Pandas: implementing DataFrame combining using merge with different date formats and timezone handling
Quick question that's been bugging me - I'm trying to figure out I'm working on a personal project and I'm updating my dependencies and I tried several approaches but none seem to work... This might be a silly question, but I'm working with a scenario when trying to merge two DataFrames in pandas that contain date columns, but the formats and timezones vary between them. For instance, I have `df1` with a date column formatted as `YYYY-MM-DD` without timezone, and `df2` with a date column formatted as `MM/DD/YYYY` with UTC timezone. Here's a simplified version of my code: ```python import pandas as pd df1 = pd.DataFrame({ 'id': [1, 2, 3], 'date': ['2023-01-01', '2023-02-01', '2023-03-01'] }) df1['date'] = pd.to_datetime(df1['date']) # Converts to datetime df2 = pd.DataFrame({ 'id': [1, 2, 3], 'date': ['01/01/2023', '02/01/2023', '03/01/2023'] }) df2['date'] = pd.to_datetime(df2['date'], format='%m/%d/%Y', utc=True) # Converts with timezone merged_df = pd.merge(df1, df2, on='date', how='inner') ``` When I run this code, I get the following behavior message: ``` ValueError: need to merge with different levels: Index(['date'], dtype='object') Index(['date'], dtype='object') ``` I’ve tried converting the `df1['date']` column to UTC as well using `df1['date'] = df1['date'].dt.tz_localize('UTC')`, but it still doesn’t work. What’s the best way to handle merging DataFrames with varying date formats and timezones in pandas? Is there a more effective approach to ensure that both date columns align correctly for the merge? Has anyone else encountered this? For context: I'm using Python on Windows. Thanks in advance! For reference, this is a production application. Any advice would be much appreciated. For reference, this is a production mobile app. Am I missing something obvious? I've been using Python for about a year now. Any feedback is welcome!