Python中优化工作流程的八个实用小工具

from pathlib import Path

# 创建一个路径对象
p = Path("/home/user/documents")

# 输出路径信息
print("父目录:", p.parent)
print("名称:", p.name)
print("是否为文件:", p.is_file())
print("是否存在:", p.exists())

# 拼接子路径
sub_path = p / "new_folder"
print("新路径:", sub_path)

# 创建目录
sub_path.mkdir(parents=True, exist_ok=True)

from rich import print
from rich.table import Table
from rich.console import Console

console = Console()

# 打印彩色文本
print("[bold red]Hello, World![/bold red]")

# 创建一个表格
table = Table(title="员工信息")
table.add_column("姓名", style="cyan", no_wrap=True)
table.add_column("年龄", justify="right", style="green")
table.add_column("部门", style="magenta")

# 添加数据
table.add_row("张三", "25", "技术部")
table.add_row("李四", "30", "销售部")

# 打印表格
console.print(table)

import pandas as pd

# 创建一个DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'City': ['New York', 'Los Angeles', 'Chicago']
}
df = pd.DataFrame(data)

# 显示DataFrame
print(df)

# 数据筛选
print("\n筛选年龄大于30的人:")
print(df[df['Age'] > 30])

# 数据排序
print("\n按年龄排序:")
print(df.sort_values(by='Age'))

# 数据聚合
print("\n按城市分组计算平均年龄:")
print(df.groupby('City')['Age'].mean())

import typer

app = typer.Typer()

@app.command()
def hello(name: str):
    """
    打印问候语
    """
    print(f"Hello, {name}!")

@app.command()
def goodbye(name: str, formal: bool = False):
    """
    打印告别语
    """
    if formal:
        print(f"Goodbye, Mr. {name}. Have a nice day!")
    else:
        print(f"Bye, {name}!")

if __name__ == "__main__":
    app()

import click

@click.command()
@click.option('--name', prompt='Your name', help='The person to greet.')
@click.option('--formal/--informal', default=False, help='Formal greeting or informal.')
def greet(name, formal):
    """
    打印问候语
    """
    if formal:
        click.echo(f"Hello, Mr. {name}. How are you?")
    else:
        click.echo(f"Hi, {name}!")

if __name__ == '__main__':
    greet()

import requests

# 发送GET请求
response = requests.get("https://api.github.com")
print(response.status_code)  # 输出状态码
print(response.json())  # 输出JSON数据

# 发送POST请求
url = "https://httpbin.org/post"
data = {'key': 'value'}
response = requests.post(url, data=data)
print(response.text)  # 输出响应内容

from tqdm import tqdm
import time

# 创建一个进度条
for i in tqdm(range(10)):
    # 模拟一些耗时操作
    time.sleep(0.5)

# 自定义进度条
for i in tqdm(range(10), desc="Processing", unit="item"):
    time.sleep(0.5)

import logging

# 设置日志级别和格式
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

# 记录不同级别的日志
logging.debug("This is a debug message")
logging.info("This is an info message")
logging.warning("This is a warning message")
logging.error("This is an error message")
logging.critical("This is a critical message")

import pandas as pd
from pathlib import Path

# 定义数据目录
data_dir = Path("data")

# 获取所有CSV文件
csv_files = list(data_dir.glob("*.csv"))

# 初始化空DataFrame
df = pd.DataFrame()

# 合并所有CSV文件
for file in csv_files:
    temp_df = pd.read_csv(file)
    df = pd.concat([df, temp_df], ignore_index=True)

# 保存汇总文件
output_file = Path("summary.csv")
df.to_csv(output_file, index=False)

print(f"汇总文件已保存到 {output_file}")

汇总文件已保存到 summary.csv