PySpark: How to fillna values in dataframe for specific columns?

Question

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)

Solution 1

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

Solution 2

Use a dictionary to fill values of certain columns:

df.fillna( { 'a':0, 'b':0 } )