Refactoring Array Manipulations for Feature Engineering in Python - Performance Concerns
Could someone explain I've looked through the documentation and I'm still confused about I've searched everywhere and can't find a clear answer... Currently developing a machine learning pipeline where array manipulations are crucial for feature engineering. My existing codebase has several functions that transform raw data into usable features, but they lack efficiency and have become hard to maintain. For example, I have a function that processes a list of numerical values to compute various statistics: ```python import numpy as np def compute_features(data): mean_val = np.mean(data) std_val = np.std(data) max_val = np.max(data) min_val = np.min(data) return mean_val, std_val, max_val, min_val ``` While this works fine for small datasets, performance degrades significantly as the dataset grows. I've implemented parallel processing using the `concurrent.futures` module to speed things up: ```python from concurrent.futures import ProcessPoolExecutor def parallel_compute_features(data_chunks): with ProcessPoolExecutor() as executor: results = list(executor.map(compute_features, data_chunks)) return results ``` However, I’m not sure if this is the best approach. There’s also the issue of processing large arrays where memory consumption becomes a burden. I tried using `numpy` arrays instead of lists, as suggested in some forums, but I still see performance bottlenecks. Additionally, I’m looking at utilizing `Dask`, which can handle larger-than-memory computations. Is restructuring my code to use `Dask` a worthwhile effort? Are there better design patterns I should consider for optimizing my array manipulations to ensure both performance and maintainability? Any guidance or examples on best practices would be greatly appreciated. Any ideas what could be causing this? For context: I'm using Python on macOS. Am I missing something obvious? How would you solve this? I'm working on a application that needs to handle this. Has anyone dealt with something similar? For context: I'm using Python on Windows 10.