BUG: exponential moving window covariance fails for multiIndexed DataFrame · Issue #34440 · pandas-dev/pandas (original) (raw)

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Code Sample, a copy-pastable example

import pandas as pd import numpy as np

columns = pd.MultiIndex.from_product([['a','b','c'],['x','y','w','z'], list(range(9))]) index = range(1000) df = pd.DataFrame( np.random.normal(size=(len(index), len(columns))), index=index, columns=columns )

df.ewm(alpha=0.1).cov() #Throws AssertionError: Length of order must be same as number of levels (4), got 3

Problem description

When calculating ewm covariance, pandas fails when the DataFrame has multiindex columns. However it works when columns are simple Index dataframes.
It works for:

pd.DataFrame(df.values).ewm(alpha=0.1).cov()

Expected Output

The covariance, actually only the last matrix (last level of index)

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Windows
OS-release : 10
machine : AMD64
processor : Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.None

pandas : 1.0.3
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.1.3.post20200330
Cython : 0.29.15
pytest : 5.4.1
hypothesis : 5.8.3
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.9.0
bottleneck : 1.3.2
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.3
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : None
pyarrow : 0.15.1
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.16
tables : 3.6.1
tabulate : 0.8.3
xarray : None
xlrd : 1.2.0
xlwt : 1.3.0
xlsxwriter : 1.2.8
numba : 0.49.0