BUG: Fix freq setter for DatetimeIndex/TimedeltaIndex and deprecate for PeriodIndex

doc/source/whatsnew/v0.23.0.txt

@@ -879,6 +879,7 @@ Deprecations
 - :func:`Series.rolling().apply() <pandas.core.window.Rolling.apply>`, :func:`DataFrame.rolling().apply() <pandas.core.window.Rolling.apply>`,
   :func:`Series.expanding().apply() <pandas.core.window.Expanding.apply>`, and :func:`DataFrame.expanding().apply() <pandas.core.window.Expanding.apply>` have deprecated passing an ``np.array`` by default. One will need to pass the new ``raw`` parameter to be explicit about what is passed (:issue:`20584`)
 - ``DatetimeIndex.offset`` is deprecated. Use ``DatetimeIndex.freq`` instead (:issue:`20716`)
+- Setting ``PeriodIndex.freq`` is deprecated. Use :meth:`PeriodIndex.asfreq` instead (:issue:`20678`)
 
 .. _whatsnew_0230.prior_deprecations:
 
@@ -1032,6 +1033,7 @@ Datetimelike
 - Bug in :func:`to_datetime` where passing an out-of-bounds datetime with ``errors='coerce'`` and ``utc=True`` would raise ``OutOfBoundsDatetime`` instead of parsing to ``NaT`` (:issue:`19612`)
 - Bug in :class:`DatetimeIndex` and :class:`TimedeltaIndex` addition and subtraction where name of the returned object was not always set consistently. (:issue:`19744`)
 - Bug in :class:`DatetimeIndex` and :class:`TimedeltaIndex` addition and subtraction where operations with numpy arrays raised ``TypeError`` (:issue:`19847`)
+- Bug in :class:`DatetimeIndex` and :class:`TimedeltaIndex` where setting the ``freq`` attribute was not fully supported (:issue:`20678`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/indexes/datetimelike.py

@@ -205,6 +205,43 @@ def floor(self, freq):
     def ceil(self, freq):
         return self._round(freq, np.ceil)
 
+    @classmethod
+    def _validate_frequency(cls, index, freq, **kwargs):
+        """
+        Validate that a frequency is compatible with the values of a given
+        DatetimeIndex or TimedeltaIndex
+
+        Parameters
+        ----------
+        index : DatetimeIndex or TimedeltaIndex
+            The index on which to determine if the given frequency is valid
+        freq : DateOffset
+            The frequency to validate
+        """
+        inferred = index.inferred_freq
+        if index.empty or inferred == freq.freqstr:
+            return None
+
+        on_freq = cls._generate(
+            index[0], None, len(index), None, freq, **kwargs)
+        if not np.array_equal(index.asi8, on_freq.asi8):
+            msg = ('Inferred frequency {infer} from passed values does not '
+                   'conform to passed frequency {passed}')
+            raise ValueError(msg.format(infer=inferred, passed=freq.freqstr))
+
+    @property
+    def freq(self):
+        """Return the frequency object if it is set, otherwise None"""
+        return self._freq
+
+    @freq.setter
+    def freq(self, value):
+        if value is not None:
+            value = frequencies.to_offset(value)
+            self._validate_frequency(self, value)
+
+        self._freq = value
+
 
 class DatetimeIndexOpsMixin(object):
     """ common ops mixin to support a unified interface datetimelike Index """
@@ -401,7 +438,7 @@ def __getitem__(self, key):
     @property
     def freqstr(self):
         """
-        Return the frequency object as a string if its set, otherwise None
+        Return the frequency object as a string if it is set, otherwise None
         """
         if self.freq is None:
             return None
@@ -410,7 +447,7 @@ def freqstr(self):
     @cache_readonly
     def inferred_freq(self):
         """
-        Tryies to return a string representing a frequency guess,
+        Tries to return a string representing a frequency guess,
         generated by infer_freq.  Returns None if it can't autodetect the
         frequency.
         """

pandas/core/indexes/datetimes.py

@@ -454,15 +454,7 @@ def __new__(cls, data=None,
 
         if verify_integrity and len(subarr) > 0:
             if freq is not None and not freq_infer:
-                inferred = subarr.inferred_freq
-                if inferred != freq.freqstr:
-                    on_freq = cls._generate(subarr[0], None, len(subarr), None,
-                                            freq, tz=tz, ambiguous=ambiguous)
-                    if not np.array_equal(subarr.asi8, on_freq.asi8):
-                        raise ValueError('Inferred frequency {0} from passed '
-                                         'dates does not conform to passed '
-                                         'frequency {1}'
-                                         .format(inferred, freq.freqstr))
+                cls._validate_frequency(subarr, freq, ambiguous=ambiguous)
 
         if freq_infer:
             inferred = subarr.inferred_freq
@@ -836,7 +828,7 @@ def __setstate__(self, state):
                 np.ndarray.__setstate__(data, nd_state)
 
                 self.name = own_state[0]
-                self.freq = own_state[1]
+                self._freq = own_state[1]
                 self._tz = timezones.tz_standardize(own_state[2])
 
                 # provide numpy < 1.7 compat
@@ -1726,16 +1718,6 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
             else:
                 raise
 
-    @property
-    def freq(self):
-        """get/set the frequency of the Index"""
-        return self._freq
-
-    @freq.setter
-    def freq(self, value):
-        """get/set the frequency of the Index"""
-        self._freq = value
-
     @property
     def offset(self):
         """get/set the frequency of the Index"""

pandas/core/indexes/period.py

@@ -219,7 +219,7 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index):
     _is_numeric_dtype = False
     _infer_as_myclass = True
 
-    freq = None
+    _freq = None
 
     _engine_type = libindex.PeriodEngine
 
@@ -367,7 +367,7 @@ def _from_ordinals(cls, values, name=None, freq=None, **kwargs):
         result.name = name
         if freq is None:
             raise ValueError('freq is not specified and cannot be inferred')
-        result.freq = Period._maybe_convert_freq(freq)
+        result._freq = Period._maybe_convert_freq(freq)
         result._reset_identity()
         return result
 
@@ -560,6 +560,19 @@ def is_full(self):
         values = self.values
         return ((values[1:] - values[:-1]) < 2).all()
 
+    @property
+    def freq(self):
+        """Return the frequency object if it is set, otherwise None"""
+        return self._freq
+
+    @freq.setter
+    def freq(self, value):
+        msg = ('Setting PeriodIndex.freq has been deprecated and will be '
+               'removed in a future version; use PeriodIndex.asfreq instead. '
+               'The PeriodIndex.freq setter is not guaranteed to work.')
+        warnings.warn(msg, FutureWarning, stacklevel=2)
+        self._freq = value
+
     def asfreq(self, freq=None, how='E'):
         """
         Convert the PeriodIndex to the specified frequency `freq`.
@@ -1060,7 +1073,7 @@ def __setstate__(self, state):
                 np.ndarray.__setstate__(data, nd_state)
 
                 # backcompat
-                self.freq = Period._maybe_convert_freq(own_state[1])
+                self._freq = Period._maybe_convert_freq(own_state[1])
 
             else:  # pragma: no cover
                 data = np.empty(state)

pandas/core/indexes/timedeltas.py

@@ -28,7 +28,8 @@
 import pandas.core.common as com
 import pandas.core.dtypes.concat as _concat
 from pandas.util._decorators import Appender, Substitution, deprecate_kwarg
-from pandas.core.indexes.datetimelike import TimelikeOps, DatetimeIndexOpsMixin
+from pandas.core.indexes.datetimelike import (
+    TimelikeOps, DatetimeIndexOpsMixin)
 from pandas.core.tools.timedeltas import (
     to_timedelta, _coerce_scalar_to_timedelta_type)
 from pandas.tseries.offsets import Tick, DateOffset
@@ -195,7 +196,7 @@ def _add_comparison_methods(cls):
     _is_numeric_dtype = True
     _infer_as_myclass = True
 
-    freq = None
+    _freq = None
 
     def __new__(cls, data=None, unit=None, freq=None, start=None, end=None,
                 periods=None, closed=None, dtype=None, copy=False,
@@ -251,15 +252,7 @@ def __new__(cls, data=None, unit=None, freq=None, start=None, end=None,
         if verify_integrity and len(data) > 0:
             if freq is not None and not freq_infer:
                 index = cls._simple_new(data, name=name)
-                inferred = index.inferred_freq
-                if inferred != freq.freqstr:
-                    on_freq = cls._generate(
-                        index[0], None, len(index), name, freq)
-                    if not np.array_equal(index.asi8, on_freq.asi8):
-                        raise ValueError('Inferred frequency {0} from passed '
-                                         'timedeltas does not conform to '
-                                         'passed frequency {1}'
-                                         .format(inferred, freq.freqstr))
+                cls._validate_frequency(index, freq)
                 index.freq = freq
                 return index
 
@@ -327,7 +320,7 @@ def _simple_new(cls, values, name=None, freq=None, **kwargs):
         result = object.__new__(cls)
         result._data = values
         result.name = name
-        result.freq = freq
+        result._freq = freq
         result._reset_identity()
         return result
 

pandas/tests/indexes/datetimes/test_ops.py

@@ -9,8 +9,9 @@
 from pandas import (DatetimeIndex, PeriodIndex, Series, Timestamp,
                     date_range, _np_version_under1p10, Index,
                     bdate_range)
-from pandas.tseries.offsets import BMonthEnd, CDay, BDay
+from pandas.tseries.offsets import BMonthEnd, CDay, BDay, Day, Hour
 from pandas.tests.test_base import Ops
+from pandas.core.dtypes.generic import ABCDateOffset
 
 
 @pytest.fixture(params=[None, 'UTC', 'Asia/Tokyo', 'US/Eastern',
@@ -405,6 +406,38 @@ def test_equals(self):
         assert not idx.equals(list(idx3))
         assert not idx.equals(pd.Series(idx3))
 
+    @pytest.mark.parametrize('values', [
+        ['20180101', '20180103', '20180105'], []])
+    @pytest.mark.parametrize('freq', [
+        '2D', Day(2), '2B', BDay(2), '48H', Hour(48)])
+    @pytest.mark.parametrize('tz', [None, 'US/Eastern'])
+    def test_freq_setter(self, values, freq, tz):
+        # GH 20678
+        idx = DatetimeIndex(values, tz=tz)
+
+        # can set to an offset, converting from string if necessary
+        idx.freq = freq
+        assert idx.freq == freq
+        assert isinstance(idx.freq, ABCDateOffset)
+
+        # can reset to None
+        idx.freq = None
+        assert idx.freq is None
+
+    def test_freq_setter_errors(self):
+        # GH 20678
+        idx = DatetimeIndex(['20180101', '20180103', '20180105'])
+
+        # setting with an incompatible freq
+        msg = ('Inferred frequency 2D from passed values does not conform to '
+               'passed frequency 5D')
+        with tm.assert_raises_regex(ValueError, msg):
+            idx.freq = '5D'
+
+        # setting with non-freq string
+        with tm.assert_raises_regex(ValueError, 'Invalid frequency'):
+            idx.freq = 'foo'
+
     def test_offset_deprecated(self):
         # GH 20716
         idx = pd.DatetimeIndex(['20180101', '20180102'])

pandas/tests/indexes/period/test_ops.py

@@ -401,6 +401,18 @@ def test_equals(self, freq):
         assert not idx.equals(list(idx3))
         assert not idx.equals(pd.Series(idx3))
 
+    def test_freq_setter_deprecated(self):
+        # GH 20678
+        idx = pd.period_range('2018Q1', periods=4, freq='Q')
+
+        # no warning for getter
+        with tm.assert_produces_warning(None):
+            idx.freq
+
+        # warning for setter
+        with tm.assert_produces_warning(FutureWarning):
+            idx.freq = pd.offsets.Day()
+
 
 class TestPeriodIndexSeriesMethods(object):
     """ Test PeriodIndex and Period Series Ops consistency """

pandas/tests/indexes/timedeltas/test_ops.py

@@ -11,6 +11,8 @@
                     _np_version_under1p10)
 from pandas._libs.tslib import iNaT
 from pandas.tests.test_base import Ops
+from pandas.tseries.offsets import Day, Hour
+from pandas.core.dtypes.generic import ABCDateOffset
 
 
 class TestTimedeltaIndexOps(Ops):
@@ -306,6 +308,40 @@ def test_equals(self):
         assert not idx.equals(list(idx2))
         assert not idx.equals(pd.Series(idx2))
 
+    @pytest.mark.parametrize('values', [['0 days', '2 days', '4 days'], []])
+    @pytest.mark.parametrize('freq', ['2D', Day(2), '48H', Hour(48)])
+    def test_freq_setter(self, values, freq):
+        # GH 20678
+        idx = TimedeltaIndex(values)
+
+        # can set to an offset, converting from string if necessary
+        idx.freq = freq
+        assert idx.freq == freq
+        assert isinstance(idx.freq, ABCDateOffset)
+
+        # can reset to None
+        idx.freq = None
+        assert idx.freq is None
+
+    def test_freq_setter_errors(self):
+        # GH 20678
+        idx = TimedeltaIndex(['0 days', '2 days', '4 days'])
+
+        # setting with an incompatible freq
+        msg = ('Inferred frequency 2D from passed values does not conform to '
+               'passed frequency 5D')
+        with tm.assert_raises_regex(ValueError, msg):
+            idx.freq = '5D'
+
+        # setting with a non-fixed frequency
+        msg = '<2 \* BusinessDays> is a non-fixed frequency'
+        with tm.assert_raises_regex(ValueError, msg):
+            idx.freq = '2B'
+
+        # setting with non-freq string
+        with tm.assert_raises_regex(ValueError, 'Invalid frequency'):
+            idx.freq = 'foo'
+
 
 class TestTimedeltas(object):