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@ryankarlos ryankarlos commented Nov 10, 2019

@ryankarlos ryankarlos force-pushed the TST-loc-axis1-singlelevel branch from 8c0c56d to e8a27bb Compare November 10, 2019 03:38
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

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

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

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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?

@gfyoung gfyoung added DataFrame DataFrame data structure Indexing Related to indexing on series/frames, not to indexes themselves Testing pandas testing functions or related to the test suite labels Nov 10, 2019
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@mroeschke all green now

@jreback jreback added this to the 1.0 milestone Nov 13, 2019
@jreback jreback merged commit 0cbf399 into pandas-dev:master Nov 13, 2019
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jreback commented Nov 13, 2019

thanks @ryankarlos

Reksbril pushed a commit to Reksbril/pandas that referenced this pull request Nov 18, 2019
proost pushed a commit to proost/pandas that referenced this pull request Dec 19, 2019
proost pushed a commit to proost/pandas that referenced this pull request Dec 19, 2019
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DataFrame DataFrame data structure Indexing Related to indexing on series/frames, not to indexes themselves Testing pandas testing functions or related to the test suite

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