Pandas从0.25.3升级至1.2.4后，Index.droplevel()在apply方法中执行报错的问题求助

Great question! Let's break down exactly why this issue popped up after your Pandas upgrade, and walk through cleaner, more sustainable fixes than the copy workaround.

Why This Happens #

Starting in newer Pandas versions (around 1.0+), the apply method was optimized to reuse the same Series object for each row instead of creating a new one every time. This is a performance improvement, but it means any in-place modifications you make to the row parameter in your function will persist across iterations.

In your code:

• The first time foo runs, you modify the shared row's index with row.index = row.index.droplevel(0), stripping it down to 1 level.
• The second time foo runs, it gets the same reused Series—but now its index only has 1 level. Trying to drop level 0 again throws the ValueError because there's nothing left to drop.

Your diagnostic print statements confirmed this: the first row had a 2-level index, but subsequent rows already had the modified 1-level index from the first iteration.

Better Solutions (No Copy Needed) #

Instead of working around the object reuse, let's adjust your code to avoid modifying the shared row entirely. Here are three solid approaches:

1. Access Values Directly via MultiIndex Tuples #

You don't need to modify the row's index to access the values. Just use the full MultiIndex tuple to reference the columns:

def foo(row):
    return row[('coz', 'beta')] > row[('coz', 'alpha')]

This is simple, avoids any index manipulation, and plays nicely with Pandas' optimized apply behavior.

2. Extract the Sub-Index Without Modifying the Original Row #

If you prefer working with a single-level index, create a new Series (instead of modifying the existing one) using droplevel:

def foo(row):
    # Create a new Series with the dropped level—leaves the original row untouched
    single_level_row = row.droplevel(0, axis=0)
    return single_level_row['beta'] > single_level_row['alpha']

This way, the shared row object used by apply never gets modified, so every iteration starts with the original 2-level index.

3. Ditch apply Entirely (Vectorized Operation) #

The best fix for performance and simplicity is to replace apply with a vectorized comparison. Pandas is built for this kind of column-wise operation, which is way faster than looping with apply:

df[('coz', 'beta')] > df[('coz', 'alpha')]

This gives you the exact same boolean Series result, without any function overhead or object reuse issues.

Why Your Copy Workaround Works (And Why It's Not Ideal) #

When you do row = row.copy(), you're creating a new, independent Series for each iteration. Modifying this copy's index doesn't affect the shared row object that apply reuses. While this works, it adds unnecessary memory overhead (copying data every time) and misses out on the performance optimizations that Pandas built into apply.

内容的提问来源于stack exchange，提问作者user3556757