pyspark-stubs

import os from pyspark.sql import SparkSession from typing import List # Instantiate SparkSession (requires PySpark to be installed and configured) spark: SparkSession = (SparkSession.builder .appName("PySparkStubsExample") .getOrCreate() ) # Example of using PySpark with type hints def process_data(data: List[int]) -> List[int]: # In a real scenario, this would involve Spark RDDs/DataFrames # This is a simplified example to show type hints in action. # For a type checker, 'pyspark-stubs' helps validate Spark-specific types. print(f"Processing data: {data}") return [x * 2 for x in data] if __name__ == '__main__': sample_data: List[int] = [1, 2, 3] processed_result = process_data(sample_data) print(f"Processed result: {processed_result}") # Example with a Spark DataFrame (for demonstration of type support) # This part requires a running SparkSession and actual PySpark code. # For type checking, `pyspark-stubs` ensures `spark` is typed correctly. data_df = spark.createDataFrame([("Alice", 1), ("Bob", 2)], ["name", "age"]) data_df.printSchema() data_df.show() spark.stop()