REF: remove freq arg from PeriodArray constructor

doc/source/whatsnew/v2.1.0.rst

@@ -173,7 +173,8 @@ Deprecations
 - Deprecated the methods :meth:`Series.bool` and :meth:`DataFrame.bool` (:issue:`51749`)
 - Deprecated :meth:`DataFrame.swapaxes` and :meth:`Series.swapaxes`, use :meth:`DataFrame.transpose` or :meth:`Series.transpose` instead (:issue:`51946`)
 - Deprecated parameter ``convert_type`` in :meth:`Series.apply` (:issue:`52140`)
-
+- Deprecated ``freq`` parameter in :class:`PeriodArray` constructor, pass ``dtype`` instead (:issue:52462`)
+-
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_210.performance:

pandas/core/arrays/datetimelike.py

@@ -1106,7 +1106,8 @@ def _add_period(self, other: Period) -> PeriodArray:
         from pandas.core.arrays.period import PeriodArray
 
         i8vals = np.broadcast_to(other.ordinal, self.shape)
-        parr = PeriodArray(i8vals, freq=other.freq)
+        dtype = PeriodDtype(other.freq)
+        parr = PeriodArray(i8vals, dtype=dtype)
         return parr + self
 
     def _add_offset(self, offset):
@@ -1282,9 +1283,7 @@ def _accumulate(self, name: str, *, skipna: bool = True, **kwargs) -> Self:
         op = getattr(datetimelike_accumulations, name)
         result = op(self.copy(), skipna=skipna, **kwargs)
 
-        return type(self)._simple_new(
-            result, freq=None, dtype=self.dtype  # type: ignore[call-arg]
-        )
+        return type(self)._simple_new(result, dtype=self.dtype)
 
     @unpack_zerodim_and_defer("__add__")
     def __add__(self, other):

pandas/core/arrays/period.py

@@ -9,8 +9,10 @@
     Literal,
     Sequence,
     TypeVar,
+    cast,
     overload,
 )
+import warnings
 
 import numpy as np
 
@@ -49,6 +51,7 @@
     cache_readonly,
     doc,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     ensure_object,
@@ -116,9 +119,7 @@ class PeriodArray(dtl.DatelikeOps, libperiod.PeriodMixin):  # type: ignore[misc]
     """
     Pandas ExtensionArray for storing Period data.
 
-    Users should use :func:`~pandas.period_array` to create new instances.
-    Alternatively, :func:`~pandas.array` can be used to create new instances
-    from a sequence of Period scalars.
+    Users should use :func:`~pandas.array` to create new instances.
 
     Parameters
     ----------
@@ -213,10 +214,21 @@ def _scalar_type(self) -> type[Period]:
     def __init__(
         self, values, dtype: Dtype | None = None, freq=None, copy: bool = False
     ) -> None:
-        freq = validate_dtype_freq(dtype, freq)
-
         if freq is not None:
-            freq = Period._maybe_convert_freq(freq)
+            # GH#52462
+            warnings.warn(
+                "The 'freq' keyword in the PeriodArray constructor is deprecated "
+                "and will be removed in a future version. Pass 'dtype' instead",
+                FutureWarning,
+                stacklevel=find_stack_level(),
+            )
+            freq = validate_dtype_freq(dtype, freq)
+            dtype = PeriodDtype(freq)
+
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
+            if not isinstance(dtype, PeriodDtype):
+                raise ValueError(f"Invalid dtype {dtype} for PeriodArray")
 
         if isinstance(values, ABCSeries):
             values = values._values
@@ -227,36 +239,38 @@ def __init__(
             values = values._values
 
         if isinstance(values, type(self)):
-            if freq is not None and freq != values.freq:
-                raise raise_on_incompatible(values, freq)
-            values, freq = values._ndarray, values.freq
+            if dtype is not None and dtype != values.dtype:
+                raise raise_on_incompatible(values, dtype.freq)
+            values, dtype = values._ndarray, values.dtype
 
         values = np.array(values, dtype="int64", copy=copy)
-        if freq is None:
-            raise ValueError("freq is not specified and cannot be inferred")
-        NDArrayBacked.__init__(self, values, PeriodDtype(freq))
+        if dtype is None:
+            raise ValueError("dtype is not specified and cannot be inferred")
+        dtype = cast(PeriodDtype, dtype)
+        NDArrayBacked.__init__(self, values, dtype)
 
     # error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
     @classmethod
     def _simple_new(  # type: ignore[override]
         cls,
         values: npt.NDArray[np.int64],
-        freq: BaseOffset | None = None,
-        dtype: Dtype | None = None,
+        dtype: PeriodDtype,
     ) -> Self:
         # alias for PeriodArray.__init__
         assertion_msg = "Should be numpy array of type i8"
         assert isinstance(values, np.ndarray) and values.dtype == "i8", assertion_msg
-        return cls(values, freq=freq, dtype=dtype)
+        return cls(values, dtype=dtype)
 
     @classmethod
     def _from_sequence(
         cls,
-        scalars: Sequence[Period | None] | AnyArrayLike,
+        scalars,
         *,
         dtype: Dtype | None = None,
         copy: bool = False,
     ) -> Self:
+        if dtype is not None:
+            dtype = pandas_dtype(dtype)
         if dtype and isinstance(dtype, PeriodDtype):
             freq = dtype.freq
         else:
@@ -266,16 +280,14 @@ def _from_sequence(
             validate_dtype_freq(scalars.dtype, freq)
             if copy:
                 scalars = scalars.copy()
-            # error: Incompatible return value type
-            # (got "Union[Sequence[Optional[Period]], Union[Union[ExtensionArray,
-            # ndarray[Any, Any]], Index, Series]]", expected "PeriodArray")
-            return scalars  # type: ignore[return-value]
+            return scalars
 
         periods = np.asarray(scalars, dtype=object)
 
         freq = freq or libperiod.extract_freq(periods)
         ordinals = libperiod.extract_ordinals(periods, freq)
-        return cls(ordinals, freq=freq)
+        dtype = PeriodDtype(freq)
+        return cls(ordinals, dtype=dtype)
 
     @classmethod
     def _from_sequence_of_strings(
@@ -299,7 +311,8 @@ def _from_datetime64(cls, data, freq, tz=None) -> Self:
         PeriodArray[freq]
         """
         data, freq = dt64arr_to_periodarr(data, freq, tz)
-        return cls(data, freq=freq)
+        dtype = PeriodDtype(freq)
+        return cls(data, dtype=dtype)
 
     @classmethod
     def _generate_range(cls, start, end, periods, freq, fields):
@@ -610,7 +623,8 @@ def asfreq(self, freq=None, how: str = "E") -> Self:
         if self._hasna:
             new_data[self._isnan] = iNaT
 
-        return type(self)(new_data, freq=freq)
+        dtype = PeriodDtype(freq)
+        return type(self)(new_data, dtype=dtype)
 
     # ------------------------------------------------------------------
     # Rendering Methods
@@ -697,7 +711,7 @@ def _addsub_int_array_or_scalar(
         if op is operator.sub:
             other = -other
         res_values = algos.checked_add_with_arr(self.asi8, other, arr_mask=self._isnan)
-        return type(self)(res_values, freq=self.freq)
+        return type(self)(res_values, dtype=self.dtype)
 
     def _add_offset(self, other: BaseOffset):
         assert not isinstance(other, Tick)
@@ -768,7 +782,7 @@ def _add_timedelta_arraylike(
             self.asi8, delta.view("i8"), arr_mask=self._isnan, b_mask=b_mask
         )
         np.putmask(res_values, self._isnan | b_mask, iNaT)
-        return type(self)(res_values, freq=self.freq)
+        return type(self)(res_values, dtype=self.dtype)
 
     def _check_timedeltalike_freq_compat(self, other):
         """
@@ -904,7 +918,12 @@ def period_array(
     if is_datetime64_dtype(data_dtype):
         return PeriodArray._from_datetime64(data, freq)
     if isinstance(data_dtype, PeriodDtype):
-        return PeriodArray(data, freq=freq)
+        out = PeriodArray(data)
+        if freq is not None:
+            if freq == data_dtype.freq:
+                return out
+            return out.asfreq(freq)
+        return out
 
     # other iterable of some kind
     if not isinstance(data, (np.ndarray, list, tuple, ABCSeries)):

pandas/core/dtypes/dtypes.py

@@ -1013,14 +1013,14 @@ def __from_arrow__(
         results = []
         for arr in chunks:
             data, mask = pyarrow_array_to_numpy_and_mask(arr, dtype=np.dtype(np.int64))
-            parr = PeriodArray(data.copy(), freq=self.freq, copy=False)
+            parr = PeriodArray(data.copy(), dtype=self, copy=False)
             # error: Invalid index type "ndarray[Any, dtype[bool_]]" for "PeriodArray";
             # expected type "Union[int, Sequence[int], Sequence[bool], slice]"
             parr[~mask] = NaT  # type: ignore[index]
             results.append(parr)
 
         if not results:
-            return PeriodArray(np.array([], dtype="int64"), freq=self.freq, copy=False)
+            return PeriodArray(np.array([], dtype="int64"), dtype=self, copy=False)
         return PeriodArray._concat_same_type(results)
 
 

pandas/core/indexes/period.py

@@ -25,6 +25,7 @@
 )
 
 from pandas.core.dtypes.common import is_integer
+from pandas.core.dtypes.dtypes import PeriodDtype
 from pandas.core.dtypes.generic import ABCSeries
 from pandas.core.dtypes.missing import is_valid_na_for_dtype
 
@@ -52,7 +53,6 @@
         npt,
     )
 
-    from pandas.core.dtypes.dtypes import PeriodDtype
 
 _index_doc_kwargs = dict(ibase._index_doc_kwargs)
 _index_doc_kwargs.update({"target_klass": "PeriodIndex or list of Periods"})
@@ -68,7 +68,8 @@ def _new_PeriodIndex(cls, **d):
     values = d.pop("data")
     if values.dtype == "int64":
         freq = d.pop("freq", None)
-        values = PeriodArray(values, freq=freq)
+        dtype = PeriodDtype(freq)
+        values = PeriodArray(values, dtype=dtype)
         return cls._simple_new(values, **d)
     else:
         return cls(values, **d)
@@ -246,7 +247,8 @@ def __new__(
             # empty when really using the range-based constructor.
             freq = freq2
 
-            data = PeriodArray(data, freq=freq)
+            dtype = PeriodDtype(freq)
+            data = PeriodArray(data, dtype=dtype)
         else:
             freq = validate_dtype_freq(dtype, freq)
 
@@ -261,7 +263,8 @@ def __new__(
             if data is None and ordinal is not None:
                 # we strangely ignore `ordinal` if data is passed.
                 ordinal = np.asarray(ordinal, dtype=np.int64)
-                data = PeriodArray(ordinal, freq=freq)
+                dtype = PeriodDtype(freq)
+                data = PeriodArray(ordinal, dtype=dtype)
             else:
                 # don't pass copy here, since we copy later.
                 data = period_array(data=data, freq=freq)
@@ -535,5 +538,6 @@ def period_range(
         freq = "D"
 
     data, freq = PeriodArray._generate_range(start, end, periods, freq, fields={})
-    data = PeriodArray(data, freq=freq)
+    dtype = PeriodDtype(freq)
+    data = PeriodArray(data, dtype=dtype)
     return PeriodIndex(data, name=name)

pandas/io/pytables.py

@@ -68,6 +68,7 @@
     CategoricalDtype,
     DatetimeTZDtype,
     ExtensionDtype,
+    PeriodDtype,
 )
 from pandas.core.dtypes.missing import array_equivalent
 
@@ -2790,7 +2791,8 @@ def f(values, freq=None, tz=None):
         elif index_class == PeriodIndex:
 
             def f(values, freq=None, tz=None):
-                parr = PeriodArray._simple_new(values, freq=freq)
+                dtype = PeriodDtype(freq)
+                parr = PeriodArray._simple_new(values, dtype=dtype)
                 return PeriodIndex._simple_new(parr, name=None)
 
             factory = f

pandas/tests/arrays/period/test_arrow_compat.py

@@ -62,7 +62,7 @@ def test_arrow_array(data, freq):
 def test_arrow_array_missing():
     from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
 
-    arr = PeriodArray([1, 2, 3], freq="D")
+    arr = PeriodArray([1, 2, 3], dtype="period[D]")
     arr[1] = pd.NaT
 
     result = pa.array(arr)
@@ -75,7 +75,7 @@ def test_arrow_array_missing():
 def test_arrow_table_roundtrip():
     from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
 
-    arr = PeriodArray([1, 2, 3], freq="D")
+    arr = PeriodArray([1, 2, 3], dtype="period[D]")
     arr[1] = pd.NaT
     df = pd.DataFrame({"a": arr})
 
@@ -96,7 +96,7 @@ def test_arrow_load_from_zero_chunks():
 
     from pandas.core.arrays.arrow.extension_types import ArrowPeriodType
 
-    arr = PeriodArray([], freq="D")
+    arr = PeriodArray([], dtype="period[D]")
     df = pd.DataFrame({"a": arr})
 
     table = pa.table(df)
@@ -110,7 +110,7 @@ def test_arrow_load_from_zero_chunks():
 
 
 def test_arrow_table_roundtrip_without_metadata():
-    arr = PeriodArray([1, 2, 3], freq="H")
+    arr = PeriodArray([1, 2, 3], dtype="period[H]")
     arr[1] = pd.NaT
     df = pd.DataFrame({"a": arr})
 

pandas/tests/arrays/period/test_constructors.py

@@ -75,16 +75,17 @@ def test_period_array_raises(data, freq, msg):
 def test_period_array_non_period_series_raies():
     ser = pd.Series([1, 2, 3])
     with pytest.raises(TypeError, match="dtype"):
-        PeriodArray(ser, freq="D")
+        PeriodArray(ser, dtype="period[D]")
 
 
 def test_period_array_freq_mismatch():
     arr = period_array(["2000", "2001"], freq="D")
     with pytest.raises(IncompatibleFrequency, match="freq"):
-        PeriodArray(arr, freq="M")
+        PeriodArray(arr, dtype="period[M]")
 
+    dtype = pd.PeriodDtype(pd.tseries.offsets.MonthEnd())
     with pytest.raises(IncompatibleFrequency, match="freq"):
-        PeriodArray(arr, freq=pd.tseries.offsets.MonthEnd())
+        PeriodArray(arr, dtype=dtype)
 
 
 def test_from_sequence_disallows_i8():
@@ -121,3 +122,14 @@ def test_from_td64nat_sequence_raises():
         pd.Series(arr, dtype=dtype)
     with pytest.raises(ValueError, match=msg):
         pd.DataFrame(arr, dtype=dtype)
+
+
+def test_freq_deprecated():
+    # GH#52462
+    data = np.arange(5).astype(np.int64)
+    msg = "The 'freq' keyword in the PeriodArray constructor is deprecated"
+    with tm.assert_produces_warning(FutureWarning, match=msg):
+        res = PeriodArray(data, freq="M")
+
+    expected = PeriodArray(data, dtype="period[M]")
+    tm.assert_equal(res, expected)