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
获取过去 N 天的日期
import datetimedef 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_daysa = 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’]
生成一段时间内的日期
import datetimedef 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_listd_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”) 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(”) 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(”)
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 家族里被引用得最多的第三方库，足见其江湖地位之高大！
发送 GET 请求
import requestsheaders = { ‘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 requestspayload={}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 redisdef 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_poolrd = redis_conn_pool()rd.set(‘test_data’, ‘mytest’)
操作 MongoDB
连接 MongoDB
from pymongo import MongoClientconn = MongoClient(“mongodb://%s:%s@ipaddress:49974/mydb” % (‘username’, ‘password’))db = conn.mydbmongo_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 pdimport osdf_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 threadingimport timeexitFlag = 0class 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 asyncioimport aiohttpimport aiofilesasync 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)))
好了，今天的分享到这啦，希望对你们有所收获！！
