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https://issues.apache.org/jira/browse/SPARK-22417?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16245204#comment-16245204
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Apache Spark commented on SPARK-22417:
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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|
> +-------------------+
> |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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