Spark assign value if null to column (python)

Question

Assuming that I have the following data

+--------------------+-----+--------------------+
|              values|count|             values2|
+--------------------+-----+--------------------+
|              aaaaaa|  249|                null|
|              bbbbbb|  166|                  b2|
|              cccccc| 1680|           something|
+--------------------+-----+--------------------+

So if there is a null value in values2 column how to assign the values1 column to it? So the result should be:

+--------------------+-----+--------------------+
|              values|count|             values2|
+--------------------+-----+--------------------+
|              aaaaaa|  249|              aaaaaa|
|              bbbbbb|  166|                  b2|
|              cccccc| 1680|           something|
+--------------------+-----+--------------------+

I thought of something of the following but it doesnt work:

df.na.fill({"values2":df['values']}).show()

I found this way to solve it but there should be something more clear forward:

def change_null_values(a,b):
    if b:
        return b
    else:
        return a

udf_change_null = udf(change_null_values,StringType())

df.withColumn("values2",udf_change_null("values","values2")).show()

Solution

You can use https://spark.apache.org/docs/1.6.2/api/python/pyspark.sql.html#pyspark.sql.functions.coalesce

df.withColumn('values2', coalesce(df.values2, df.values)).show()

Comments

[proutray]

Doesn't provide complete solution to the issue. Only hints towards the solution.

[vegetarianCoder]

Can we ingest our own value like 0 or something ?