Partitioning by multiple columns in PySpark with columns in a list

Question

My question is similar to this thread: Partitioning by multiple columns in Spark SQL

but I'm working in Pyspark rather than Scala and I want to pass in my list of columns as a list. I want to do something like this:

column_list = ["col1","col2"]
win_spec = Window.partitionBy(column_list)

I can get the following to work:

win_spec = Window.partitionBy(col("col1"))

This also works:

col_name = "col1"
win_spec = Window.partitionBy(col(col_name))

And this also works:

win_spec = Window.partitionBy([col("col1"), col("col2")])

Answer 1

Convert column names to column expressions with a list comprehension [col(x) for x in column_list]:

from pyspark.sql.functions import col
column_list = ["col1","col2"]
win_spec = Window.partitionBy([col(x) for x in column_list])

Answer 2

Your first attempt should work.

Consider the following example:

import pyspark.sql.functions as f
from pyspark.sql import Window

df = sqlCtx.createDataFrame(
    [
        ("a", "apple", 1),
        ("a", "orange", 2),
        ("a", "orange", 3),
        ("b", "orange", 3),
        ("b", "orange", 5)
    ],
    ["name", "fruit","value"]
)
df.show()
#+----+------+-----+
#|name| fruit|value|
#+----+------+-----+
#|   a| apple|    1|
#|   a|orange|    2|
#|   a|orange|    3|
#|   b|orange|    3|
#|   b|orange|    5|
#+----+------+-----+

Suppose you wanted to calculate a fraction of the sum for each row, grouping by the first two columns:

cols = ["name", "fruit"]
w = Window.partitionBy(cols)
df.select(cols + [(f.col('value') / f.sum('value').over(w)).alias('fraction')]).show()

#+----+------+--------+
#|name| fruit|fraction|
#+----+------+--------+
#|   a| apple|     1.0|
#|   b|orange|   0.375|
#|   b|orange|   0.625|
#|   a|orange|     0.6|
#|   a|orange|     0.4|
#+----+------+--------+