API: Index doesn't results in PeriodIndex if Period contains NaT

Author: sinhrks <sinhrks@gmail.com>

Closes #13664 from sinhrks/period_infer2 and squashes the following commits:

b208a9e [sinhrks] API: Index doesn't results in PeriodIndex if Period contains NaT

doc/source/whatsnew/v0.19.0.txt

@@ -269,6 +269,8 @@ API changes
 - ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
 - ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
 - ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`)
+- Passing ``Period`` with multiple frequencies to normal ``Index`` now returns ``Index`` with ``object`` dtype (:issue:`13664`)
+- ``PeriodIndex.fillna`` with ``Period`` has different freq now coerces to ``object`` dtype (:issue:`13664`)
 
 
 .. _whatsnew_0190.api.tolist:
@@ -645,7 +647,6 @@ Bug Fixes
 - Bug in ``.unstack`` with ``Categorical`` dtype resets ``.ordered`` to ``True`` (:issue:`13249`)
 - Clean some compile time warnings in datetime parsing (:issue:`13607`)
 
-
 - Bug in ``Series`` comparison operators when dealing with zero dim NumPy arrays (:issue:`13006`)
 - Bug in ``groupby`` where ``apply`` returns different result depending on whether first result is ``None`` or not (:issue:`12824`)
 - Bug in ``groupby(..).nth()`` where the group key is included inconsistently if called after ``.head()/.tail()`` (:issue:`12839`)

pandas/core/ops.py

@@ -31,7 +31,7 @@
                                  is_list_like,
                                  _ensure_object)
 from pandas.types.cast import _maybe_upcast_putmask
-from pandas.types.generic import ABCSeries, ABCIndex
+from pandas.types.generic import ABCSeries, ABCIndex, ABCPeriodIndex
 
 # -----------------------------------------------------------------------------
 # Functions that add arithmetic methods to objects, given arithmetic factory
@@ -773,6 +773,15 @@ def wrapper(self, other, axis=None):
             if (not lib.isscalar(lib.item_from_zerodim(other)) and
                     len(self) != len(other)):
                 raise ValueError('Lengths must match to compare')
+
+            if isinstance(other, ABCPeriodIndex):
+                # temp workaround until fixing GH 13637
+                # tested in test_nat_comparisons
+                # (pandas.tests.series.test_operators.TestSeriesOperators)
+                return self._constructor(na_op(self.values,
+                                               other.asobject.values),
+                                         index=self.index)
+
             return self._constructor(na_op(self.values, np.asarray(other)),
                                      index=self.index).__finalize__(self)
         elif isinstance(other, pd.Categorical):

pandas/indexes/base.py

@@ -224,7 +224,8 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
                     pass
 
             # maybe coerce to a sub-class
-            from pandas.tseries.period import PeriodIndex
+            from pandas.tseries.period import (PeriodIndex,
+                                               IncompatibleFrequency)
             if isinstance(data, PeriodIndex):
                 return PeriodIndex(data, copy=copy, name=name, **kwargs)
             if issubclass(data.dtype.type, np.integer):
@@ -265,13 +266,15 @@ def __new__(cls, data=None, dtype=None, copy=False, name=None,
                             return DatetimeIndex(subarr, copy=copy, name=name,
                                                  **kwargs)
 
-                    elif (inferred.startswith('timedelta') or
-                          lib.is_timedelta_array(subarr)):
+                    elif inferred.startswith('timedelta'):
                         from pandas.tseries.tdi import TimedeltaIndex
                         return TimedeltaIndex(subarr, copy=copy, name=name,
                                               **kwargs)
                     elif inferred == 'period':
-                        return PeriodIndex(subarr, name=name, **kwargs)
+                        try:
+                            return PeriodIndex(subarr, name=name, **kwargs)
+                        except IncompatibleFrequency:
+                            pass
             return cls._simple_new(subarr, name)
 
         elif hasattr(data, '__array__'):
@@ -866,6 +869,16 @@ def _convert_can_do_setop(self, other):
             result_name = self.name if self.name == other.name else None
         return other, result_name
 
+    def _convert_for_op(self, value):
+        """ Convert value to be insertable to ndarray """
+        return value
+
+    def _assert_can_do_op(self, value):
+        """ Check value is valid for scalar op """
+        if not lib.isscalar(value):
+            msg = "'value' must be a scalar, passed: {0}"
+            raise TypeError(msg.format(type(value).__name__))
+
     @property
     def nlevels(self):
         return 1
@@ -1508,16 +1521,6 @@ def hasnans(self):
         else:
             return False
 
-    def _convert_for_op(self, value):
-        """ Convert value to be insertable to ndarray """
-        return value
-
-    def _assert_can_do_op(self, value):
-        """ Check value is valid for scalar op """
-        if not is_scalar(value):
-            msg = "'value' must be a scalar, passed: {0}"
-            raise TypeError(msg.format(type(value).__name__))
-
     def putmask(self, mask, value):
         """
         return a new Index of the values set with the mask

pandas/src/inference.pyx

@@ -270,7 +270,7 @@ cdef inline bint is_null_datetimelike(v):
 
 
 cdef inline bint is_null_datetime64(v):
-    # determine if we have a null for a datetime (or integer versions)x,
+    # determine if we have a null for a datetime (or integer versions),
     # excluding np.timedelta64('nat')
     if util._checknull(v):
         return True
@@ -282,7 +282,7 @@ cdef inline bint is_null_datetime64(v):
 
 
 cdef inline bint is_null_timedelta64(v):
-    # determine if we have a null for a timedelta (or integer versions)x,
+    # determine if we have a null for a timedelta (or integer versions),
     # excluding np.datetime64('nat')
     if util._checknull(v):
         return True
@@ -293,6 +293,16 @@ cdef inline bint is_null_timedelta64(v):
     return False
 
 
+cdef inline bint is_null_period(v):
+    # determine if we have a null for a Period (or integer versions),
+    # excluding np.datetime64('nat') and np.timedelta64('nat')
+    if util._checknull(v):
+        return True
+    elif v is NaT:
+        return True
+    return False
+
+
 cdef inline bint is_datetime(object o):
     return PyDateTime_Check(o)
 
@@ -531,6 +541,7 @@ def is_timedelta_array(ndarray values):
             return False
     return null_count != n
 
+
 def is_timedelta64_array(ndarray values):
     cdef Py_ssize_t i, null_count = 0, n = len(values)
     cdef object v
@@ -546,6 +557,7 @@ def is_timedelta64_array(ndarray values):
             return False
     return null_count != n
 
+
 def is_timedelta_or_timedelta64_array(ndarray values):
     """ infer with timedeltas and/or nat/none """
     cdef Py_ssize_t i, null_count = 0, n = len(values)
@@ -562,6 +574,7 @@ def is_timedelta_or_timedelta64_array(ndarray values):
             return False
     return null_count != n
 
+
 def is_date_array(ndarray[object] values):
     cdef Py_ssize_t i, n = len(values)
     if n == 0:
@@ -571,6 +584,7 @@ def is_date_array(ndarray[object] values):
             return False
     return True
 
+
 def is_time_array(ndarray[object] values):
     cdef Py_ssize_t i, n = len(values)
     if n == 0:
@@ -582,15 +596,21 @@ def is_time_array(ndarray[object] values):
 
 
 def is_period_array(ndarray[object] values):
-    cdef Py_ssize_t i, n = len(values)
-    from pandas.tseries.period import Period
-
+    cdef Py_ssize_t i, null_count = 0, n = len(values)
+    cdef object v
     if n == 0:
         return False
+
+    # return False for all nulls
     for i in range(n):
-        if not isinstance(values[i], Period):
+        v = values[i]
+        if is_null_period(v):
+            # we are a regular null
+            if util._checknull(v):
+                null_count += 1
+        elif not is_period(v):
             return False
-    return True
+    return null_count != n
 
 
 cdef extern from "parse_helper.h":

pandas/tests/indexes/test_datetimelike.py

@@ -119,10 +119,10 @@ def test_pickle_compat_construction(self):
     def test_construction_index_with_mixed_timezones(self):
         # GH 11488
         # no tz results in DatetimeIndex
-        result = Index(
-            [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
-        exp = DatetimeIndex(
-            [Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
+        result = Index([Timestamp('2011-01-01'),
+                        Timestamp('2011-01-02')], name='idx')
+        exp = DatetimeIndex([Timestamp('2011-01-01'),
+                             Timestamp('2011-01-02')], name='idx')
         self.assert_index_equal(result, exp, exact=True)
         self.assertTrue(isinstance(result, DatetimeIndex))
         self.assertIsNone(result.tz)
@@ -295,9 +295,9 @@ def test_construction_dti_with_mixed_timezones(self):
                                 Timestamp('2011-01-02 10:00',
                                           tz='Asia/Tokyo')],
                                name='idx')
-        exp = DatetimeIndex(
-            [Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00')
-             ], tz='Asia/Tokyo', name='idx')
+        exp = DatetimeIndex([Timestamp('2011-01-01 10:00'),
+                             Timestamp('2011-01-02 10:00')],
+                            tz='Asia/Tokyo', name='idx')
         self.assert_index_equal(result, exp, exact=True)
         self.assertTrue(isinstance(result, DatetimeIndex))
 
@@ -338,6 +338,17 @@ def test_construction_dti_with_mixed_timezones(self):
                            Timestamp('2011-01-02 10:00', tz='US/Eastern')],
                           tz='US/Eastern', name='idx')
 
+    def test_construction_base_constructor(self):
+        arr = [pd.Timestamp('2011-01-01'), pd.NaT, pd.Timestamp('2011-01-03')]
+        tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.DatetimeIndex(np.array(arr)))
+
+        arr = [np.nan, pd.NaT, pd.Timestamp('2011-01-03')]
+        tm.assert_index_equal(pd.Index(arr), pd.DatetimeIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.DatetimeIndex(np.array(arr)))
+
     def test_astype(self):
         # GH 13149, GH 13209
         idx = DatetimeIndex(['2016-05-16', 'NaT', NaT, np.NaN])
@@ -699,12 +710,11 @@ def test_fillna_datetime64(self):
                             pd.Timestamp('2011-01-01 11:00')], dtype=object)
             self.assert_index_equal(idx.fillna('x'), exp)
 
-            idx = pd.DatetimeIndex(
-                ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], tz=tz)
+            idx = pd.DatetimeIndex(['2011-01-01 09:00', pd.NaT,
+                                    '2011-01-01 11:00'], tz=tz)
 
-            exp = pd.DatetimeIndex(
-                ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'
-                 ], tz=tz)
+            exp = pd.DatetimeIndex(['2011-01-01 09:00', '2011-01-01 10:00',
+                                    '2011-01-01 11:00'], tz=tz)
             self.assert_index_equal(
                 idx.fillna(pd.Timestamp('2011-01-01 10:00', tz=tz)), exp)
 
@@ -734,6 +744,26 @@ def setUp(self):
     def create_index(self):
         return period_range('20130101', periods=5, freq='D')
 
+    def test_construction_base_constructor(self):
+        # GH 13664
+        arr = [pd.Period('2011-01', freq='M'), pd.NaT,
+               pd.Period('2011-03', freq='M')]
+        tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.PeriodIndex(np.array(arr)))
+
+        arr = [np.nan, pd.NaT, pd.Period('2011-03', freq='M')]
+        tm.assert_index_equal(pd.Index(arr), pd.PeriodIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.PeriodIndex(np.array(arr)))
+
+        arr = [pd.Period('2011-01', freq='M'), pd.NaT,
+               pd.Period('2011-03', freq='D')]
+        tm.assert_index_equal(pd.Index(arr), pd.Index(arr, dtype=object))
+
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.Index(np.array(arr), dtype=object))
+
     def test_astype(self):
         # GH 13149, GH 13209
         idx = PeriodIndex(['2016-05-16', 'NaT', NaT, np.NaN], freq='D')
@@ -874,7 +904,6 @@ def test_repeat(self):
         self.assertEqual(res.freqstr, 'D')
 
     def test_period_index_indexer(self):
-
         # GH4125
         idx = pd.period_range('2002-01', '2003-12', freq='M')
         df = pd.DataFrame(pd.np.random.randn(24, 10), index=idx)
@@ -886,23 +915,23 @@ def test_period_index_indexer(self):
 
     def test_fillna_period(self):
         # GH 11343
-        idx = pd.PeriodIndex(
-            ['2011-01-01 09:00', pd.NaT, '2011-01-01 11:00'], freq='H')
+        idx = pd.PeriodIndex(['2011-01-01 09:00', pd.NaT,
+                              '2011-01-01 11:00'], freq='H')
 
-        exp = pd.PeriodIndex(
-            ['2011-01-01 09:00', '2011-01-01 10:00', '2011-01-01 11:00'
-             ], freq='H')
+        exp = pd.PeriodIndex(['2011-01-01 09:00', '2011-01-01 10:00',
+                              '2011-01-01 11:00'], freq='H')
         self.assert_index_equal(
             idx.fillna(pd.Period('2011-01-01 10:00', freq='H')), exp)
 
         exp = pd.Index([pd.Period('2011-01-01 09:00', freq='H'), 'x',
                         pd.Period('2011-01-01 11:00', freq='H')], dtype=object)
         self.assert_index_equal(idx.fillna('x'), exp)
 
-        with tm.assertRaisesRegexp(
-                ValueError,
-                'Input has different freq=D from PeriodIndex\\(freq=H\\)'):
-            idx.fillna(pd.Period('2011-01-01', freq='D'))
+        exp = pd.Index([pd.Period('2011-01-01 09:00', freq='H'),
+                        pd.Period('2011-01-01', freq='D'),
+                        pd.Period('2011-01-01 11:00', freq='H')], dtype=object)
+        self.assert_index_equal(idx.fillna(pd.Period('2011-01-01', freq='D')),
+                                exp)
 
     def test_no_millisecond_field(self):
         with self.assertRaises(AttributeError):
@@ -923,6 +952,17 @@ def setUp(self):
     def create_index(self):
         return pd.to_timedelta(range(5), unit='d') + pd.offsets.Hour(1)
 
+    def test_construction_base_constructor(self):
+        arr = [pd.Timedelta('1 days'), pd.NaT, pd.Timedelta('3 days')]
+        tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.TimedeltaIndex(np.array(arr)))
+
+        arr = [np.nan, pd.NaT, pd.Timedelta('1 days')]
+        tm.assert_index_equal(pd.Index(arr), pd.TimedeltaIndex(arr))
+        tm.assert_index_equal(pd.Index(np.array(arr)),
+                              pd.TimedeltaIndex(np.array(arr)))
+
     def test_shift(self):
         # test shift for TimedeltaIndex
         # err8083

pandas/tests/types/test_inference.py

@@ -431,6 +431,33 @@ def test_infer_dtype_timedelta(self):
                        dtype=object)
         self.assertEqual(lib.infer_dtype(arr), 'mixed')
 
+    def test_infer_dtype_period(self):
+        # GH 13664
+        arr = np.array([pd.Period('2011-01', freq='D'),
+                        pd.Period('2011-02', freq='D')])
+        self.assertEqual(pd.lib.infer_dtype(arr), 'period')
+
+        arr = np.array([pd.Period('2011-01', freq='D'),
+                        pd.Period('2011-02', freq='M')])
+        self.assertEqual(pd.lib.infer_dtype(arr), 'period')
+
+        # starts with nan
+        for n in [pd.NaT, np.nan]:
+            arr = np.array([n, pd.Period('2011-01', freq='D')])
+            self.assertEqual(pd.lib.infer_dtype(arr), 'period')
+
+            arr = np.array([n, pd.Period('2011-01', freq='D'), n])
+            self.assertEqual(pd.lib.infer_dtype(arr), 'period')
+
+        # different type of nat
+        arr = np.array([np.datetime64('nat'), pd.Period('2011-01', freq='M')],
+                       dtype=object)
+        self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')
+
+        arr = np.array([pd.Period('2011-01', freq='M'), np.datetime64('nat')],
+                       dtype=object)
+        self.assertEqual(pd.lib.infer_dtype(arr), 'mixed')
+
     def test_infer_dtype_all_nan_nat_like(self):
         arr = np.array([np.nan, np.nan])
         self.assertEqual(lib.infer_dtype(arr), 'floating')

pandas/tseries/base.py

@@ -800,12 +800,15 @@ def _ensure_datetimelike_to_i8(other):
     if lib.isscalar(other) and isnull(other):
         other = tslib.iNaT
     elif isinstance(other, ABCIndexClass):
-
         # convert tz if needed
         if getattr(other, 'tz', None) is not None:
             other = other.tz_localize(None).asi8
         else:
             other = other.asi8
     else:
-        other = np.array(other, copy=False).view('i8')
+        try:
+            other = np.array(other, copy=False).view('i8')
+        except TypeError:
+            # period array cannot be coerces to int
+            other = Index(other).asi8
     return other