pyspark - assign non-null columns to new columns - apache-spark

Question

I have a dataframe of the following scheme in pyspark:
user_id datadate page_1.A page_1.B page_1.C page_2.A page_2.B \
0 111 20220203 NaN NaN NaN NaN NaN
1 222 20220203 5 5 5 5.0 5.0
2 333 20220203 3 3 3 3.0 3.0
page_2.C page_3.A page_3.B page_3.C
0 NaN 1.0 1.0 2.0
1 5.0 NaN NaN NaN
2 4.0 NaN NaN NaN
So it contains columns like user_id, datadate, and few columns for each page (got 3 pages), which are the result of 2 joins. In this example, i have page_1, page_2, page_3, and each has 3 columns: A,B,C. Additionally, for each page columns, for each row, they will either be all null or all full, like in my example.
I don't care about the values of each of the columns per page, I just want to get for each row, the [A,B,C] values that are not null.
example for a wanted result table:
user_id datadate A B C
0 111 20220203 1 1 2
1 222 20220203 5 5 5
2 333 20220203 3 3 3
so the logic will be something like:
df[A] = page_1.A or page_2.A or page_3.A, whichever is not null
df[B] = page_1.B or page_2.B or page_3.B, whichever is not null
df[C] = page_1.C or page_2.C or page_3.C, whichever is not null
for all of the rows..
and of course, I would like to do it in an efficient way.
Thanks a lot.

Answer 1

You can use the sql functions greatest to extract the greatest values in a list of columns.
You can find the documentation here: https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.functions.greatest.html
from pyspark.sql import functions as F
(df.withColumn('A', F.greates(F.col('page_1.A'), F.col('page_2.A), F.col('page_3.A'))
.withColumn('B', F.greates(F.col('page_1.B'), F.col('page_2.B), F.col('page_3.B'))
.select('userid', 'datadate', 'A', 'B'))