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TST: Add tests for single level indexing with loc(axis=1) #29519

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Merged
merged 6 commits into from
Nov 13, 2019
Merged

TST: Add tests for single level indexing with loc(axis=1) #29519

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60c3f9f
Add tests for single level indexing with loc(axis=1)
ryankarlos Nov 10, 2019
e8a27bb
remove dtype check to pass CI
ryankarlos Nov 10, 2019
184169e
Split simple df test into diff function
ryankarlos Nov 10, 2019
ed9075b
Remove check_dtype=False
ryankarlos Nov 10, 2019
bd31841
Split first test into sepaarate funcs
ryankarlos Nov 10, 2019
f47c322
change from list to np.array
ryankarlos Nov 12, 2019
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23 changes: 23 additions & 0 deletions pandas/tests/indexing/multiindex/test_slice.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,6 +492,29 @@ def test_loc_axis_arguments(self):
with pytest.raises(ValueError):
df.loc(axis="foo")[:, :, ["C1", "C3"]]

def test_loc_axis_single_level_indexer(self):

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Could you add the issue number?

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Sure

# test single level indexing on Multindex column dataframe
df1 = pd.DataFrame(
np.arange(27).reshape(3, 9),
columns=pd.MultiIndex.from_product(
[["a1", "a2", "a3"], ["b1", "b2", "b3"]]
),
)
result = df1.loc(axis=1)["a1":"a2"]
expected = df1.iloc[:, :-3]
tm.assert_frame_equal(result, expected)
result = df1.loc(axis=1)["a1"]
expected = df1.iloc[:, :3]
expected.columns = ["b1", "b2", "b3"]
tm.assert_frame_equal(result, expected)
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Could you make 3 different functions for these tests?

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@mroeschke Is it ok if I split it into 2 functions - one for Multindex column df and one for simple df --- and for the Multiindex bit I can comment on the two different tests - i.e one is slicing on multi columns on single level and other is slicing on single col. Otherwise, having concise names for the functions is tricky.

Something like this:

        # GH29519
        # test with single level multiple columns slice
        df1 = pd.DataFrame(
            np.arange(27).reshape(3, 9),
            columns=pd.MultiIndex.from_product(
                [["a1", "a2", "a3"], ["b1", "b2", "b3"]]
            ),
        )
        result = df1.loc(axis=1)["a1":"a2"]
        expected = df1.iloc[:, :-3]

        tm.assert_frame_equal(result, expected)

        # GH29519
        # test with single level single column slice
        result = df1.loc(axis=1)["a1"]
        expected = df1.iloc[:, :3]
        expected.columns = ["b1", "b2", "b3"]

        tm.assert_frame_equal(result, expected)

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Verbose function names are fine. Helps isolate the failing test as separate methods.

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Ok just pushed with changes as you originally suggested


# test single level indexing on single index column data frame
df2 = pd.DataFrame(np.arange(9).reshape(3, 3), columns=["a", "b", "c"])
result = df2.loc(axis=1)["a"]
expected = pd.Series([0, 3, 6], name="a")
tm.assert_series_equal(result, expected, check_dtype=False)
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Why is check_dtype=False set?

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@ryankarlos ryankarlos Nov 10, 2019 •
edited
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@mroeschke Since CI was failing for some of the 32 bit OS builds because of int32 vs int64 dtype comparison - im having a similar issue in #29522. Don't know if there is any other work around.

================================== FAILURES ===================================
__________ TestMultiIndexSlicers.test_loc_axis_single_level_indexer ___________
[gw0] win32 -- Python 3.6.7 C:\Miniconda\envs\pandas-dev\python.exe

self = <pandas.tests.indexing.multiindex.test_slice.TestMultiIndexSlicers object at 0x00000156C517C898>

    def test_loc_axis_single_level_indexer(self):
    
        # test single level indexing on Multindex column dataframe
        df1 = pd.DataFrame(
            np.arange(27).reshape(3, 9),
            columns=pd.MultiIndex.from_product(
                [["a1", "a2", "a3"], ["b1", "b2", "b3"]]
            ),
        )
        result = df1.loc(axis=1)["a1":"a2"]
        expected = df1.iloc[:, :-3]
        tm.assert_frame_equal(result, expected)
        result = df1.loc(axis=1)["a1"]
        expected = df1.iloc[:, :3]
        expected.columns = ["b1", "b2", "b3"]
        tm.assert_frame_equal(result, expected)
    
        # test single level indexing on single index column data frame
        df2 = pd.DataFrame(np.arange(9).reshape(3, 3), columns=["a", "b", "c"])
        result = df2.loc(axis=1)["a"]
        expected = pd.Series([0, 3, 6], dtype="int64", name="a")
>       tm.assert_series_equal(result, expected)
E       AssertionError: Attributes of Series are different
E       
E       Attribute "dtype" are different
E       [left]:  int32
E       [right]: int64

pandas\tests\indexing\multiindex\test_slice.py:516: AssertionError

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I see you're not assigning dtype="int64" anymore. Could you remove check_dtype=False now?


def test_per_axis_per_level_setitem(self):

# test index maker
Expand Down