DOC: Can't figure out how to get exponential working

[tavurth]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

---

Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

import pandas as pd

to_test = pd.DataFrame({ "data": [1,2,3,4,5,6,7,8,9,10] })
exponent = to_test.rolling((3,4), win_type="exponential").mean()

print(exponent)

ValueError: exponential window requires tau

import pandas as pd

to_test = pd.DataFrame({ "data": [1,2,3,4,5,6,7,8,9,10] })
exponent = to_test.rolling(3, win_type="exponential", tau=4).mean()

print(exponent)

TypeError: rolling() got an unexpected keyword argument 'tau'

Problem description

I can't get exponential rolling working, and there's no documentation on this.

Expected Output

The exponential mean of the first 4 values

Output of pd.show_versions()

INSTALLED VERSIONS

---

commit : None
python : 3.7.7.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_GB.UTF-8
LOCALE : en_GB.UTF-8

pandas : 1.0.4
numpy : 1.18.5
pytz : 2020.1
dateutil : 2.8.1
pip : 20.1.1
setuptools : 46.0.0
Cython : 0.29.17
pytest : 5.4.2
hypothesis : None
sphinx : None
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.11.2
IPython : 7.14.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.2.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.17.0
pytables : None
pytest : 5.4.2
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : None
tables : None
tabulate : 0.8.7
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None

[tavurth]

I finally fixed this by changing the code as follows:

import pandas as pd

to_test = pd.DataFrame({ "data": [1,2,3,4,5,6,7,8,9,10] })

exponent = to_test.rolling(3, win_type="exponential").mean(tau=5)

print(exponent)

   data
0   NaN
1   NaN
2   2.0
3   3.0
4   4.0
5   5.0
6   6.0
7   7.0
8   8.0
9   9.0

Sorry I marked this as a bug, I just figured it out.

Perhaps this could be documented somewhere? I can't find any other reference online about this.

[MarcoGorelli]

Thanks @tavurth for the report

there's no documentation on this.

Here it is https://pandas.pydata.org/pandas-docs/dev/reference/api/pandas.Series.rolling.html

Though to be fair, perhaps exponential (needs tau) doesn't make it too clear where the tau needs to go, so if you have an idea on how to clarify this, I think a PR would be welcome

[linnndachen]

How about exponential (needs tau in operation argument)?

[MarcoGorelli]

Yes, I think that would be clearer - would you like to submit a pull request, @wchen928 ? If so, perhaps reword the others where this happens too (e.g. gaussian (needs std))

[linnndachen]

Yes, I think that would be clearer - would you like to submit a pull request, @wchen928 ? If so, perhaps reword the others where this happens too (e.g. gaussian (needs std))

Sounds good. I will do that.

[linnndachen]

I hope I did it right. I am not sure what the 5 tasks are. Do I need to check them off? or the reviewer will check them off? Sorry about the entry-level question... this is my first commit.

[dsaxton]

I hope I did it right. I am not sure what the 5 tasks are. Do I need to check them off? or the reviewer will check them off? Sorry about the entry-level question... this is my first commit.

You did, for documentation fixes like this most of those boxes don't really apply (and actually checking them off isn't a big deal, more important is the content of the changes)

[linnndachen]

You did, for documentation fixes like this most of those boxes don't really apply (and actually checking them off isn't a big deal, more important is the content of the changes)

Thank you so much for the explanation!