在一个函数中并行运行多个数据帧

import threading
import pandas as pd

lock = threading.Lock()
data_frames = [pd.DataFrame({'A': range(1000)}) for _ in range(5)]

def process_data_frame(df):
    with lock:
        # 处理数据帧
        result = df.sum()
        print(result)

threads = []
for df in data_frames:
    thread = threading.Thread(target=process_data_frame, args=(df,))
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()

import multiprocessing
import pandas as pd

def process_data_frame(queue, df):
    # 处理数据帧
    result = df.sum()
    queue.put(result)

if __name__ == '__main__':
    data_frames = [pd.DataFrame({'A': range(1000)}) for _ in range(5)]
    queue = multiprocessing.Queue()
    processes = []

    for df in data_frames:
        process = multiprocessing.Process(target=process_data_frame, args=(queue, df))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    while not queue.empty():
        print(queue.get())

import concurrent.futures
import pandas as pd

data_frames = [pd.DataFrame({'A': range(1000)}) for _ in range(5)]

def process_data_frame(df):
    # 处理数据帧
    return df.sum()

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(process_data_frame, data_frames))

for result in results:
    print(result)