The easiest way to clean and transform data using PySpark in Databricks is by leveraging the DataFrame API. Start by loading data into a Spark DataFrame with spark.read. Use built-in functions like dropna, fillna, and withColumn to handle missing values and create new columns. Apply filter or select for subsetting data, and use groupBy with aggregation for summaries. Databricks’ interactive notebooks make it easy to visualize results instantly. Finally, write the cleaned data back to storage using df.write in formats like Parquet or Delta Lake.