Unlocking the Power of transform() in Spark: Custom Transformations Made Easy
transform() is used to chain custom transformations and it returns a new DataFrame after applying the functions. It takes the following parameters: func: custom function name *args: arguments to pass in func *kwargs: keyword arguments to pass in func Creating custom functions: Adding a new column which converts it to uppercase: def to_upper_str(df: DataFrame) -> DataFrame:
    return df.withColumn("name", upper(col("name")))
Unlocking the Power of transform() in Spark: Custom Transformations Made Easy transform() is used to chain custom transformations and it returns a new DataFrame after applying the functions. It takes the following parameters: func: custom function name *args: arguments to pass in func *kwargs: keyword arguments to pass in func Creating custom functions: Adding a new column which converts it to uppercase: def to_upper_str(df: DataFrame) -> DataFrame:
    return df.withColumn("name", upper(col("name"))) data-science
Unlocking Power transform Spark Custom Transformations Made Easy transform used chain custom transformation return new DataFrame applying function take following parameter func custom function name args argument pas func kwargs keyword argument pas func Creating custom function Adding new column convert uppercase def toupperstrdf DataFrame DataFrame return dfwithColumnname uppercolname datascience