remove uses of (ts)?lib.(NaT|iNaT|Timestamp) (#23562)

pandas/core/generic.py

@@ -10,7 +10,7 @@
 import numpy as np
 import pandas as pd
 
-from pandas._libs import tslib, properties
+from pandas._libs import properties, Timestamp, iNaT
 from pandas.core.dtypes.common import (
     ensure_int64,
     ensure_object,
@@ -9273,9 +9273,9 @@ def describe_categorical_1d(data):
                     tz = data.dt.tz
                     asint = data.dropna().values.view('i8')
                     names += ['top', 'freq', 'first', 'last']
-                    result += [tslib.Timestamp(top, tz=tz), freq,
-                               tslib.Timestamp(asint.min(), tz=tz),
-                               tslib.Timestamp(asint.max(), tz=tz)]
+                    result += [Timestamp(top, tz=tz), freq,
+                               Timestamp(asint.min(), tz=tz),
+                               Timestamp(asint.max(), tz=tz)]
                 else:
                     names += ['top', 'freq']
                     result += [top, freq]
@@ -10613,7 +10613,7 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
                 issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):
             result = accum_func(y, axis)
             mask = isna(self)
-            np.putmask(result, mask, tslib.iNaT)
+            np.putmask(result, mask, iNaT)
         elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
             mask = isna(self)
             np.putmask(y, mask, mask_a)

pandas/core/indexes/period.py

@@ -22,11 +22,11 @@
 )
 from pandas.core.tools.datetimes import parse_time_string, DateParseError
 
-from pandas._libs import tslib, index as libindex
+from pandas._libs import index as libindex
 from pandas._libs.tslibs.period import (Period, IncompatibleFrequency,
                                         DIFFERENT_FREQ_INDEX)
 
-from pandas._libs.tslibs import resolution
+from pandas._libs.tslibs import resolution, NaT, iNaT
 
 from pandas.core.algorithms import unique1d
 import pandas.core.arrays.datetimelike as dtl
@@ -336,7 +336,7 @@ def _box_func(self):
         # places outside of indexes/period.py are calling this _box_func,
         # but passing data that's already boxed.
         def func(x):
-            if isinstance(x, Period) or x is tslib.NaT:
+            if isinstance(x, Period) or x is NaT:
                 return x
             else:
                 return Period._from_ordinal(ordinal=x, freq=self.freq)
@@ -726,7 +726,7 @@ def get_loc(self, key, method=None, tolerance=None):
                 raise KeyError(key)
 
             try:
-                ordinal = tslib.iNaT if key is tslib.NaT else key.ordinal
+                ordinal = iNaT if key is NaT else key.ordinal
                 if tolerance is not None:
                     tolerance = self._convert_tolerance(tolerance,
                                                         np.asarray(key))

pandas/tests/dtypes/test_inference.py

@@ -15,7 +15,7 @@
 import pytest
 
 import pandas as pd
-from pandas._libs import tslib, lib, missing as libmissing
+from pandas._libs import lib, iNaT, missing as libmissing
 from pandas import (Series, Index, DataFrame, Timedelta,
                     DatetimeIndex, TimedeltaIndex, Timestamp,
                     Panel, Period, Categorical, isna, Interval,
@@ -1263,7 +1263,7 @@ def test_nan_to_nat_conversions():
     }))
     df.iloc[3:6, :] = np.nan
     result = df.loc[4, 'B'].value
-    assert (result == tslib.iNaT)
+    assert (result == iNaT)
 
     s = df['B'].copy()
     s._data = s._data.setitem(indexer=tuple([slice(8, 9)]), value=np.nan)

pandas/tests/frame/test_constructors.py

@@ -711,7 +711,7 @@ def test_constructor_maskedarray_nonfloat(self):
         assert 1 == frame['A'][1]
         assert 2 == frame['C'][2]
 
-        # masked np.datetime64 stays (use lib.NaT as null)
+        # masked np.datetime64 stays (use NaT as null)
         mat = ma.masked_all((2, 3), dtype='M8[ns]')
         # 2-D input
         frame = DataFrame(mat, columns=['A', 'B', 'C'], index=[1, 2])

pandas/tests/indexes/datetimes/test_tools.py

@@ -13,7 +13,7 @@
 import pytz
 
 from pandas._libs import tslib
-from pandas._libs.tslibs import parsing
+from pandas._libs.tslibs import iNaT, parsing
 from pandas.compat import PY3, lmap
 from pandas.errors import OutOfBoundsDatetime
 import pandas.util._test_decorators as td
@@ -652,7 +652,7 @@ def test_unit(self, cache):
         with pytest.raises(ValueError):
             to_datetime([1], unit='D', format='%Y%m%d', cache=cache)
 
-        values = [11111111, 1, 1.0, tslib.iNaT, NaT, np.nan,
+        values = [11111111, 1, 1.0, iNaT, NaT, np.nan,
                   'NaT', '']
         result = to_datetime(values, unit='D', errors='ignore', cache=cache)
         expected = Index([11111111, Timestamp('1970-01-02'),
@@ -669,7 +669,7 @@ def test_unit(self, cache):
         with pytest.raises(tslib.OutOfBoundsDatetime):
             to_datetime(values, unit='D', errors='raise', cache=cache)
 
-        values = [1420043460000, tslib.iNaT, NaT, np.nan, 'NaT']
+        values = [1420043460000, iNaT, NaT, np.nan, 'NaT']
 
         result = to_datetime(values, errors='ignore', unit='s', cache=cache)
         expected = Index([1420043460000, NaT, NaT,
@@ -1104,7 +1104,7 @@ def test_string_na_nat_conversion(self, cache):
         expected = np.empty(4, dtype='M8[ns]')
         for i, val in enumerate(strings):
             if isna(val):
-                expected[i] = tslib.iNaT
+                expected[i] = iNaT
             else:
                 expected[i] = parse_date(val)
 
@@ -1145,7 +1145,7 @@ def test_string_na_nat_conversion(self, cache):
         for i in range(5):
             x = series[i]
             if isna(x):
-                expected[i] = tslib.iNaT
+                expected[i] = iNaT
             else:
                 expected[i] = to_datetime(x, cache=cache)
 
@@ -1420,10 +1420,10 @@ def test_parsers_nat(self):
         result2 = to_datetime('NaT')
         result3 = Timestamp('NaT')
         result4 = DatetimeIndex(['NaT'])[0]
-        assert result1 is tslib.NaT
-        assert result2 is tslib.NaT
-        assert result3 is tslib.NaT
-        assert result4 is tslib.NaT
+        assert result1 is NaT
+        assert result2 is NaT
+        assert result3 is NaT
+        assert result4 is NaT
 
     @pytest.mark.parametrize('cache', [True, False])
     def test_parsers_dayfirst_yearfirst(self, cache):

pandas/tests/indexes/period/test_ops.py

@@ -2,10 +2,8 @@
 import numpy as np
 import pytest
 
-import pandas._libs.tslib as tslib
-
 import pandas as pd
-from pandas import DatetimeIndex, Index, Period, PeriodIndex, Series
+from pandas import DatetimeIndex, Index, NaT, Period, PeriodIndex, Series
 from pandas.core.arrays import PeriodArray
 from pandas.tests.test_base import Ops
 import pandas.util.testing as tm
@@ -29,13 +27,13 @@ def test_ops_properties(self):
     def test_minmax(self):
 
         # monotonic
-        idx1 = pd.PeriodIndex([pd.NaT, '2011-01-01', '2011-01-02',
+        idx1 = pd.PeriodIndex([NaT, '2011-01-01', '2011-01-02',
                                '2011-01-03'], freq='D')
         assert idx1.is_monotonic
 
         # non-monotonic
-        idx2 = pd.PeriodIndex(['2011-01-01', pd.NaT, '2011-01-03',
-                               '2011-01-02', pd.NaT], freq='D')
+        idx2 = pd.PeriodIndex(['2011-01-01', NaT, '2011-01-03',
+                               '2011-01-02', NaT], freq='D')
         assert not idx2.is_monotonic
 
         for idx in [idx1, idx2]:
@@ -50,15 +48,15 @@ def test_minmax(self):
             # Return NaT
             obj = PeriodIndex([], freq='M')
             result = getattr(obj, op)()
-            assert result is tslib.NaT
+            assert result is NaT
 
-            obj = PeriodIndex([pd.NaT], freq='M')
+            obj = PeriodIndex([NaT], freq='M')
             result = getattr(obj, op)()
-            assert result is tslib.NaT
+            assert result is NaT
 
-            obj = PeriodIndex([pd.NaT, pd.NaT, pd.NaT], freq='M')
+            obj = PeriodIndex([NaT, NaT, NaT], freq='M')
             result = getattr(obj, op)()
-            assert result is tslib.NaT
+            assert result is NaT
 
     def test_numpy_minmax(self):
         pr = pd.period_range(start='2016-01-15', end='2016-01-20')
@@ -113,7 +111,7 @@ def test_value_counts_unique(self):
 
         idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 09:00',
                            '2013-01-01 09:00', '2013-01-01 08:00',
-                           '2013-01-01 08:00', pd.NaT], freq='H')
+                           '2013-01-01 08:00', NaT], freq='H')
 
         exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00'],
                               freq='H')
@@ -123,7 +121,7 @@ def test_value_counts_unique(self):
             tm.assert_series_equal(obj.value_counts(), expected)
 
         exp_idx = PeriodIndex(['2013-01-01 09:00', '2013-01-01 08:00',
-                               pd.NaT], freq='H')
+                               NaT], freq='H')
         expected = Series([3, 2, 1], index=exp_idx)
 
         for obj in [idx, Series(idx)]:
@@ -284,9 +282,9 @@ def test_order(self):
                             '2011-01-03', '2011-01-05'],
                            freq='D', name='idx2')
 
-        idx3 = PeriodIndex([pd.NaT, '2011-01-03', '2011-01-05',
-                            '2011-01-02', pd.NaT], freq='D', name='idx3')
-        exp3 = PeriodIndex([pd.NaT, pd.NaT, '2011-01-02', '2011-01-03',
+        idx3 = PeriodIndex([NaT, '2011-01-03', '2011-01-05',
+                            '2011-01-02', NaT], freq='D', name='idx3')
+        exp3 = PeriodIndex([NaT, NaT, '2011-01-02', '2011-01-03',
                             '2011-01-05'], freq='D', name='idx3')
 
         for idx, expected in [(idx1, exp1), (idx2, exp2), (idx3, exp3)]:
@@ -338,8 +336,8 @@ def test_repeat(self):
             tm.assert_index_equal(res, exp)
 
     def test_nat(self):
-        assert pd.PeriodIndex._na_value is pd.NaT
-        assert pd.PeriodIndex([], freq='M')._na_value is pd.NaT
+        assert pd.PeriodIndex._na_value is NaT
+        assert pd.PeriodIndex([], freq='M')._na_value is NaT
 
         idx = pd.PeriodIndex(['2011-01-01', '2011-01-02'], freq='D')
         assert idx._can_hold_na
@@ -460,10 +458,10 @@ def test_pi_comp_period_nat(self):
         f = lambda x: pd.Period('2011-03', freq='M') == x
         self._check(idx, f, exp)
 
-        f = lambda x: x == tslib.NaT
+        f = lambda x: x == NaT
         exp = np.array([False, False, False, False], dtype=np.bool)
         self._check(idx, f, exp)
-        f = lambda x: tslib.NaT == x
+        f = lambda x: NaT == x
         self._check(idx, f, exp)
 
         f = lambda x: x != pd.Period('2011-03', freq='M')
@@ -472,10 +470,10 @@ def test_pi_comp_period_nat(self):
         f = lambda x: pd.Period('2011-03', freq='M') != x
         self._check(idx, f, exp)
 
-        f = lambda x: x != tslib.NaT
+        f = lambda x: x != NaT
         exp = np.array([True, True, True, True], dtype=np.bool)
         self._check(idx, f, exp)
-        f = lambda x: tslib.NaT != x
+        f = lambda x: NaT != x
         self._check(idx, f, exp)
 
         f = lambda x: pd.Period('2011-03', freq='M') >= x
@@ -486,11 +484,11 @@ def test_pi_comp_period_nat(self):
         exp = np.array([True, False, False, False], dtype=np.bool)
         self._check(idx, f, exp)
 
-        f = lambda x: x > tslib.NaT
+        f = lambda x: x > NaT
         exp = np.array([False, False, False, False], dtype=np.bool)
         self._check(idx, f, exp)
 
-        f = lambda x: tslib.NaT >= x
+        f = lambda x: NaT >= x
         exp = np.array([False, False, False, False], dtype=np.bool)
         self._check(idx, f, exp)