assert_frame_equal not differentiating NaN and None within object dtype

Code Sample, a copy-pastable example if possible

df1 = pd.DataFrame([['foo', 'bar', 'baz'], [None, None, None]])
df2 = pd.DataFrame([['foo', 'bar', 'baz'], [np.nan, np.nan, np.nan]])
tm.assert_frame_equal(df1, df2)

Problem description

No AssertionError gets raised in this case, even though I would not expect None and np.nan to be considered equal values (I found this while testing #18450)

Note that if you simply compared a DataFrame with only np.nan with another containing only None you would get an AttributeError that the dtypes are different (float64 vs object) but because the missing values are mixed into object dtypes here I think that differentiation gets lost

Expected Output

AssertionError: DataFrame.iloc[1, :] are different

Output of pd.show_versions()

[paste the output of pd.show_versions() here below this line] INSTALLED VERSIONS

commit: d64995a4fcc3269dff4366988230563b8aeffb9f python: 3.6.2.final.0 python-bits: 64 OS: Darwin OS-release: 17.2.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.22.0.dev0+205.gd64995a4f pytest: 3.2.5 pip: 9.0.1 setuptools: 36.4.0 Cython: 0.26 numpy: 1.13.1 scipy: None pyarrow: None xarray: None IPython: 6.2.1 sphinx: 1.6.3 patsy: None dateutil: 2.6.1 pytz: 2017.2 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: None openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None sqlalchemy: None pymysql: None psycopg2: None jinja2: 2.9.6 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

[jreback]

from above, this is actually wrong and we should strictly check this.

In [18]: df1.equals(df2)
Out[18]: True

[TomAugspurger]

I think this also affects pd.NA.

In [45]: a = pd.Series([1, np.nan], dtype=object)

In [46]: b = pd.Series([1, pd.NA], dtype=object)

In [47]: pd.testing.assert_series_equal(a, b)

A keyword to control this would be great. Based on the recent experience with check_freq, we should perhaps not make things strict by default. But we could do non-strict + a warning by default, with the option to override in the function or via a global option.

def assert_series_equal(..., check_na=None):
    if check_na is None:
        check_na = pandas.config.get("testing.check_na")
    if check_na is None;
        warnings.warn("default to false, changing to strict in the future")
        check_na = False