Details
Minor Description
What I am trying to do: Group data based on time intervals (e.g., 15 days window) and perform some operations on dataframe using (pandas) UDFs. I don't know if there is a better/cleaner way to do it.
Below is the sample code that I tried and error message I am getting.
df = sparkSession.createDataFrame([(17.00, "2018-03-10T15:27:18+00:00"), (13.00, "2018-03-11T12:27:18+00:00"), (25.00, "2018-03-12T11:27:18+00:00"), (20.00, "2018-03-13T15:27:18+00:00"), (17.00, "2018-03-14T12:27:18+00:00"), (99.00, "2018-03-15T11:27:18+00:00"), (156.00, "2018-03-22T11:27:18+00:00"), (17.00, "2018-03-31T11:27:18+00:00"), (25.00, "2018-03-15T11:27:18+00:00"), (25.00, "2018-03-16T11:27:18+00:00") ], ["id", "ts"]) df = df.withColumn('ts', df.ts.cast('timestamp')) schema = StructType([ StructField("id", IntegerType()), StructField("ts", TimestampType()) ]) @pandas_udf(schema, PandasUDFType.GROUPED_MAP) def some_udf(df): # some computation return df df.groupby('id', F.window("ts", "15 days")).apply(some_udf).show()
This throws following exception:
TypeError: Unsupported type in conversion from Arrow: struct<start: timestamp[us, tz=America/Chicago], end: timestamp[us, tz=America/Chicago]>
However, if I use builtin agg method then it works all fine. For example,
df.groupby('id', F.window("ts", "15 days")).mean().show(truncate=False)
Output
+-----+------------------------------------------+-------+ |id |window |avg(id)| +-----+------------------------------------------+-------+ |13.0 |[2018-03-05 00:00:00, 2018-03-20 00:00:00]|13.0 | |17.0 |[2018-03-20 00:00:00, 2018-04-04 00:00:00]|17.0 | |156.0|[2018-03-20 00:00:00, 2018-04-04 00:00:00]|156.0 | |99.0 |[2018-03-05 00:00:00, 2018-03-20 00:00:00]|99.0 | |20.0 |[2018-03-05 00:00:00, 2018-03-20 00:00:00]|20.0 | |17.0 |[2018-03-05 00:00:00, 2018-03-20 00:00:00]|17.0 | |25.0 |[2018-03-05 00:00:00, 2018-03-20 00:00:00]|25.0 | +-----+------------------------------------------+-------+
Attachments
Issue Links
- relates to
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SPARK-29402 Add tests for grouped map pandas_udf using window
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- Resolved
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