I am trying to convert a SAS proc transpose statement to pyspark in databricks. With the following data as a sample:
data = [{"duns":1234, "finc stress":100,"ver":6.0},{"duns":1234, "finc stress":125,"ver":7.0},{"duns":1234, "finc stress":135,"ver":7.1},{"duns":12345, "finc stress":125,"ver":7.6}]
I would expect the result to look like this
I tried using the pandas pivot_table() function with the following code however I ran into some performance issues with the size of the data:
tst = (df.pivot_table(index=['duns'], columns=['ver'], values='finc stress') .add_prefix('ver') .reset_index())
Is there a way to translate the PROC Transpose SAS logic to Pyspark instead of using pandas?
I am trying something like this but am getting an error
tst= sparkdf.groupBy('duns').pivot('ver').agg('finc_stress').withColumn('ver') AssertionError: all exprs should be Column --------------------------------------------------------------------------- AssertionError Traceback (most recent call last) <command-2507760044487307> in <module> 4 df = pd.DataFrame(data) # pandas 5 ----> 6 tst= sparkdf.groupBy('duns').pivot('ver').agg('finc_stress').withColumn('ver') 7 8 /databricks/spark/python/pyspark/sql/group.py in agg(self, *exprs) 115 else: 116 # Columns --> 117 assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column" 118 jdf = self._jgd.agg(exprs[0]._jc, 119 _to_seq(self.sql_ctx._sc, [c._jc for c in exprs[1:]])) AssertionError: all exprs should be Column
If you could help me out I would so appreciate it! Thank you so much.
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Answer
I don’t know how you create df from data but here is what I did:
import pyspark.pandas as ps df = ps.DataFrame(data) df['ver'] = df['ver'].astype('str')
Then your pandas code worked.
To use PySpark method, here is what I did:
sparkdf.groupBy('duns').pivot('ver').agg(F.first('finc stress'))