COMPAT: np 1.18 wants explicit dtype=object)

pandas/core/indexes/base.py

@@ -331,12 +331,14 @@ def __new__(
 
         # extension dtype
         elif is_extension_array_dtype(data) or is_extension_array_dtype(dtype):
-            data = np.asarray(data)
             if not (dtype is None or is_object_dtype(dtype)):
                 # coerce to the provided dtype
                 ea_cls = dtype.construct_array_type()
                 data = ea_cls._from_sequence(data, dtype=dtype, copy=False)
 
+            else:
+                data = np.asarray(data, dtype=object)
+
             # coerce to the object dtype
             data = data.astype(object)
             return Index(data, dtype=object, copy=copy, name=name, **kwargs)

pandas/core/internals/blocks.py

@@ -1764,6 +1764,9 @@ def get_values(self, dtype=None):
         return values
 
     def to_dense(self):
+        if self.dtype.kind == "O":
+            # See https://github.com/numpy/numpy/issues/15041
+            return np.asarray(self.values, dtype=object)
         return np.asarray(self.values)
 
     def to_native_types(self, slicer=None, na_rep="nan", quoting=None, **kwargs):

pandas/core/ops/dispatch.py

@@ -2,6 +2,7 @@
 Functions for defining unary operations.
 """
 from typing import Any, Callable, Union
+import warnings
 
 import numpy as np
 
@@ -132,19 +133,24 @@ def dispatch_to_extension_op(
     # The op calls will raise TypeError if the op is not defined
     # on the ExtensionArray
 
-    try:
-        res_values = op(left, right)
-    except NullFrequencyError:
-        # DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
-        # on add/sub of integers (or int-like).  We re-raise as a TypeError.
-        if keep_null_freq:
-            # TODO: remove keep_null_freq after Timestamp+int deprecation
-            #  GH#22535 is enforced
-            raise
-        raise TypeError(
-            "incompatible type for a datetime/timedelta "
-            "operation [{name}]".format(name=op.__name__)
-        )
+    with warnings.catch_warnings():
+        # See https://github.com/numpy/numpy/issues/15041
+        warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+
+        try:
+            res_values = op(left, right)
+        except NullFrequencyError:
+            # DatetimeIndex and TimedeltaIndex with freq == None raise ValueError
+            # on add/sub of integers (or int-like).  We re-raise as a TypeError.
+            if keep_null_freq:
+                # TODO: remove keep_null_freq after Timestamp+int deprecation
+                #  GH#22535 is enforced
+                raise
+            raise TypeError(
+                "incompatible type for a datetime/timedelta "
+                "operation [{name}]".format(name=op.__name__)
+            )
+
     return res_values
 
 

pandas/core/strings.py

@@ -74,10 +74,20 @@ def cat_core(list_of_columns: List, sep: str):
     """
     if sep == "":
         # no need to interleave sep if it is empty
-        return np.sum(list_of_columns, axis=0)
+        with warnings.catch_warnings():
+            # See https://github.com/numpy/numpy/issues/15041
+            warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+            out = np.sum(list_of_columns, axis=0)
+        return out
+
     list_with_sep = [sep] * (2 * len(list_of_columns) - 1)
     list_with_sep[::2] = list_of_columns
-    return np.sum(list_with_sep, axis=0)
+
+    with warnings.catch_warnings():
+        # See https://github.com/numpy/numpy/issues/15041
+        warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+        out = np.sum(list_with_sep, axis=0)
+    return out
 
 
 def cat_safe(list_of_columns: List, sep: str):

pandas/io/formats/format.py

@@ -1482,6 +1482,11 @@ def _format_strings(self) -> List[str]:
         if is_categorical_dtype(values.dtype):
             # Categorical is special for now, so that we can preserve tzinfo
             array = values._internal_get_values()
+        elif values.dtype.kind == "O":
+            # numpy>=1.18 wants object dtype passed explicitly
+            # Note: dtype.kind check works for json extension tests, while
+            #  dtype == object check does not.
+            array = np.asarray(values, dtype=object)
         else:
             array = np.asarray(values)
 

pandas/tests/extension/test_numpy.py

@@ -51,7 +51,7 @@ def data_missing(allow_in_pandas, dtype):
     if dtype.numpy_dtype == "object":
         if _np_version_under1p16:
             raise pytest.skip("Skipping for NumPy <1.16")
-        return PandasArray(np.array([np.nan, (1,)]))
+        return PandasArray(np.array([np.nan, (1,)], dtype=object))
     return PandasArray(np.array([np.nan, 1.0]))
 
 
@@ -78,7 +78,7 @@ def data_for_sorting(allow_in_pandas, dtype):
     if dtype.numpy_dtype == "object":
         # Use an empty tuple for first element, then remove,
         # to disable np.array's shape inference.
-        return PandasArray(np.array([(), (2,), (3,), (1,)])[1:])
+        return PandasArray(np.array([(), (2,), (3,), (1,)], dtype=object)[1:])
     return PandasArray(np.array([1, 2, 0]))
 
 
@@ -90,7 +90,7 @@ def data_missing_for_sorting(allow_in_pandas, dtype):
     A < B and NA missing.
     """
     if dtype.numpy_dtype == "object":
-        return PandasArray(np.array([(1,), np.nan, (0,)]))
+        return PandasArray(np.array([(1,), np.nan, (0,)], dtype=object))
     return PandasArray(np.array([1, np.nan, 0]))
 
 
@@ -106,7 +106,9 @@ def data_for_grouping(allow_in_pandas, dtype):
         a, b, c = (1,), (2,), (3,)
     else:
         a, b, c = np.arange(3)
-    return PandasArray(np.array([b, b, np.nan, np.nan, a, a, b, c]))
+    return PandasArray(
+        np.array([b, b, np.nan, np.nan, a, a, b, c], dtype=dtype.numpy_dtype)
+    )
 
 
 @pytest.fixture

pandas/tests/io/json/test_ujson.py

@@ -761,8 +761,9 @@ def test_array_list(self):
             ["a", "b"],
             {"key": "val"},
         ]
-        arr = np.array(arr_list)
-        tm.assert_numpy_array_equal(np.array(ujson.decode(ujson.encode(arr))), arr)
+        arr = np.array(arr_list, dtype=object)
+        result = np.array(ujson.decode(ujson.encode(arr)), dtype=object)
+        tm.assert_numpy_array_equal(result, arr)
 
     def test_array_float(self):
         dtype = np.float32

pandas/tests/test_multilevel.py

@@ -121,7 +121,8 @@ def test_append_index(self):
                     (1.2, tz.localize(datetime.datetime(2011, 1, 2)), "B"),
                     (1.3, tz.localize(datetime.datetime(2011, 1, 3)), "C"),
                 ]
-                + expected_tuples
+                + expected_tuples,
+                dtype=object,
             ),
             None,
         )

pandas/tests/test_strings.py

@@ -2853,7 +2853,8 @@ def test_partition_index(self):
         result = values.str.partition("_", expand=False)
         exp = Index(
             np.array(
-                [("a", "_", "b_c"), ("c", "_", "d_e"), ("f", "_", "g_h"), np.nan, None]
+                [("a", "_", "b_c"), ("c", "_", "d_e"), ("f", "_", "g_h"), np.nan, None],
+                dtype=object,
             )
         )
         tm.assert_index_equal(result, exp)
@@ -2862,7 +2863,8 @@ def test_partition_index(self):
         result = values.str.rpartition("_", expand=False)
         exp = Index(
             np.array(
-                [("a_b", "_", "c"), ("c_d", "_", "e"), ("f_g", "_", "h"), np.nan, None]
+                [("a_b", "_", "c"), ("c_d", "_", "e"), ("f_g", "_", "h"), np.nan, None],
+                dtype=object,
             )
         )
         tm.assert_index_equal(result, exp)