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From "Bryan Cutler (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-22417) createDataFrame from a pandas.DataFrame reads datetime64 values as longs
Date Wed, 01 Nov 2017 22:42:00 GMT
Bryan Cutler created SPARK-22417:
------------------------------------

             Summary: 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|
+-------------------+
|1509411661000000000|
+-------------------+


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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