How to replace all Null values of a dataframe in Pyspark

问题

I have a data frame in pyspark with more than 300 columns. In these columns there are some columns with values null.

For example:

Column_1 column_2
null     null
null     null
234      null
125      124
365      187
and so on

When I want to do a sum of column_1 I am getting a Null as a result, instead of 724.

Now I want to replace the null in all columns of the data frame with empty space. So when I try to do a sum of these columns I don't get a null value but I will get a numerical value.

How can we achieve that in pyspark

回答1:

You can use df.na.fill to replace nulls with zeros, for example:

>>> df = spark.createDataFrame([(1,), (2,), (3,), (None,)], ['col'])
>>> df.show()
+----+
| col|
+----+
|   1|
|   2|
|   3|
|null|
+----+

>>> df.na.fill(0).show()
+---+
|col|
+---+
|  1|
|  2|
|  3|
|  0|
+---+

回答2:

You can use fillna() func.

>>> df = spark.createDataFrame([(1,), (2,), (3,), (None,)], ['col'])
>>> df.show()
+----+
| col|
+----+
|   1|
|   2|
|   3|
|null|
+----+

>>> df = df.fillna({'col':'4'})
>>> df.show()

or df.fillna({'col':'4'}).show()

+---+
|col|
+---+
|  1|
|  2|
|  3|
|  4|
+---+

来源：https://stackoverflow.com/questions/42312042/how-to-replace-all-null-values-of-a-dataframe-in-pyspark