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Posted to issues@spark.apache.org by "Nicolas Renkamp (Jira)" <ji...@apache.org> on 2020/02/26 16:27:00 UTC
[jira] [Updated] (SPARK-30961) Arrow enabled: to_pandas with date
column fails
[ https://issues.apache.org/jira/browse/SPARK-30961?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nicolas Renkamp updated SPARK-30961:
------------------------------------
Description:
Hi,
there seems to be a bug in the arrow enabled to_pandas conversion from spark dataframe to pandas dataframe when the dataframe has a column of type DateType. Here is a minimal example to reproduce the issue:
{code:java}
spark = SparkSession.builder.getOrCreate()
is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
print("Arrow optimization is enabled: " + is_arrow_enabled)
spark_df = spark.createDataFrame(
[['2019-12-06']], 'created_at: string') \
.withColumn('created_at', F.to_date('created_at'))
# works
spark_df.toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", 'true')
is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
print("Arrow optimization is enabled: " + is_arrow_enabled)
# raises AttributeError: Can only use .dt accessor with datetimelike values
# series is still of type object, .dt does not exist
spark_df.toPandas(){code}
A fix would be to modify the _check_series_convert_date function in pyspark.sql.types to:
{code:java}
def _check_series_convert_date(series, data_type):
"""
Cast the series to datetime.date if it's a date type, otherwise returns the original series. :param series: pandas.Series
:param data_type: a Spark data type for the series
"""
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version() from pandas import to_datetime
if type(data_type) == DateType:
return to_datetime(series).dt.date
else:
return series
{code}
Let me know if I should prepare a Pull Request for the 2.4.5 branch.
I have not tested the behavior on master branch.
Thanks,
Nicolas
was:
Hi,
there seems to be a bug in the arrow enabled to_pandas conversion from spark dataframe to pandas dataframe when the dataframe has a column of type DateType. Here is a minimal example to reproduce the issue:
{code:java}
spark = SparkSession.builder.getOrCreate()
is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
print("Arrow optimization is enabled: " + is_arrow_enabled)
spark_df = spark.createDataFrame(
[['2019-12-06']], 'created_at: string') \
.withColumn('created_at', F.to_date('created_at'))
# works
spark_df.toPandas()
spark.conf.set("spark.sql.execution.arrow.enabled", 'true')
is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
print("Arrow optimization is enabled: " + is_arrow_enabled)
# raises AttributeError
spark_df.toPandas(){code}
A fix would be to modify the _check_series_convert_date function in pyspark.sql.types to:
{code:java}
def _check_series_convert_date(series, data_type):
"""
Cast the series to datetime.date if it's a date type, otherwise returns the original series. :param series: pandas.Series
:param data_type: a Spark data type for the series
"""
from pyspark.sql.utils import require_minimum_pandas_version
require_minimum_pandas_version() from pandas import to_datetime
if type(data_type) == DateType:
return to_datetime(series).dt.date
else:
return series
{code}
Let me know if I should prepare a Pull Request for the 2.4.5 branch.
I have not tested the behavior on master branch.
Thanks,
Nicolas
> Arrow enabled: to_pandas with date column fails
> -----------------------------------------------
>
> Key: SPARK-30961
> URL: https://issues.apache.org/jira/browse/SPARK-30961
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.4.5
> Environment: Apache Spark 2.4.5
> Reporter: Nicolas Renkamp
> Priority: Major
> Labels: ready-to-commit
>
> Hi,
> there seems to be a bug in the arrow enabled to_pandas conversion from spark dataframe to pandas dataframe when the dataframe has a column of type DateType. Here is a minimal example to reproduce the issue:
> {code:java}
> spark = SparkSession.builder.getOrCreate()
> is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
> print("Arrow optimization is enabled: " + is_arrow_enabled)
> spark_df = spark.createDataFrame(
> [['2019-12-06']], 'created_at: string') \
> .withColumn('created_at', F.to_date('created_at'))
> # works
> spark_df.toPandas()
> spark.conf.set("spark.sql.execution.arrow.enabled", 'true')
> is_arrow_enabled = spark.conf.get("spark.sql.execution.arrow.enabled")
> print("Arrow optimization is enabled: " + is_arrow_enabled)
> # raises AttributeError: Can only use .dt accessor with datetimelike values
> # series is still of type object, .dt does not exist
> spark_df.toPandas(){code}
> A fix would be to modify the _check_series_convert_date function in pyspark.sql.types to:
> {code:java}
> def _check_series_convert_date(series, data_type):
> """
> Cast the series to datetime.date if it's a date type, otherwise returns the original series. :param series: pandas.Series
> :param data_type: a Spark data type for the series
> """
> from pyspark.sql.utils import require_minimum_pandas_version
> require_minimum_pandas_version() from pandas import to_datetime
> if type(data_type) == DateType:
> return to_datetime(series).dt.date
> else:
> return series
> {code}
> Let me know if I should prepare a Pull Request for the 2.4.5 branch.
> I have not tested the behavior on master branch.
>
> Thanks,
> Nicolas
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