suppress more

pandas-dev · jbrockmendel · Dec 4, 2019 · Dec 4, 2019 · Dec 4, 2019 · Dec 4, 2019
commit af573eb44fde1b682d49a9da51a5592652c16f9c
diff --git a/pandas/core/arrays/numpy_.py b/pandas/core/arrays/numpy_.py
@@ -1,5 +1,6 @@
 import numbers
 from typing import Union
+import warnings
 
 import numpy as np
 from numpy.lib.mixins import NDArrayOperatorsMixin
@@ -147,7 +148,11 @@ def _from_sequence(cls, scalars, dtype=None, copy=False):
         if isinstance(dtype, PandasDtype):
             dtype = dtype._dtype
 
-        result = np.asarray(scalars, dtype=dtype)
+        with warnings.catch_warnings():
+            # See https://github.com/numpy/numpy/issues/15041
+            warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+            result = np.asarray(scalars, dtype=dtype)
+
         if copy and result is scalars:
             result = result.copy()
         return cls(result)

diff --git a/pandas/core/common.py b/pandas/core/common.py
@@ -10,6 +10,7 @@
 from functools import partial
 import inspect
 from typing import Any, Iterable, Union
+import warnings
 
 import numpy as np
 
@@ -224,7 +225,13 @@ def asarray_tuplesafe(values, dtype=None):
     if isinstance(values, list) and dtype in [np.object_, object]:
         return construct_1d_object_array_from_listlike(values)
 
-    result = np.asarray(values, dtype=dtype)
+    if dtype is None:
+        with warnings.catch_warnings():
+            # See https://github.com/numpy/numpy/issues/15041
+            warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+            result = np.asarray(values)
+    else:
+        result = np.asarray(values, dtype=dtype)
 
     if issubclass(result.dtype.type, str):
         result = np.asarray(values, dtype=object)

diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py
@@ -1,6 +1,7 @@
 """ routings for casting """
 
 from datetime import datetime, timedelta
+import warnings
 
 import numpy as np
 
@@ -1029,7 +1030,11 @@ def maybe_infer_to_datetimelike(value, convert_dates: bool = False):
 
     if not is_list_like(v):
         v = [v]
-    v = np.array(v, copy=False)
+
+    with warnings.catch_warnings():
+        # See https://github.com/numpy/numpy/issues/15041
+        warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+        v = np.array(v, copy=False)
 
     # we only care about object dtypes
     if not is_object_dtype(v):

diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
@@ -1,6 +1,7 @@
 """
 Utility functions related to concat
 """
+import warnings
 
 import numpy as np
 
@@ -134,6 +135,7 @@ def is_nonempty(x) -> bool:
                 # coerce to object
                 to_concat = [x.astype("object") for x in to_concat]
 
+    to_concat = [_safe_array(x) for x in to_concat]
     return np.concatenate(to_concat, axis=axis)
 
 
@@ -172,7 +174,7 @@ def concat_categorical(to_concat, axis: int = 0):
     to_concat = [
         x._internal_get_values()
         if is_categorical_dtype(x.dtype)
-        else np.asarray(x).ravel()
+        else _safe_array(x).ravel()
         if not is_datetime64tz_dtype(x)
         else np.asarray(x.astype(object))
         for x in to_concat
@@ -183,6 +185,15 @@ def concat_categorical(to_concat, axis: int 
6D40
= 0):
     return result
 
 
+def _safe_array(x):
+    # FIXME: kludge
+    with warnings.catch_warnings():
+        # See https://github.com/numpy/numpy/issues/15041
+        warnings.filterwarnings("ignore", ".*with automatic object dtype.*")
+        arr = np.asarray(x)
+    return arr
+
+
 def union_categoricals(
     to_union, sort_categories: bool = False, ignore_order: bool = False
 ):

diff --git a/pandas/tests/dtypes/test_inference.py b/pandas/tests/dtypes/test_inference.py
@@ -449,7 +449,7 @@ def test_scientific_no_exponent(self):
     def test_convert_non_hashable(self):
         # GH13324
         # make sure that we are handing non-hashables
-        arr = np.array([[10.0, 2], 1.0, "apple"])
+        arr = np.array([[10.0, 2], 1.0, "apple"], dtype=object)
         result = lib.maybe_convert_numeric(arr, set(), False, True)
         tm.assert_numpy_array_equal(result, np.array([np.nan, 1.0, np.nan]))
 

diff --git a/pandas/tests/extension/base/interface.py b/pandas/tests/extension/base/interface.py
@@ -32,7 +32,11 @@ def test_memory_usage(self, data):
         assert result == s.nbytes
 
     def test_array_interface(self, data):
-        result = np.array(data)
+        if hasattr(data, "dtype") and data.dtype.kind == "O":
+            # e.g. JSONArray
+            result = np.array(data, dtype=object)
+        else:
+            result = np.array(data)
         assert result[0] == data[0]
 
         result = np.array(data, dtype=object)

diff --git a/pandas/tests/io/test_html.py b/pandas/tests/io/test_html.py
@@ -84,6 +84,7 @@ def test_same_ordering(datapath):
     assert_framelist_equal(dfs_lxml, dfs_bs4)
 
 
+@td.skip_if_no("bs4")
 @pytest.mark.parametrize(
     "flavor",
     [