Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18664#discussion_r144168006
--- Diff: python/pyspark/sql/types.py ---
@@ -1619,11 +1619,47 @@ 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:
+ arrow_type = pa.timestamp('us', tz='UTC')
else:
raise TypeError("Unsupported type in conversion to Arrow: " +
str(dt))
return arrow_type
+def _check_localize_series_timestamps(s):
+ from pandas.types.common import is_datetime64_dtype
+ # TODO: handle nested timestamps?
+ if is_datetime64_dtype(s.dtype):
+ # TODO: pyarrow.Column.to_pandas keeps data in UTC but removes
timezone
+ return
s.dt.tz_localize('UTC').dt.tz_convert('tzlocal()').dt.tz_localize(None)
+ else:
+ return s
+
+
+def _check_localize_dataframe_timestamps(df):
+ from pandas.types.common import is_datetime64tz_dtype
+ for column, series in df.iteritems():
+ # TODO: handle nested timestamps?
+ if is_datetime64tz_dtype(series.dtype):
+ df[column] =
series.dt.tz_convert('tzlocal()').dt.tz_localize(None)
+ return df
+
+
+def _utc_normalize_series_timestamps(s):
--- End diff --
I think maybe "convert_to_spark_internal" could be a better name for this
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