BryanCutler commented on a change in pull request #33980:
URL: https://github.com/apache/spark/pull/33980#discussion_r718964803
##########
File path: python/pyspark/sql/pandas/types.py
##########
@@ -296,7 +337,34 @@ def _check_series_convert_timestamps_localize(s,
from_timezone, to_timezone):
return s
-def _check_series_convert_timestamps_local_tz(s, timezone):
+def __handle_array_of_timestamps(series, to_tz, from_tz=None):
+ """
+
+ :param series: Pandas series
+ :param to_tz: to timezone
+ :param from_tz: from time zone
+ :return: return series respecting timezone
+ """
+ from pandas.api.types import is_datetime64tz_dtype, is_datetime64_dtype
+ import pandas as pd
+ from pandas import Series
+ data_after_conversion = []
+ for data in series:
Review comment:
So this is iterating over each timestamp array in the series, applying
conversions each time and then building a new series back from a list? That
seems pretty inefficient and looks to be a lot of specialized conversion going
on here for just arrays of timestamps.
##########
File path: python/pyspark/sql/pandas/types.py
##########
@@ -253,14 +278,29 @@ def _check_series_convert_timestamps_internal(s,
timezone):
# >>> str(tz.localize(t))
# '2015-11-01 01:30:00-05:00'
tz = timezone or _get_local_timezone()
- return s.dt.tz_localize(tz, ambiguous=False).dt.tz_convert('UTC')
+ data = s.dt.tz_localize(tz, ambiguous=False).dt.tz_convert('UTC')
+ return __modified_series(data, is_array)
elif is_datetime64tz_dtype(s.dtype):
- return s.dt.tz_convert('UTC')
+ data = s.dt.tz_convert('UTC')
+ return __modified_series(data, is_array)
else:
- return s
+ return __modified_series(s, is_array)
+
+
+def __modified_series(data, is_array):
+ """
+ :param data: Converted data
+ :param is_array: If the input data type is type of array
+ :return: If input type is array ,then return series with array of data
+ else return series as it is.
+ """
+ from pandas import Series
+ if is_array:
+ return Series([data])
Review comment:
Sorry, I don't quite understand what this function is doing, it looks
like it's making a Series with 1 array?
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