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implement _local_timestamps in DatetimeArrayMixin #21721

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Merged
merged 3 commits into from
Jul 5, 2018
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implement _local_timestamps in DatetimeArrayMixin #21721

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989806d
implement _local_timestamps
jbrockmendel Jul 4, 2018
8d20e09
docstring, comment
jbrockmendel Jul 4, 2018
70e4047
Merge branch 'master' of https://github.com/pandas-dev/pandas into lo…
jbrockmendel Jul 5, 2018
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22 changes: 21 additions & 1 deletion pandas/core/arrays/datetimes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,11 @@
import warnings

import numpy as np
from pytz import utc

from pandas._libs import tslib
from pandas._libs.tslib import Timestamp, NaT, iNaT
from pandas._libs.tslibs import timezones
from pandas._libs.tslibs import conversion, timezones

from pandas.util._decorators import cache_readonly

Expand Down Expand Up @@ -110,6 +111,25 @@ def _sub_datelike_dti(self, other):
new_values[mask] = iNaT
return new_values.view('timedelta64[ns]')

# -----------------------------------------------------------------
# Timezone Conversion and Localization Methods

def _local_timestamps(self):
"""
Convert to an i8 (unix-like nanosecond timestamp) representation
while keeping the local timezone and not using UTC.
This is used to calculate time-of-day information as if the timestamps
were timezone-naive.
"""
values = self.asi8
indexer = values.argsort()
result = conversion.tz_convert(values.take(indexer), utc, self.tz)
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Does this only make sense if tz_convert requires its input to be sorted? I don't think it does.

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yeah not sure why that indexer is there

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In the next pass I'll see if this can be simplified.


n = len(indexer)
reverse = np.empty(n, dtype=np.int_)
reverse.put(indexer, np.arange(n))
return result.take(reverse)

# ----------------------------------------------------------------
# Conversion Methods - Vectorized analogues of Timedelta methods

Expand Down
9 changes: 1 addition & 8 deletions pandas/core/indexes/datetimes.py
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Expand Up @@ -603,14 +603,7 @@ def _local_timestamps(self):
if self.is_monotonic:
return conversion.tz_convert(self.asi8, utc, self.tz)
else:
values = self.asi8
indexer = values.argsort()
result = conversion.tz_convert(values.take(indexer), utc, self.tz)

n = len(indexer)
reverse = np.empty(n, dtype=np.int_)
reverse.put(indexer, np.arange(n))
return result.take(reverse)
return DatetimeArrayMixin._local_timestamps(self)

@classmethod
def _simple_new(cls, values, name=None, freq=None, tz=None,
Expand Down