fromtqdm.contrib.concurrentimportthread_mapimport timedef process_item(x):time.sleep(0.01)returnx*2# 使用thread_map替代map,并自动管理多线程进度条results = thread_map(process_item, range(100)) 在这个例子中,我们通过使用thread_map函数替代传统的map函数,实现了多线程任务的进度显示。这种方式不仅提升了...
from tqdm import tqdm from concurrent.futures import ThreadPoolExecutor def process_data(data): # 处理数据 pass data_to_process = range(100) with ThreadPoolExecutor() as executor: list(tqdm(executor.map(process_data, data_to_process), total=len(data_to_process))) 性能考虑 当处理大规模数据...
将定义的函数包装在tqdm中,以便显示进度条。 results=list(tqdm(executor.map(task,range(10)),total=10))# 使用tqdm包装函数并指定总数 1. 三、类图 ThreadPoolExecutor+__init__(max_workers)+map(func, *iterables)tqdm+__init__(**kwargs) 以上就是在Python多线程中使用tqdm库的完整教程。希望这篇文章...
tmap <= map add concurrent module thread_map <= concurrent.futures.ThreadPoolExecutor.map process_map <= concurrent.futures.ProcessPoolExecutor.map add itertools module (#225) stub product 🔍 add & update tests📘 add & update documentation ...
import time import concurrent.futures from tqdm import tqdm def f(x): time.sleep(0.001) # to visualize the progress return x**2 def run(f, my_iter): with concurrent.futures.ThreadPoolExecutor() as executor: results = list(tqdm(executor.map(f, my_iter), total=len(my_iter))) return ...
t=threading.Thread(target=haha,args=(10,)) #将线程设置为守护线程 t.setDaemon(True) #线程准备就绪,随时等候cpu调度 t.start() 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.
tqdm是通用的,能以很多种方式使用。下面给出主要的三种方式: 1)基于迭代器的方法: 即将tqdm封装在任意迭代器中 # conding:utf-8fromtqdm import tqdm import time text=""forcharintqdm(["a","b","c","d"]): time.sleep(0.25) text= text +char ...
Monitoring thread, intervals and miniters Merch Contributions Ports to Other Languages LICENCE Installation Latest PyPI stable release pip install tqdm Latest development release on GitHub Pull and install pre-releasedevelbranch: pip install"git+https://github.com/tqdm/tqdm.git@devel#egg=tqdm" ...
bar.update(flush_num)defstart():# 提前计算此次需要生成的文件个数bar_thrad=Thread(target=get_files,args=(total_num,))bar_thrad.start()task=[]foriin[]:task.append((i,))withPool(num)aspool:pool.startmap(writer_csv,task)
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