ZeroDivisionError when groupby rank with method="dense" and pct=True #23666

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opened this issue Nov 13, 2018 · 4 comments

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njryo commented Nov 13, 2018 • edited

When I tried to use groupby rank function with method="dense", pct=True options, I encountered the ZeroDivisionError.

Code Sample, a copy-pastable example if possible

```import pandas as pd

df = pd.DataFrame({"A": [1, 1, 1, 2, 2, 2],
"B": [1, 1, 1, 1, 2, 2],
"C": [1, 2, 1, 1, 1, 2]})
df.groupby(["A", "B"])["C"].rank(method="dense", pct=True)```

error:

``````Traceback (most recent call last):
File "c:/Users/<user_name>/Documents/test.py", line 6, in <module>
df.groupby(["A", "B"])["C"].rank(method="dense", pct=True)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 1906, in rank
na_option=na_option, pct=pct, axis=axis)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 1025, in _cython_transform
**kwargs)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 2630, in transform
return self._cython_operation('transform', values, how, axis, **kwargs)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 2590, in _cython_operation
**kwargs)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 2664, in _transform
transform_func(result, values, comp_ids, is_datetimelike, **kwargs)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 2479, in wrapper
return f(afunc, *args, **kwargs)
File "C:\Users\<user_name>\Anaconda3\lib\site-packages\pandas\core\groupby\groupby.py", line 2431, in <lambda>
kwargs.get('na_option', 'keep')
File "pandas\_libs\groupby_helper.pxi", line 1292, in pandas._libs.groupby.group_rank_int64
ZeroDivisionError: float division
``````

Problem description

I encountered ZeroDivisionError when I tried to use the groupby rank function.

I can't find out exactly what a problem is. But when I drop either method="dense" or pct=True option, the above code works.

If some elements in the above DataFrame are changed, this error disappear. For example, the following code gives the expected output.

```df = pd.DataFrame({"A": [1, 1, 1, 2, 2, 2],
"B": [1, 1, 1, 1, 2, 2],
"C": [1, 2, 1, 0, 1, 2]}) # a little change in column C
df.groupby(["A", "B"])["C"].rank(method="dense", pct=True)```

output:

``````0    0.5
1    1.0
2    0.5
3    1.0
4    0.5
5    1.0
Name: C, dtype: float64
``````

Output of `pd.show_versions()`

[paste the output of `pd.show_versions()` here below this line]

INSTALLED VERSIONS

commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.5
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 3.0.0
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.5
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None

Contributor

Koustav-Samaddar commented Nov 22, 2018 • edited

 I think this is caused due to groups of size 1. By removing (A, B) = (2, 1) group, the error goes away. @WillAyd Do you mind if I tackle this? It's my first time contributing to pandas but I think I have a rough idea on how to fix the problem.
Member

WillAyd commented Nov 22, 2018

 Go for it!
Contributor

Koustav-Samaddar commented Nov 22, 2018 • edited

 @WillAyd Sorry for bothering you, but I had a quick question related to bug fix contributions. In my rough testing of the fix, I found another bug in groupby rank (checked to make sure that the bug also existed without my fix). Should I fix the bug in another commit in the same PR or should I create a new bug tracker and go from there? The new bug isn't directly related to the current bug but arises from nearby code.
Member

WillAyd commented Nov 23, 2018

 Typically best to create a separate issue and PR for tracking and review purposes

Merged