Filter dictionary in pyspark with key names

Given a dictionary like column in a dataset, I want to grab the value from a key given that the value from another key is satisfied.

Example: Say I have a column ‘statistics’ in a dataset, where each data row looks as:

array
0: {"hair": "black", "eye": "white", "metric": "feet"}
1: {"hair": "blue", "eye": "white", "metric": "m"}
2: {"hair": "red", "eye": "brown", "metric": "feet"}
3: {"hair": "yellow", "eye": "white", "metric": "cm"}

I want to get the value of ‘eye’ whenever hair is ‘black’

I tried:

select
statistics.eye("*").filter(statistics.hair, x -> x == 'black')
from arrayData

but it gives an error and I’m unable to grab the value for eye, please assist.

Answer

I eventually figured it out without having to first convert to a dataframe.

The aggregate command allows you to grab the value from a key given that the value from another key is satisfied. For this instance, the command below will suffice:

select 
aggregate(statistics,"",(agg,item)->concat(agg,CASE WHEN item.hair == 'black' THEN item.eye ELSE "" END)) as EyeColor
from arrayData

For more details on how to use this function, see here