The groupby filter output differs if we run an aggregate function before

[arnaudlegout]

Code Sample, a copy-pastable example if possible

# I create a test DataFrame
d = pd.DataFrame({'data': range(6), 'key': list('ABCABC')})
# I groupby the column 'key'
g = d.groupby('key')
# I filter with always True (not that useful, just for the example)
print(g.filter(lambda x: True))
g.sum()
print(g.filter(lambda x: True))

Problem description

Here is the output of the above code

   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C
   data
0     0
1     1
2     2
3     3
4     4
5     5

I don't understand why the column key is in the output in the first run of filter, whereas when running an aggregate function (here g.sum()) before the filter, the key column disappear. If I use the as_index=False for the groupby, then the column is correctly preserved (as expected).

It looks like the aggregate function somehow change the groupby object, whereas my understanding of the groupby object is that each function call return a new object (and do not modify the original groupby object).

Expected Output

   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C

   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: None.None

pandas: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

[gfyoung]

Hmm...that's really odd. Surprise inplace modifications aren't nice. PR to patch this is welcome!

@gfyoung and @arnaudlegout I ran into the same problem, and did some investigation on this. Turns out that when calling g.sum(), the member _group_selection in g is modified in the function _set_group_selection, but never got changed back.

Also, test_grouper_creation_bug in file test_groupby.py is inaccurate:

        df = DataFrame({'A': [0, 0, 1, 1, 2, 2], 'B': [1, 2, 3, 4, 5, 6]})
        g = df.groupby('A')
        expected = g.sum()

        g = df.groupby(pd.Grouper(key='A'))
        result = g.sum()
        assert_frame_equal(result, expected)

        result = g.apply(lambda x: x.sum())
        assert_frame_equal(result, expected)

If you do this:

        df = DataFrame({'A': [0, 0, 1, 1, 2, 2], 'B': [1, 2, 3, 4, 5, 6]})
        g = df.groupby('A')
        expected = g.sum()

        g = df.groupby(pd.Grouper(key='A'))
        result = g.sum()
        assert_frame_equal(result, expected)

        df = DataFrame({'A': [0, 0, 1, 1, 2, 2], 'B': [1, 2, 3, 4, 5, 6]})
        g = df.groupby('A')
        result = g.apply(lambda x: x.sum())
        assert_frame_equal(result, expected)

The test will fail. It only passes because of this bug. g.apply(lambda x: x.sum()) returns a 3x2 data frame if g is 3x2. This bug causes b to be 3x1, hence result turns to have the same dimension as expected.

Am I missing anything here? Many thanks.

[jreback]

so groupers can keep state, though they really shouldn't. this is might be a wider problem. would be helpful for you to look at it.

I'll be happy to!

[rhshadrach]

This is essentially a duplicate of #34656, which was closed by #35314. I've confirmed that this is fixed on master. However, I think tests should be added using filter, similar to the tests that were added as part of #35314.