👀 Views: 2 💬 Answers: 1 📅 Created: 2025-06-11

pandas dataframe apply python

I'm stuck on something that should probably be simple. After trying multiple solutions online, I still can't figure this out. I’m facing an issue when using the `DataFrame.apply()` method in pandas to perform complex calculations on a DataFrame. Specifically, I’m trying to compute a custom metric based on multiple columns, but I’m getting unexpected NaN results in the output, which is disrupting my analysis. Here’s a simplified version of the DataFrame I'm working with: ```python import pandas as pd data = { 'A': [1, 2, 3, None, 5], 'B': [10, 20, None, 40, 50], 'C': [5, None, 15, 20, 25] } df = pd.DataFrame(data) ``` I want to calculate a new metric, say the ratio of the sum of columns 'A' and 'B' to column 'C', but I’m running into issues with rows that contain NaN values. My current approach looks like this: ```python def custom_metric(row): if pd.notna(row['C']): return (row['A'] + row['B']) / row['C'] return None # or np.nan result = df.apply(custom_metric, axis=1) df['metric'] = result ``` However, when I check the `df`, I see that the 'metric' column is filled with NaNs for the rows where either 'A' or 'B' is NaN: ```python print(df) ``` This leads to: ``` A B C metric 0 1.0 10.0 5.0 2.2 1 2.0 20.0 NaN NaN 2 3.0 NaN 15.0 NaN 3 NaN 40.0 20.0 NaN 4 5.0 50.0 25.0 2.2 ``` I expected to skip the NaN values and still compute the metric where possible. I’ve tried using `fillna()` before applying the function, but that doesn’t yield the correct results either. Is there a way to adjust my custom function to handle these NaN cases more effectively, or is there a better pattern for this type of calculation? I’m using pandas version 1.5.3. My development environment is Ubuntu. What's the best practice here? Any ideas how to fix this?