BUG: RollingGroupby.corr() producing incorrect results · Issue #43386 · pandas-dev/pandas (original) (raw)

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Code Sample, a copy-pastable example

import pandas as pd

sample dataframe. We order it by column c

df = pd.DataFrame({
"a": ["g1"]*4 + ["g2"]*4,
"b": range(8),
"c": [0, 4, 6, 1, 2, 7, 3, 5]
}).sort_values("c")
df

a b c
0 g1 0 0
3 g1 3 1
4 g2 4 2
6 g2 6 3
1 g1 1 4
7 g2 7 5
2 g1 2 6
5 g2 5 7

(df.groupby("a")[["b", "c"]] .rolling(3) .corr() .unstack() .iloc[:, 1] .droplevel("a") .sort_index())

0         NaN
1         NaN
2    0.155543
3         NaN
4    0.960769
5   -0.397360
6    0.981981
7         NaN
Name: (b, c), dtype: float64

this result should be no different than above - sorting by groupby key should not be necessary

(df.sort_values(["a", "c"]).groupby("a")[["b", "c"]] .rolling(3) .corr() .unstack() .iloc[:, 1] .droplevel("a") .sort_index())

0         NaN
1    0.052414
2   -0.596040
3         NaN
4         NaN
5   -0.500000
6         NaN
7    0.928571
Name: (b, c), dtype: float64

Problem description

This RollingGroupby.corr() operation is producing incorrect results. The example above shows how sorting the dataframe with respect to the groupby key fixes the issue, but that should not be necessary since it is the groupby key. I have tested and this bug does not exist in version 1.2.5. It first appears in 1.3.0.

Expected Output

The bottom part of the above repro contains expected output. Here's another way of showing expected output by filtering to one group:

(df[df['a'] == "g1"] .groupby("a")[["b", "c"]] .rolling(3) .corr() .unstack() .iloc[:, 1] .droplevel("a") .sort_index())

0         NaN
1    0.052414
2   -0.596040
3         NaN
Name: (b, c), dtype: float64

Output of pd.show_versions()

INSTALLED VERSIONS

commit : 5f648bf
python : 3.8.10.final.0
python-bits : 64
OS : Linux
OS-release : 4.18.0-240.22.1.el8_3.x86_64
Version : #1 SMP Thu Mar 25 14:36:04 EDT 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.2
numpy : 1.21.2
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 57.4.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.27.0
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : 0.8.7
fastparquet : None
gcsfs : None
matplotlib : 3.4.3
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 0.4.2
scipy : 1.7.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
xlwt : None
numba : None