Pandas: How to efficiently update existing DataFrame rows using a condition based on another DataFrame?
After trying multiple solutions online, I still can't figure this out. Hey everyone, I'm running into an issue that's driving me crazy. I've been struggling with this for a few days now and could really use some help. I'm trying to update specific rows in a pandas DataFrame based on the values in another DataFrame, but I'm working with performance optimization when the DataFrames are quite large (over 1 million rows). I'm using pandas version 1.3.5 and I've tried using `DataFrame.update()` and `loc`, but both seem inefficient for my use case. Here's a simplified version of my code: ```python import pandas as pd # Original DataFrame original_df = pd.DataFrame({ 'id': range(1, 1000001), 'value': range(1000000) }) # DataFrame with updates updates_df = pd.DataFrame({ 'id': [100, 200, 300], 'value': [9999, 8888, 7777] }) # Attempting to update values for idx, row in updates_df.iterrows(): original_df.loc[original_df['id'] == row['id'], 'value'] = row['value'] ``` While this works, it takes an extremely long time to run. I've also tried using `merge()` to combine the DataFrames and then update, but I'm running into issues with duplicated rows and performance as well. Is there a more efficient way to achieve this update operation without significantly increasing execution time? Any suggestions for optimizing this process would be greatly appreciated! Also, if there's a way to handle potential duplicate `id` values in the updates, that would be a bonus! Any help would be greatly appreciated! I'm working on a service that needs to handle this. I'm coming from a different tech stack and learning Python. Thanks, I really appreciate it!