Revert "CI: workaround numpydev bug (#29433)"

ci/azure/posix.yml

@@ -44,16 +44,13 @@ jobs:
           PATTERN: "not slow and not network"
           LOCALE_OVERRIDE: "zh_CN.UTF-8"
 
-        # https://github.com/pandas-dev/pandas/issues/29432
-        # py37_np_dev:
-        #   ENV_FILE: ci/deps/azure-37-numpydev.yaml
-        #   CONDA_PY: "37"
-        #   PATTERN: "not slow and not network"
-        #   TEST_ARGS: "-W error"
-        #   PANDAS_TESTING_MODE: "deprecate"
-        #   EXTRA_APT: "xsel"
-        #   # TODO:
-        #   continueOnError: true
+        py37_np_dev:
+          ENV_FILE: ci/deps/azure-37-numpydev.yaml
+          CONDA_PY: "37"
+          PATTERN: "not slow and not network"
+          TEST_ARGS: "-W error"
+          PANDAS_TESTING_MODE: "deprecate"
+          EXTRA_APT: "xsel"
 
   steps:
     - script: |

pandas/core/dtypes/common.py

@@ -1278,6 +1278,124 @@ def _is_unorderable_exception(e: TypeError) -> bool:
     return "'>' not supported between instances of" in str(e)
 
 
+# This exists to silence numpy deprecation warnings, see GH#29553
+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)
+    )
+
+
+# This exists to silence numpy deprecation warnings, see GH#29553
+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) -> bool:
     """
     Check whether the array or dtype should be converted to int64.

pandas/core/dtypes/missing.py

@@ -17,6 +17,7 @@
     is_complex_dtype,
     is_datetime64_dtype,
     is_datetime64tz_dtype,
+    is_datetimelike_v_numeric,
     is_dtype_equal,
     is_extension_array_dtype,
     is_float_dtype,
@@ -465,6 +466,10 @@ def array_equivalent(left, right, strict_nan: bool = False) -> bool:
             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) or needs_i8_conversion(right):
         # datetime64, timedelta64, Period
         if not is_dtype_equal(left.dtype, right.dtype):

pandas/core/internals/managers.py

@@ -18,8 +18,10 @@
 )
 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,
 )
@@ -1924,7 +1926,11 @@ def _compare_or_regex_search(a, b, regex=False):
     is_a_array = isinstance(a, np.ndarray)
     is_b_array = isinstance(b, np.ndarray)
 
-    result = op(a)
+    if is_datetimelike_v_numeric(a, b) or is_numeric_v_string_like(a, b):
+        # GH#29553 avoid deprecation warnings from numpy
+        result = False
+    else:
+        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

@@ -1,6 +1,7 @@
 """
 Routines for filling missing data.
 """
+
 import numpy as np
 
 from pandas._libs import algos, lib
@@ -12,6 +13,7 @@
     is_datetime64_dtype,
     is_datetime64tz_dtype,
     is_integer_dtype,
+    is_numeric_v_string_like,
     is_scalar,
     is_timedelta64_dtype,
     needs_i8_conversion,
@@ -38,14 +40,22 @@ def mask_missing(arr, values_to_mask):
     mask = None
     for x in nonna:
         if mask is None:
-            mask = arr == x
+            if is_numeric_v_string_like(arr, x):
+                # GH#29553 prevent numpy deprecation warnings
+                mask = False
+            else:
+                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:
-            mask |= arr == x
+            if is_numeric_v_string_like(arr, x):
+                # GH#29553 prevent numpy deprecation warnings
+                mask |= False
+            else:
+                mask |= arr == x
 
     if na_mask.any():
         if mask is None:

pandas/tests/dtypes/test_common.py

@@ -493,6 +493,34 @@ 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)