CLN: Partially revert pandas-dev#29553

Author: lithomas1

pandas/core/array_algos/replace.py

@@ -19,8 +19,6 @@
 )
 
 from pandas.core.dtypes.common import (
-    is_datetimelike_v_numeric,
-    is_numeric_v_string_like,
     is_re,
     is_re_compilable,
     is_scalar,
@@ -44,7 +42,7 @@ def should_use_regex(regex: bool, to_replace: Any) -> bool:
 
 def compare_or_regex_search(
     a: ArrayLike, b: Scalar | Pattern, regex: bool, mask: npt.NDArray[np.bool_]
-) -> ArrayLike | bool:
+) -> ArrayLike:
     """
     Compare two array-like inputs of the same shape or two scalar values
 
@@ -95,15 +93,6 @@ def _check_comparison_types(
     if isinstance(a, np.ndarray):
         a = a[mask]
 
-    if is_numeric_v_string_like(a, b):
-        # GH#29553 avoid deprecation warnings from numpy
-        return np.zeros(a.shape, dtype=bool)
-
-    elif is_datetimelike_v_numeric(a, b):
-        # GH#29553 avoid deprecation warnings from numpy
-        _check_comparison_types(False, a, b)
-        return False
-
     result = op(a)
 
     if isinstance(result, np.ndarray) and mask is not None:

pandas/core/dtypes/common.py

@@ -1067,64 +1067,6 @@ def is_numeric_v_string_like(a: ArrayLike, b) -> bool:
     )
 
 
-# This exists to silence numpy deprecation warnings, see GH#29553
-def is_datetimelike_v_numeric(a, b) -> bool:
-    """
-    Check if we are comparing a datetime-like object to a numeric object.
-    By "numeric," we mean an object that is either of an int or float dtype.
-
-    Parameters
-    ----------
-    a : array-like, scalar
-        The first object to check.
-    b : array-like, scalar
-        The second object to check.
-
-    Returns
-    -------
-    boolean
-        Whether we return a comparing a datetime-like to a numeric object.
-
-    Examples
-    --------
-    >>> from datetime import datetime
-    >>> dt = np.datetime64(datetime(2017, 1, 1))
-    >>>
-    >>> is_datetimelike_v_numeric(1, 1)
-    False
-    >>> is_datetimelike_v_numeric(dt, dt)
-    False
-    >>> is_datetimelike_v_numeric(1, dt)
-    True
-    >>> is_datetimelike_v_numeric(dt, 1)  # symmetric check
-    True
-    >>> is_datetimelike_v_numeric(np.array([dt]), 1)
-    True
-    >>> is_datetimelike_v_numeric(np.array([1]), dt)
-    True
-    >>> is_datetimelike_v_numeric(np.array([dt]), np.array([1]))
-    True
-    >>> is_datetimelike_v_numeric(np.array([1]), np.array([2]))
-    False
-    >>> is_datetimelike_v_numeric(np.array([dt]), np.array([dt]))
-    False
-    """
-    if not hasattr(a, "dtype"):
-        a = np.asarray(a)
-    if not hasattr(b, "dtype"):
-        b = np.asarray(b)
-
-    def is_numeric(x):
-        """
-        Check if an object has a numeric dtype (i.e. integer or float).
-        """
-        return is_integer_dtype(x) or is_float_dtype(x)
-
-    return (needs_i8_conversion(a) and is_numeric(b)) or (
-        needs_i8_conversion(b) and is_numeric(a)
-    )
-
-
 def needs_i8_conversion(arr_or_dtype) -> bool:
     """
     Check whether the array or dtype should be converted to int64.
@@ -1790,7 +1732,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_datetime64_dtype",
     "is_datetime64_ns_dtype",
     "is_datetime64tz_dtype",
-    "is_datetimelike_v_numeric",
     "is_datetime_or_timedelta_dtype",
     "is_decimal",
     "is_dict_like",
@@ -1809,7 +1750,6 @@ def is_all_strings(value: ArrayLike) -> bool:
     "is_number",
     "is_numeric_dtype",
     "is_any_numeric_dtype",
-    "is_numeric_v_string_like",
     "is_object_dtype",
     "is_period_dtype",
     "is_re",

pandas/core/dtypes/missing.py

@@ -28,7 +28,6 @@
     is_bool_dtype,
     is_categorical_dtype,
     is_complex_dtype,
-    is_datetimelike_v_numeric,
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
@@ -505,8 +504,6 @@ def array_equivalent(
         # fastpath when we require that the dtypes match (Block.equals)
         if left.dtype.kind in ["f", "c"]:
             return _array_equivalent_float(left, right)
-        elif is_datetimelike_v_numeric(left.dtype, right.dtype):
-            return False
         elif needs_i8_conversion(left.dtype):
             return _array_equivalent_datetimelike(left, right)
         elif is_string_or_object_np_dtype(left.dtype):
@@ -529,10 +526,6 @@ def array_equivalent(
             return True
         return ((left == right) | (isna(left) & isna(right))).all()
 
-    elif is_datetimelike_v_numeric(left, right):
-        # GH#29553 avoid numpy deprecation warning
-        return False
-
     elif needs_i8_conversion(left.dtype) or needs_i8_conversion(right.dtype):
         # datetime64, timedelta64, Period
         if not is_dtype_equal(left.dtype, right.dtype):

pandas/core/missing.py

@@ -31,7 +31,6 @@
 from pandas.core.dtypes.cast import infer_dtype_from
 from pandas.core.dtypes.common import (
     is_array_like,
-    is_numeric_v_string_like,
     is_object_dtype,
     needs_i8_conversion,
 )
@@ -96,20 +95,16 @@ def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
     # GH 21977
     mask = np.zeros(arr.shape, dtype=bool)
     for x in nonna:
-        if is_numeric_v_string_like(arr, x):
-            # GH#29553 prevent numpy deprecation warnings
-            pass
+        if potential_na:
+            new_mask = np.zeros(arr.shape, dtype=np.bool_)
+            new_mask[arr_mask] = arr[arr_mask] == x
         else:
-            if potential_na:
-                new_mask = np.zeros(arr.shape, dtype=np.bool_)
-                new_mask[arr_mask] = arr[arr_mask] == x
-            else:
-                new_mask = arr == x
-
-                if not isinstance(new_mask, np.ndarray):
-                    # usually BooleanArray
-                    new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
-            mask |= new_mask
+            new_mask = arr == x
+
+            if not isinstance(new_mask, np.ndarray):
+                # usually BooleanArray
+                new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
+        mask |= new_mask
 
     if na_mask.any():
         mask |= isna(arr)

pandas/tests/dtypes/test_common.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-from datetime import datetime
-
 import numpy as np
 import pytest
 
@@ -517,21 +515,6 @@ def test_is_numeric_v_string_like():
     assert com.is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
 
 
-def test_is_datetimelike_v_numeric():
-    dt = np.datetime64(datetime(2017, 1, 1))
-
-    assert not com.is_datetimelike_v_numeric(1, 1)
-    assert not com.is_datetimelike_v_numeric(dt, dt)
-    assert not com.is_datetimelike_v_numeric(np.array([1]), np.array([2]))
-    assert not com.is_datetimelike_v_numeric(np.array([dt]), np.array([dt]))
-
-    assert com.is_datetimelike_v_numeric(1, dt)
-    assert com.is_datetimelike_v_numeric(1, dt)
-    assert com.is_datetimelike_v_numeric(np.array([dt]), 1)
-    assert com.is_datetimelike_v_numeric(np.array([1]), dt)
-    assert com.is_datetimelike_v_numeric(np.array([dt]), np.array([1]))
-
-
 def test_needs_i8_conversion():
     assert not com.needs_i8_conversion(str)
     assert not com.needs_i8_conversion(np.int64)