BUG: roll_std compute different result when input same data with diffrent length

[qianyun210603]

Pandas version checks

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

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

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

# the data file used in the example https://1drv.ms/u/s!AnPL7Q5hAP8rk12MkTUQZs2RnVwv?e=gmZMFI (CSV file stored in MS OneDrive)

import pandas as pd
pd.set_option('display.precision',12)
series = pd.read_csv(r"E:\pd_rollingstd_issue.csv", index_col=[0], parse_dates=["date"]).loc[:, '0']
series.loc["2017-05-20":"2017-06-10"]
date
2017-05-22    10.0242441
2017-05-23    10.0242441
2017-05-24    10.0242441
2017-05-25    10.0242441
2017-05-26    10.0242441
2017-05-31    10.0242441
2017-06-01    10.0242436
2017-06-02    10.0242436
2017-06-05    10.0242436
2017-06-06    10.0242436
2017-06-07    10.0242436
2017-06-08    10.0242436
2017-06-09    10.0242436
Name: 0, dtype: float64
series.loc["2017-05-20":"2017-06-10"].rolling(5).std()
date
2017-05-22               NaN
2017-05-23               NaN
2017-05-24               NaN
2017-05-25               NaN
2017-05-26    0.000000000000
2017-05-31    0.000000000000
2017-06-01    0.000000223607
2017-06-02    0.000000273861
2017-06-05    0.000000273861
2017-06-06    0.000000223607
2017-06-07    0.000000000000
2017-06-08    0.000000000000
2017-06-09    0.000000000000
Name: 0, dtype: float64
series.rolling(5).std().loc["2017-05-20":"2017-06-10"]
date
2017-05-22    0.0
2017-05-23    0.0
2017-05-24    0.0
2017-05-25    0.0
2017-05-26    0.0
2017-05-31    0.0
2017-06-01    0.0
2017-06-02    0.0
2017-06-05    0.0
2017-06-06    0.0
2017-06-07    0.0
2017-06-08    0.0
2017-06-09    0.0
Name: 0, dtype: float64

Issue Description

As one can see in the example, rolling std with window size 5 generates non-zero number between 2017-06-01 and 2017-06-06 when we use part of the series, which is correct as we see in source series value slightly changes on 2017-06-01. However, when using the whole series, it generates all 0 number, which is not correct.

Expected Behavior

With same number in the 5-length window, the whole series should generate same non-zero result for dates between 2017-06-01 and 2017-06-06.

Installed Versions

INSTALLED VERSIONS

commit : e86ed37
python : 3.9.16.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.19045
machine : AMD64
processor : AMD64 Family 23 Model 113 Stepping 0, AuthenticAMD
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.936

pandas : 2.1.1
numpy : 1.24.2
pytz : 2022.7.1
dateutil : 2.8.2
setuptools : 65.6.3
pip : 22.3.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.2
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.10.0
pandas_datareader : None
bs4 : 4.11.2
bottleneck : None
dataframe-api-compat: None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.0
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None

[PiotrekB416]

take

[PiotrekB416]

Could you recheck if the problem exists. I just checked and got this output

import pandas as pd
pd.set_option('display.precision',12)
series = pd.read_csv(r"./test.csv", index_col=[0], parse_dates=["date"]).loc[:,'0']
series.loc["2017-05-20":"2017-06-10"]
date
2017-05-22    10.0242441
2017-05-23    10.0242441
2017-05-24    10.0242441
2017-05-25    10.0242441
2017-05-26    10.0242441
2017-05-31    10.0242441
2017-06-01    10.0242436
2017-06-02    10.0242436
2017-06-05    10.0242436
2017-06-06    10.0242436
2017-06-07    10.0242436
2017-06-08    10.0242436
2017-06-09    10.0242436
Name: 0, dtype: float64
series.loc["2017-05-20":"2017-06-10"].rolling(5).std()
date
2017-05-22               NaN
2017-05-23               NaN
2017-05-24               NaN
2017-05-25               NaN
2017-05-26    0.000000000000
2017-05-31    0.000000000000
2017-06-01    0.000000223607
2017-06-02    0.000000273861
2017-06-05    0.000000273861
2017-06-06    0.000000223607
2017-06-07    0.000000000000
2017-06-08    0.000000000000
2017-06-09    0.000000000000
Name: 0, dtype: float64
series.rolling(5).std().loc["2017-05-20":"2017-06-10"]
date
2017-05-22               NaN
2017-05-23               NaN
2017-05-24               NaN
2017-05-25               NaN
2017-05-26    0.000000000000
2017-05-31    0.000000000000
2017-06-01    0.000000223607
2017-06-02    0.000000273861
2017-06-05    0.000000273861
2017-06-06    0.000000223607
2017-06-07    0.000000000000
2017-06-08    0.000000000000
2017-06-09    0.000000000000
Name: 0, dtype: float64

[qianyun210603]

@PiotrekB416

Thanks for prompt reply!

I retried on both Linux and Windows, and confirmed the issue exists with latest release. Sorry I had some issue building main branch which I cannot solve quickly.

I suspect you assigned series.loc["2017-05-20":"2017-06-10"] to series somewhere before you run series.rolling(5).std().loc["2017-05-20":"2017-06-10"], otherwise there shouldn't be NaNs at the beginning of the results, as with the whole series participating the rolling, there are enough elements to calculate MA5 for dates from 2017-05-22 to 05-25.

Also maybe it's caused by similar reason of #54380?

[qianyun210603]

Another weird phenomenon I observed is, when you run rolling with different start date, the numbers from 2017-06-01 to 2017-06-06 changes, e.g.

>>> series.loc["2015-05-20":"2017-06-10"].rolling(5).std().loc["2017-05-20":"2017-06-10"]
date
2017-05-22    0.000000000000
2017-05-23    0.000000000000
2017-05-24    0.000000000000
2017-05-25    0.000000000000
2017-05-26    0.000000000000
2017-05-31    0.000000000000
2017-06-01    0.000000504119
2017-06-02    0.000000528333
2017-06-05    0.000000528333
2017-06-06    0.000000504119
2017-06-07    0.000000000000
2017-06-08    0.000000000000
2017-06-09    0.000000000000
Name: 0, dtype: float64

[PiotrekB416]

Could you send the CVS data in the reply. The link didn't work so I just used the data from the issue and I'd like to verify that is not the data that's causing the weird behavior.

[qianyun210603]

Attached please find the whole csv file. From: Piotr Bartoszewicz ***@***.***> Sent: Sunday, October 1, 2023 23:13 To: pandas-dev/pandas ***@***.***> Cc: YQ Tsui ***@***.***>; Author ***@***.***> Subject: Re: [pandas-dev/pandas] BUG: roll_std compute different result when input same data with diffrent length (Issue #55343) Could you send the CVS data in the reply. The link didn't work so I just used the data from the issue and I'd like to verify that is not the data that's causing the weird behavior. — Reply to this email directly, view it on GitHub<#55343 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AEMWMPGUQNASKRBHUQHQR5DX5GB7ZANCNFSM6AAAAAA5OANKRQ>. You are receiving this because you authored the thread.Message ID: ***@***.******@***.***>>

[qianyun210603]

@PiotrekB416

Replied email with csv attached, but it's a github address, not sure if you can receive it.

Re-uploaded the csv to onedrive, zipped to avoid unwanted conversion by MS.
https://1drv.ms/u/s!AnPL7Q5hAP8rk12MkTUQZs2RnVwv?e=gmZMFI

[qianyun210603]

@PiotrekB416 Any finding on this please?