PySpark: How to fill values in dataframe for specific columns?
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) Firstly, you have to 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 } )To pyspark fillna, follow the above mentioned steps.