I have the following sample DataFrame:
a | b | c | 1 | 2 | 4 | 0 | null | null| null | 3 | 4 | And I want to replace null values only in the first 2 columns - Column "a" and "b":
a | b | c | 1 | 2 | 4 | 0 | 0 | null| 0 | 3 | 4 | Here is the code to create sample dataframe:
rdd = sc.parallelize([(1,2,4), (0,None,None), (None,3,4)]) df2 = sqlContext.createDataFrame(rdd, ["a", "b", "c"]) I know how to replace all null values using:
df2 = df2.fillna(0) And when I try this, I lose the third column:
df2 = df2.select(df2.columns[0:1]).fillna(0) 2 Answers
df.fillna(0, subset=['a', 'b']) There is a parameter named subset to choose the columns unless your spark version is lower than 1.3.1
Use a dictionary to fill values of certain columns:
df.fillna( { 'a':0, 'b':0 } ) 
