Python中经常使用的代码片段
作者：rs勿忘初心
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针对工作生活中基础的功能和操作，梳理了下对应的几个Python代码片段，供参考：
日期生成
获取过去 N 天的日期
import datetime
def get_nday_list(n):
before_n_days = []
# [::-1]控制日期排序
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)输出：
['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', '2022-01-22', '2022-01-23', '2022-01-24']
生成一段时间区间内的日期
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')
print(d_list)输出：
['2021-12-27', '2021-12-28', '2021-12-29', '2021-12-30']
保存数据到CSV
保存数据到 CSV 算是比较常见的操作了，下面代码如果运行正确会生成"2022_data_2022-01-25.csv"文件。
import os
def save_data(data, date):
"""
:param data:
:param date:
:return:
"""
if not os.path.exists(r'2022_data_%s.csv' % date):
with open("2022_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("2022_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')
data = [{"title": "demo", "extra": "hello", "time": "1998-01-01", "url": "https://www.baidu.com/"}]
date = "2022-01-25"
save_data(data, date)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)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()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
异步爬取网站代码示例：
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)))总结
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