Pandas: ValueError when merging DataFrames with MultiIndex columns and differing levels
I'm relatively new to this, so bear with me. I'm working with a `ValueError` when trying to merge two DataFrames that have MultiIndex columns. The DataFrames that I'm working with are structured as follows: ```python import pandas as pd # First DataFrame with a MultiIndex index1 = pd.MultiIndex.from_tuples([('A', 'foo'), ('A', 'bar'), ('B', 'foo')]) df1 = pd.DataFrame({'value': [1, 2, 3]}, index=index1) # Second DataFrame with a different MultiIndex index2 = pd.MultiIndex.from_tuples([('A', 'foo'), ('B', 'baz'), ('B', 'foo')]) df2 = pd.DataFrame({'value': [4, 5, 6]}, index=index2) print(df1) print(df2) ``` When I try to merge these DataFrames using: ```python result = pd.merge(df1, df2, left_index=True, right_index=True, how='outer') ``` I get the following behavior: ``` ValueError: Length of passed values is 2, index implies 3 ``` I'm confused because I thought the merge operation would handle the differing levels of the MultiIndex. In this case, I want to keep all the existing data, and I'm unsure how to resolve this scenario. I've tried resetting the index using `df1.reset_index()` and `df2.reset_index()` before merging, but that didn't help either. Is there a specific way to merge DataFrames with MultiIndex columns when they have differing levels? Any advice on how to handle this situation would be greatly appreciated! For context: I'm using Python on Windows. What's the best practice here? This is part of a larger CLI tool I'm building.