Python 2.7: Performance implementing large datasets when using pandas groupby and apply
Quick question that's been bugging me - Hey everyone, I'm running into an issue that's driving me crazy. I'm stuck on something that should probably be simple... I'm currently working on a data processing script where I need to analyze a large dataset (around 1 million rows) using pandas in Python 2.7. My goal is to group the data by a certain column and then apply a custom function to each group. However, I'm working with important performance optimization, and the operation takes an unusually long time to complete. Here's a simplified version of what I've tried: ```python import pandas as pd df = pd.DataFrame({ 'group': ['A'] * 500000 + ['B'] * 500000, 'value': range(1000000) }) def custom_function(group): return group['value'].sum() / len(group) result = df.groupby('group').apply(custom_function) print(result) ``` When I run this code, it seems to be exploring for several minutes before producing any output. I suspect that the scenario might be related to the size of the dataset and the efficiency of the `apply` method with my custom function. I've considered using `numba` for just-in-time compilation, but I'm unsure how to apply it in this context. I've also read that using `agg` instead of `apply` might be faster, but I'm not sure how to translate my custom logic into an aggregation function. Does anyone have suggestions on how to improve the performance of this operation, or is there a more efficient way to achieve my goal in pandas while working with large datasets? Any insights or best practices for optimizing groupby operations in Python 2.7 would be greatly appreciated! I'm working on a application that needs to handle this. Has anyone else encountered this? What's the correct way to implement this? I'm working in a macOS environment. What are your experiences with this?