Pandas: Difficulty in Resampling Time Series Data with Irregular Frequency and Missing Timestamps
I'm reviewing some code and I just started working with This might be a silly question, but I'm trying to resample a time series DataFrame with irregular frequency and I'm working with issues when it comes to handling missing timestamps. Specifically, I'm using Pandas version 1.3.3 and I have a DataFrame with a datetime index that includes several gaps. When I attempt to resample the data to a daily frequency and fill the missing values using `ffill`, I end up with unexpected results where some dates are completely missing in the output. Here's a minimal example of my DataFrame: ```python import pandas as pd import numpy as np dates = pd.to_datetime(['2023-01-01 10:00', '2023-01-01 12:00', '2023-01-02 15:00']) values = [1, 2, 3] df = pd.DataFrame({'value': values}, index=dates) ``` If I then try to resample the DataFrame: ```python daily_resampled = df.resample('D').ffill() ``` I expect to see an entry for '2023-01-01' and '2023-01-02', but the output looks like this: ``` value 2023-01-01 2.0 2023-01-02 3.0 ``` What's puzzling is that I do not see a forward fill for '2023-01-01 00:00'. I would have expected that the value from '2023-01-01 12:00' would carry forward to fill the missing '00:00' time for that day. I've tried using `fill_value` and `method='ffill'`, but it doesn't seem to affect the resampling output. Could someone explain why this is happening and how I can ensure that all expected timestamps are filled correctly in the resulting DataFrame? Any insights or workarounds would be greatly appreciated! Thanks in advance! My team is using Python for this service. Thanks for taking the time to read this! My development environment is Windows 10.