👀 Views: 93 💬 Answers: 1 📅 Created: 2025-08-07

python pandas datetime Python

I'm trying to figure out I'm working on a data analysis project using Pandas in Python 3.9, and I'm running into issues when trying to handle timestamps that fall within Daylight Saving Time (DST) transitions... When I attempt to convert a DataFrame column of UTC timestamps to a local timezone, I notice unexpected behavior for dates that are around the DST change. For instance, the following code snippet is supposed to convert UTC timestamps to US/Eastern timezone: ```python import pandas as pd import pytz df = pd.DataFrame({'timestamp': ['2023-03-12 01:30:00', '2023-03-12 03:30:00', '2023-11-05 01:30:00', '2023-11-05 03:30:00']}) df['timestamp'] = pd.to_datetime(df['timestamp']) df['timestamp_utc'] = df['timestamp'].dt.tz_localize('UTC') df['timestamp_est'] = df['timestamp_utc'].dt.tz_convert('US/Eastern') print(df) ``` The output for the timestamps around the DST change seems correct at first glance, but when I check the values, I see that `2023-03-12 03:30:00` shows up as `2023-03-12 03:30:00-04:00`, while I expect it to show `2023-03-12 04:30:00-04:00` because DST should have started. Similarly, for `2023-11-05 01:30:00`, it shows `2023-11-05 01:30:00-04:00` instead of `2023-11-05 01:30:00-05:00` after the time change back to standard time. I’ve read about the `pytz` library and its timezone definitions, but I’m still confused about how exactly it interacts with Pandas' `tz_localize` and `tz_convert`. I tried using `df['timestamp'].dt.tz_localize('US/Eastern', ambiguous='infer')`, but that didn’t resolve the scenario either. I also checked the `ambiguous` parameter options, but I’m not fully sure how to implement it correctly in this context. Is there a recommended best practice for handling such cases? Any guidance would be greatly appreciated! I'm using Python 3.11 in this project. I'd love to hear your thoughts on this. Any pointers in the right direction?