您可以使用when：

from pyspark.sql import functions as F

# Loop over all the columns you want to fill
for col in ('Col1', 'Col2', 'Col3'):
    # compute here conditions to fill using a value or another
    fill_a = F.col(col).isNull() & (F.col('Group') == 'A')
    fill_b = F.col(col).isNull() & (F.col('Group') == 'B')

    # Fill the column based on the different conditions 
    # using nested `when` - `otherwise`.
    #
    # Do not forget to add the last `otherwise` with the original 
    # values if none of the previous conditions have been met
    filled_col = (
        F.when(fill_a, -999)
        .otherwise(
            F.when(fill_b, 0)
            .otherwise(F.col(col))
        )
    )

    # 'overwrite' the original column with the filled column
    df = df.withColumn(col, filled_col)

另一个可能的选择是对每一列使用coalesce，并使用“filler”列保存替换值：

import pyspark.sql.functions as F

for c in ['Col1', 'Col2', 'Col3']:
  df = df.withColumn(c, F.coalesce(c, F.when(F.col('group') == 'A', -999)
                                       .when(F.col('group') == 'B', 0)))