👀 Views: 70 💬 Answers: 1 📅 Created: 2025-06-12

pandas datetime timezone dataframe Python

I can't seem to get I've been struggling with this for a few days now and could really use some help... I'm working with a DataFrame in Pandas that contains a column of timestamps with mixed time zones. Some timestamps are in UTC while others are in local time (Eastern Standard Time). When I try to convert all timestamps to a single timezone (UTC), I encounter the following behavior: `TypeError: need to convert to Timestamp`. Here's a snippet of my DataFrame: ```python import pandas as pd import pytz data = { 'timestamp': [ '2023-10-01 12:00:00', # UTC '2023-10-01 08:00:00', # EST '2023-10-01 10:00:00', # UTC '2023-10-01 13:00:00', # EST ], 'value': [10, 20, 30, 40] } df = pd.DataFrame(data) df['timestamp'] = pd.to_datetime(df['timestamp']) df['timezone'] = [ 'UTC', 'EST', 'UTC', 'EST' ] ``` I've tried using `df['timestamp'].dt.tz_localize()` to assign time zones, but I get the behavior mentioned above. When I attempt to set the timezone directly like this: ```python df.loc[df['timezone'] == 'UTC', 'timestamp'] = df.loc[df['timezone'] == 'UTC', 'timestamp'].dt.tz_localize('UTC') df.loc[df['timezone'] == 'EST', 'timestamp'] = df.loc[df['timezone'] == 'EST', 'timestamp'].dt.tz_localize('America/New_York') ``` It seems to work for the EST timestamps but not for the UTC ones. I end up with mixed types in the timestamp column after conversion, which complicates further analysis. How can I consistently convert all timestamps to UTC without running into type issues? Is there a best practice for handling DataFrames with mixed time zones in Pandas, especially when dealing with time series data? Any insights would be greatly appreciated! Any help would be greatly appreciated!