How to implement guide with calculating rolling mean on a dataframe with irregular time index in pandas
I've been struggling with this for a few days now and could really use some help. I'm working with a scenario when calculating the rolling mean on a DataFrame that has an irregular time index. My DataFrame looks like this: ```python import pandas as pd import numpy as np dates = pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-04', '2023-01-05', '2023-01-08']) values = [1, 2, 3, 4, 5] df = pd.DataFrame({'value': values}, index=dates) ``` When I try to compute the rolling mean using a window of 3 days, I expect to get an output that considers only the available dates within that window. However, I get an unexpected result: ```python rolling_mean = df['value'].rolling(window='3D').mean() print(rolling_mean) ``` This outputs: ```plaintext 2023-01-01 NaN 2023-01-02 1.5 2023-01-04 2.5 2023-01-05 3.0 2023-01-08 4.0 ``` The output seems incorrect because the rolling mean for `2023-01-04`, which has only one preceding value (`2`), should reflect a mean of `2.0` instead of `2.5`. I expected it to account only for the dates that fall within the window—`2023-01-02` and `2023-01-04`—but it seems to be averaging over dates where values are missing. I've checked the Pandas documentation for the `rolling` method but couldn't find a clear explanation on how to handle this situation specifically. I'm using Pandas version 1.3.3. Is there a better way to specify the rolling window so it only considers the actual data points? Any insights would be greatly appreciated! Is there a better approach?