👀 Views: 69 💬 Answers: 1 📅 Created: 2025-06-03

pandas dataframe merge Python

I can't seem to get I'm sure I'm missing something obvious here, but I'm trying to merge two pandas DataFrames based on multiple keys using the `merge` function, but I want to ensure that all matching records are retained in the result. I'm using pandas version 1.3.0 and want to achieve a full outer join, but I'm encountering unexpected results where some rows seem to drop out of the final DataFrame. Here are the two DataFrames I'm working with: ```python import pandas as pd df1 = pd.DataFrame({ 'key1': ['A', 'B', 'C', 'B'], 'key2': [1, 2, 3, 2], 'value1': [10, 20, 30, 25] }) df2 = pd.DataFrame({ 'key1': ['B', 'C', 'A'], 'key2': [2, 3, 1], 'value2': [200, 300, 100] }) ``` When I perform the merge using the following code: ```python result = pd.merge(df1, df2, on=['key1', 'key2'], how='outer') print(result) ``` I expect the output to retain all records from both DataFrames, but I'm getting this: ``` key1 key2 value1 value2 0 A 1 10.0 100.0 1 B 2 20.0 200.0 2 B 2 25.0 NaN 3 C 3 NaN 300.0 4 C 3 NaN NaN ``` It seems like duplicate rows in `df1` for the keys are causing some unexpected behavior. I've tried using `drop_duplicates()` on `df1` before merging, but that doesn't yield the results I'm expecting either. Am I missing something in my merge setup, or is there a better way to approach this to ensure all records are included without losing any? Additionally, if possible, I'd love to know how to handle cases where the keys are not unique, as this is a common scenario in my dataset. I'd really appreciate any guidance on this. Could someone point me to the right documentation? This is part of a larger service I'm building. Am I approaching this the right way?