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1. Pandas + glob获取指定目录下的文件列表

import pandas as pd
import globdata_dir = "/public/data/"
# 获取文件后缀为.txt的文件列表
df_all = pd.concat([pd.read_csv(f, sep='\t') for f in glob.glob(data_dir + '*.txt')])
print(df_all)

2. 使用 enumerate 函数获取索引和值

# A-K 字母列表
letter = [chr(ord('A') + i) for i in range(0, 11)]# 输出索引和值
for idx, value in enumerate(letter):print(f"{idx}\t{value}")

3. 使用 zip 函数同时遍历多个列表

# 0-10 数字列表
number = [n for n in range(0, 11)]
# A-K 字母列表
letter = [chr(ord('A') + i) for i in range(0, 11)]for number, letter in zip(letter, number):print(f"{letter}: {number}")# 0: A
# 1: B
# 2: C
# 3: D
# 4: E
# 5: F
# 6: G
# 7: H
# 8: I
# 9: J
# 10: K

4. 内置函数map + filter 过滤数据

number = [n for n in range(0, 11)]# 获取平方数
squared_numbers = list(map(lambda x: x**2, number)
print(squared_numbers) 
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]# 获取偶数
even_numbers = list(filter(lambda x: x % 2 == 0, number))
print(even_numbers)
# [0, 2, 4, 6, 8, 10]

5. 使用concurrent.futures模块实现循环的并发处理，提高计算效率

import concurrent.futures
def square(num):return num ** 2with concurrent.futures.ThreadPoolExecutor() as executor:res = list(executor.map(square, number))print(res)

6. 使用asyncio模块实现异步处理，提高并发性能

import asyncio
import math
async def sqrt(num):return math.sqrt(num)async def calculate():run_tasks = [sqrt(num) for num in number]results = await asyncio.gather(*run_tasks)print(results)asyncio.run(calculate())

7. 程序运行分析装饰器

import timedef analysis_time(func):def warpper(*args, **kwargs):start_time = time.time()res = func(*args, *kwargs)end_time = time.time()print(f"{func.__name__} program run time: {end_time - start_time}s")return resreturn warpper# 并行计算
import concurrent.futures
def square(num):return num ** 2@analysis_time
def calulate(number):with concurrent.futures.ThreadPoolExecutor() as executor:res = list(executor.map(square, number))return resprint(calulate(number))
# calulate program run time: 0.002947568893432617s
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100]