PySpark – Dataframes Table Of Contents: What Is PySpark DataFrames. Why Use DataFrames In PySpark? How To Create DataFrames In PySSark? Common DataFrame Operations. Lazy Evaluation. Under the Hood: Catalyst & Tungsten (1) What Is PySpark DataFrames. (2) Why Use DataFrames in PySpark? (3) How to Create a DataFrame? From A List: from pyspark.sql import SparkSession spark = SparkSession.builder.appName("Example").getOrCreate() data = [("Alice", 30), ("Bob", 25), ("Charlie", 35)] columns = ["Name", "Age"] df = spark.createDataFrame(data, columns) df.show() From A CSV File: df = spark.read.csv("employees.csv", header=True, inferSchema=True) df.show() (4) Common DataFrame Operations Filtering Rows: df.filter(df.Age > 30).show() Selecting Columns: df.select("Name").show() Group and

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