The requirement is to remove any record that contains a null value in any of its fields. The pyspark.sql.DataFrameNaFunctions.drop() method is used for this purpose. The how parameter controls the dropping logic. Setting how='any' instructs Spark to drop a row if it contains one or more null values across any of its columns. This directly fulfills the data scientist's requirement. The default behavior of df.na.drop() is also equivalent to how='any'.

A. filtereddf = usersrawdf.na.drop(thresh=0): The thresh parameter keeps rows with at least that many non-null values. thresh=0 would keep all rows, as every row has at least zero non-nulls.

B. filtereddf = usersrawdf.na.drop(how='all'): This option drops a row only if all of its values are null, which is too restrictive and does not meet the requirement to drop rows with any null value.

D. filtereddf = usersrawdf.na.drop(how='all', thresh=None): This is functionally identical to option B. Specifying thresh=None is redundant as it's the default and does not alter the incorrect how='all' logic.

1. Apache Spark Official Documentation: The pyspark.sql.DataFrameNaFunctions.drop documentation explicitly defines the how parameter.

Source: Apache Spark 3.5.1 Documentation

pyspark.sql.DataFrameNaFunctions.drop.

Reference: "how – str

optional – 'any' or 'all'. If 'any'

drop a row if it contains any nulls. If 'all'

drop a row only if all its values are null."

2. Databricks Official Documentation: The Databricks guide on data cleaning confirms this behavior.

Source: Databricks Documentation

"Handle missing data".

Reference: In the section on dropping rows

it states: "You can drop rows with any null or NaN values using df.na.drop() or df.dropna(). This is the default." It further clarifies that df.na.drop("any") is the explicit form.

3. University Courseware: Reputable university courses on data science with Spark teach this fundamental operation.

Source: University of California

Berkeley

Data 100: Principles and Techniques of Data Science.

Reference: Course materials on data cleaning with PySpark demonstrate that df.dropna(how='any') is the standard method to remove rows containing any missing values. The dropna() alias is interchangeable with na.drop().