DEPR: is_period, is_interval

doc/source/whatsnew/v2.2.0.rst

@@ -262,6 +262,7 @@ For example:
 Other Deprecations
 ^^^^^^^^^^^^^^^^^^
 - Changed :meth:`Timedelta.resolution_string` to return ``h``, ``min``, ``s``, ``ms``, ``us``, and ``ns`` instead of ``H``, ``T``, ``S``, ``L``, ``U``, and ``N``, for compatibility with respective deprecations in frequency aliases (:issue:`52536`)
+- Deprecated :func:`pandas.api.types.is_interval` and :func:`pandas.api.types.is_period`, use ``isinstance(obj, pd.Interval)`` and ``isinstance(obj, pd.Period)`` instead (:issue:`55264`)
 - Deprecated :func:`read_gbq` and :meth:`DataFrame.to_gbq`. Use ``pandas_gbq.read_gbq`` and ``pandas_gbq.to_gbq`` instead https://pandas-gbq.readthedocs.io/en/latest/api.html (:issue:`55525`)
 - Deprecated :meth:`.DataFrameGroupBy.fillna` and :meth:`.SeriesGroupBy.fillna`; use :meth:`.DataFrameGroupBy.ffill`, :meth:`.DataFrameGroupBy.bfill` for forward and backward filling or :meth:`.DataFrame.fillna` to fill with a single value (or the Series equivalents) (:issue:`55718`)
 - Deprecated :meth:`Index.format`, use ``index.astype(str)`` or ``index.map(formatter)`` instead (:issue:`55413`)

pandas/_libs/lib.pyx

@@ -65,6 +65,7 @@ from numpy cimport (
 )
 
 cnp.import_array()
+from pandas._libs.interval import Interval
 
 
 cdef extern from "numpy/arrayobject.h":
@@ -245,7 +246,7 @@ def is_scalar(val: object) -> bool:
     # Note: PyNumber_Check check includes Decimal, Fraction, numbers.Number
     return (PyNumber_Check(val)
             or is_period_object(val)
-            or is_interval(val)
+            or isinstance(val, Interval)
             or is_offset_object(val))
 
 
@@ -1178,6 +1179,17 @@ cpdef bint is_decimal(object obj):
 
 
 cpdef bint is_interval(object obj):
+    import warnings
+
+    from pandas.util._exceptions import find_stack_level
+
+    warnings.warn(
+        # GH#55264
+        "is_interval is deprecated and will be removed in a future version. "
+        "Use isinstance(obj, pd.Interval) instead.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
     return getattr(obj, "_typ", "_typ") == "interval"
 
 
@@ -1189,6 +1201,17 @@ def is_period(val: object) -> bool:
     -------
     bool
     """
+    import warnings
+
+    from pandas.util._exceptions import find_stack_level
+
+    warnings.warn(
+        # GH#55264
+        "is_period is deprecated and will be removed in a future version. "
+        "Use isinstance(obj, pd.Period) instead.",
+        FutureWarning,
+        stacklevel=find_stack_level(),
+    )
     return is_period_object(val)
 
 
@@ -1711,7 +1734,7 @@ def infer_dtype(value: object, skipna: bool = True) -> str:
         if is_period_array(values, skipna=skipna):
             return "period"
 
-    elif is_interval(val):
+    elif isinstance(val, Interval):
         if is_interval_array(values):
             return "interval"
 
@@ -2186,7 +2209,7 @@ cpdef bint is_interval_array(ndarray values):
     for i in range(n):
         val = values[i]
 
-        if is_interval(val):
+        if isinstance(val, Interval):
             if closed is None:
                 closed = val.closed
                 numeric = (
@@ -2647,7 +2670,7 @@ def maybe_convert_objects(ndarray[object] objects,
             except (ValueError, TypeError):
                 seen.object_ = True
                 break
-        elif is_interval(val):
+        elif isinstance(val, Interval):
             if convert_non_numeric:
                 seen.interval_ = True
                 break

pandas/core/dtypes/cast.py

@@ -20,7 +20,11 @@
 
 from pandas._config import using_pyarrow_string_dtype
 
-from pandas._libs import lib
+from pandas._libs import (
+    Interval,
+    Period,
+    lib,
+)
 from pandas._libs.missing import (
     NA,
     NAType,
@@ -850,9 +854,9 @@ def infer_dtype_from_scalar(val) -> tuple[DtypeObj, Any]:
     elif is_complex(val):
         dtype = np.dtype(np.complex128)
 
-    if lib.is_period(val):
+    if isinstance(val, Period):
         dtype = PeriodDtype(freq=val.freq)
-    elif lib.is_interval(val):
+    elif isinstance(val, Interval):
         subtype = infer_dtype_from_scalar(val.left)[0]
         dtype = IntervalDtype(subtype=subtype, closed=val.closed)
 

pandas/core/indexes/interval.py

@@ -22,6 +22,7 @@
 )
 from pandas._libs.tslibs import (
     BaseOffset,
+    Period,
     Timedelta,
     Timestamp,
     to_offset,
@@ -561,7 +562,7 @@ def _maybe_convert_i8(self, key):
         if scalar:
             # Timestamp/Timedelta
             key_dtype, key_i8 = infer_dtype_from_scalar(key)
-            if lib.is_period(key):
+            if isinstance(key, Period):
                 key_i8 = key.ordinal
             elif isinstance(key_i8, Timestamp):
                 key_i8 = key_i8._value

pandas/tests/dtypes/test_inference.py

@@ -1622,11 +1622,23 @@ def test_to_object_array_width(self):
         tm.assert_numpy_array_equal(out, expected)
 
     def test_is_period(self):
-        assert lib.is_period(Period("2011-01", freq="M"))
-        assert not lib.is_period(PeriodIndex(["2011-01"], freq="M"))
-        assert not lib.is_period(Timestamp("2011-01"))
-        assert not lib.is_period(1)
-        assert not lib.is_period(np.nan)
+        # GH#55264
+        msg = "is_period is deprecated and will be removed in a future version"
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_period(Period("2011-01", freq="M"))
+            assert not lib.is_period(PeriodIndex(["2011-01"], freq="M"))
+            assert not lib.is_period(Timestamp("2011-01"))
+            assert not lib.is_period(1)
+            assert not lib.is_period(np.nan)
+
+    def test_is_interval(self):
+        # GH#55264
+        msg = "is_interval is deprecated and will be removed in a future version"
+        item = Interval(1, 2)
+        with tm.assert_produces_warning(FutureWarning, match=msg):
+            assert lib.is_interval(item)
+            assert not lib.is_interval(pd.IntervalIndex([item]))
+            assert not lib.is_interval(pd.IntervalIndex([item])._engine)
 
     def test_categorical(self):
         # GH 8974