Data Automation & Cleaning
Advanced
Process 100+ CSV Files in Parallel Using Python Multiprocessing Pool
Supercharge data processing speeds 4x by mapping folder CSV cleaning tasks across multi-core CPU threads in Python using multiprocessing.
#pandas#numpy
Problem Statement
Processing 500 monthly trading logs sequentially takes minutes. Python's `multiprocessing` pool distributes file processing across all CPU cores simultaneously.
Formula / Core Logic
Uses multiprocessing.Pool().map(process_func, file_list) to utilize 100% of CPU cores.
Installation
pip install pandas numpy
multiprocessing-parallel-csv-processing-python.py
import pandas as pd
from pathlib import Path
from multiprocessing import Pool
.def clean_single_file(filepath):
df = pd.read_csv(filepath)
# Apply cleaning
return f'Processed {filepath} ({len(df)} rows)'
\.def parallel_process_folder(folder_path):
files = list(Path(folder_path).glob('*.csv'))
with Pool() as pool:
results = pool.map(clean_single_file, files)
return results
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Buy Full Bundle on Gumroad ($19) →Frequently Asked Questions
Q: Why use multiprocessing instead of threading in Python?
Python's Global Interpreter Lock (GIL) prevents threads from running CPU-bound Pandas tasks simultaneously; multiprocessing bypasses the GIL using separate CPU processes.