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https://issues.apache.org/jira/browse/SPARK-38833?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon resolved SPARK-38833.
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Fix Version/s: 3.3.0
Resolution: Fixed
Issue resolved by pull request 36120
[https://github.com/apache/spark/pull/36120]
> PySpark applyInPandas should allow to return empty DataFrame without columns
> ----------------------------------------------------------------------------
>
> Key: SPARK-38833
> URL: https://issues.apache.org/jira/browse/SPARK-38833
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 3.4.0
> Reporter: Enrico Minack
> Assignee: Enrico Minack
> Priority: Major
> Fix For: 3.3.0
>
>
> Currently, returning an empty Pandas DataFrame from {{applyInPandas}} raises
> an error:
> {noformat}
> RuntimeError: Number of columns of the returned pandas.DataFrame doesn't
> match specified schema. Expected: 2 Actual: 0
> {noformat}
> Here is an example:
> {code}
> import pandas as pd
> from pyspark.sql.functions import pandas_udf, ceil
> df = spark.createDataFrame(
> [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
> ("id", "v"))
> def mean_func(key, pdf):
> if key == (1,):
> return pd.DataFrame([])
> else:
> return pd.DataFrame([key + (pdf.v.mean(),)])
> df.groupby('id').applyInPandas(mean_func, schema="id long, v double").show()
> {code}
> Since the schema is defined when calling {{applyInPandas()}}, it looks
> redundant to define the columns when returning an empty {{pd.DataFrame}}.
> Returning a non-empty DataFrame does not require defining columns, so
> returning an empty DataFrame shouldn't require that either.
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