TST/COMPAT: test Python 3.7

[jorisvandenbossche]

Python 3.7.0b3 is released, it would be good to test against it.

Not sure what the best way is. Anaconda/conda-forge does not seem to have packages for 3.7, travis has a 3.7-dev python (then we need to switch our dev build from conda to pip).

[TomAugspurger]

I'm running the test suite locally now. Will post that.

@jjhelmus are there plans for a conda-forge 3.7 beta python release? If not, I can (try) to build it and install from my channel, but other projects will likely find this useful.

[TomAugspurger]

Here's the output of my local run: https://gist.github.com/b119546b318ab8f31ee4c7ffc8dce5f6

Once #20551 is fixed (which breaks everything), things don't look too bad.

Looks like some additional deprecation warnings caused several.

The datetime.timedelta repr changed from datetime.timedelta(1) to datetime.timedelta(days=1).

[jjhelmus]

@jjhelmus are there plans for a conda-forge 3.7 beta python release?

There have been a few requests for Python 3.7 beta packages on conda-forge. I'll look into putting together a recipe.

[TomAugspurger]

I think conda install -c c3i_test python=3.7 will get 3.7

[cgohlke]

FWIW, on Windows, I get the following test results:

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.0.beta.4
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 79 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.0
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: 0.10.3
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: 1.5.1
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: 2.7.4 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: 0.1.5
fastparquet: None
pandas_gbq: None
pandas_datareader: None

running: pytest --skip-slow --skip-network X:\Python37\lib\site-packages\pandas
============================= test session starts =============================
platform win32 -- Python 3.7.0b4, pytest-3.5.1, py-1.5.3, pluggy-0.6.0
rootdir: C:\Users\--\test, inifile:
plugins: faulthandler-1.5.0, hypothesis-3.56.9
collected 26198 items / 2 skipped

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================================== FAILURES ===================================
________________________ TestDtype_object.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_object object at 0x0000015F07592C50>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_________________________ TestDtype_int8.test_dtypes __________________________

self = <pandas.tests.test_window.TestDtype_int8 object at 0x0000015F0631B0B8>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_________________________ TestDtype_int16.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_int16 object at 0x0000015F022653C8>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_________________________ TestDtype_int32.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_int32 object at 0x0000015F02718160>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_________________________ TestDtype_int64.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_int64 object at 0x0000015F07FB4E10>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_________________________ TestDtype_uint8.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_uint8 object at 0x0000015F06324160>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_uint16.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_uint16 object at 0x0000015F06399A20>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_uint32.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_uint32 object at 0x0000015F075B75C0>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_uint64.test_dtypes _________________________

self = <pandas.tests.test_window.TestDtype_uint64 object at 0x0000015F03675160>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_float16.test_dtypes ________________________

self = <pandas.tests.test_window.TestDtype_float16 object at 0x0000015F7B1EE5F8>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_float32.test_dtypes ________________________

self = <pandas.tests.test_window.TestDtype_float32 object at 0x0000015F07AE50B8>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestDtype_float64.test_dtypes ________________________

self = <pandas.tests.test_window.TestDtype_float64 object at 0x0000015F04110F98>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_______________________ TestDtype_category.test_dtypes ________________________

self = <pandas.tests.test_window.TestDtype_category object at 0x0000015F01B07AC8>

    def test_dtypes(self):
        self._create_data()
        for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

            f = self.funcs[f_name]
            d = self.data[d_name]
            exp = self.expects[d_name][f_name]
>           self.check_dtypes(f, f_name, d, d_name, exp)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:772:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:777: in check_dtypes
    tm.assert_almost_equal(result, exp)
X:\Python37\lib\site-packages\pandas\util\testing.py:241: in assert_almost_equal
    **kwargs)
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (80.0 %)'
left = '[nan, nan, nan, nan, nan]'
right = '[nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (80.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [nan, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476, 0.7071067811865476]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestMoments.test_rolling_std _________________________

self = <pandas.tests.test_window.TestMoments object at 0x0000015F0266A240>

    def test_rolling_std(self):
        self._check_moment_func(lambda x: np.std(x, ddof=1),
>                               name='std')

X:\Python37\lib\site-packages\pandas\tests\test_window.py:1307:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:1370: in _check_moment_func
    static_comp(self.series[-50:]))
X:\Python37\lib\site-packages\pandas\util\testing.py:269: in assert_almost_equal
    **kwargs)
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   AssertionError: expected 1.11175 but got nan, with decimal 5

pandas\_libs\testing.pyx:209: AssertionError
______________________ TestMoments.test_rolling_std_1obs ______________________

self = <pandas.tests.test_window.TestMoments object at 0x0000015F01366A20>

    def test_rolling_std_1obs(self):
        vals = pd.Series([1., 2., 3., 4., 5.])

        result = vals.rolling(1, min_periods=1).std()
        expected = pd.Series([np.nan] * 5)
        tm.assert_series_equal(result, expected)

        result = vals.rolling(1, min_periods=1).std(ddof=0)
        expected = pd.Series([0.] * 5)
>       tm.assert_series_equal(result, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:1320:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (100.0 %)'
left = '[nan, nan, nan, nan, nan]', right = '[0.0, 0.0, 0.0, 0.0, 0.0]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (100.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [0.0, 0.0, 0.0, 0.0, 0.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
____________________ TestMoments.test_rolling_std_neg_sqrt ____________________

self = <pandas.tests.test_window.TestMoments object at 0x0000015F0135C198>

    def test_rolling_std_neg_sqrt(self):
        # unit test from Bottleneck

        # Test move_nanstd for neg sqrt.

        a = pd.Series([0.0011448196318903589, 0.00028718669878572767,
                       0.00028718669878572767, 0.00028718669878572767,
                       0.00028718669878572767])
        b = a.rolling(window=3).std()
>       assert np.isfinite(b[2:]).all()
E       AssertionError: assert False
E        +  where False = <bound method Series.all of 2    False\n3    False\n4    False\ndtype: bool>()
E        +    where <bound method Series.all of 2    False\n3    False\n4    False\ndtype: bool> = 2    False\n3    False\n4    False\ndtype: bool.all
E        +      where 2    False\n3    False\n4    False\ndtype: bool = <ufunc 'isfinite'>(2   NaN\n3   NaN\n4   NaN\ndtype: float64)
E        +        where <ufunc 'isfinite'> = np.isfinite

X:\Python37\lib\site-packages\pandas\tests\test_window.py:1335: AssertionError
________________________ TestMoments.test_rolling_var _________________________

self = <pandas.tests.test_window.TestMoments object at 0x0000015F0266A630>

    def test_rolling_var(self):
        self._check_moment_func(lambda x: np.var(x, ddof=1),
>                               name='var')

X:\Python37\lib\site-packages\pandas\tests\test_window.py:1342:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\tests\test_window.py:1370: in _check_moment_func
    static_comp(self.series[-50:]))
X:\Python37\lib\site-packages\pandas\util\testing.py:269: in assert_almost_equal
    **kwargs)
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   AssertionError: expected 0.72512 but got nan, with decimal 5

pandas\_libs\testing.pyx:209: AssertionError
__________________ TestMomentsConsistency.test_rolling_corr ___________________

self = <pandas.tests.test_window.TestMomentsConsistency object at 0x0000015F6AC25FD0>

    def test_rolling_corr(self):
        A = self.series
        B = A + randn(len(A))

        result = A.rolling(window=50, min_periods=25).corr(B)
>       tm.assert_almost_equal(result[-1], np.corrcoef(A[-50:], B[-50:])[0, 1])

X:\Python37\lib\site-packages\pandas\tests\test_window.py:2402:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:269: in assert_almost_equal
    **kwargs)
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

>   ???
E   AssertionError: expected 0.49155 but got nan, with decimal 5

pandas\_libs\testing.pyx:209: AssertionError
____________ TestMomentsConsistency.test_expanding_corr_diff_index ____________

self = <pandas.tests.test_window.TestMomentsConsistency object at 0x0000015F01CDEEB8>

    def test_expanding_corr_diff_index(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.expanding().corr(s2)
        expected = Series([None, None, 1.0])
>       tm.assert_series_equal(result, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:2615:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (33.33333 %)'
left = '[nan, nan, nan]', right = '[nan, nan, 1.0]', diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (33.33333 %)
E       [left]:  [nan, nan, nan]
E       [right]: [nan, nan, 1.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
____________ TestMomentsConsistency.test_rolling_corr_diff_length _____________

self = <pandas.tests.test_window.TestMomentsConsistency object at 0x0000015F01CD5CF8>

    def test_rolling_corr_diff_length(self):
        # GH 7512
        s1 = Series([1, 2, 3], index=[0, 1, 2])
        s2 = Series([1, 3], index=[0, 2])
        result = s1.rolling(window=3, min_periods=2).corr(s2)
        expected = Series([None, None, 1.0])
>       tm.assert_series_equal(result, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:2645:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series values are different (33.33333 %)'
left = '[nan, nan, nan]', right = '[nan, nan, 1.0]', diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series values are different (33.33333 %)
E       [left]:  [nan, nan, nan]
E       [right]: [nan, nan, 1.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
_______ TestMomentsConsistency.test_expanding_corr_pairwise_diff_length _______

self = <pandas.tests.test_window.TestMomentsConsistency object at 0x0000015F0266BD30>

    def test_expanding_corr_pairwise_diff_length(self):
        # GH 7512
        df1 = DataFrame([[1, 2], [3, 2], [3, 4]],
                        columns=['A', 'B'],
                        index=Index(range(3), name='bar'))
        df1a = DataFrame([[1, 2], [3, 4]],
                         index=Index([0, 2], name='bar'),
                         columns=['A', 'B'])
        df2 = DataFrame([[5, 6], [None, None], [2, 1]],
                        columns=['X', 'Y'],
                        index=Index(range(3), name='bar'))
        df2a = DataFrame([[5, 6], [2, 1]],
                         index=Index([0, 2], name='bar'),
                         columns=['X', 'Y'])
        result1 = df1.expanding().corr(df2, pairwise=True).loc[2]
        result2 = df1.expanding().corr(df2a, pairwise=True).loc[2]
        result3 = df1a.expanding().corr(df2, pairwise=True).loc[2]
        result4 = df1a.expanding().corr(df2a, pairwise=True).loc[2]
        expected = DataFrame([[-1.0, -1.0], [-1.0, -1.0]],
                             columns=['A', 'B'],
                             index=Index(['X', 'Y']))
>       tm.assert_frame_equal(result1, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:2859:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1348: in assert_frame_equal
    obj='DataFrame.iloc[:, {idx}]'.format(idx=i))
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'DataFrame.iloc[:, 0]'
message = 'DataFrame.iloc[:, 0] values are different (100.0 %)'
left = '[nan, nan]', right = '[-1.0, -1.0]', diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: DataFrame.iloc[:, 0] are different
E
E       DataFrame.iloc[:, 0] values are different (100.0 %)
E       [left]:  [nan, nan]
E       [right]: [-1.0, -1.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestRollingTS.test_ragged_std ________________________

self = <pandas.tests.test_window.TestRollingTS object at 0x0000015F001B7F98>

    def test_ragged_std(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).std(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
>       tm.assert_frame_equal(result, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:3607:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1348: in assert_frame_equal
    obj='DataFrame.iloc[:, {idx}]'.format(idx=i))
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'DataFrame.iloc[:, 0]'
message = 'DataFrame.iloc[:, 0] values are different (100.0 %)'
left = '[nan, nan, nan, nan, nan]', right = '[0.0, 0.0, 0.0, 0.0, 0.0]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: DataFrame.iloc[:, 0] are different
E
E       DataFrame.iloc[:, 0] values are different (100.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [0.0, 0.0, 0.0, 0.0, 0.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
________________________ TestRollingTS.test_ragged_var ________________________

self = <pandas.tests.test_window.TestRollingTS object at 0x0000015F08AFE198>

    def test_ragged_var(self):

        df = self.ragged
        result = df.rolling(window='1s', min_periods=1).var(ddof=0)
        expected = df.copy()
        expected['B'] = [0.0] * 5
>       tm.assert_frame_equal(result, expected)

X:\Python37\lib\site-packages\pandas\tests\test_window.py:3630:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1348: in assert_frame_equal
    obj='DataFrame.iloc[:, {idx}]'.format(idx=i))
X:\Python37\lib\site-packages\pandas\util\testing.py:1227: in assert_series_equal
    obj='{obj}'.format(obj=obj))
pandas\_libs\testing.pyx:59: in pandas._libs.testing.assert_almost_equal
    ???
pandas\_libs\testing.pyx:173: in pandas._libs.testing.assert_almost_equal
    ???
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'DataFrame.iloc[:, 0]'
message = 'DataFrame.iloc[:, 0] values are different (100.0 %)'
left = '[nan, nan, nan, nan, nan]', right = '[0.0, 0.0, 0.0, 0.0, 0.0]'
diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: DataFrame.iloc[:, 0] are different
E
E       DataFrame.iloc[:, 0] values are different (100.0 %)
E       [left]:  [nan, nan, nan, nan, nan]
E       [right]: [0.0, 0.0, 0.0, 0.0, 0.0]

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
____________________ TestXlrdReader.test_usecols_int[.xls] ____________________

self = <pandas.tests.io.test_excel.TestXlrdReader object at 0x0000015F07A59748>
ext = '.xls'

    def test_usecols_int(self, ext):

        dfref = self.get_csv_refdf('test1')
        dfref = dfref.reindex(columns=['A', 'B', 'C'])
        df1 = self.get_exceldf('test1', ext, 'Sheet1', index_col=0, usecols=3)
        df2 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
                               index_col=0, usecols=3)

        with tm.assert_produces_warning(FutureWarning):
            df3 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
>                                  index_col=0, parse_cols=3)

X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py:120:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\contextlib.py:119: in __exit__
    next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_warning = <class 'FutureWarning'>, filter_level = 'always'
clear = None, check_stacklevel = True

    @contextmanager
    def assert_produces_warning(expected_warning=Warning, filter_level="always",
                                clear=None, check_stacklevel=True):
        """
        Context manager for running code expected to either raise a specific
        warning, or not raise any warnings. Verifies that the code raises the
        expected warning, and that it does not raise any other unexpected
        warnings. It is basically a wrapper around ``warnings.catch_warnings``.

        Parameters
        ----------
        expected_warning : {Warning, False, None}, default Warning
            The type of Exception raised. ``exception.Warning`` is the base
            class for all warnings. To check that no warning is returned,
            specify ``False`` or ``None``.
        filter_level : str, default "always"
            Specifies whether warnings are ignored, displayed, or turned
            into errors.
            Valid values are:

            * "error" - turns matching warnings into exceptions
            * "ignore" - discard the warning
            * "always" - always emit a warning
            * "default" - print the warning the first time it is generated
              from each location
            * "module" - print the warning the first time it is generated
              from each module
            * "once" - print the warning the first time it is generated

        clear : str, default None
            If not ``None`` then remove any previously raised warnings from
            the ``__warningsregistry__`` to ensure that no warning messages are
            suppressed by this context manager. If ``None`` is specified,
            the ``__warningsregistry__`` keeps track of which warnings have been
             shown, and does not show them again.
        check_stacklevel : bool, default True
            If True, displays the line that called the function containing
            the warning to show were the function is called. Otherwise, the
            line that implements the function is displayed.

        Examples
        --------
        >>> import warnings
        >>> with assert_produces_warning():
        ...     warnings.warn(UserWarning())
        ...
        >>> with assert_produces_warning(False):
        ...     warnings.warn(RuntimeWarning())
        ...
        Traceback (most recent call last):
            ...
        AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
        >>> with assert_produces_warning(UserWarning):
        ...     warnings.warn(RuntimeWarning())
        Traceback (most recent call last):
            ...
        AssertionError: Did not see expected warning of class 'UserWarning'.

        ..warn:: This is *not* thread-safe.
        """
        with warnings.catch_warnings(record=True) as w:

            if clear is not None:
                # make sure that we are clearning these warnings
                # if they have happened before
                # to guarantee that we will catch them
                if not is_list_like(clear):
                    clear = [clear]
                for m in clear:
                    try:
                        m.__warningregistry__.clear()
                    except Exception:
                        pass

            saw_warning = False
            warnings.simplefilter(filter_level)
            yield w
            extra_warnings = []

            for actual_warning in w:
                if (expected_warning and issubclass(actual_warning.category,
                                                    expected_warning)):
                    saw_warning = True

                    if check_stacklevel and issubclass(actual_warning.category,
                                                       (FutureWarning,
                                                        DeprecationWarning)):
                        from inspect import getframeinfo, stack
                        caller = getframeinfo(stack()[2][0])
                        msg = ("Warning not set with correct stacklevel. "
                               "File where warning is raised: {actual} != "
                               "{caller}. Warning message: {message}"
                               ).format(actual=actual_warning.filename,
                                        caller=caller.filename,
                                        message=actual_warning.message)
                        assert actual_warning.filename == caller.filename, msg
                else:
                    extra_warnings.append(actual_warning.category.__name__)
            if expected_warning:
                msg = "Did not see expected warning of class {name!r}.".format(
                    name=expected_warning.__name__)
                assert saw_warning, msg
            assert not extra_warnings, ("Caused unexpected warning(s): {extra!r}."
>                                       ).format(extra=extra_warnings)
E           AssertionError: Caused unexpected warning(s): ['DeprecationWarning', 'DeprecationWarning', 'DeprecationWarning'].

X:\Python37\lib\site-packages\pandas\util\testing.py:2540: AssertionError
___________________ TestXlrdReader.test_usecols_list[.xls] ____________________

self = <pandas.tests.io.test_excel.TestXlrdReader object at 0x0000015F0762C860>
ext = '.xls'

    def test_usecols_list(self, ext):

        dfref = self.get_csv_refdf('test1')
        dfref = dfref.reindex(columns=['B', 'C'])
        df1 = self.get_exceldf('test1', ext, 'Sheet1', index_col=0,
                               usecols=[0, 2, 3])
        df2 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
                               index_col=0, usecols=[0, 2, 3])

        with tm.assert_produces_warning(FutureWarning):
            df3 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
>                                  index_col=0, parse_cols=[0, 2, 3])

X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py:138:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\contextlib.py:119: in __exit__
    next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_warning = <class 'FutureWarning'>, filter_level = 'always'
clear = None, check_stacklevel = True

    @contextmanager
    def assert_produces_warning(expected_warning=Warning, filter_level="always",
                                clear=None, check_stacklevel=True):
        """
        Context manager for running code expected to either raise a specific
        warning, or not raise any warnings. Verifies that the code raises the
        expected warning, and that it does not raise any other unexpected
        warnings. It is basically a wrapper around ``warnings.catch_warnings``.

        Parameters
        ----------
        expected_warning : {Warning, False, None}, default Warning
            The type of Exception raised. ``exception.Warning`` is the base
            class for all warnings. To check that no warning is returned,
            specify ``False`` or ``None``.
        filter_level : str, default "always"
            Specifies whether warnings are ignored, displayed, or turned
            into errors.
            Valid values are:

            * "error" - turns matching warnings into exceptions
            * "ignore" - discard the warning
            * "always" - always emit a warning
            * "default" - print the warning the first time it is generated
              from each location
            * "module" - print the warning the first time it is generated
              from each module
            * "once" - print the warning the first time it is generated

        clear : str, default None
            If not ``None`` then remove any previously raised warnings from
            the ``__warningsregistry__`` to ensure that no warning messages are
            suppressed by this context manager. If ``None`` is specified,
            the ``__warningsregistry__`` keeps track of which warnings have been
             shown, and does not show them again.
        check_stacklevel : bool, default True
            If True, displays the line that called the function containing
            the warning to show were the function is called. Otherwise, the
            line that implements the function is displayed.

        Examples
        --------
        >>> import warnings
        >>> with assert_produces_warning():
        ...     warnings.warn(UserWarning())
        ...
        >>> with assert_produces_warning(False):
        ...     warnings.warn(RuntimeWarning())
        ...
        Traceback (most recent call last):
            ...
        AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
        >>> with assert_produces_warning(UserWarning):
        ...     warnings.warn(RuntimeWarning())
        Traceback (most recent call last):
            ...
        AssertionError: Did not see expected warning of class 'UserWarning'.

        ..warn:: This is *not* thread-safe.
        """
        with warnings.catch_warnings(record=True) as w:

            if clear is not None:
                # make sure that we are clearning these warnings
                # if they have happened before
                # to guarantee that we will catch them
                if not is_list_like(clear):
                    clear = [clear]
                for m in clear:
                    try:
                        m.__warningregistry__.clear()
                    except Exception:
                        pass

            saw_warning = False
            warnings.simplefilter(filter_level)
            yield w
            extra_warnings = []

            for actual_warning in w:
                if (expected_warning and issubclass(actual_warning.category,
                                                    expected_warning)):
                    saw_warning = True

                    if check_stacklevel and issubclass(actual_warning.category,
                                                       (FutureWarning,
                                                        DeprecationWarning)):
                        from inspect import getframeinfo, stack
                        caller = getframeinfo(stack()[2][0])
                        msg = ("Warning not set with correct stacklevel. "
                               "File where warning is raised: {actual} != "
                               "{caller}. Warning message: {message}"
                               ).format(actual=actual_warning.filename,
                                        caller=caller.filename,
                                        message=actual_warning.message)
                        assert actual_warning.filename == caller.filename, msg
                else:
                    extra_warnings.append(actual_warning.category.__name__)
            if expected_warning:
                msg = "Did not see expected warning of class {name!r}.".format(
                    name=expected_warning.__name__)
                assert saw_warning, msg
            assert not extra_warnings, ("Caused unexpected warning(s): {extra!r}."
>                                       ).format(extra=extra_warnings)
E           AssertionError: Caused unexpected warning(s): ['DeprecationWarning', 'DeprecationWarning', 'DeprecationWarning'].

X:\Python37\lib\site-packages\pandas\util\testing.py:2540: AssertionError
____________________ TestXlrdReader.test_usecols_str[.xls] ____________________

self = <pandas.tests.io.test_excel.TestXlrdReader object at 0x0000015F071EFDD8>
ext = '.xls'

    def test_usecols_str(self, ext):

        dfref = self.get_csv_refdf('test1')

        df1 = dfref.reindex(columns=['A', 'B', 'C'])
        df2 = self.get_exceldf('test1', ext, 'Sheet1', index_col=0,
                               usecols='A:D')
        df3 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
                               index_col=0, usecols='A:D')

        with tm.assert_produces_warning(FutureWarning):
            df4 = self.get_exceldf('test1', ext, 'Sheet2', skiprows=[1],
>                                  index_col=0, parse_cols='A:D')

X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py:157:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\contextlib.py:119: in __exit__
    next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_warning = <class 'FutureWarning'>, filter_level = 'always'
clear = None, check_stacklevel = True

    @contextmanager
    def assert_produces_warning(expected_warning=Warning, filter_level="always",
                                clear=None, check_stacklevel=True):
        """
        Context manager for running code expected to either raise a specific
        warning, or not raise any warnings. Verifies that the code raises the
        expected warning, and that it does not raise any other unexpected
        warnings. It is basically a wrapper around ``warnings.catch_warnings``.

        Parameters
        ----------
        expected_warning : {Warning, False, None}, default Warning
            The type of Exception raised. ``exception.Warning`` is the base
            class for all warnings. To check that no warning is returned,
            specify ``False`` or ``None``.
        filter_level : str, default "always"
            Specifies whether warnings are ignored, displayed, or turned
            into errors.
            Valid values are:

            * "error" - turns matching warnings into exceptions
            * "ignore" - discard the warning
            * "always" - always emit a warning
            * "default" - print the warning the first time it is generated
              from each location
            * "module" - print the warning the first time it is generated
              from each module
            * "once" - print the warning the first time it is generated

        clear : str, default None
            If not ``None`` then remove any previously raised warnings from
            the ``__warningsregistry__`` to ensure that no warning messages are
            suppressed by this context manager. If ``None`` is specified,
            the ``__warningsregistry__`` keeps track of which warnings have been
             shown, and does not show them again.
        check_stacklevel : bool, default True
            If True, displays the line that called the function containing
            the warning to show were the function is called. Otherwise, the
            line that implements the function is displayed.

        Examples
        --------
        >>> import warnings
        >>> with assert_produces_warning():
        ...     warnings.warn(UserWarning())
        ...
        >>> with assert_produces_warning(False):
        ...     warnings.warn(RuntimeWarning())
        ...
        Traceback (most recent call last):
            ...
        AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
        >>> with assert_produces_warning(UserWarning):
        ...     warnings.warn(RuntimeWarning())
        Traceback (most recent call last):
            ...
        AssertionError: Did not see expected warning of class 'UserWarning'.

        ..warn:: This is *not* thread-safe.
        """
        with warnings.catch_warnings(record=True) as w:

            if clear is not None:
                # make sure that we are clearning these warnings
                # if they have happened before
                # to guarantee that we will catch them
                if not is_list_like(clear):
                    clear = [clear]
                for m in clear:
                    try:
                        m.__warningregistry__.clear()
                    except Exception:
                        pass

            saw_warning = False
            warnings.simplefilter(filter_level)
            yield w
            extra_warnings = []

            for actual_warning in w:
                if (expected_warning and issubclass(actual_warning.category,
                                                    expected_warning)):
                    saw_warning = True

                    if check_stacklevel and issubclass(actual_warning.category,
                                                       (FutureWarning,
                                                        DeprecationWarning)):
                        from inspect import getframeinfo, stack
                        caller = getframeinfo(stack()[2][0])
                        msg = ("Warning not set with correct stacklevel. "
                               "File where warning is raised: {actual} != "
                               "{caller}. Warning message: {message}"
                               ).format(actual=actual_warning.filename,
                                        caller=caller.filename,
                                        message=actual_warning.message)
                        assert actual_warning.filename == caller.filename, msg
                else:
                    extra_warnings.append(actual_warning.category.__name__)
            if expected_warning:
                msg = "Did not see expected warning of class {name!r}.".format(
                    name=expected_warning.__name__)
                assert saw_warning, msg
            assert not extra_warnings, ("Caused unexpected warning(s): {extra!r}."
>                                       ).format(extra=extra_warnings)
E           AssertionError: Caused unexpected warning(s): ['DeprecationWarning', 'DeprecationWarning', 'DeprecationWarning'].

X:\Python37\lib\site-packages\pandas\util\testing.py:2540: AssertionError
_____________ TestXlrdReader.test_sheet_name_and_sheetname[.xls] ______________

self = <pandas.tests.io.test_excel.TestXlrdReader object at 0x0000015F07EC5518>
ext = '.xls'

    def test_sheet_name_and_sheetname(self, ext):
        # GH10559: Minor improvement: Change "sheet_name" to "sheetname"
        # GH10969: DOC: Consistent var names (sheetname vs sheet_name)
        # GH12604: CLN GH10559 Rename sheetname variable to sheet_name
        # GH20920: ExcelFile.parse() and pd.read_xlsx() have different
        #          behavior for "sheetname" argument
        dfref = self.get_csv_refdf('test1')
        df1 = self.get_exceldf('test1', ext,
                               sheet_name='Sheet1')  # doc
        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
            df2 = self.get_exceldf('test1', ext,
>                                  sheetname='Sheet1')  # bkwrd compat

X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py:513:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\contextlib.py:119: in __exit__
    next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_warning = <class 'FutureWarning'>, filter_level = 'always'
clear = None, check_stacklevel = False

    @contextmanager
    def assert_produces_warning(expected_warning=Warning, filter_level="always",
                                clear=None, check_stacklevel=True):
        """
        Context manager for running code expected to either raise a specific
        warning, or not raise any warnings. Verifies that the code raises the
        expected warning, and that it does not raise any other unexpected
        warnings. It is basically a wrapper around ``warnings.catch_warnings``.

        Parameters
        ----------
        expected_warning : {Warning, False, None}, default Warning
            The type of Exception raised. ``exception.Warning`` is the base
            class for all warnings. To check that no warning is returned,
            specify ``False`` or ``None``.
        filter_level : str, default "always"
            Specifies whether warnings are ignored, displayed, or turned
            into errors.
            Valid values are:

            * "error" - turns matching warnings into exceptions
            * "ignore" - discard the warning
            * "always" - always emit a warning
            * "default" - print the warning the first time it is generated
              from each location
            * "module" - print the warning the first time it is generated
              from each module
            * "once" - print the warning the first time it is generated

        clear : str, default None
            If not ``None`` then remove any previously raised warnings from
            the ``__warningsregistry__`` to ensure that no warning messages are
            suppressed by this context manager. If ``None`` is specified,
            the ``__warningsregistry__`` keeps track of which warnings have been
             shown, and does not show them again.
        check_stacklevel : bool, default True
            If True, displays the line that called the function containing
            the warning to show were the function is called. Otherwise, the
            line that implements the function is displayed.

        Examples
        --------
        >>> import warnings
        >>> with assert_produces_warning():
        ...     warnings.warn(UserWarning())
        ...
        >>> with assert_produces_warning(False):
        ...     warnings.warn(RuntimeWarning())
        ...
        Traceback (most recent call last):
            ...
        AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
        >>> with assert_produces_warning(UserWarning):
        ...     warnings.warn(RuntimeWarning())
        Traceback (most recent call last):
            ...
        AssertionError: Did not see expected warning of class 'UserWarning'.

        ..warn:: This is *not* thread-safe.
        """
        with warnings.catch_warnings(record=True) as w:

            if clear is not None:
                # make sure that we are clearning these warnings
                # if they have happened before
                # to guarantee that we will catch them
                if not is_list_like(clear):
                    clear = [clear]
                for m in clear:
                    try:
                        m.__warningregistry__.clear()
                    except Exception:
                        pass

            saw_warning = False
            warnings.simplefilter(filter_level)
            yield w
            extra_warnings = []

            for actual_warning in w:
                if (expected_warning and issubclass(actual_warning.category,
                                                    expected_warning)):
                    saw_warning = True

                    if check_stacklevel and issubclass(actual_warning.category,
                                                       (FutureWarning,
                                                        DeprecationWarning)):
                        from inspect import getframeinfo, stack
                        caller = getframeinfo(stack()[2][0])
                        msg = ("Warning not set with correct stacklevel. "
                               "File where warning is raised: {actual} != "
                               "{caller}. Warning message: {message}"
                               ).format(actual=actual_warning.filename,
                                        caller=caller.filename,
                                        message=actual_warning.message)
                        assert actual_warning.filename == caller.filename, msg
                else:
                    extra_warnings.append(actual_warning.category.__name__)
            if expected_warning:
                msg = "Did not see expected warning of class {name!r}.".format(
                    name=expected_warning.__name__)
                assert saw_warning, msg
            assert not extra_warnings, ("Caused unexpected warning(s): {extra!r}."
>                                       ).format(extra=extra_warnings)
E           AssertionError: Caused unexpected warning(s): ['DeprecationWarning', 'DeprecationWarning', 'DeprecationWarning'].

X:\Python37\lib\site-packages\pandas\util\testing.py:2540: AssertionError
______________ TestXlrdReader.test_read_excel_parse_dates[.xls] _______________

self = <pandas.tests.io.test_excel.TestXlrdReader object at 0x0000015F0C0CEE10>
ext = '.xls'

    @td.skip_if_no('openpyxl')
    @td.skip_if_no('xlwt')
    def test_read_excel_parse_dates(self, ext):
        # GH 11544, 12051
        df = DataFrame(
            {'col': [1, 2, 3],
             'date_strings': pd.date_range('2012-01-01', periods=3)})
        df2 = df.copy()
        df2['date_strings'] = df2['date_strings'].dt.strftime('%m/%d/%Y')

        with ensure_clean(ext) as pth:
            df2.to_excel(pth)

            res = read_excel(pth)
            tm.assert_frame_equal(df2, res)

            # no index_col specified when parse_dates is True
            with tm.assert_produces_warning():
                res = read_excel(pth, parse_dates=True)
>               tm.assert_frame_equal(df2, res)

X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py:949:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\contextlib.py:119: in __exit__
    next(self.gen)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

expected_warning = <class 'Warning'>, filter_level = 'always', clear = None
check_stacklevel = True

    @contextmanager
    def assert_produces_warning(expected_warning=Warning, filter_level="always",
                                clear=None, check_stacklevel=True):
        """
        Context manager for running code expected to either raise a specific
        warning, or not raise any warnings. Verifies that the code raises the
        expected warning, and that it does not raise any other unexpected
        warnings. It is basically a wrapper around ``warnings.catch_warnings``.

        Parameters
        ----------
        expected_warning : {Warning, False, None}, default Warning
            The type of Exception raised. ``exception.Warning`` is the base
            class for all warnings. To check that no warning is returned,
            specify ``False`` or ``None``.
        filter_level : str, default "always"
            Specifies whether warnings are ignored, displayed, or turned
            into errors.
            Valid values are:

            * "error" - turns matching warnings into exceptions
            * "ignore" - discard the warning
            * "always" - always emit a warning
            * "default" - print the warning the first time it is generated
              from each location
            * "module" - print the warning the first time it is generated
              from each module
            * "once" - print the warning the first time it is generated

        clear : str, default None
            If not ``None`` then remove any previously raised warnings from
            the ``__warningsregistry__`` to ensure that no warning messages are
            suppressed by this context manager. If ``None`` is specified,
            the ``__warningsregistry__`` keeps track of which warnings have been
             shown, and does not show them again.
        check_stacklevel : bool, default True
            If True, displays the line that called the function containing
            the warning to show were the function is called. Otherwise, the
            line that implements the function is displayed.

        Examples
        --------
        >>> import warnings
        >>> with assert_produces_warning():
        ...     warnings.warn(UserWarning())
        ...
        >>> with assert_produces_warning(False):
        ...     warnings.warn(RuntimeWarning())
        ...
        Traceback (most recent call last):
            ...
        AssertionError: Caused unexpected warning(s): ['RuntimeWarning'].
        >>> with assert_produces_warning(UserWarning):
        ...     warnings.warn(RuntimeWarning())
        Traceback (most recent call last):
            ...
        AssertionError: Did not see expected warning of class 'UserWarning'.

        ..warn:: This is *not* thread-safe.
        """
        with warnings.catch_warnings(record=True) as w:

            if clear is not None:
                # make sure that we are clearning these warnings
                # if they have happened before
                # to guarantee that we will catch them
                if not is_list_like(clear):
                    clear = [clear]
                for m in clear:
                    try:
                        m.__warningregistry__.clear()
                    except Exception:
                        pass

            saw_warning = False
            warnings.simplefilter(filter_level)
            yield w
            extra_warnings = []

            for actual_warning in w:
                if (expected_warning and issubclass(actual_warning.category,
                                                    expected_warning)):
                    saw_warning = True

                    if check_stacklevel and issubclass(actual_warning.category,
                                                       (FutureWarning,
                                                        DeprecationWarning)):
                        from inspect import getframeinfo, stack
                        caller = getframeinfo(stack()[2][0])
                        msg = ("Warning not set with correct stacklevel. "
                               "File where warning is raised: {actual} != "
                               "{caller}. Warning message: {message}"
                               ).format(actual=actual_warning.filename,
                                        caller=caller.filename,
                                        message=actual_warning.message)
>                       assert actual_warning.filename == caller.filename, msg
E                       AssertionError: Warning not set with correct stacklevel. File where warning is raised: X:\Python37\lib\site-packages\xlrd\book.py != X:\Python37\lib\site-packages\pandas\tests\io\test_excel.py. Warning message: time.clock has been deprecated in Python 3.3 and will be removed from Python 3.8: use time.perf_counter or time.process_time instead

X:\Python37\lib\site-packages\pandas\util\testing.py:2532: AssertionError
________________________ TestHDFStore.test_categorical ________________________

self = <pandas.tests.io.test_pytables.TestHDFStore object at 0x0000015F0C47E5F8>

    def test_categorical(self):

        with ensure_clean_store(self.path) as store:

            # Basic
            _maybe_remove(store, 's')
            s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=[
                       'a', 'b', 'c', 'd'], ordered=False))
            store.append('s', s, format='table')
            result = store.select('s')
            tm.assert_series_equal(s, result)

            _maybe_remove(store, 's_ordered')
            s = Series(Categorical(['a', 'b', 'b', 'a', 'a', 'c'], categories=[
                       'a', 'b', 'c', 'd'], ordered=True))
            store.append('s_ordered', s, format='table')
            result = store.select('s_ordered')
            tm.assert_series_equal(s, result)

            _maybe_remove(store, 'df')

            df = DataFrame({"s": s, "vals": [1, 2, 3, 4, 5, 6]})
            store.append('df', df, format='table')
            result = store.select('df')
            tm.assert_frame_equal(result, df)

            # Dtypes
            s = Series([1, 1, 2, 2, 3, 4, 5]).astype('category')
            store.append('si', s)
            result = store.select('si')
>           tm.assert_series_equal(result, s)

X:\Python37\lib\site-packages\pandas\tests\io\test_pytables.py:4700:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
X:\Python37\lib\site-packages\pandas\util\testing.py:1175: in assert_series_equal
    raise_assert_detail(obj, 'Series length are different', msg1, msg2)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

obj = 'Series', message = 'Series length are different'
left = "14, Int64Index([0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype='int64')"
right = '7, RangeIndex(start=0, stop=7, step=1)', diff = None

    def raise_assert_detail(obj, message, left, right, diff=None):
        if isinstance(left, np.ndarray):
            left = pprint_thing(left)
        elif is_categorical_dtype(left):
            left = repr(left)

        if PY2 and isinstance(left, string_types):
            # left needs to be printable in native text type in python2
            left = left.encode('utf-8')

        if isinstance(right, np.ndarray):
            right = pprint_thing(right)
        elif is_categorical_dtype(right):
            right = repr(right)

        if PY2 and isinstance(right, string_types):
            # right needs to be printable in native text type in python2
            right = right.encode('utf-8')

        msg = """{obj} are different

    {message}
    [left]:  {left}
    [right]: {right}""".format(obj=obj, message=message, left=left, right=right)

        if diff is not None:
            msg += "\n[diff]: {diff}".format(diff=diff)

>       raise AssertionError(msg)
E       AssertionError: Series are different
E
E       Series length are different
E       [left]:  14, Int64Index([0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6], dtype='int64')
E       [right]: 7, RangeIndex(start=0, stop=7, step=1)

X:\Python37\lib\site-packages\pandas\util\testing.py:1018: AssertionError
__________________________ TestBusinessDay.test_repr __________________________

self = <pandas.tests.tseries.offsets.test_offsets.TestBusinessDay object at 0x0000015F7DFFA710>

    def test_repr(self):
        assert repr(self.offset) == '<BusinessDay>'
        assert repr(self.offset2) == '<2 * BusinessDays>'

        expected = '<BusinessDay: offset=datetime.timedelta(1)>'
>       assert repr(self.offset + timedelta(1)) == expected
E       AssertionError: assert '<BusinessDay...elta(days=1)>' == '<BusinessDay:...timedelta(1)>'
E         - <BusinessDay: offset=datetime.timedelta(days=1)>
E         ?                                         -----
E         + <BusinessDay: offset=datetime.timedelta(1)>

X:\Python37\lib\site-packages\pandas\tests\tseries\offsets\test_offsets.py:532: AssertionError
_______________________ TestCustomBusinessDay.test_repr _______________________

self = <pandas.tests.tseries.offsets.test_offsets.TestCustomBusinessDay object at 0x0000015F1B69A9B0>

    def test_repr(self):
        assert repr(self.offset) == '<CustomBusinessDay>'
        assert repr(self.offset2) == '<2 * CustomBusinessDays>'

        expected = '<BusinessDay: offset=datetime.timedelta(1)>'
>       assert repr(self.offset + timedelta(1)) == expected
E       AssertionError: assert '<BusinessDay...elta(days=1)>' == '<BusinessDay:...timedelta(1)>'
E         - <BusinessDay: offset=datetime.timedelta(days=1)>
E         ?                                         -----
E         + <BusinessDay: offset=datetime.timedelta(1)>

X:\Python37\lib\site-packages\pandas\tests\tseries\offsets\test_offsets.py:1646: AssertionError
============================== warnings summary ===============================
pandas/tests/test_multilevel.py::TestMultiLevel::()::test_frame_group_ops[True]
  X:\Python37\lib\site-packages\numpy\core\_methods.py:29: RuntimeWarning: invalid value encountered in reduce
    return umr_minimum(a, axis, None, out, keepdims)
  X:\Python37\lib\site-packages\numpy\core\_methods.py:26: RuntimeWarning: invalid value encountered in reduce
    return umr_maximum(a, axis, None, out, keepdims)

pandas/tests/test_multilevel.py::TestMultiLevel::()::test_frame_group_ops[False]
  X:\Python37\lib\site-packages\numpy\core\_methods.py:29: RuntimeWarning: invalid value encountered in reduce
    return umr_minimum(a, axis, None, out, keepdims)
  X:\Python37\lib\site-packages\numpy\core\_methods.py:26: RuntimeWarning: invalid value encountered in reduce
    return umr_maximum(a, axis, None, out, keepdims)

pandas/tests/frame/test_missing.py::TestDataFrameMissingData::()::test_fillna_categorical_nan
  X:\Python37\lib\site-packages\numpy\lib\function_base.py:4033: RuntimeWarning: All-NaN slice encountered
    r = func(a, **kwargs)

pandas/tests/groupby/test_filters.py::test_filter_and_transform_with_non_unique_float_index
  X:\Python37\lib\site-packages\pandas\core\indexes\base.py:3063: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
    return self._engine.get_loc(key)

pandas/tests/plotting/test_misc.py::TestDataFramePlots::()::test_parallel_coordinates_with_sorted_labels
  X:\Python37\lib\site-packages\matplotlib\axes\_base.py:3149: UserWarning: Attempting to set identical left==right results
  in singular transformations; automatically expanding.
  left=0, right=0
    'left=%s, right=%s') % (left, right))

pandas/tests/scalar/timestamp/test_timezones.py::TestTimestampTZOperations::()::test_tz_localize_pushes_out_of_bounds
  X:\Python37\lib\site-packages\pandas\tests\scalar\timestamp\test_timezones.py:32: RuntimeWarning: overflow encountered in longlong_scalars
    Timestamp.min.tz_localize('Asia/Tokyo')
  X:\Python37\lib\site-packages\pandas\tests\scalar\timestamp\test_timezones.py:39: RuntimeWarning: overflow encountered in longlong_scalars
    Timestamp.max.tz_localize('US/Pacific')

-- Docs: http://doc.pytest.org/en/latest/warnings.html
 31 failed, 24779 passed, 1280 skipped, 80 xfailed, 30 xpassed, 9 warnings in 519.10 seconds
sys:1: ResourceWarning: unclosed <socket.socket fd=3200, family=AddressFamily.AF_INET, type=SocketKind.SOCK_DGRAM, proto=0>

[jreback]

is there a reason u are running 3.7? (which is not covered by pandas currently)

[cgohlke]

is there a reason u are running 3.7? (which is not covered by pandas currently)

Just testing packages for compatibility with Python 3.7

[jreback]

ok thanks @cgohlke we have never run this against 3.7 on windows so bound to be some issues.