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https://issues.apache.org/jira/browse/SPARK-36143?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xinrong Meng updated SPARK-36143:
---------------------------------
Description:
{code:java}
>>> pser = pd.Series([1, 2, np.nan], dtype=float)
>>> psser = ps.from_pandas(pser)
>>> pser.astype(int)
...
ValueError: Cannot convert non-finite values (NA or inf) to integer
>>> psser.astype(int)
0 1.0
1 2.0
2 NaN
dtype: float64
{code}
As shown above, astype of Series with fractional missing values doesn't behave
the same as pandas, we ought to adjust that.
was:
{code:java}
>>> pser = pd.Series([1, 2, np.nan], dtype=float)
>>> psser = ps.from_pandas(pser)
>>> pser.astype(int)
...
ValueError: Cannot convert non-finite values (NA or inf) to integer
>>> psser.astype(int)
0 1.0
1 2.0
2 NaN
dtype: float64
{code}
As shown above, astype of Series with missing values doesn't behave the same as
pandas, we ought to adjust that.
> Adjust `astype` of fractional Series with missing values to follow pandas
> -------------------------------------------------------------------------
>
> Key: SPARK-36143
> URL: https://issues.apache.org/jira/browse/SPARK-36143
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark
> Affects Versions: 3.2.0
> Reporter: Xinrong Meng
> Priority: Major
>
> {code:java}
> >>> pser = pd.Series([1, 2, np.nan], dtype=float)
> >>> psser = ps.from_pandas(pser)
> >>> pser.astype(int)
> ...
> ValueError: Cannot convert non-finite values (NA or inf) to integer
> >>> psser.astype(int)
> 0 1.0
> 1 2.0
> 2 NaN
> dtype: float64
> {code}
> As shown above, astype of Series with fractional missing values doesn't
> behave the same as pandas, we ought to adjust that.
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