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GroupBy Not Throwing KeyError When Names Exist in MultiIndex #25704

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vss888 opened this issue Mar 13, 2019 · 6 comments

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@vss888
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commented Mar 13, 2019

Here is a link to the discussion: pandas Series groupby with one group

Code Sample, a copy-pastable example if possible

# from the stackoverflow link above
import pandas as pd
data = pd.DataFrame(data={'date':[pd.Timestamp('2016-02-15')]*3, 
    'time':[pd.Timedelta(x) for x in ('07:30:00','10:10:00','11:10:00')],'name':['A']*3, 'N':[1,2,3]}
).set_index(['date','time','name']).sort_index()
data = data[ data.index.get_level_values('time')>=pd.to_timedelta('09:30:00') ]
dataGB = data['N'].groupby(['date','name'])
print(data)
print('Number of groups:',len(dataGB))
print(dataGB.sum())
print(pd.__version__)

Problem description

  1. The code produces 2 groups while clearly there should be only one.
  2. dataGB.sum() result is incorrect

Real Output

>>> print(data)
                          N
date       time     name   
2016-02-15 10:10:00 A     2
           11:10:00 A     3
>>> print('Number of groups:',len(dataGB))
Number of groups: 2
>>> print(dataGB.sum())
date    2
name    3
Name: N, dtype: int64
>>> print(pd.__version__)
0.24.1

Expected Output

>>> print(data)
                          N
date       time     name   
2016-02-15 10:10:00 A     2
           11:10:00 A     3
>>> print('Number of groups:',len(dataGB))
Number of groups: 1
>>> dataGB.sum()
date        name
2016-02-15  A       5
Name: N, dtype: int64
>>> print(pd.__version__)
0.24.1

Output of pd.show_versions()

pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-862.11.6.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.utf-8
LANG: en_US.utf-8
LOCALE: en_US.UTF-8

pandas: 0.24.1
pytest: 3.3.2
pip: 19.0.3
setuptools: 39.0.1
Cython: 0.27.3
numpy: 1.16.1
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 3.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml.etree: None
bs4: None
html5lib: 0.9999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

@WillAyd

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commented Mar 13, 2019

You should get the desired behavior by selecting levels:

In [11]: data['N'].groupby(level=['date', 'name']).sum()
Out[11]:
date        name
2016-02-15  A       5
Name: N, dtype: int64

This should be raising a KeyError as dates and names aren't actually column labels.

PRs to make that happen would certainly be welcome

@WillAyd WillAyd added this to the Contributions Welcome milestone Mar 13, 2019

@WillAyd WillAyd changed the title Pontential bug in Series groupby GroupBy Not Throwing KeyError When Names Exist in MultiIndex Mar 13, 2019

@vss888

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commented Mar 13, 2019

@WillAyd According to Grouping DataFrame with Index Levels and Columns : "Index level names may be specified as keys directly to groupby" (starting with version 0.20, see In/Out[51] on the page). So, what I did should be correct. Am I misunderstanding anything?

@vss888

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commented Mar 13, 2019

I think, it has something to do with the following selection line in my example:

data = data[ data.index.get_level_values('time')>=pd.to_timedelta('09:30:00') ]

Without it, the code works correctly.

@vss888

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commented Mar 13, 2019

Or it might have something to do with the Series input to groupby having only two rows, since the following example (without any selection) also produces incorrect result:

import pandas as pd
data = pd.DataFrame(data={'date':[pd.Timestamp('2016-02-15')]*2, 
    'time':[pd.Timedelta(x) for x in ('10:10:00','11:10:00')],'name':['A']*2, 
    'N':[2,3]}).set_index(['date','time','name']).sort_index()
dataGB = data['N'].groupby(['date','name'])
print(data)
print('Number of groups:',len(dataGB))
print(dataGB.sum())
print(pd.__version__)
@WillAyd

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commented Mar 13, 2019

Hmm OK thanks for sharing that. I see this was actually implemented in #14432

Looking at the test coverage there I don't see anything that has multiple index levels without a column selection, so that may be the culprit here. Investigation and PRs would be welcome

@ArtificialQualia

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commented Mar 15, 2019

I can take this one, I see where the problem is. It appears to be in core/groupby/grouper.py:_get_grouper where all_in_columns_index isn't properly checking for series like it does for DataFrame. And since len(keys) == len(group_axis) in this specific case, it isn't grouping properly.

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