18个Python脚本可加速你的编码速度("提升编程效率：18个Python脚本助你快速编码")

原创

ithorizon 6个月前 (10-20) 阅读数 22 #后端开发

提升编程高效能：18个Python脚本助你迅捷编码

一、引言

作为一名Python开发者，尽也许缩减损耗编码高效能是提升工作高效能的关键。本文将为您介绍18个实用的Python脚本，帮助您在日常编程中节省时间，尽也许缩减损耗高效能。

二、代码模板生成

1. 生成类模板


class MyClass:
    def __init__(self):
        pass
    def my_method(self):
        pass

2. 生成函数模板


def my_function():
    pass

三、文件操作

1. 文件复制


import shutil
def copy_file(src, dst):
    shutil.copy(src, dst)

2. 文件移动


import os
def move_file(src, dst):
    os.rename(src, dst)

3. 文件删除


import os
def delete_file(file_path):
    os.remove(file_path)

4. 递归删除文件夹


import os
import shutil
def delete_folder(folder_path):
    shutil.rmtree(folder_path)

四、字符串操作

1. 字符串反转


def reverse_string(s):
    return s[::-1]

2. 字符串查找


def find_string(s, sub):
    return s.find(sub)

3. 字符串替换


def replace_string(s, old, new):
    return s.replace(old, new)

4. 字符串分割


def split_string(s, delimiter):
    return s.split(delimiter)

五、日期和时间操作

1. 获取当前时间


from datetime import datetime
def get_current_time():
    return datetime.now()

2. 格式化时间


from datetime import datetime
def format_time(time_str, format_str):
    return datetime.strptime(time_str, format_str)

3. 时间戳转换


import time
def timestamp_to_time(timestamp):
    return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(timestamp))

4. 时间加减


from datetime import datetime, timedelta
def add_time(time_str, days):
    time_obj = datetime.strptime(time_str, '%Y-%m-%d %H:%M:%S')
    return (time_obj + timedelta(days=days)).strftime('%Y-%m-%d %H:%M:%S')

六、网络编程

1. 发送HTTP请求


import requests
def http_get(url):
    response = requests.get(url)
    return response.text
def http_post(url, data):
    response = requests.post(url, data=data)
    return response.text

2. 网络爬虫


import requests
from bs4 import BeautifulSoup
def get_html(url):
    response = requests.get(url)
    return response.text
def parse_html(html):
    soup = BeautifulSoup(html, 'html.parser')
    return soup

七、数据处理

1. 数据排序


def sort_data(data):
    return sorted(data)

2. 数据去重


def remove_duplicates(data):
    return list(set(data))

3. 数据分组


from itertools import groupby
def group_data(data, key_func):
    return groupby(data, key_func)

4. 数据统计


from collections import Counter
def count_data(data):
    return Counter(data)

八、文件解析

1. 解析CSV文件


import csv
def read_csv(file_path):
    with open(file_path, 'r') as f:
        reader = csv.reader(f)
        return list(reader)
def write_csv(file_path, data):
    with open(file_path, 'w', newline='') as f:
        writer = csv.writer(f)
        writer.writerows(data)

2. 解析JSON文件


import json
def read_json(file_path):
    with open(file_path, 'r') as f:
        return json.load(f)
def write_json(file_path, data):
    with open(file_path, 'w') as f:
        json.dump(data, f)

3. 解析XML文件


import xml.etree.ElementTree as ET
def read_xml(file_path):
    tree = ET.parse(file_path)
    return tree.getroot()
def write_xml(file_path, data):
    root = ET.Element(data['tag'])
    for key, value in data['attributes'].items():
        root.set(key, value)
    for child in data['children']:
        child_element = ET.SubElement(root, child['tag'])
        child_element.text = child['text']
    tree = ET.ElementTree(root)
    tree.write(file_path)