分类： python

import time
import threading
from concurrent.futures import ThreadPoolExecutor, as_completed
import _thread
import demo_requests
import asyncio


def task(task_id):
    for run_count in range(5):
        print(f"id: {task_id} run times: {run_count}")
        time.sleep(3)

def handle_result(future):  
    print(future)  
    pass

async def handle():
    print('handle')
    pass

def start_multi_thread(threadPoolNum, count):
    pool = ThreadPoolExecutor(max_workers=threadPoolNum)
    futures = []
    for i in range(count):
        future = pool.submit(task, i)
        futures.append(future)
    print(len(futures))
    for future in as_completed(futures):
        print("for each futures start")
        future.add_done_callback(handle_result)
        print("for each futures end")
    pool.shutdown()

if __name__ == '__main__':
    # start_multi_thread(5, 10)
    # print('done')
    t = _thread.start_new_thread(asyncio.run, [handle()])
    print(t)

读文件

import pandas as pd

df = pd.read_excel('data.xlsx')
df = pd.read_csv('data.csv')
df.columns.tolist()
merged_data = pd.merge(df_a, df_b, left_on='id', right_on='aid', how='inner')
df.drop('id', axis=1)
df = df[df['name'] == 'nnname']
df.to_excel('result.xlsx', index=False)
df.head()
df.groupby('callno')
df['name'].apply(lambda x: is_valid_text(x))
result = df['name'].copy()
faq_list = df['答案文本'].dropna().unique().tolist()
faq_list = df.iloc[:, 2].dropna().unique().tolist()
result_df = pd.DataFrame(results)
filtered_df = result_df[result_df['Q相似度得分'] > 90].copy()
result_df['Q相似度得分'].mean():.2f
filtered_df['更适合的答案'].value_counts().to_string()
merged = pd.merge(
    lx_grouped, 
    yx_grouped, 
    on=['intent', 'best_match'], 
    suffixes=('_lx', '_yx'),
    how='outer'
)
for _, row in merged.iterrows():
rows.append({})
df.values:
df.to_dict(orient='records')
list(df_e['会话id'])
df = df.sort_values(['callno', 'speake_no']).reset_index(drop=True)
df.sheet_names
df.count()

import asyncio
import threading


# 传入name参数:
async def hello(name):
    # 打印name和当前线程:
    print("Hello %s! (%s)" % (name, threading.current_thread))
    # 异步调用asyncio.sleep(1):
    await asyncio.sleep(10)
    print("Hello %s again! (%s)" % (name, threading.current_thread))
    return name

# syncio.ensure_future是Python asyncio库中用于安排协程或Future对象执行的函数，其核心作用是将可等待对象（如协程或Future）加入任务队列并确保其被调度执行，即使未被显式await

async def main():
    L = await asyncio.gather(hello("Bob"), hello("Alice"))
    #L = await asyncio.wait(hello("Bob"), hello("Alice"))
    print(L)

asyncio.run(main())

import asyncio
import time

print("\n\n=== 2. 异步模式开始 (请注意观察心跳) ===")

# 这是一个背景任务，模拟 UI 动画或者心跳检测
async def background_heartbeat():
    while True:
        print("   💓 (背景任务) 咚-咚-咚... 程序还活着！")
        await asyncio.sleep(0.3) # 每0.3秒跳动一次

# 异步生成器
async def async_stream():
    for i in range(3):
        print(f"🔄 (生成器内部) 正在用力下载第 {i+1} 个包...")
        # 【挂起】告诉 Python：我要等1秒，这期间你去处理那个“心跳”任务吧
        await asyncio.sleep(1) 
        yield f"📦 包 {i+1} 下载完成"

async def main():
    # 1. 启动背景心跳任务
    task = asyncio.create_task(background_heartbeat())

    start_time = time.time()

    # 2. 开始 Async For 循环
    # 关键点：每次在这里“等”数据的时候，上面的 background_heartbeat 就会插队运行
    async for data in async_stream():
        print(f"✅ 主程序收到: {data}\n")

    # 停止心跳任务
    task.cancel()
    print(f"=== 异步模式结束，总耗时: {time.time() - start_time:.2f}秒 ===")

# 运行
asyncio.run(main())

集合

# set

s1 = {1, 2, 3, 4}
print(type(s1))
print(s1)
s1.add(5)
s1.add(6)
print(s1)
s2 = {5, 6, 7, 8}
s3 = s1 | s2
print(s3)
s3.remove(3)
s3.discard(4)
print(s3)

s4 = {s for s in range(10, 20)}
print(s4)


t0 = (1, 2, 3, 4)
print(t0)


m = {
    "a": "a",
    "b": "",
    "c": None
}

print(m.get("a"))
print(m.get("b"))
print(m.get("c"))
print(m.get("d"))


print("--------")
print(m.get("a", "aa"))
print(m.get("b", "bb"))
print(m.get("c", "cc"))
print(m.get("d", "dd"))
print("--------")
print(m.get("a") if m.get("a") else "aa")
print(m.get("b") if m.get("b") else "bb")
print(m.get("c") if m.get("c") else "cc")
print(m.get("d") if m.get("d") else "dd")

内置函数

################## zip
list0 = ["a", "b", "c"]
list1 = [1, 2, 3]
list2 = ["A", "B", "C"]

result = zip(list0, list1, list2)
print(str(result))
# for item in result:
#     print(item)
list3 = list(result)
print(list3)
print('----------')
print(locals())
print(globals())


################## sorted
list0 = ["a", "abb", "adddd", "accc"]
result = sorted(list0, key=lambda k: len(k))
print(result)

################## filter
result = filter(lambda s: "adddd" == s, list0)
print(list(result))

################## map
result = map(lambda i : i + i, list0)
print(list(result))

################## reduce
s0 = {0, 1, 2, 3}
s1 = {1, 2, 3}
from functools import reduce
result = reduce(lambda a, b: a | b, [s0, s1])

print('67' in '67@1@dhxy')

################## zip
list0 = ["a", "b", "c"]
list1 = [1, 2, 3]
list2 = ["A", "B", "C"]

result = zip(list0, list1, list2)
print(str(result))
# for item in result:
#     print(item)
list3 = list(result)
print(list3)
print('----------')
print(locals())
print(globals())


################## sorted
list0 = ["a", "abb", "adddd", "accc"]
result = sorted(list0, key=lambda k: len(k))
print(result)

################## filter
result = filter(lambda s: "adddd" == s, list0)
print(list(result))

################## map
result = map(lambda i : i + i, list0)
print(list(result))

################## reduce
s0 = {0, 1, 2, 3}
s1 = {1, 2, 3}
from functools import reduce
result = reduce(lambda a, b: a | b, [s0, s1])
print(result)