DEPR: deprecate Index.is_numeric

doc/source/user_guide/scale.rst

@@ -333,6 +333,7 @@ Dask implements the most used parts of the pandas API. For example, we can do
 a familiar groupby aggregation.
 
 .. ipython:: python
+   :okwarning:
 
    %time ddf.groupby("name")[["x", "y"]].mean().compute().head()
 
@@ -356,6 +357,7 @@ we need to supply the divisions manually.
 Now we can do things like fast random access with ``.loc``.
 
 .. ipython:: python
+   :okwarning:
 
    ddf.loc["2002-01-01 12:01":"2002-01-01 12:05"].compute()
 
@@ -369,6 +371,7 @@ results will fit in memory, so we can safely call ``compute`` without running
 out of memory. At that point it's just a regular pandas object.
 
 .. ipython:: python
+   :okwarning:
 
    @savefig dask_resample.png
    ddf[["x", "y"]].resample("1D").mean().cumsum().compute().plot()

doc/source/whatsnew/v2.0.0.rst

@@ -749,6 +749,7 @@ 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_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

@@ -47,6 +47,7 @@
 from pandas.core.dtypes.common import (
     ensure_int64,
     ensure_platform_int,
+    is_any_numeric_dtype,
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_dtype,
@@ -595,7 +596,7 @@ def _from_inferred_categories(
 
         if known_categories:
             # Convert to a specialized type with `dtype` if specified.
-            if dtype.categories.is_numeric():
+            if is_any_numeric_dtype(dtype.categories):
                 cats = to_numeric(inferred_categories, errors="coerce")
             elif is_datetime64_dtype(dtype.categories):
                 cats = to_datetime(inferred_categories, errors="coerce")
@@ -1758,7 +1759,7 @@ def _values_for_rank(self):
             if mask.any():
                 values = values.astype("float64")
                 values[mask] = np.nan
-        elif self.categories.is_numeric():
+        elif is_any_numeric_dtype(self.categories):
             values = np.array(self)
         else:
             #  reorder the categories (so rank can use the float codes)

pandas/core/dtypes/common.py

@@ -1219,6 +1219,40 @@ def is_numeric_dtype(arr_or_dtype) -> bool:
     )
 
 
+def is_any_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of a real number dtype
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        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)
+    True
+    >>> is_any_numeric_dtype(float)
+    True
+    >>> is_any_numeric_dtype(complex(1,2))
+    False
+    >>> is_any_numeric_dtype(bool)
+    False
+    """
+    return (
+        is_numeric_dtype(arr_or_dtype)
+        and not is_complex_dtype(arr_or_dtype)
+        and not is_bool_dtype(arr_or_dtype)
+    )
+
+
 def is_float_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of a float dtype.
@@ -1774,6 +1808,7 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_nested_list_like",
     "is_number",
     "is_numeric_dtype",
+    "is_any_numeric_dtype",
     "is_numeric_v_string_like",
     "is_object_dtype",
     "is_period_dtype",

pandas/core/indexes/base.py

@@ -88,6 +88,7 @@
     ensure_int64,
     ensure_object,
     ensure_platform_int,
+    is_any_numeric_dtype,
     is_bool_dtype,
     is_categorical_dtype,
     is_dtype_equal,
@@ -2278,7 +2279,7 @@ def is_boolean(self) -> bool:
         --------
         is_integer : Check if the Index only consists of integers (deprecated).
         is_floating : Check if the Index is a floating type (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_object : Check if the Index is of the object dtype (deprecated).
         is_categorical : Check if the Index holds categorical data.
         is_interval : Check if the Index holds Interval objects (deprecated).
@@ -2322,7 +2323,7 @@ def is_integer(self) -> bool:
         --------
         is_boolean : Check if the Index only consists of booleans (deprecated).
         is_floating : Check if the Index is a floating type (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_object : Check if the Index is of the object dtype. (deprecated).
         is_categorical : Check if the Index holds categorical data (deprecated).
         is_interval : Check if the Index holds Interval objects (deprecated).
@@ -2370,7 +2371,7 @@ def is_floating(self) -> bool:
         --------
         is_boolean : Check if the Index only consists of booleans (deprecated).
         is_integer : Check if the Index only consists of integers (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_object : Check if the Index is of the object dtype. (deprecated).
         is_categorical : Check if the Index holds categorical data (deprecated).
         is_interval : Check if the Index holds Interval objects (deprecated).
@@ -2406,6 +2407,9 @@ def is_numeric(self) -> bool:
         """
         Check if the Index only consists of numeric data.
 
+        .. deprecated:: 2.0.0
+            Use `pandas.api.types.is_numeric_dtype` instead.
+
         Returns
         -------
         bool
@@ -2442,6 +2446,12 @@ def is_numeric(self) -> bool:
         >>> idx.is_numeric()
         False
         """
+        warnings.warn(
+            f"{type(self).__name__}.is_numeric is deprecated. "
+            "Use pandas.api.types.is_numeric_dtype instead",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
         return self.inferred_type in ["integer", "floating"]
 
     @final
@@ -2462,7 +2472,7 @@ def is_object(self) -> bool:
         is_boolean : Check if the Index only consists of booleans (deprecated).
         is_integer : Check if the Index only consists of integers (deprecated).
         is_floating : Check if the Index is a floating type (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_categorical : Check if the Index holds categorical data (deprecated).
         is_interval : Check if the Index holds Interval objects (deprecated).
 
@@ -2512,7 +2522,7 @@ def is_categorical(self) -> bool:
         is_boolean : Check if the Index only consists of booleans (deprecated).
         is_integer : Check if the Index only consists of integers (deprecated).
         is_floating : Check if the Index is a floating type (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_object : Check if the Index is of the object dtype. (deprecated).
         is_interval : Check if the Index holds Interval objects (deprecated).
 
@@ -2565,7 +2575,7 @@ def is_interval(self) -> bool:
         is_boolean : Check if the Index only consists of booleans (deprecated).
         is_integer : Check if the Index only consists of integers (deprecated).
         is_floating : Check if the Index is a floating type (deprecated).
-        is_numeric : Check if the Index only consists of numeric data.
+        is_numeric : Check if the Index only consists of numeric data (deprecated).
         is_object : Check if the Index is of the object dtype. (deprecated).
         is_categorical : Check if the Index holds categorical data (deprecated).
 
@@ -3360,7 +3370,7 @@ def _intersection(self, other: Index, sort: bool = False):
                 pass
             else:
                 # TODO: algos.unique1d should preserve DTA/TDA
-                if self.is_numeric():
+                if is_numeric_dtype(self):
                     # This is faster, because Index.unique() checks for uniqueness
                     # before calculating the unique values.
                     res = algos.unique1d(res_indexer)
@@ -6035,8 +6045,8 @@ def _should_compare(self, other: Index) -> bool:
         Check if `self == other` can ever have non-False entries.
         """
 
-        if (is_bool_dtype(other) and self.is_numeric()) or (
-            is_bool_dtype(self) and other.is_numeric()
+        if (is_bool_dtype(other) and is_any_numeric_dtype(self)) or (
+            is_bool_dtype(self) and is_any_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

@@ -27,6 +27,7 @@
 from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
+    is_any_numeric_dtype,
     is_categorical_dtype,
     is_extension_array_dtype,
     is_float,
@@ -840,7 +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 index.is_numeric():
+            elif is_any_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/indexes/common.py

@@ -813,6 +813,15 @@ def test_holds_integer_deprecated(self, simple_index):
         with tm.assert_produces_warning(FutureWarning, match=msg):
             idx.holds_integer()
 
+    def test_is_numeric_is_deprecated(self, simple_index):
+        # GH50042
+        idx = simple_index
+        with tm.assert_produces_warning(
+            FutureWarning,
+            match=f"{type(idx).__name__}.is_numeric is deprecated. ",
+        ):
+            idx.is_numeric()
+
     def test_is_categorical_is_deprecated(self, simple_index):
         # GH50042
         idx = simple_index

pandas/tests/indexes/multi/test_equivalence.py

@@ -1,6 +1,8 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.common import is_any_numeric_dtype
+
 import pandas as pd
 from pandas import (
     Index,
@@ -253,7 +255,7 @@ def test_is_all_dates(idx):
 
 def test_is_numeric(idx):
     # MultiIndex is never numeric
-    assert not idx.is_numeric()
+    assert not is_any_numeric_dtype(idx)
 
 
 def test_multiindex_compare():

pandas/tests/indexes/test_base.py

@@ -19,6 +19,7 @@
 from pandas.util._test_decorators import async_mark
 
 from pandas.core.dtypes.common import (
+    is_any_numeric_dtype,
     is_numeric_dtype,
     is_object_dtype,
 )
@@ -659,7 +660,7 @@ def test_append_empty_preserve_name(self, name, expected):
         indirect=["index"],
     )
     def test_is_numeric(self, index, expected):
-        assert index.is_numeric() is expected
+        assert is_any_numeric_dtype(index) is expected
 
     @pytest.mark.parametrize(
         "index, expected",