Handling Sorting of a List of Dates with Time Zones in Python - implementing Naive vs Aware Datetime Objects
I'm updating my dependencies and I'm dealing with I've been struggling with this for a few days now and could really use some help... I'm working on a Python application using Pandas (version 1.3.3) that involves sorting a list of date strings with varying time zones. My goal is to convert these strings to `datetime` objects and then sort them. However, I'm working with unexpected behaviors when some of the dates are naive (without time zone) and others are aware (with time zone). For example, my list looks like this: ```python import pandas as pd from datetime import datetime date_strings = [ '2023-09-01T14:30:00Z', # UTC '2023-09-02T08:15:00-05:00', # EDT '2023-09-01T09:45:00', # naive '2023-09-01T12:00:00+02:00' # CEST ] ``` When I try to convert these strings to datetime objects and sort them, I use the following code: ```python # Convert strings to datetime objects converted_dates = [pd.to_datetime(date) for date in date_strings] # Attempt to sort sorted_dates = sorted(converted_dates) print(sorted_dates) ``` However, I'm running into a `TypeError` that states `must be aware or naive, not mix of both`. I tried using `date.replace(tzinfo=pytz.UTC)` to make naive dates aware, but that only leads to incorrect ordering since they should represent different local times. How can I handle sorting this list correctly without losing the integrity of the date-time values? Any insights on best practices or specific functions in Pandas that might help with this situation would be greatly appreciated! Am I approaching this the right way? This issue appeared after updating to Python stable. Any examples would be super helpful. This is for a CLI tool running on Windows 10. Thanks, I really appreciate it! Thanks in advance!