👀 Views: 3 💬 Answers: 1 📅 Created: 2025-06-06

pandas performance dataframe vectorization Python

I'm currently working with a large DataFrame in Pandas (version 1.4.2) and I'm running into important performance optimization when applying a custom function to each row using a loop. My DataFrame has around 1 million rows and I'm using the `apply()` method to process each row based on several conditions. Here’s a simplified version of what I’ve tried: ```python import pandas as pd def custom_function(row): # Sample processing logic if row['value'] > 10: return row['value'] * 2 else: return row['value'] + 5 # Sample DataFrame creation data = {'value': range(1, 1000001)} df = pd.DataFrame(data) # Applying the function using apply() df['result'] = df.apply(custom_function, axis=1) ``` When I run this, it takes an unreasonable amount of time to complete. I received a warning stating that the operation is taking longer than expected due to the size of the DataFrame, which is definitely true. I tried using `numba` to compile the function, but even that didn’t yield important improvements. Is there a more efficient way to achieve the same result without using `apply()` in a loop? I’ve also considered using vectorized operations but I’m not sure how to translate my conditional logic into that format. Any suggestions or best practices would be greatly appreciated!