[jira] [Commented] (SPARK-22417) createDataFrame from a pandas.DataFrame reads datetime64 values as longs

2017-11-08 Thread Apache Spark (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-22417?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16245204#comment-16245204
 ] 

Apache Spark commented on SPARK-22417:
--

User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/19704

> createDataFrame from a pandas.DataFrame reads datetime64 values as longs
> 
>
> Key: SPARK-22417
> URL: https://issues.apache.org/jira/browse/SPARK-22417
> Project: Spark
>  Issue Type: Bug
>  Components: PySpark
>Affects Versions: 2.2.0
>Reporter: Bryan Cutler
>Assignee: Bryan Cutler
> Fix For: 2.2.1, 2.3.0
>
>
> When trying to create a Spark DataFrame from an existing Pandas DataFrame 
> using {{createDataFrame}}, columns with datetime64 values are converted as 
> long values.  This is only when the schema is not specified.  
> {code}
> In [2]: import pandas as pd
>...: from datetime import datetime
>...: 
> In [3]: pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)]})
> In [4]: df = spark.createDataFrame(pdf)
> In [5]: df.show()
> +---+
> | ts|
> +---+
> |15094116610|
> +---+
> In [6]: df.schema
> Out[6]: StructType(List(StructField(ts,LongType,true)))
> {code}
> Spark should interpret a datetime64\[D\] value to DateType and other 
> datetime64 values to TImestampType.



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[jira] [Commented] (SPARK-22417) createDataFrame from a pandas.DataFrame reads datetime64 values as longs

2017-11-03 Thread Apache Spark (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-22417?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16237772#comment-16237772
 ] 

Apache Spark commented on SPARK-22417:
--

User 'BryanCutler' has created a pull request for this issue:
https://github.com/apache/spark/pull/19646

> createDataFrame from a pandas.DataFrame reads datetime64 values as longs
> 
>
> Key: SPARK-22417
> URL: https://issues.apache.org/jira/browse/SPARK-22417
> Project: Spark
>  Issue Type: Bug
>  Components: PySpark
>Affects Versions: 2.2.0
>Reporter: Bryan Cutler
>Priority: Normal
>
> When trying to create a Spark DataFrame from an existing Pandas DataFrame 
> using {{createDataFrame}}, columns with datetime64 values are converted as 
> long values.  This is only when the schema is not specified.  
> {code}
> In [2]: import pandas as pd
>...: from datetime import datetime
>...: 
> In [3]: pdf = pd.DataFrame({"ts": [datetime(2017, 10, 31, 1, 1, 1)]})
> In [4]: df = spark.createDataFrame(pdf)
> In [5]: df.show()
> +---+
> | ts|
> +---+
> |15094116610|
> +---+
> In [6]: df.schema
> Out[6]: StructType(List(StructField(ts,LongType,true)))
> {code}
> Spark should interpret a datetime64\[D\] value to DateType and other 
> datetime64 values to TImestampType.



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