👀 Views: 208 💬 Answers: 1 📅 Created: 2025-06-05

pandas dataframe fillna Python

I'm trying to debug I've encountered a strange issue with I keep running into I'm working on a project and hit a roadblock... I'm facing an unexpected behavior while using the `fillna()` method in Pandas, particularly when dealing with a DataFrame that contains mixed data types. I have a DataFrame with both numeric and string columns, and I want to replace NaN values in a specific numeric column with the mean of that column while leaving other columns unchanged. However, the operation seems to be affecting string columns as well, resulting in unexpected data type conversions. Here's a snippet of the code I'm using: ```python import pandas as pd import numpy as np # Sample DataFrame with mixed types data = { 'A': [1, 2, np.nan, 4], # Numeric column 'B': ['foo', 'bar', np.nan, 'baz'], # String column 'C': [10.0, np.nan, np.nan, 40.0] # Another numeric column } df = pd.DataFrame(data) # Attempting to fill NaN in column 'A' with its mean mean_A = df['A'].mean() df['A'] = df['A'].fillna(mean_A) print(df) ``` The output I expect is that column 'A' should have NaN values filled with the mean, while column 'B' should remain unchanged, but instead, I'm getting this: ``` A B C 0 1.0 foo 10.0 1 2.0 bar NaN 2 2.333333 baz 25.0 3 4.0 baz 40.0 ``` Notice how the mean calculation seems to influence other rows in column 'C', which is also numeric. I was expecting `fillna()` to operate only on the specified column but it seems that the operation inadvertently changes the string values in column 'B' as well. I have checked the version of Pandas I'm using, which is 1.4.2. What am I doing wrong? Is there something specific I need to consider when using fillna() in a DataFrame with mixed data types? Any help would be greatly appreciated. Cheers for any assistance! Thanks for any help you can provide! What am I doing wrong?