Pandas DataFrame Merge with Different Timestamp Formats Leads to Missing Rows
I'm confused about I'm wondering if anyone has experience with I need help solving This might be a silly question, but I'm maintaining legacy code that This might be a silly question, but I'm working with an scenario when trying to merge two DataFrames on a timestamp column, but I'm seeing missing rows in the final output..... One DataFrame has timestamps formatted as strings, while the other uses Pandas datetime objects. Here’s what my DataFrames look like: ```python import pandas as pd # DataFrame A with string timestamps data_a = {'timestamp': ['2023-10-01 12:00:00', '2023-10-02 12:00:00'], 'value_a': [1, 2]} df_a = pd.DataFrame(data_a) # DataFrame B with datetime timestamps data_b = {'timestamp': [pd.Timestamp('2023-10-01 12:00:00'), pd.Timestamp('2023-10-03 12:00:00')], 'value_b': [3, 4]} df_b = pd.DataFrame(data_b) ``` When I try to merge these two DataFrames using: ```python merged_df = pd.merge(df_a, df_b, on='timestamp', how='inner') ``` I expect to see the row with `2023-10-01 12:00:00` in the merged DataFrame, but it's missing. Instead, I get this result: ``` Empty DataFrame Columns: [] Index: [] ``` I've tried converting the timestamp column in `df_a` to datetime with: ```python df_a['timestamp'] = pd.to_datetime(df_a['timestamp']) ``` But this still doesn't resolve the scenario. The merge is not finding any matching rows. I suspect it might be due to the format differences or maybe something else entirely. Any ideas on how I can resolve this and successfully merge these two DataFrames? I'm using Pandas version 1.5.3. Am I missing something obvious? I'd love to hear your thoughts on this. For context: I'm using Python on Ubuntu 20.04. Am I approaching this the right way? Is this even possible? Thanks, I really appreciate it! My team is using Python for this desktop app. Am I approaching this the right way? Is there a better approach? For context: I'm using Python on macOS. What's the correct way to implement this?