今天分享幾段作業生活中常用的代碼，都是最為基礎的功能和操作，而且大多還都是出現頻率比較高的，很多都是可以拿來直接
使用或者簡單修改就可以放到自己的專案當中
日期生成
很多時候我們需要批量生成日期，方法有很多，這里分享兩段代碼
Python學習交流Q群：906715085### 獲取過去 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']
生成一段時間區間內的日期
Python學習交流Q群：906715085### import datetime def create_assist_date(datestart = None,dateend = None): # 創建日期輔助表 if datestart is None: datestart = '2016-01-01' if dateend is None: dateend = datetime.datetime.now().strftime('%Y-%m-%d') # 轉為日期格式 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: # 日期疊加一天 datestart+=datetime.timedelta(days=+1) # 日期轉字串存入串列 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']
保存資料到CSV
保存資料到 CSV 是太常見的操作了，分享一段我個人比較喜歡的寫法
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("標題,熱度,時間,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')
帶背景顏色的 Pyecharts
Pyecharts 作為 Echarts 的優秀 Python 實作，受到眾多開發者的青睞，用 Pyecharts 作圖時，使用一個舒服的背景也會給我們的圖表增色不少
以餅圖為例，通過添加 JavaScript 代碼來改變背景顏色
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 庫呼叫
據統計，requests 庫是 Python 家族里被參考的最多的第三方庫，足見其江湖地位之高大！
發送 GET 請求
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) 發送 POST 請求 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=https://www.cnblogs.com/123456feng/archive/2022/05/04/payload, files=files) 根據某些條件回圈請求，比如根據生成的日期 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=https://www.cnblogs.com/123456feng/archive/2022/05/04/payload, files=files) items = response.json()['data']['items'] if items: save_data(items, d) else: break
Python 操作各種資料庫
操作 Redis
連接 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 寫入 Redis from redis_conn import redis_conn_pool rd = redis_conn_pool() rd.set('test_data', 'mytest')
操作 MongoDB
連接 MongoDB
from pymongo import MongoClient conn = MongoClient("mongodb://%s:%s@ipaddress:49974/mydb" % ('username', 'password')) db = conn.mydb mongo_collection = db.mydata 批量插入資料 res = requests.get(url, params=query).json() commentList = res['data']['commentList'] mongo_collection.insert_many(commentList)
操作 MySQL
連接 MySQL
import MySQLdb # 打開資料庫連接 db = MySQLdb.connect("localhost", "testuser", "test123", "TESTDB", charset='utf8' ) # 使用cursor()方法獲取操作游標 cursor = db.cursor() 執行 SQL 陳述句 # 使用 execute 方法執行 SQL 陳述句 cursor.execute("SELECT VERSION()") # 使用 fetchone() 方法獲取一條資料 data =https://www.cnblogs.com/123456feng/archive/2022/05/04/ cursor.fetchone() print "Database version : %s " % data # 關閉資料庫連接 db.close() Output: Database version : 5.0.45 本地檔案整理 整理檔案涉及需求的比較多，這里分享的是將本地多個 CSV 檔案整合成一個檔案 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)
多執行緒代碼
多執行緒也有很多實作方式，我們選擇自己最為熟悉順手的方式即可
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 ("開始執行緒：" + self.name) print_time(self.name, self.delay, 5) print ("退出執行緒：" + 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 # 創建新執行緒 thread1 = myThread(1, "Thread-1", 1) thread2 = myThread(2, "Thread-2", 2) # 開啟新執行緒 thread1.start() thread2.start() thread1.join() thread2.join() print ("退出主執行緒")
異步編程代碼
異步爬取網站
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, "出現錯誤") else: resp.encoding = 'utf-8' text = await resp.text() return text except Exception as e: print("出現錯誤", e) await get_html(session, url)
使用異步請求之后，對應的檔案保存也需要使用異步，即是一處異步，處處異步
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)))
以上就是我平時用的最多的代碼片段，希望對你有所幫助好了，這就是今天分享的全部內容，喜歡就點個贊+在看吧！！！
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