👀 Views: 1 💬 Answers: 1 📅 Created: 2025-06-11

pandas csv datetime Python

I recently switched to Could someone explain I'm integrating two systems and I'm working with a question when importing a CSV file into a Pandas DataFrame where the datetime columns are not being parsed correctly, resulting in timezone inconsistencies. I’m using Pandas version 1.3.3 and Python 3.9. The CSV file contains a datetime column in UTC, but when I import it, the resulting DataFrame seems to interpret the datetimes in the local timezone instead of keeping them as UTC. My code for importing the CSV looks like this: ```python import pandas as pd df = pd.read_csv('data.csv', parse_dates=['timestamp']) ``` After importing, I check the timezone with: ```python print(df['timestamp'].dt.tz) ``` However, it shows `None`, which indicates that it is not timezone-aware. I've tried explicitly setting the timezone after the import: ```python # Attempt to set timezone manually df['timestamp'] = df['timestamp'].dt.tz_localize('UTC') ``` But this leads to an behavior message: ``` ValueError: Index has duplicates. ``` The CSV file has unique timestamps, so I'm puzzled by this behavior. I’ve also tried using the `date_parser` argument: ```python from dateutil import parser df = pd.read_csv('data.csv', parse_dates=['timestamp'], date_parser=parser.parse) ``` But it still results in the same scenario with timezones. I need to ensure that the datetime is parsed correctly as UTC right from the import. Any ideas on how to resolve this scenario, or is there a better approach to handle datetime parsing while preserving the timezone information? My development environment is Windows 11. I'd really appreciate any guidance on this. I'm working with Python in a Docker container on CentOS. I'm open to any suggestions. My development environment is CentOS. Has anyone else encountered this? Has anyone dealt with something similar? The stack includes Python and several other technologies. I'd be grateful for any help.