BUG: Add is_any_real_numeric_dtype to solve discrepancy between Index.is_numeric() and is_numeric_dtype() (#51160)

Author: ABCPAN-rank

ci/code_checks.sh

@@ -590,6 +590,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         pandas.Series.sparse.sp_values \
         pandas.Timestamp.fromtimestamp \
         pandas.api.types.infer_dtype \
+        pandas.api.types.is_any_real_numeric_dtype \
         pandas.api.types.is_bool_dtype \
         pandas.api.types.is_categorical_dtype \
         pandas.api.types.is_complex_dtype \

doc/source/reference/arrays.rst

@@ -653,6 +653,7 @@ Data type introspection
 .. autosummary::
    :toctree: api/
 
+    api.types.is_any_real_numeric_dtype
     api.types.is_bool_dtype
     api.types.is_categorical_dtype
     api.types.is_complex_dtype

doc/source/whatsnew/v2.0.0.rst

@@ -760,6 +760,7 @@ Other API changes
 - The levels of the index of the :class:`Series` returned from ``Series.sparse.from_coo`` now always have dtype ``int32``. Previously they had dtype ``int64`` (:issue:`50926`)
 - :func:`to_datetime` with ``unit`` of either "Y" or "M" will now raise if a sequence contains a non-round ``float`` value, matching the ``Timestamp`` behavior (:issue:`50301`)
 - The methods :meth:`Series.round`, :meth:`DataFrame.__invert__`, :meth:`Series.__invert__`, :meth:`DataFrame.swapaxes`, :meth:`DataFrame.first`, :meth:`DataFrame.last`, :meth:`Series.first`, :meth:`Series.last` and :meth:`DataFrame.align` will now always return new objects (:issue:`51032`)
+- Added :func:`pandas.api.types.is_any_real_numeric_dtype` to check for real numeric dtypes (:issue:`51152`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_200.deprecations:
@@ -774,7 +775,7 @@ Deprecations
 - :meth:`Index.is_integer` has been deprecated. Use :func:`pandas.api.types.is_integer_dtype` instead (:issue:`50042`)
 - :meth:`Index.is_floating` has been deprecated. Use :func:`pandas.api.types.is_float_dtype` instead (:issue:`50042`)
 - :meth:`Index.holds_integer` has been deprecated. Use :func:`pandas.api.types.infer_dtype` instead (:issue:`50243`)
-- :meth:`Index.is_numeric` has been deprecated. Use :func:`pandas.api.types.is_numeric_dtype` instead (:issue:`50042`)
+- :meth:`Index.is_numeric` has been deprecated. Use :func:`pandas.api.types.is_any_real_numeric_dtype` instead (:issue:`50042`,:issue:`51152`)
 - :meth:`Index.is_categorical` has been deprecated. Use :func:`pandas.api.types.is_categorical_dtype` instead (:issue:`50042`)
 - :meth:`Index.is_object` has been deprecated. Use :func:`pandas.api.types.is_object_dtype` instead (:issue:`50042`)
 - :meth:`Index.is_interval` has been deprecated. Use :func:`pandas.api.types.is_intterval_dtype` instead (:issue:`50042`)

pandas/core/arrays/categorical.py

@@ -47,7 +47,7 @@
 from pandas.core.dtypes.common import (
     ensure_int64,
     ensure_platform_int,
-    is_any_numeric_dtype,
+    is_any_real_numeric_dtype,
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_dtype,
@@ -596,7 +596,7 @@ def _from_inferred_categories(
 
         if known_categories:
             # Convert to a specialized type with `dtype` if specified.
-            if is_any_numeric_dtype(dtype.categories):
+            if is_any_real_numeric_dtype(dtype.categories):
                 cats = to_numeric(inferred_categories, errors="coerce")
             elif is_datetime64_dtype(dtype.categories):
                 cats = to_datetime(inferred_categories, errors="coerce")
@@ -1752,7 +1752,7 @@ def _values_for_rank(self):
             if mask.any():
                 values = values.astype("float64")
                 values[mask] = np.nan
-        elif is_any_numeric_dtype(self.categories):
+        elif is_any_real_numeric_dtype(self.categories):
             values = np.array(self)
         else:
             #  reorder the categories (so rank can use the float codes)

pandas/core/dtypes/api.py

@@ -1,4 +1,5 @@
 from pandas.core.dtypes.common import (
+    is_any_real_numeric_dtype,
     is_array_like,
     is_bool,
     is_bool_dtype,
@@ -41,6 +42,7 @@
 )
 
 __all__ = [
+    "is_any_real_numeric_dtype",
     "is_array_like",
     "is_bool",
     "is_bool_dtype",

pandas/core/dtypes/common.py

@@ -1161,9 +1161,9 @@ def is_numeric_dtype(arr_or_dtype) -> bool:
     )
 
 
-def is_any_numeric_dtype(arr_or_dtype) -> bool:
+def is_any_real_numeric_dtype(arr_or_dtype) -> bool:
     """
-    Check whether the provided array or dtype is of a real number dtype
+    Check whether the provided array or dtype is of a real number dtype.
 
     Parameters
     ----------
@@ -1173,19 +1173,21 @@ def is_any_numeric_dtype(arr_or_dtype) -> bool:
     Returns
     -------
     boolean
-        Whether or not the array or dtype is of a real number dtype
+        Whether or not the array or dtype is of a real number dtype.
 
     Examples
-    -------
-    >>> is_any_numeric_dtype(str)
-    False
-    >>> is_any_numeric_dtype(int)
+    --------
+    >>> is_any_real_numeric_dtype(int)
     True
-    >>> is_any_numeric_dtype(float)
+    >>> is_any_real_numeric_dtype(float)
     True
-    >>> is_any_numeric_dtype(complex(1,2))
+    >>> is_any_real_numeric_dtype(object)
+    False
+    >>> is_any_real_numeric_dtype(str)
+    False
+    >>> is_any_real_numeric_dtype(complex(1, 2))
     False
-    >>> is_any_numeric_dtype(bool)
+    >>> is_any_real_numeric_dtype(bool)
     False
     """
     return (
@@ -1721,6 +1723,7 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_1d_only_ea_obj",
     "is_all_strings",
     "is_any_int_dtype",
+    "is_any_real_numeric_dtype",
     "is_array_like",
     "is_bool",
     "is_bool_dtype",
@@ -1749,7 +1752,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_nested_list_like",
     "is_number",
     "is_numeric_dtype",
-    "is_any_numeric_dtype",
     "is_object_dtype",
     "is_period_dtype",
     "is_re",

pandas/core/indexes/base.py

@@ -88,7 +88,7 @@
     ensure_int64,
     ensure_object,
     ensure_platform_int,
-    is_any_numeric_dtype,
+    is_any_real_numeric_dtype,
     is_bool_dtype,
     is_categorical_dtype,
     is_dtype_equal,
@@ -2429,7 +2429,7 @@ def is_numeric(self) -> bool:
         """
         warnings.warn(
             f"{type(self).__name__}.is_numeric is deprecated. "
-            "Use pandas.api.types.is_numeric_dtype instead",
+            "Use pandas.api.types.is_any_real_numeric_dtype instead",
             FutureWarning,
             stacklevel=find_stack_level(),
         )
@@ -6029,8 +6029,8 @@ def _should_compare(self, other: Index) -> bool:
         Check if `self == other` can ever have non-False entries.
         """
 
-        if (is_bool_dtype(other) and is_any_numeric_dtype(self)) or (
-            is_bool_dtype(self) and is_any_numeric_dtype(other)
+        if (is_bool_dtype(other) and is_any_real_numeric_dtype(self)) or (
+            is_bool_dtype(self) and is_any_real_numeric_dtype(other)
         ):
             # GH#16877 Treat boolean labels passed to a numeric index as not
             #  found. Without this fix False and True would be treated as 0 and 1

pandas/plotting/_matplotlib/core.py

@@ -27,7 +27,7 @@
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
-    is_any_numeric_dtype,
+    is_any_real_numeric_dtype,
     is_categorical_dtype,
     is_extension_array_dtype,
     is_float,
@@ -841,7 +841,7 @@ def _get_xticks(self, convert_period: bool = False):
             if convert_period and isinstance(index, ABCPeriodIndex):
                 self.data = self.data.reindex(index=index.sort_values())
                 x = self.data.index.to_timestamp()._mpl_repr()
-            elif is_any_numeric_dtype(index):
+            elif is_any_real_numeric_dtype(index):
                 # Matplotlib supports numeric values or datetime objects as
                 # xaxis values. Taking LBYL approach here, by the time
                 # matplotlib raises exception when using non numeric/datetime

pandas/tests/api/test_types.py

@@ -8,6 +8,7 @@
 class TestTypes(Base):
 
     allowed = [
+        "is_any_real_numeric_dtype",
         "is_bool",
         "is_bool_dtype",
         "is_categorical_dtype",

pandas/tests/dtypes/test_common.py

@@ -558,6 +558,20 @@ def _is_numeric(self) -> bool:
     assert com.is_numeric_dtype(MyNumericDType())
 
 
+def test_is_any_real_numeric_dtype():
+    assert not com.is_any_real_numeric_dtype(str)
+    assert not com.is_any_real_numeric_dtype(bool)
+    assert not com.is_any_real_numeric_dtype(complex)
+    assert not com.is_any_real_numeric_dtype(object)
+    assert not com.is_any_real_numeric_dtype(np.datetime64)
+    assert not com.is_any_real_numeric_dtype(np.array(["a", "b", complex(1, 2)]))
+    assert not com.is_any_real_numeric_dtype(pd.DataFrame([complex(1, 2), True]))
+
+    assert com.is_any_real_numeric_dtype(int)
+    assert com.is_any_real_numeric_dtype(float)
+    assert com.is_any_real_numeric_dtype(np.array([1, 2.5]))
+
+
 def test_is_float_dtype():
     assert not com.is_float_dtype(str)
     assert not com.is_float_dtype(int)

pandas/tests/indexes/multi/test_equivalence.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from pandas.core.dtypes.common import is_any_numeric_dtype
+from pandas.core.dtypes.common import is_any_real_numeric_dtype
 
 import pandas as pd
 from pandas import (
@@ -255,7 +255,7 @@ def test_is_all_dates(idx):
 
 def test_is_numeric(idx):
     # MultiIndex is never numeric
-    assert not is_any_numeric_dtype(idx)
+    assert not is_any_real_numeric_dtype(idx)
 
 
 def test_multiindex_compare():

pandas/tests/indexes/test_base.py

@@ -19,7 +19,7 @@
 from pandas.util._test_decorators import async_mark
 
 from pandas.core.dtypes.common import (
-    is_any_numeric_dtype,
+    is_any_real_numeric_dtype,
     is_numeric_dtype,
     is_object_dtype,
 )
@@ -659,7 +659,7 @@ def test_append_empty_preserve_name(self, name, expected):
         indirect=["index"],
     )
     def test_is_numeric(self, index, expected):
-        assert is_any_numeric_dtype(index) is expected
+        assert is_any_real_numeric_dtype(index) is expected
 
     @pytest.mark.parametrize(
         "index, expected",