Pandas: guide with applying custom function using apply and accessing multiple columns in DataFrame
I'm stuck trying to I've been banging my head against this for hours. I'm working with a question while trying to apply a custom function to my DataFrame that requires access to multiple columns. I'm using Pandas version 1.5.0 and I have the following DataFrame structure: ```python import pandas as pd # Sample DataFrame data = { 'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8], 'C': [9, 10, 11, 12] } df = pd.DataFrame(data) ``` I want to create a new column 'D' that is calculated from columns 'A', 'B', and 'C' using a custom function. My function should compute the average of 'A' and 'B' and then subtract 'C'. Here’s how I’m currently trying to do it: ```python def custom_function(row): return (row['A'] + row['B']) / 2 - row['C'] # Applying the function try: df['D'] = df.apply(custom_function, axis=1) except Exception as e: print(f'behavior: {e}') ``` However, I'm getting the following behavior message: ``` behavior: "KeyError: 'A'" ``` I’ve checked that the columns are spelled correctly and exist in the DataFrame. I also tried using `df.iterrows()` but it was very slow for my large DataFrame. Is there a more efficient way to achieve this? I’ve also heard that using `.apply()` can be slow with large DataFrames. Any advice on how to optimize this process while correctly accessing multiple columns would be greatly appreciated! For context: I'm using Python on Ubuntu. This is happening in both development and production on Debian. I appreciate any insights! I'm open to any suggestions.