[jira] [Assigned] (SPARK-23290) inadvertent change in handling of DateType when converting to pandas dataframe
[
https://issues.apache.org/jira/browse/SPARK-23290?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Hyukjin Kwon reassigned SPARK-23290:
Assignee: Takuya Ueshin
> inadvertent change in handling of DateType when converting to pandas dataframe
> --
>
> Key: SPARK-23290
> URL: https://issues.apache.org/jira/browse/SPARK-23290
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Andre Menck
>Assignee: Takuya Ueshin
>Priority: Blocker
> Fix For: 2.3.0
>
>
> In [this
> PR|https://github.com/apache/spark/pull/18664/files#diff-6fc344560230bf0ef711bb9b5573f1faR1968]
> there was a change in how `DateType` is being returned to users (line 1968
> in dataframe.py). This can cause client code to fail, as in the following
> example from a python terminal:
> {code:python}
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> 02015-01-01
> Name: date, dtype: object
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'] = pd.to_datetime(pdf['date'])
> >>> pdf.dtypes
> datedatetime64[ns]
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> Traceback (most recent call last):
> File "", line 1, in
> File
> "/Users/amenck/anaconda2/lib/python2.7/site-packages/pandas/core/series.py",
> line 2355, in apply
> mapped = lib.map_infer(values, f, convert=convert_dtype)
> File "pandas/_libs/src/inference.pyx", line 1574, in
> pandas._libs.lib.map_infer
> File "", line 1, in
> TypeError: strptime() argument 1 must be string, not Timestamp
> >>>
> {code}
> Above we show both the old behavior (returning an "object" col) and the new
> behavior (returning a datetime column). Since there may be user code relying
> on the old behavior, I'd suggest reverting this specific part of this change.
> Also note that the NOTE on the docstring for the "_to_corrected_pandas_type"
> seems to be off, referring to the old behavior and not the current one.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23290) inadvertent change in handling of DateType when converting to pandas dataframe
[
https://issues.apache.org/jira/browse/SPARK-23290?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23290:
Assignee: Apache Spark
> inadvertent change in handling of DateType when converting to pandas dataframe
> --
>
> Key: SPARK-23290
> URL: https://issues.apache.org/jira/browse/SPARK-23290
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Andre Menck
>Assignee: Apache Spark
>Priority: Blocker
>
> In [this
> PR|https://github.com/apache/spark/pull/18664/files#diff-6fc344560230bf0ef711bb9b5573f1faR1968]
> there was a change in how `DateType` is being returned to users (line 1968
> in dataframe.py). This can cause client code to fail, as in the following
> example from a python terminal:
> {code:python}
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> 02015-01-01
> Name: date, dtype: object
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'] = pd.to_datetime(pdf['date'])
> >>> pdf.dtypes
> datedatetime64[ns]
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> Traceback (most recent call last):
> File "", line 1, in
> File
> "/Users/amenck/anaconda2/lib/python2.7/site-packages/pandas/core/series.py",
> line 2355, in apply
> mapped = lib.map_infer(values, f, convert=convert_dtype)
> File "pandas/_libs/src/inference.pyx", line 1574, in
> pandas._libs.lib.map_infer
> File "", line 1, in
> TypeError: strptime() argument 1 must be string, not Timestamp
> >>>
> {code}
> Above we show both the old behavior (returning an "object" col) and the new
> behavior (returning a datetime column). Since there may be user code relying
> on the old behavior, I'd suggest reverting this specific part of this change.
> Also note that the NOTE on the docstring for the "_to_corrected_pandas_type"
> seems to be off, referring to the old behavior and not the current one.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Assigned] (SPARK-23290) inadvertent change in handling of DateType when converting to pandas dataframe
[
https://issues.apache.org/jira/browse/SPARK-23290?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Apache Spark reassigned SPARK-23290:
Assignee: (was: Apache Spark)
> inadvertent change in handling of DateType when converting to pandas dataframe
> --
>
> Key: SPARK-23290
> URL: https://issues.apache.org/jira/browse/SPARK-23290
> Project: Spark
> Issue Type: Bug
> Components: PySpark
>Affects Versions: 2.3.0
>Reporter: Andre Menck
>Priority: Blocker
>
> In [this
> PR|https://github.com/apache/spark/pull/18664/files#diff-6fc344560230bf0ef711bb9b5573f1faR1968]
> there was a change in how `DateType` is being returned to users (line 1968
> in dataframe.py). This can cause client code to fail, as in the following
> example from a python terminal:
> {code:python}
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> 02015-01-01
> Name: date, dtype: object
> >>> pdf = pd.DataFrame([['2015-01-01',1]], columns=['date', 'num'])
> >>> pdf.dtypes
> dateobject
> num int64
> dtype: object
> >>> pdf['date'] = pd.to_datetime(pdf['date'])
> >>> pdf.dtypes
> datedatetime64[ns]
> num int64
> dtype: object
> >>> pdf['date'].apply(lambda d: dt.datetime.strptime(d, '%Y-%m-%d').date() )
> Traceback (most recent call last):
> File "", line 1, in
> File
> "/Users/amenck/anaconda2/lib/python2.7/site-packages/pandas/core/series.py",
> line 2355, in apply
> mapped = lib.map_infer(values, f, convert=convert_dtype)
> File "pandas/_libs/src/inference.pyx", line 1574, in
> pandas._libs.lib.map_infer
> File "", line 1, in
> TypeError: strptime() argument 1 must be string, not Timestamp
> >>>
> {code}
> Above we show both the old behavior (returning an "object" col) and the new
> behavior (returning a datetime column). Since there may be user code relying
> on the old behavior, I'd suggest reverting this specific part of this change.
> Also note that the NOTE on the docstring for the "_to_corrected_pandas_type"
> seems to be off, referring to the old behavior and not the current one.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
-
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
