BUG: Renaming a dataframe columns with a series containing duplicated index corrupts the dataframe about pandas

[mixmixmix]

Hi @mixmixmix, I was able to reproduce what you provided. However, from my personal view, why not just use a dict instead of a Series? As stated in the documentation example, a dict might be more preferable.

Thanks @luke396 , and: yes absolutely using dicts makes more sense!. However, still if the option of using Series is possible, I think it should return an error if it cannot create a valid columns for the dataframe.

[luke396]

Hi @mixmixmix, I was able to reproduce what you provided. However, from my personal view, why not just use a dict instead of a Series? As stated in the documentation example, a dict might be more preferable.

import pandas as pd
import numpy as np

# Define a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

df.rename(columns={'A': 'X', 'B': 'Y', 'C': 'W'}, inplace=True, errors="raise")
#    X  Y  W
# 0  1  4  7
# 1  2  5  8
# 2  3  6  9

# Define a DataFrame of size 20x60
data = np.random.randint(1, 100, size=(20, 60))
columns = [f'Col_{i}' for i in range(60)]
df = pd.DataFrame(data, columns=columns)

# Create a Series for renaming columns, ensuring all names are unique except for two in the middle
new_names = [f'New_{i}' for i in range(61)]
old_names = [f'Col_{i}' for i in range(30)] + ['Col_29'] + [f'Col_{i}' for i in range(30, 60)]

df.rename(columns={old_names[i]: new_names[i] for i in range(61)}, inplace=True, errors="raise")
df['New_0']  # works

[shoaib-moeen]

@luke396 I can look into this. If you want

[luke396]

@luke396 I can look into this. If you want

Of course, anyone can contribute to pandas. Replying 'take' will assign the issue to you.