CLN: remove unnecessary type checks

pandas/core/dtypes/common.py

@@ -1285,124 +1285,6 @@ def _is_unorderable_exception(e: TypeError) -> bool:
     return "unorderable" in str(e)
 
 
-def is_numeric_v_string_like(a, b):
-    """
-    Check if we are comparing a string-like object to a numeric ndarray.
-
-    NumPy doesn't like to compare such objects, especially numeric arrays
-    and scalar string-likes.
-
-    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 string-like object to a numeric array.
-
-    Examples
-    --------
-    >>> is_numeric_v_string_like(1, 1)
-    False
-    >>> is_numeric_v_string_like("foo", "foo")
-    False
-    >>> is_numeric_v_string_like(1, "foo")  # non-array numeric
-    False
-    >>> is_numeric_v_string_like(np.array([1]), "foo")
-    True
-    >>> is_numeric_v_string_like("foo", np.array([1]))  # symmetric check
-    True
-    >>> is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
-    True
-    >>> is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
-    True
-    >>> is_numeric_v_string_like(np.array([1]), np.array([2]))
-    False
-    >>> is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
-    False
-    """
-
-    is_a_array = isinstance(a, np.ndarray)
-    is_b_array = isinstance(b, np.ndarray)
-
-    is_a_numeric_array = is_a_array and is_numeric_dtype(a)
-    is_b_numeric_array = is_b_array and is_numeric_dtype(b)
-    is_a_string_array = is_a_array and is_string_like_dtype(a)
-    is_b_string_array = is_b_array and is_string_like_dtype(b)
-
-    is_a_scalar_string_like = not is_a_array and isinstance(a, str)
-    is_b_scalar_string_like = not is_b_array and isinstance(b, str)
-
-    return (
-        (is_a_numeric_array and is_b_scalar_string_like)
-        or (is_b_numeric_array and is_a_scalar_string_like)
-        or (is_a_numeric_array and is_b_string_array)
-        or (is_b_numeric_array and is_a_string_array)
-    )
-
-
-def is_datetimelike_v_numeric(a, b):
-    """
-    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
-    --------
-    >>> dt = np.datetime64(pd.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):
     """
     Check whether the array or dtype should be converted to int64.

pandas/core/dtypes/missing.py

@@ -17,7 +17,6 @@
     is_complex_dtype,
     is_datetime64_dtype,
     is_datetime64tz_dtype,
-    is_datetimelike_v_numeric,
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
@@ -463,12 +462,8 @@ def array_equivalent(left, right, strict_nan=False):
             return True
         return ((left == right) | (isna(left) & isna(right))).all()
 
-    # numpy will will not allow this type of datetimelike vs integer comparison
-    elif is_datetimelike_v_numeric(left, right):
-        return False
-
-    # M8/m8
-    elif needs_i8_conversion(left) and needs_i8_conversion(right):
+    elif needs_i8_conversion(left) or needs_i8_conversion(right):
+        # datetime64, timedelta64, Period
         if not is_dtype_equal(left.dtype, right.dtype):
             return False
 

pandas/core/internals/managers.py

@@ -18,10 +18,8 @@
 )
 from pandas.core.dtypes.common import (
     _NS_DTYPE,
-    is_datetimelike_v_numeric,
     is_extension_array_dtype,
     is_list_like,
-    is_numeric_v_string_like,
     is_scalar,
     is_sparse,
 )
@@ -1926,15 +1924,7 @@ def _compare_or_regex_search(a, b, regex=False):
     is_a_array = isinstance(a, np.ndarray)
     is_b_array = isinstance(b, np.ndarray)
 
-    # numpy deprecation warning to have i8 vs integer comparisons
-    if is_datetimelike_v_numeric(a, b):
-        result = False
-
-    # numpy deprecation warning if comparing numeric vs string-like
-    elif is_numeric_v_string_like(a, b):
-        result = False
-    else:
-        result = op(a)
+    result = op(a)
 
     if is_scalar(result) and (is_a_array or is_b_array):
         type_names = [type(a).__name__, type(b).__name__]

pandas/core/missing.py

@@ -12,7 +12,6 @@
     is_datetime64_dtype,
     is_datetime64tz_dtype,
     is_integer_dtype,
-    is_numeric_v_string_like,
     is_scalar,
     is_timedelta64_dtype,
     needs_i8_conversion,
@@ -39,24 +38,14 @@ def mask_missing(arr, values_to_mask):
     mask = None
     for x in nonna:
         if mask is None:
-
-            # numpy elementwise comparison warning
-            if is_numeric_v_string_like(arr, x):
-                mask = False
-            else:
-                mask = arr == x
+            mask = arr == x
 
             # if x is a string and arr is not, then we get False and we must
             # expand the mask to size arr.shape
             if is_scalar(mask):
                 mask = np.zeros(arr.shape, dtype=bool)
         else:
-
-            # numpy elementwise comparison warning
-            if is_numeric_v_string_like(arr, x):
-                mask |= False
-            else:
-                mask |= arr == x
+            mask |= arr == x
 
     if na_mask.any():
         if mask is None:

pandas/tests/dtypes/test_common.py

@@ -493,34 +493,6 @@ def test_is_datetime_or_timedelta_dtype():
     assert com.is_datetime_or_timedelta_dtype(np.array([], dtype=np.datetime64))
 
 
-def test_is_numeric_v_string_like():
-    assert not com.is_numeric_v_string_like(1, 1)
-    assert not com.is_numeric_v_string_like(1, "foo")
-    assert not com.is_numeric_v_string_like("foo", "foo")
-    assert not com.is_numeric_v_string_like(np.array([1]), np.array([2]))
-    assert not com.is_numeric_v_string_like(np.array(["foo"]), np.array(["foo"]))
-
-    assert com.is_numeric_v_string_like(np.array([1]), "foo")
-    assert com.is_numeric_v_string_like("foo", np.array([1]))
-    assert com.is_numeric_v_string_like(np.array([1, 2]), np.array(["foo"]))
-    assert com.is_numeric_v_string_like(np.array(["foo"]), np.array([1, 2]))
-
-
-def test_is_datetimelike_v_numeric():
-    dt = np.datetime64(pd.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)