[
https://issues.apache.org/jira/browse/SPARK-60656?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Ruifeng Zheng reassigned SPARK-60656:
-------------------------------------
Assignee: asddfl
> pandas-on-Spark np.maximum fails to propagate a missing operand generated by
> log
> --------------------------------------------------------------------------------
>
> Key: SPARK-60656
> URL: https://issues.apache.org/jira/browse/SPARK-60656
> Project: Spark
> Issue Type: Bug
> Components: Pandas API on Spark, PySpark
> Affects Versions: 4.2.0
> Environment: Platform: Ubuntu 24.04 Linux-6.17.0-29-generic x86_64
> Python: 3.11.17
> pyspark 4.2.0
> findspark 2.0.1
> pandas 3.0.6
> numpy 2.4.6
> Reporter: asddfl
> Assignee: asddfl
> Priority: Major
> Labels: pull-request-available
>
> When using the pandas API on Spark (pyspark.pandas), np.maximum does not
> preserve the missing result of np.log(-1.0). Pandas/NumPy returns NaN, while
> pandas-on-Spark returns the other operand, -1.0.
> The example contains one float64 value and computes the missing operand at
> runtime. Both queries complete successfully, but the result changes from
> missing to a finite number.
> {code:python}
> import pandas as pd
> import numpy as np
> from pyspark.sql import SparkSession
> import pyspark.pandas as ps
> t0 = pd.DataFrame({'c0': [-1.0]})
> print("Pandas:", flush=True)
> result = np.maximum(np.log(t0['c0']), t0['c0'])
> print(result, flush=True)
> spark = (
> SparkSession.builder
> .master("local[2]")
> .config("spark.sql.ansi.enabled", "false")
> .config("spark.sql.shuffle.partitions", "2")
> .getOrCreate()
> )
> ps.set_option("compute.ops_on_diff_frames", True)
> t0 = ps.DataFrame({'c0': [-1.0]})
> print("PySpark Pandas:", flush=True)
> result = np.maximum(np.log(t0['c0']), t0['c0'])
> print(result, flush=True)
> spark.stop()
> {code}
> {code:bash}
> Pandas:
> 0 NaN
> Name: c0, dtype: float64
> PySpark Pandas:
> 0 -1.0
> Name: c0, dtype: float64
> {code}
> *Expected behavior*
> np.maximum(np.log(-1.0), -1.0) should propagate the missing operand and
> return NaN, matching Pandas/NumPy.
> *Actual behavior*
> Pandas prints NaN. pandas-on-Spark prints -1.0. Both result Series have dtype
> float64.
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