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明天分享几段工作生存中罕用的代码，都是最为根底的性能和操作，而且大多还都是呈现频率比拟高的，很多都是能够拿来间接应用或者简略批改就能够放到本人的我的项目当中
日期生成
很多时候咱们须要批量生成日期，办法有很多，这里分享两段代码
获取过来 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
import datetime
def create_assist_date(datestart = None,dateend = None):
# 创立日期辅助表
if datestart is None:
datestart = '2016-01-01'
if dateend is None:
dateend = datetime.datetime.now().strftime('%Y-%m-%d')
# 转为日期格局
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_listOutput:
['2021-12-27', '2021-12-28', '2021-12-29', '2021-12-30']保留数据到 CSV
保留数据到 CSV 是太常见的操作了，分享一段我集体比拟喜爱的写法
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("题目, 热度, 工夫,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')带背景色彩的 Pyecharts
Pyecharts 作为 Echarts 的优良 Python 实现，受到泛滥开发者的青眼，用 Pyecharts 作图时，应用一个舒服的背景也会给咱们的图表增色不少
以饼图为例，通过增加 JavaScript 代码来扭转背景色彩
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 crequests 库调用
据统计，requests 库是 Python 家族里被援用的最多的第三方库，足见其江湖位置之高大！
发送 GET 申请
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 申请
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)依据某些条件循环申请，比方依据生成的日期
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:
breakPython 操作各种数据库
操作 Redis
连贯 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
连贯 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
连贯 MySQL
import MySQLdb
# 关上数据库连贯
db = MySQLdb.connect("localhost", "testuser", "test123", "TESTDB", charset='utf8')
# 应用 cursor() 办法获取操作游标
cursor = db.cursor()执行 SQL 语句
# 应用 execute 办法执行 SQL 语句
cursor.execute("SELECT VERSION()")
# 应用 fetchone() 办法获取一条数据
data = cursor.fetchone()
print "Database version : %s" % data
# 敞开数据库连贯
db.close()Output:
Database version : 5.0.45本地文件整顿
整顿文件波及需要的比拟多，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形式，咱们抉择本人最为相熟棘手的形式即可
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
# 创立新线程
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, "呈现谬误")
else:
resp.encoding = 'utf-8'
text = await resp.text()
return text
except Exception as e:
print("呈现谬误", e)
await get_html(session, url)应用异步申请之后，对应的文件保留也须要应用异步，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)))正文完