👀 Views: 83 💬 Answers: 1 📅 Created: 2025-06-14

pandas csv datetime Python

I keep running into I'm experiencing issues with parsing dates from a CSV file using Pandas... The question arises when I read the CSV file that contains date strings along with timezone information which isn't being handled correctly. For example, my date column has entries like `2023-09-15T13:00:00Z`, and when I try to read the CSV file, I get unexpected `NaT` values for some rows after parsing. Here's the code I am currently using: ```python import pandas as pd # Attempting to read the CSV file with date parsing file_path = 'data.csv' df = pd.read_csv(file_path, parse_dates=['date_column']) ``` The `date_column` is supposed to contain ISO 8601 format dates. However, I noticed that for some rows, the entries are missing the timezone info, e.g., `2023-09-15T13:00:00`, and Pandas seems to be treating these inconsistently, resulting in `NaT`. I tried using the `date_parser` parameter in `read_csv` to handle different formats, like this: ```python def custom_date_parser(x): return pd.to_datetime(x, utc=True) df = pd.read_csv(file_path, parse_dates=['date_column'], date_parser=custom_date_parser) ``` But this didn't solve the scenario, and I'm still working with `NaT` values for the rows with missing timezone info. I also set `errors='coerce'`, but it doesn't help in converting the non-standard date formats. I would appreciate any tips on how to handle these date inconsistencies effectively. Is there a better approach to ensure that all date values are parsed correctly, despite the missing timezone information? I’m using Pandas version 1.5.0. Thanks in advance! I'm on Windows 11 using the latest version of Python. Thanks, I really appreciate it!