Pandas DataFrame Merge Results in Unexpected Duplicates When Joining on Multiple Columns
I'm having trouble with I'm stuck on something that should probably be simple. I'm sure I'm missing something obvious here, but I'm stuck on something that should probably be simple. I'm experiencing an issue when merging two DataFrames in Pandas and ending up with unexpected duplicate rows. I'm using Pandas version 1.5.1 and trying to merge two DataFrames based on multiple keys, but after the merge, some rows appear multiple times, which shouldn't happen. Here’s a simplified version of my DataFrames: ```python import pandas as pd # Creating the first DataFrame left = pd.DataFrame({ 'key1': ['A', 'B', 'C'], 'key2': ['D', 'E', 'F'], 'value': [1, 2, 3] }) # Creating the second DataFrame with additional duplicate keys right = pd.DataFrame({ 'key1': ['A', 'A', 'B'], 'key2': ['D', 'D', 'E'], 'other_value': [4, 5, 6] }) # Attempting to merge on multiple keys result = pd.merge(left, right, on=['key1', 'key2'], how='inner') print(result) ``` I expect only one row for each matching pair of keys, but instead, I'm getting: ``` key1 key2 value other_value 0 A D 1 4 1 A D 1 5 2 B E 2 6 ``` It seems that the duplicate entries in the `right` DataFrame are causing this behavior, but I was anticipating that the merge would handle this somehow. I've also tried using `drop_duplicates()` on the `right` DataFrame before merging, but it didn't resolve the duplication issue in the merged DataFrame. Here’s what I tried: ```python right_unique = right.drop_duplicates() result_unique = pd.merge(left, right_unique, on=['key1', 'key2'], how='inner') print(result_unique) ``` However, I still see the same issue. How can I achieve a clean merge that does not result in such duplicates? Is there a particular approach or parameter I should be using to handle this situation effectively? I'd really appreciate any guidance on this. The stack includes Python and several other technologies. Is there a better approach? My team is using Python for this desktop app. I'm open to any suggestions.