Unexpected performance drop in Python loop when handling large datasets with Pandas
I've spent hours debugging this and I've searched everywhere and can't find a clear answer. I'm experiencing a important performance drop when looping through a large dataset using Pandas version 1.3.3. The dataset has around 1 million rows, and I'm trying to filter rows based on multiple conditions. My current approach employs a for loop, but the operation takes an unreasonably long time to execute. Here’s a simplified version of my code: ```python import pandas as pd df = pd.read_csv('large_dataset.csv') filtered_rows = [] for index, row in df.iterrows(): if row['column_a'] > 10 and row['column_b'] == 'active': filtered_rows.append(row) ``` I’ve heard that using `iterrows()` can be slow for large datasets, but I’m unsure how to optimize this. I attempted to apply a boolean mask instead, but I ended up with a memory behavior due to the large size of the data: ```python mask = (df['column_a'] > 10) & (df['column_b'] == 'active') filtered_df = df[mask] ``` The memory behavior suggests that the operation may be trying to create an intermediate object that is too large. Is there a more efficient way to filter large datasets in Pandas without running into performance or memory issues? Any suggestions or best practices would be greatly appreciated. For context: I'm using Python on Windows. How would you solve this? I'm coming from a different tech stack and learning Python.