Row(user_id=2, email="jane@example.com", age=None),
        Row(user_id=3, email="john@example.com", age=30),
        Row(user_id=4, email="alice@example.com", age=20),
    ]

    schema = StructType([
        StructField("user_id", IntegerType(), True),
        StructField("email", StringType(), True),
        StructField("age", IntegerType(), True),
    ])

    example_df = spark.createDataFrame(data, schema)

    # Display the example DataFrame
    print("Example DataFrame:")
    example_df.show()

    # Perform data quality checks
    result_df = perform_data_quality_checks(example_df)

    # Display the results of data quality checks
    print("Results of Data Quality Checks:")
    result_df.show(truncate=False)

Popularity 1/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: baseline data-quality pyspark python

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Contributed on Jan 05 2024

Allie Ubisse

0 Answers Avg Quality 2/10

Pyspark baseline data quality checks with example to test

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