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)

Answer 1

Firstly, you will create your dataframe:

Now, in order to replace null values only in the first 2 columns - Column "a" and "b", and that too without losing the third column, you can use:

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

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