Drop if all entries in a spark dataframe's specific column is null

Question

Using Pyspark, how can I select/keep all columns of a DataFrame which contain a non-null value; or equivalently remove all columns which contain no data.

Answer 1

for me it worked in a bit different way than @Suresh answer:

nonNull_cols = [c for c in original_df.columns if original_df.filter(func.col(c).isNotNull()).count() > 0]
new_df = original_df.select(*nonNull_cols)

Answer 2

Here's a much more efficient solution that doesn't involve looping over the columns. It is much faster when you have many columns. I tested the other methods here on a dataframe with 800 columns, which took 17 mins to run. The following method takes only 1 min in my tests on the same dataset.

def drop_fully_null_columns(df, but_keep_these=[]):
    """Drops DataFrame columns that are fully null
    (i.e. the maximum value is null)

    Arguments:
        df {spark DataFrame} -- spark dataframe
        but_keep_these {list} -- list of columns to keep without checking for nulls

    Returns:
        spark DataFrame -- dataframe with fully null columns removed
    """

    # skip checking some columns
    cols_to_check = [col for col in df.columns if col not in but_keep_these]
    if len(cols_to_check) > 0:
        # drop columns for which the max is None
        rows_with_data = df.select(*cols_to_check).groupby().agg(*[F.max(c).alias(c) for c in cols_to_check]).take(1)[0]
        cols_to_drop = [c for c, const in rows_with_data.asDict().items() if const == None]
        new_df = df.drop(*cols_to_drop)

        return new_df
    else:
        return df