simplify timestamp precision issue

Author: reidy-p

doc/source/whatsnew/v0.23.0.txt

@@ -598,12 +598,12 @@ Returning a ``Series`` allows one to control the exact return structure and colu
 .. _whatsnew_0230.api_breaking.end_time:
 
 Time values in ``dt.end_time`` and ``to_timestamp(how='end')``
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The time values in ``Period`` and ``PeriodIndex`` objects are now adjusted
-appropriately when calling :attr:`Series.dt.end_time`, :attr:`Period.end_time`,
+The time values in :class:`Period` and :class:`PeriodIndex` objects are now set
+to '23:59:59.999999999' when calling :attr:`Series.dt.end_time`, :attr:`Period.end_time`,
 :attr:`PeriodIndex.end_time`, :func:`Period.to_timestamp()` with ``how='end'``,
-and :func:`PeriodIndex.to_timestamp()` with ``how='end'`` (:issue:`17157`)
+or :func:`PeriodIndex.to_timestamp()` with ``how='end'`` (:issue:`17157`)
 
 Previous Behavior:
 
@@ -615,22 +615,11 @@ Previous Behavior:
    In [4]: pd.Series(p).dt.end_time[0]
    Out[4]: Timestamp(2017-01-01 00:00:00)
 
-   In [5]: pd.Series(pi).dt.end_time[0]
+   In [5]: pi.end_time[0]
    Out[5]: Timestamp(2017-01-01 00:00:00)
 
-   In [6]: p.end_time
-   Out[6]: Timestamp(2017-01-01 23:59:59.999999999)
-
-   In [7]: pi.end_time[0]
-   Out[7]: Timestamp(2017-01-01 00:00:00)
-
-   In [8]: p.to_timestamp(how='end')
-   Out[8]: Timestamp(2017-01-01 00:00:00)
-
-   In [9]: pi.to_timestamp(how='end')[0]
-   Out[9]: Timestamp(2017-01-01 00:00:00)
-
-Current Behavior
+   In [6]: pi.to_timestamp(how='end')
+   Out[6]: Timestamp(2017-01-01 00:00:00)
 
 .. ipython:: python
 
@@ -639,15 +628,9 @@ Current Behavior
 
    pd.Series(p).dt.end_time[0]
 
-   pd.Series(pi).dt.end_time[0]
-
-   p.end_time
-
    pi.end_time[0]
 
-   p.to_timestamp(how='end')
-
-   pi.to_timestamp(how='end')[0]
+   pi.to_timestamp(how='end')
 
 
 Build Changes

pandas/core/indexes/datetimes.py

@@ -53,7 +53,7 @@
 import pandas.core.tools.datetimes as tools
 
 from pandas._libs import (lib, index as libindex, tslib as libts,
-                          join as libjoin, Timestamp, Timedelta)
+                          join as libjoin, Timestamp)
 from pandas._libs.tslibs import (timezones, conversion, fields, parsing,
                                  resolution as libresolution)
 
@@ -2545,28 +2545,14 @@ def _generate_regular_range(start, end, periods, offset):
         data = np.arange(b, e, stride, dtype=np.int64)
         data = DatetimeIndex._simple_new(data, None, tz=tz)
     else:
-        if isinstance(start, Timestamp):
-            start_date = start.to_pydatetime()
-        else:
-            start_date = start
-
-        if isinstance(end, Timestamp):
-            end_date = end.to_pydatetime()
-        else:
-            end_date = end
 
-        xdr = generate_range(start=start_date, end=end_date,
+        xdr = generate_range(start=start, end=end,
                              periods=periods, offset=offset)
 
         dates = list(xdr)
         # utc = len(dates) > 0 and dates[0].tzinfo is not None
         data = tools.to_datetime(dates)
 
-        # Add back in the lost nanoseconds
-        if isinstance(start, Timestamp) and isinstance(end, Timestamp):
-            if start.nanosecond == 999 and end.nanosecond == 999:
-                data = data + Timedelta(999, 'ns')
-
     return data
 
 

pandas/core/indexes/period.py

@@ -692,19 +692,7 @@ def to_timestamp(self, freq=None, how='start'):
         base, mult = _gfc(freq)
         new_data = self.asfreq(freq, how)
 
-        end = how == 'E'
-        if end:
-            indexer = np.where(new_data.notnull())
-            # move forward one period
-            new_data._values[indexer] += 1
-            ndarray_vals = new_data._ndarray_values
-            new_data = period.periodarr_to_dt64arr(ndarray_vals, base)
-            # subtract one nanosecond
-            new_data[indexer] -= 1
-        else:
-            ndarray_vals = new_data._ndarray_values
-            new_data = period.periodarr_to_dt64arr(ndarray_vals, base)
-
+        new_data = period.periodarr_to_dt64arr(new_data._ndarray_values, base)
         return DatetimeIndex(new_data, freq='infer', name=self.name)
 
     def _maybe_convert_timedelta(self, other):

pandas/tests/indexes/period/test_period.py

@@ -367,11 +367,13 @@ def test_periods_number_check(self):
             period_range('2011-1-1', '2012-1-1', 'B')
 
     def test_start_time(self):
+        # GH 17157
         index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31')
         expected_index = date_range('2016-01-01', end='2016-05-31', freq='MS')
         tm.assert_index_equal(index.start_time, expected_index)
 
     def test_end_time(self):
+        # GH 17157
         index = PeriodIndex(freq='M', start='2016-01-01', end='2016-05-31')
         expected_index = date_range('2016-01-01', end='2016-05-31', freq='M')
         expected_index = expected_index.shift(1, freq='D').shift(-1, freq='ns')