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Reproducible Example

from pathlib import Path import numpy as np import pandas as pd path = Path("file.csv") # Empty values in MultiIndex for both index & columns df = pd.DataFrame( np.arange(6).reshape((2, 3)), columns=pd.MultiIndex.from_tuples((("a", ""), ("b", ""), ("b", "b2"))), index=pd.MultiIndex.from_tuples((("i1", ""), ("i2", ""))), ) df.to_csv(path) # CSV has empty cells in multiindex; just like the df: # , ,a ,b ,b # , , , ,b2 # i1 , ,0 ,1 ,2 # i2 , ,3 ,4 ,5 df_read = pd.read_csv(path, header=[0, 1], index_col=[0, 1]) # Reading from CSV fills multiindex xwith "Unnamed: ..." # , ,a ,b ,b # , ,Unnamed: 2_level_1 ,Unnamed: 3_level_1 ,b2 # i1 , ,0 ,1 ,2 # i2 , ,3 ,4 ,5

Issue Description

Hi,

Closely related to #51252 #51824 #50953. I opened a new issue as it's not exactly the same example, but feel free to close if it's not relevant. Not even sure it's a bug or not, sorry if i'm mistaking.

When reading a dataframe from CSV, if the columns have empty values, it gets filled with "Unnamed: ..." pattern. This makes sense for regular 1D columns, as they need to be unique. When it comes to columns using multiindex, the uniqueness can be ensured even with empty values, if the other levels are different. Current implementation uses "Unnamed: ..." in both cases.

When reading index with Multiindex, empty values don't get replaced. The index read from CSV in the minimal example is identical to the initial index.

Expected Behavior

• I would expect consistency in the handling of MultiIndex by index & columns. Maybe it's not feasible due to the uniqueness required by columns & not by index

• Empty values should be left unfilled if the index remains unique. This would require an identification of duplicates in the multiindex before filling the values, thus losing consistency within the handling of MultiIndex for columns

Installed Versions