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https://issues.apache.org/jira/browse/SPARK-60655?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-60655:
-----------------------------------
Labels: pull-request-available (was: )
> pandas-on-Spark np.mod returns NaN for a finite value with a positive
> infinite divisor
> --------------------------------------------------------------------------------------
>
> Key: SPARK-60655
> URL: https://issues.apache.org/jira/browse/SPARK-60655
> 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
> Priority: Critical
> Labels: pull-request-available
>
> When using the pandas API on Spark (pyspark.pandas), applying np.mod to a
> float64 Series containing 1.0 with positive infinity as the divisor returns
> NaN. The equivalent Pandas/NumPy operation returns 1.0 for the same input.
> The example constructs a single float64 value and supplies the infinite
> divisor explicitly. Both queries complete successfully, but the
> pandas-on-Spark result introduces a missing value.
> {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.mod(t0['c0'], float('inf'))
> 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.mod(t0['c0'], float('inf'))
> print(result, flush=True)
> spark.stop()
> {code}
> {code:bash}
> Pandas:
> 0 1.0
> Name: c0, dtype: float64
> PySpark Pandas:
> 0 NaN
> Name: c0, dtype: float64
> {code}
> *Expected behavior*
> np.mod(1.0, float('inf')) should return 1.0. The result should remain a
> float64 Series containing the finite value, matching Pandas/NumPy.
> *Actual behavior*
> Pandas returns 1.0, while pandas-on-Spark returns NaN. Both result Series
> have dtype float64, so the difference concerns the computed value rather than
> its displayed dtype.
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