Github user icexelloss commented on a diff in the pull request:
https://github.com/apache/spark/pull/20537#discussion_r167266079
--- Diff: python/pyspark/sql/types.py ---
@@ -1744,8 +1744,27 @@ def _check_series_convert_timestamps_internal(s,
timezone):
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
# TODO: handle nested timestamps, such as ArrayType(TimestampType())?
if is_datetime64_dtype(s.dtype):
+ # tz_localize with ambiguous=False has the same behavior of
pytz.localize
+ # >>> import datetime
+ # >>> import pandas as pd
+ # >>> import pytz
+ # >>>
+ # >>> t = datetime.datetime(2015, 11, 1, 1, 23, 24)
+ # >>> ts = pd.Series([t])
+ # >>> tz = pytz.timezone('America/New_York')
+ # >>>
+ # >>> ts.dt.tz_localize(tz, ambiguous=False)
+ # 0 2015-11-01 01:23:24-05:00
+ # dtype: datetime64[ns, America/New_York]
+ # >>>
+ # >>> ts.dt.tz_localize(tz, ambiguous=True)
+ # 0 2015-11-01 01:23:24-04:00
+ # dtype: datetime64[ns, America/New_York]
+ # >>>
+ # >>> str(tz.localize(t))
+ # '2015-11-01 01:23:24-05:00'
--- End diff --
I add comment to explain this.
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