I'm using PySpark and I have a Spark dataframe with a bunch of numeric columns. I want to add a column that is the sum of all the other columns.
Suppose my dataframe had columns "a", "b", and "c". I know I can do this:
df.withColumn('total_col', df.a + df.b + df.c)
The problem is that I don't want to type out each column individually and add them, especially if I have a lot of columns. I want to be able to do this automatically or by specifying a list of column names that I want to add. Is there another way to do this?
This was not obvious. I see no row-based sum of the columns defined in the spark Dataframes API.
This can be done in a fairly simple way:
newdf = df.withColumn('total', sum(df[col] for col in df.columns))
df.columns
is supplied by pyspark as a list of strings giving all of the column names in the Spark Dataframe. For a different sum, you can supply any other list of column names instead.
I did not try this as my first solution because I wasn't certain how it would behave. But it works.
This is overly complicated, but works as well.
You can do this:
df.columns
to get a list of the names of the columnsWith python's reduce, some knowledge of how operator overloading works, and the pyspark code for columns here that becomes:
def column_add(a,b):
return a.__add__(b)
newdf = df.withColumn('total_col',
reduce(column_add, ( df[col] for col in df.columns ) ))
Note this is a python reduce, not a spark RDD reduce, and the parenthesis term in the second parameter to reduce requires the parenthesis because it is a list generator expression.
Tested, Works!
$ pyspark
>>> df = sc.parallelize([{'a': 1, 'b':2, 'c':3}, {'a':8, 'b':5, 'c':6}, {'a':3, 'b':1, 'c':0}]).toDF().cache()
>>> df
DataFrame[a: bigint, b: bigint, c: bigint]
>>> df.columns
['a', 'b', 'c']
>>> def column_add(a,b):
... return a.__add__(b)
...
>>> df.withColumn('total', reduce(column_add, ( df[col] for col in df.columns ) )).collect()
[Row(a=1, b=2, c=3, total=6), Row(a=8, b=5, c=6, total=19), Row(a=3, b=1, c=0, total=4)]