Pandas DataFrame Merging Results in Unexpected Duplicate Rows with Same Key
I've looked through the documentation and I'm still confused about I just started working with I'm working on a project and hit a roadblock. I've been working on this all day and I've been banging my head against this for hours... I'm encountering an issue when merging two DataFrames in pandas, and it results in unexpected duplicate rows. I am using pandas version 1.5.2. Here's the scenario: I have two DataFrames, `df1` and `df2`, that I want to merge on a single key column called 'id'. However, after performing the merge, I notice that some rows in `df1` are duplicated in the merged result, even when they shouldn't be. I've ensured that the 'id' values in `df1` are unique, but `df2` contains multiple entries for the same 'id'. Here are the DataFrames: ```python import pandas as pd df1 = pd.DataFrame({ 'id': [1, 2, 3], 'value': ['A', 'B', 'C'] }) df2 = pd.DataFrame({ 'id': [2, 2, 3], 'value': ['D', 'E', 'F'] }) ``` I perform the merge like this: ```python merged_df = pd.merge(df1, df2, on='id', how='inner') ``` The resulting `merged_df` looks like this: ```python id value_x value_y 0 2 B D 1 2 B E 2 3 C F ``` As seen, the row with 'id' 2 is duplicated, which is expected because `df2` has two entries for 'id' 2, but I'm unsure how to handle this situation properly. I tried using `drop_duplicates()` on the merged DataFrame, but that removes other potentially important rows. Is there a recommended practice for merging in such a situation where the key might have duplicates in the second DataFrame? Should I consider a different merge strategy or preprocess my DataFrames in a specific way? Any insights on how to prevent this kind of duplication would be greatly appreciated. This is part of a larger CLI tool I'm building. What's the best practice here? Thanks for any help you can provide! I recently upgraded to Python stable. Thanks, I really appreciate it! I'm working with Python in a Docker container on Linux.