Pandas: Difficulty in handling non-unique multi-index DataFrame during groupby operation
I've spent hours debugging this and I'm currently working with a Pandas DataFrame that has a multi-index created from two columns: 'category' and 'subcategory'. I need to perform a groupby operation to calculate the mean of a 'value' column, but I'm running into problems due to the non-unique nature of the index after the operation. Here's the code I've tried so far: ```python import pandas as pd # Sample data index = pd.MultiIndex.from_tuples([ ('A', 'a'), ('A', 'a'), ('A', 'b'), ('B', 'c'), ('B', 'c'), ('B', 'd') ], names=['category', 'subcategory']) data = { 'value': [10, 20, 30, 40, 50, 60] } df = pd.DataFrame(data, index=index) # Attempting to group by the multi-index and calculate mean result = df.groupby(level=['category', 'subcategory']).mean() print(result) ``` The output I receive is: ``` value category subcategory A a 15.0 b 30.0 B c 50.0 d 60.0 ``` While this is correct, I would like to reset the index to avoid confusion later on, especially since I want to merge this result with another DataFrame that doesn't use a multi-index. However, when I run `result.reset_index()`, the 'subcategory' column seems to contain duplicate entries: ```python reset_result = result.reset_index() print(reset_result) ``` The output looks like this: ``` category subcategory value 0 A a 15.0 1 A b 30.0 2 B c 50.0 3 B d 60.0 ``` Which is what I expected, but I am concerned about potential issues with merging later since I will be using this DataFrame with other data that may also have a multi-index. Is there a recommended approach or best practice to ensure that merging or other operations won't lead to unexpected results later? Any insights would be greatly appreciated! My team is using Python for this microservice. I'd really appreciate any guidance on this.