Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18664#discussion_r146636005
--- Diff: python/pyspark/sql/types.py ---
@@ -1619,11 +1619,33 @@ def to_arrow_type(dt):
arrow_type = pa.decimal(dt.precision, dt.scale)
elif type(dt) == StringType:
arrow_type = pa.string()
+ elif type(dt) == DateType:
+ arrow_type = pa.date32()
+ elif type(dt) == TimestampType:
+ # Timestamps should be in UTC, JVM Arrow timestamps require a
timezone to be read
+ arrow_type = pa.timestamp('us', tz='UTC')
else:
raise TypeError("Unsupported type in conversion to Arrow: " +
str(dt))
return arrow_type
+def _check_dataframe_localize_timestamps(df):
+ """ Convert timezone aware timestamps to timezone-naive in local time
+ """
+ from pandas.api.types import is_datetime64tz_dtype
+ for column, series in df.iteritems():
+ # TODO: handle nested timestamps, such as
ArrayType(TimestampType())?
+ if is_datetime64tz_dtype(series.dtype):
+ df[column] =
series.dt.tz_convert('tzlocal()').dt.tz_localize(None)
+ return df
+
+
+def _series_convert_timestamps_internal(s):
+ """ Convert a tz-naive timestamp in local tz to UTC normalized for
Spark internal storage
+ """
+ return s.dt.tz_localize('tzlocal()').dt.tz_convert('UTC')
--- End diff --
Yeah, you're right. I figured we are already checking that it is a
timestamp type, but it's true the user could have created tz-aware timestamps
so we need to check.
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