Handling DateTime Conversion with Time Zone Awareness in Pandas
I'm building a feature where I've spent hours debugging this and I'm currently working with a DataFrame that has a column of date strings in various formats, and I need to convert this column into a timezone-aware datetime format... The original data looks like this: ```python import pandas as pd date_data = {'dates': ['2023-01-01 12:00:00', '2023/01/02 15:30:00', '01-03-2023 09:45']} df = pd.DataFrame(date_data) print(df) ``` I attempted to use `pd.to_datetime()` to handle the conversion, but I ran into issues with inconsistent formats. Here's the code I tried: ```python df['datetime'] = pd.to_datetime(df['dates'], errors='coerce') print(df) ``` However, this resulted in `NaT` values for the dates that didn't match the default format, leading me to believe that the coercion wasn't handling my various formats as expected. I also tried specifying `format` parameter explicitly, but I couldn't find a way to accommodate multiple date formats at once. I would like to convert these to a datetime format and assign a specific timezone (e.g., 'America/New_York') to all of them. Is there an efficient way to achieve this, or do I need to preprocess the strings into a consistent format first? I would appreciate any tips or best practices for dealing with timezones in Pandas, especially in this context where formats vary significantly. Thanks in advance! I'm working on a service that needs to handle this. I'd really appreciate any guidance on this. My development environment is Ubuntu 20.04. I'm working in a Windows 11 environment. Any pointers in the right direction?