DEPR: Deprecate range-based PeriodIndex construction

asv_bench/benchmarks/period.py

@@ -1,5 +1,5 @@
-from pandas import (DataFrame, Series, Period, PeriodIndex, date_range,
-                    period_range)
+from pandas import (
+    DataFrame, Period, PeriodIndex, Series, date_range, period_range)
 
 
 class PeriodProperties(object):
@@ -94,7 +94,7 @@ def time_value_counts(self, typ):
 class Indexing(object):
 
     def setup(self):
-        self.index = PeriodIndex(start='1985', periods=1000, freq='D')
+        self.index = period_range(start='1985', periods=1000, freq='D')
         self.series = Series(range(1000), index=self.index)
         self.period = self.index[500]
 

doc/source/whatsnew/v0.21.0.rst

@@ -654,7 +654,7 @@ Previous Behavior:
 
 .. code-block:: ipython
 
-   In [1]: pi = pd.PeriodIndex(start='2000-01-01', freq='D', periods=10)
+   In [1]: pi = pd.period_range(start='2000-01-01', freq='D', periods=10)
 
    In [2]: s = pd.Series(np.arange(10), index=pi)
 
@@ -674,7 +674,7 @@ New Behavior:
 
 .. ipython:: python
 
-   pi = pd.PeriodIndex(start='2000-01-01', freq='D', periods=10)
+   pi = pd.period_range(start='2000-01-01', freq='D', periods=10)
 
    s = pd.Series(np.arange(10), index=pi)
 

doc/source/whatsnew/v0.24.0.rst

@@ -1148,7 +1148,7 @@ Deprecations
 - Timezone converting a tz-aware ``datetime.datetime`` or :class:`Timestamp` with :class:`Timestamp` and the ``tz`` argument is now deprecated. Instead, use :meth:`Timestamp.tz_convert` (:issue:`23579`)
 - :func:`pandas.api.types.is_period` is deprecated in favor of `pandas.api.types.is_period_dtype` (:issue:`23917`)
 - :func:`pandas.api.types.is_datetimetz` is deprecated in favor of `pandas.api.types.is_datetime64tz` (:issue:`23917`)
-- Creating a :class:`TimedeltaIndex` or :class:`DatetimeIndex` by passing range arguments `start`, `end`, and `periods` is deprecated in favor of :func:`timedelta_range` and :func:`date_range` (:issue:`23919`)
+- Creating a :class:`TimedeltaIndex`, :class:`DatetimeIndex`, or :class:`PeriodIndex` by passing range arguments `start`, `end`, and `periods` is deprecated in favor of :func:`timedelta_range`, :func:`date_range`, or :func:`period_range` (:issue:`23919`)
 - Passing a string alias like ``'datetime64[ns, UTC]'`` as the ``unit`` parameter to :class:`DatetimeTZDtype` is deprecated. Use :class:`DatetimeTZDtype.construct_from_string` instead (:issue:`23990`).
 - In :meth:`Series.where` with Categorical data, providing an ``other`` that is not present in the categories is deprecated. Convert the categorical to a different dtype or add the ``other`` to the categories first (:issue:`24077`).
 - :meth:`Series.clip_lower`, :meth:`Series.clip_upper`, :meth:`DataFrame.clip_lower` and :meth:`DataFrame.clip_upper` are deprecated and will be removed in a future version. Use ``Series.clip(lower=threshold)``, ``Series.clip(upper=threshold)`` and the equivalent ``DataFrame`` methods (:issue:`24203`)
@@ -1310,6 +1310,7 @@ Datetimelike
 - Bug in :meth:`Series.combine_first` not properly aligning categoricals, so that missing values in ``self`` where not filled by valid values from ``other`` (:issue:`24147`)
 - Bug in :func:`DataFrame.combine` with datetimelike values raising a TypeError (:issue:`23079`)
 - Bug in :func:`date_range` with frequency of ``Day`` or higher where dates sufficiently far in the future could wrap around to the past instead of raising ``OutOfBoundsDatetime`` (:issue:`14187`)
+- Bug in :func:`period_range` ignoring the frequency of ``start`` and ``end`` when those are provided as :class:`Period` objects (:issue:`20535`).
 - Bug in :class:`PeriodIndex` with attribute ``freq.n`` greater than 1 where adding a :class:`DateOffset` object would return incorrect results (:issue:`23215`)
 - Bug in :class:`Series` that interpreted string indices as lists of characters when setting datetimelike values (:issue:`23451`)
 - Bug in :class:`Timestamp` constructor which would drop the frequency of an input :class:`Timestamp` (:issue:`22311`)

pandas/core/indexes/datetimes.py

@@ -81,15 +81,27 @@ class DatetimeIndex(DatetimeArray, DatetimeIndexOpsMixin, Int64Index):
     start : starting value, datetime-like, optional
         If data is None, start is used as the start point in generating regular
         timestamp data.
+
+        .. deprecated:: 0.24.0
+
     periods  : int, optional, > 0
         Number of periods to generate, if generating index. Takes precedence
         over end argument
-    end   : end time, datetime-like, optional
+
+        .. deprecated:: 0.24.0
+
+    end : end time, datetime-like, optional
         If periods is none, generated index will extend to first conforming
         time on or just past end argument
+
+        .. deprecated:: 0.24.0
+
     closed : string or None, default None
         Make the interval closed with respect to the given frequency to
         the 'left', 'right', or both sides (None)
+
+        .. deprecated:: 0.24. 0
+
     tz : pytz.timezone or dateutil.tz.tzfile
     ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
         When clocks moved backward due to DST, ambiguous times may arise.
@@ -166,12 +178,16 @@ class DatetimeIndex(DatetimeArray, DatetimeIndexOpsMixin, Int64Index):
     To learn more about the frequency strings, please see `this link
     <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
 
+    Creating a DatetimeIndex based on `start`, `periods`, and `end` has
+    been deprecated in favor of :func:`date_range`.
+
     See Also
     ---------
     Index : The base pandas Index type.
     TimedeltaIndex : Index of timedelta64 data.
     PeriodIndex : Index of Period data.
-    pandas.to_datetime : Convert argument to datetime.
+    to_datetime : Convert argument to datetime.
+    date_range : Create a fixed-frequency DatetimeIndex.
     """
     _typ = 'datetimeindex'
     _join_precedence = 10
@@ -223,14 +239,15 @@ def __new__(cls, data=None,
             verify_integrity = True
 
         if data is None:
-            warnings.warn("Creating a DatetimeIndex by passing range "
-                          "endpoints is deprecated.  Use "
-                          "`pandas.date_range` instead.",
-                          FutureWarning, stacklevel=2)
             dtarr = DatetimeArray._generate_range(
                 start, end, periods,
                 freq=freq, tz=tz, normalize=normalize,
                 closed=closed, ambiguous=ambiguous)
+            warnings.warn("Creating a DatetimeIndex by passing range "
+                          "endpoints is deprecated.  Use "
+                          "`pandas.date_range` instead.",
+                          FutureWarning, stacklevel=2)
+
             return cls(dtarr, name=name)
 
         if is_scalar(data):

pandas/core/indexes/period.py

@@ -92,12 +92,21 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index,
     start : starting value, period-like, optional
         If data is None, used as the start point in generating regular
         period data.
+
+        .. deprecated:: 0.24.0
+
     periods : int, optional, > 0
         Number of periods to generate, if generating index. Takes precedence
         over end argument
+
+        .. deprecated:: 0.24.0
+
     end : end value, period-like, optional
         If periods is none, generated index will extend to first conforming
         period on or just past end argument
+
+        .. deprecated:: 0.24.0
+
     year : int, array, or Series, default None
     month : int, array, or Series, default None
     quarter : int, array, or Series, default None
@@ -138,18 +147,22 @@ class PeriodIndex(DatelikeOps, DatetimeIndexOpsMixin, Int64Index,
     strftime
     to_timestamp
 
+    Notes
+    -----
+    Creating a PeriodIndex based on `start`, `periods`, and `end` has
+    been deprecated in favor of :func:`period_range`.
+
     Examples
     --------
     >>> idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)
 
-    >>> idx2 = pd.PeriodIndex(start='2000', end='2010', freq='A')
-
     See Also
     ---------
     Index : The base pandas Index type.
     Period : Represents a period of time.
     DatetimeIndex : Index with datetime64 data.
     TimedeltaIndex : Index of timedelta64 data.
+    period_range : Create a fixed-frequency PeriodIndex.
     """
     _typ = 'periodindex'
     _attributes = ['name', 'freq']
@@ -181,8 +194,32 @@ def __new__(cls, data=None, ordinal=None, freq=None, start=None, end=None,
 
         if data is None and ordinal is None:
             # range-based.
-            data, freq = PeriodArray._generate_range(start, end, periods,
-                                                     freq, fields)
+            data, freq2 = PeriodArray._generate_range(start, end, periods,
+                                                      freq, fields)
+            # PeriodArray._generate range does validate that fields is
+            # empty when really using the range-based constructor.
+            if not fields:
+                msg = ("Creating a PeriodIndex by passing range "
+                       "endpoints is deprecated.  Use "
+                       "`pandas.period_range` instead.")
+                # period_range differs from PeriodIndex for cases like
+                # start="2000", periods=4
+                # PeriodIndex interprets that as A-DEC freq.
+                # period_range interprets it as 'D' freq.
+                cond = (
+                    freq is None and (
+                        (start and not isinstance(start, Period)) or
+                        (end and not isinstance(end, Period))
+                    )
+                )
+                if cond:
+                    msg += (
+                        " Note that the default `freq` may differ. Pass "
+                        "'freq=\"{}\"' to ensure the same output."
+                    ).format(freq2.freqstr)
+                warnings.warn(msg, FutureWarning, stacklevel=2)
+            freq = freq2
+
             data = PeriodArray(data, freq=freq)
         else:
             freq = validate_dtype_freq(dtype, freq)
@@ -939,7 +976,7 @@ def base(self):
 PeriodIndex._add_datetimelike_methods()
 
 
-def period_range(start=None, end=None, periods=None, freq='D', name=None):
+def period_range(start=None, end=None, periods=None, freq=None, name=None):
     """
     Return a fixed frequency PeriodIndex, with day (calendar) as the default
     frequency
@@ -952,8 +989,11 @@ def period_range(start=None, end=None, periods=None, freq='D', name=None):
         Right bound for generating periods
     periods : integer, default None
         Number of periods to generate
-    freq : string or DateOffset, default 'D'
-        Frequency alias
+    freq : string or DateOffset, optional
+        Frequency alias. By default the freq is taken from `start` or `end`
+        if those are Period objects. Otherwise, the default is ``"D"`` for
+        daily frequency.
+
     name : string, default None
         Name of the resulting PeriodIndex
 
@@ -990,6 +1030,11 @@ def period_range(start=None, end=None, periods=None, freq='D', name=None):
     if com.count_not_none(start, end, periods) != 2:
         raise ValueError('Of the three parameters: start, end, and periods, '
                          'exactly two must be specified')
-
-    return PeriodIndex(start=start, end=end, periods=periods,
-                       freq=freq, name=name)
+    if freq is None and (not isinstance(start, Period)
+                         and not isinstance(end, Period)):
+        freq = 'D'
+
+    data, freq = PeriodArray._generate_range(start, end, periods, freq,
+                                             fields={})
+    data = PeriodArray(data, freq=freq)
+    return PeriodIndex(data, name=name)

pandas/core/indexes/timedeltas.py

@@ -64,15 +64,27 @@ class TimedeltaIndex(TimedeltaArray, DatetimeIndexOpsMixin,
     start : starting value, timedelta-like, optional
         If data is None, start is used as the start point in generating regular
         timedelta data.
+
+        .. deprecated:: 0.24.0
+
     periods  : int, optional, > 0
         Number of periods to generate, if generating index. Takes precedence
         over end argument
-    end   : end time, timedelta-like, optional
+
+        .. deprecated:: 0.24.0
+
+    end : end time, timedelta-like, optional
         If periods is none, generated index will extend to first conforming
         time on or just past end argument
+
+        .. deprecated:: 0.24. 0
+
     closed : string or None, default None
         Make the interval closed with respect to the given frequency to
         the 'left', 'right', or both sides (None)
+
+        .. deprecated:: 0.24. 0
+
     name : object
         Name to be stored in the index
 
@@ -100,12 +112,15 @@ class TimedeltaIndex(TimedeltaArray, DatetimeIndexOpsMixin,
     Timedelta : Represents a duration between two dates or times.
     DatetimeIndex : Index of datetime64 data.
     PeriodIndex : Index of Period data.
+    timedelta_range : Create a fixed-frequency TimedeltaIndex.
 
     Notes
     -----
-
     To learn more about the frequency strings, please see `this link
     <http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases>`__.
+
+    Creating a TimedeltaIndex based on `start`, `periods`, and `end` has
+    been deprecated in favor of :func:`timedelta_range`.
     """
 
     _typ = 'timedeltaindex'

pandas/core/resample.py

@@ -1491,10 +1491,10 @@ def _get_time_period_bins(self, ax):
             binner = labels = PeriodIndex(data=[], freq=freq, name=ax.name)
             return binner, [], labels
 
-        labels = binner = PeriodIndex(start=ax[0],
-                                      end=ax[-1],
-                                      freq=freq,
-                                      name=ax.name)
+        labels = binner = pd.period_range(start=ax[0],
+                                          end=ax[-1],
+                                          freq=freq,
+                                          name=ax.name)
 
         end_stamps = (labels + freq).asfreq(freq, 's').to_timestamp()
         if ax.tzinfo:
@@ -1543,8 +1543,8 @@ def _get_period_bins(self, ax):
             bin_shift = start_offset.n % freq_mult
             start = p_start
 
-        labels = binner = PeriodIndex(start=start, end=end,
-                                      freq=self.freq, name=ax.name)
+        labels = binner = pd.period_range(start=start, end=end,
+                                          freq=self.freq, name=ax.name)
 
         i8 = memb.asi8
 

pandas/plotting/_converter.py

@@ -22,7 +22,7 @@
 import pandas.core.common as com
 from pandas.core.index import Index
 from pandas.core.indexes.datetimes import date_range
-from pandas.core.indexes.period import Period, PeriodIndex
+from pandas.core.indexes.period import Period, PeriodIndex, period_range
 import pandas.core.tools.datetimes as tools
 
 import pandas.tseries.frequencies as frequencies
@@ -630,7 +630,7 @@ def _daily_finder(vmin, vmax, freq):
     (vmin, vmax) = (Period(ordinal=int(vmin), freq=freq),
                     Period(ordinal=int(vmax), freq=freq))
     span = vmax.ordinal - vmin.ordinal + 1
-    dates_ = PeriodIndex(start=vmin, end=vmax, freq=freq)
+    dates_ = period_range(start=vmin, end=vmax, freq=freq)
     # Initialize the output
     info = np.zeros(span,
                     dtype=[('val', np.int64), ('maj', bool),

pandas/tests/frame/test_join.py

@@ -3,7 +3,7 @@
 import pytest
 import numpy as np
 
-from pandas import DataFrame, Index, PeriodIndex
+from pandas import DataFrame, Index, period_range
 from pandas.tests.frame.common import TestData
 import pandas.util.testing as tm
 
@@ -13,7 +13,7 @@ def frame_with_period_index():
     return DataFrame(
         data=np.arange(20).reshape(4, 5),
         columns=list('abcde'),
-        index=PeriodIndex(start='2000', freq='A', periods=4))
+        index=period_range(start='2000', freq='A', periods=4))
 
 
 @pytest.fixture

pandas/tests/frame/test_period.py

@@ -44,7 +44,7 @@ def test_frame_setitem(self):
 
     def test_frame_to_time_stamp(self):
         K = 5
-        index = PeriodIndex(freq='A', start='1/1/2001', end='12/1/2009')
+        index = period_range(freq='A', start='1/1/2001', end='12/1/2009')
         df = DataFrame(randn(len(index), K), index=index)
         df['mix'] = 'a'