Pandas: Unexpected behavior when using groupby and transform on large DataFrame with NaN values
After trying multiple solutions online, I still can't figure this out. I'm encountering an unexpected behavior when I try to use `groupby` and `transform` on a large DataFrame that contains NaN values. The goal is to fill NaN values with the mean of their respective groups. However, I noticed that instead of filling in the NaNs correctly, my DataFrame seems to be returning unexpected values, and in some cases, the NaNs remain unchanged. Here’s a minimal example of what I’m working with: ```python import pandas as pd import numpy as np # Sample DataFrame data = { 'group': ['A', 'A', 'B', 'B', 'C', 'C'], 'value': [1, np.nan, 3, 4, np.nan, 6] } df = pd.DataFrame(data) # Applying groupby and transform to fill NaN values with the group mean mean_filled = df.groupby('group')['value'].transform(lambda x: x.fillna(x.mean())) df['value'] = mean_filled ``` After running this code, I expected that any NaN in the `value` column would be replaced with the mean of its group. However, the NaN for group `A` remains unchanged, while the NaN for group `C` is filled correctly. When I print the output, it shows: ``` group value 0 A 1.0 1 A NaN 2 B 3.0 3 B 4.0 4 C NaN 5 C 6.0 ``` I've tried different approaches, such as using `agg` instead of `transform`, but that only made the original DataFrame lose its structure. I also verified that all groups have sufficient data points for calculating the mean. My DataFrame has over 100,000 rows, and I'm using pandas version 1.5.3. Could this be a known issue with handling NaNs in large DataFrames, or is there something I'm missing in my approach? What's the best practice here? Could someone point me to the right documentation? I'd be grateful for any help.