百木园今天分享几段工作生活中常用的代码，都是最为基础的功能和操作，而且大多还都是出现频率比较高的，很多都是可以拿来直接
使用或者简单修改就可以放到自己的项目当中
日期生成
很多时候我们需要批量生成日期，方法有很多，这里分享两段代码
Python学习交流Q群：906715085### 获取过去 N 天的日期 import datetime def get_nday_list(n): before_n_days = [] for i in range(1, n + 1)[::-1]: before_n_days.append(str(datetime.date.today() - datetime.timedelta(days=i))) return before_n_days a = get_nday_list(30) print(a) Output: [\'2021-12-23\', \'2021-12-24\', \'2021-12-25\', \'2021-12-26\', \'2021-12-27\', \'2021-12-28\', \'2021-12-29\', \'2021-12-30\', \'2021-12-31\', \'2022-01-01\', \'2022-01-02\', \'2022-01-03\', \'2022-01-04\', \'2022-01-05\', \'2022-01-06\', \'2022-01-07\', \'2022-01-08\', \'2022-01-09\', \'2022-01-10\', \'2022-01-11\', \'2022-01-12\', \'2022-01-13\', \'2022-01-14\', \'2022-01-15\', \'2022-01-16\', \'2022-01-17\', \'2022-01-18\', \'2022-01-19\', \'2022-01-20\', \'2022-01-21\']
生成一段时间区间内的日期
Python学习交流Q群：906715085### import datetime def create_assist_date(datestart = None,dateend = None): # 创建日期辅助表 if datestart is None: datestart = \'2016-01-01\' if dateend is None: dateend = datetime.datetime.now().strftime(\'%Y-%m-%d\') # 转为日期格式 datestart=datetime.datetime.strptime(datestart,\'%Y-%m-%d\') dateend=datetime.datetime.strptime(dateend,\'%Y-%m-%d\') date_list = [] date_list.append(datestart.strftime(\'%Y-%m-%d\')) while datestart<dateend: # 日期叠加一天 datestart+=datetime.timedelta(days=+1) # 日期转字符串存入列表 date_list.append(datestart.strftime(\'%Y-%m-%d\')) return date_list d_list = create_assist_date(datestart=\'2021-12-27\', dateend=\'2021-12-30\') d_list
Output:
[\'2021-12-27\', \'2021-12-28\', \'2021-12-29\', \'2021-12-30\']
保存数据到CSV
保存数据到 CSV 是太常见的操作了，分享一段我个人比较喜欢的写法
def save_data(data, date): if not os.path.exists(r\'2021_data_%s.csv\' % date): with open(\"2021_data_%s.csv\" % date, \"a+\", encoding=\'utf-8\') as f: f.write(\"标题,热度,时间,url\\n\") for i in data: title = i[\"title\"] extra = i[\"extra\"] time = i[\'time\'] url = i[\"url\"] row = \'{},{},{},{}\'.format(title,extra,time,url) f.write(row) f.write(\'\\n\') else: with open(\"2021_data_%s.csv\" % date, \"a+\", encoding=\'utf-8\') as f: for i in data: title = i[\"title\"] extra = i[\"extra\"] time = i[\'time\'] url = i[\"url\"] row = \'{},{},{},{}\'.format(title,extra,time,url) f.write(row) f.write(\'\\n\')
带背景颜色的 Pyecharts
Pyecharts 作为 Echarts 的优秀 Python 实现，受到众多开发者的青睐，用 Pyecharts 作图时，使用一个舒服的背景也会给我们的图表增色不少
以饼图为例，通过添加 JavaScript 代码来改变背景颜色
def pie_rosetype(data) -> Pie: background_color_js = ( \"new echarts.graphic.LinearGradient(0, 0, 0, 1, \" \"[{offset: 0, color: \'#c86589\'}, {offset: 1, color: \'#06a7ff\'}], false)\" ) c = ( Pie(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js))) .add( \"\", data, radius=[\"30%\", \"75%\"], center=[\"45%\", \"50%\"], rosetype=\"radius\", label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"), ) .set_global_opts(title_opts=opts.TitleOpts(title=\"\"), ) ) return c
requests 库调用
据统计，requests 库是 Python 家族里被引用的最多的第三方库，足见其江湖地位之高大！
发送 GET 请求
import requests headers = { \'user-agent\': \'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36\', \'cookie\': \'some_cookie\' } response = requests.request(\"GET\", url, headers=headers) 发送 POST 请求 import requests payload={} files=[] headers = { \'user-agent\': \'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36\', \'cookie\': \'some_cookie\' } response = requests.request(\"POST\", url, headers=headers, data=payload, files=files) 根据某些条件循环请求，比如根据生成的日期 def get_data(mydate): date_list = create_assist_date(mydate) url = \"https://test.test\" files=[] headers = { \'user-agent\': \'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36\', \'cookie\': \'\' } for d in date_list: payload={\'p\': \'10\', \'day\': d, \'nodeid\': \'1\', \'t\': \'itemsbydate\', \'c\': \'node\'} for i in range(1, 100): payload[\'p\'] = str(i) print(\"get data of %s in page %s\" % (d, str(i))) response = requests.request(\"POST\", url, headers=headers, data=payload, files=files) items = response.json()[\'data\'][\'items\'] if items: save_data(items, d) else: break
Python 操作各种数据库
操作 Redis
连接 Redis
import redis def redis_conn_pool(): pool = redis.ConnectionPool(host=\'localhost\', port=6379, decode_responses=True) rd = redis.Redis(connection_pool=pool) return rd 写入 Redis from redis_conn import redis_conn_pool rd = redis_conn_pool() rd.set(\'test_data\', \'mytest\')
操作 MongoDB
连接 MongoDB
from pymongo import MongoClient conn = MongoClient(\"mongodb://%s:%s@ipaddress:49974/mydb\" % (\'username\', \'password\')) db = conn.mydb mongo_collection = db.mydata 批量插入数据 res = requests.get(url, params=query).json() commentList = res[\'data\'][\'commentList\'] mongo_collection.insert_many(commentList)
操作 MySQL
连接 MySQL
import MySQLdb # 打开数据库连接 db = MySQLdb.connect(\"localhost\", \"testuser\", \"test123\", \"TESTDB\", charset=\'utf8\' ) # 使用cursor()方法获取操作游标 cursor = db.cursor() 执行 SQL 语句 # 使用 execute 方法执行 SQL 语句 cursor.execute(\"SELECT VERSION()\") # 使用 fetchone() 方法获取一条数据 data = cursor.fetchone() print \"Database version : %s \" % data # 关闭数据库连接 db.close() Output: Database version : 5.0.45 本地文件整理 整理文件涉及需求的比较多，这里分享的是将本地多个 CSV 文件整合成一个文件 import pandas as pd import os df_list = [] for i in os.listdir(): if \"csv\" in i: day = i.split(\'.\')[0].split(\'_\')[-1] df = pd.read_csv(i) df[\'day\'] = day df_list.append(df) df = pd.concat(df_list, axis=0) df.to_csv(\"total.txt\", index=0)
多线程代码
多线程也有很多实现方式，我们选择自己最为熟悉顺手的方式即可
import threading import time exitFlag = 0 class myThread (threading.Thread): def __init__(self, threadID, name, delay): threading.Thread.__init__(self) self.threadID = threadID self.name = name self.delay = delay def run(self): print (\"开始线程：\" + self.name) print_time(self.name, self.delay, 5) print (\"退出线程：\" + self.name) def print_time(threadName, delay, counter): while counter: if exitFlag: threadName.exit() time.sleep(delay) print (\"%s: %s\" % (threadName, time.ctime(time.time()))) counter -= 1 # 创建新线程 thread1 = myThread(1, \"Thread-1\", 1) thread2 = myThread(2, \"Thread-2\", 2) # 开启新线程 thread1.start() thread2.start() thread1.join() thread2.join() print (\"退出主线程\")
异步编程代码
异步爬取网站
import asyncio import aiohttp import aiofiles async def get_html(session, url): try: async with session.get(url=url, timeout=8) as resp: if not resp.status // 100 == 2: print(resp.status) print(\"爬取\", url, \"出现错误\") else: resp.encoding = \'utf-8\' text = await resp.text() return text except Exception as e: print(\"出现错误\", e) await get_html(session, url)
使用异步请求之后，对应的文件保存也需要使用异步，即是一处异步，处处异步
async def download(title_list, content_list): async with aiofiles.open(\'{}.txt\'.format(title_list[0]), \'a\', encoding=\'utf-8\') as f: await f.write(\'{}\'.format(str(content_list)))
以上就是我平时用的最多的代码片段，希望对你有所帮助好了，这就是今天分享的全部内容，喜欢就点个赞+在看吧！！！
来源：https://www.cnblogs.com/123456feng/p/16203528.html
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