flask flask-pymongo flask-mongoengine 数据内容 [{ "_id": { "$oid": "62e3525f18909374eb4de53f" }, "plat": { "wechat": "bc61736d-7ed0-47bb-ba1b-33474fecde23", "alipay": "random" }, "created_at": { "$date": { "$numberLong": "1659093727910" } }, "updated_at": { "$date": { "$numberLong": "1659093727910" } }, "nickname": "su" }] 根据 plat.wechat中来搜索用户 pymongo语法: mongo.db.user.find_one(filter={"plat.wechat":openid}) mongoengine语法: User.objects(plat__wechat=openid).first()
自定义的一个小功能,线程池处理,用法很简单 def handleThread(a,b): pass main = ThreadPool(5) req = WorkRequest(handleThread, args=[1, 2], kwds={}) main.putRequest(req) #-*-encoding:utf-8-*- ''' Created on 2012-3-9 @summary: 线程池 ''' import sys import threading import queue import traceback import statistics # 定义一些Exception，用于自定义异常处理 class NoResultsPending(Exception): """All works requests have been processed""" pass class NoWorkersAvailable(Exception): """No worket threads available to process remaining requests.""" pass def _handle_thread_exception(request, exc_info): """默认的异常处理函数，只是简单的打印""" traceback.print_exception(*exc_info) #classes class WorkerThread(threading.Thread): """后台线程，真正的工作线程，从请求队列(requestQueue)中获取work， 并将执行后的结果添加到结果队列(resultQueue)""" def __init__(self,requestQueue,resultQueue,poll_timeout=5,**kwds): threading.Thread.__init__(self,**kwds) '''设置为守护进行''' self.setDaemon(True) self._requestQueue = requestQueue self....
[github]路径(https://github.com/suguer/python-tronlink-scan) python-tronlink-scan 波场链的扫块功能,如果对于需要获取波场链上的交易情况,又没有条件部署一台自己的节点,那么采用本文的方式是一个不错的选择,通过调用接口也能及时获取到交易信息 安装需要的用到的插件 pip install -r requirements.txt 运行即可,有需要的可调整为数据库的结构来存 python main.py example: 需要记录的合约 config/contract.csv 地址|名称|精度 address,name,decimals TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t,USDT,6 TEkxiTehnzSmSe2XqrBj4w32RUN966rdz8,USDC,6 TAFjULxiVgT4qWk6UZwjqwZXTSaGaqnVp4,BitTorrent,18 需要记录的钱包 config/wallet.csv 地址|密钥 address,private_key TSRg164MqUKMxDn2eQYvAg9iFNhQYXAFa8, TNV2h4c2ibuQKes5XTgwuuP9mdfQiAHxpL, TGn37A4yEjVBCXojhFNpZvovWvfNdzwC3x, 激活的要以 config/transaction.csv hash|区块|发出地址|接收地址|区块时间|数额|合约地址|状态 hash,block,from,to,block_at,amount,contract_address,status f474e2ceb883637aae1f666adc7714309bf933cfd956173c68bd0512e2190f34,40967609,TPJA5T1QaJEZnCgujC36GpX2wGLYsYnqAh,TNV2h4c2ibuQKes5XTgwuuP9mdfQiAHxpL,2022-05-25 13:32:27,28.29,TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t,success fbd03d69af7ba8e1f7c56dd11738c8d6269497193daf0efa23eff3ed477e6f12,40969058,TJDFLrCiKneK2rXjCudn4gGMcYhVeqB8ru,TGn37A4yEjVBCXojhFNpZvovWvfNdzwC3x,2022-05-25 14:45:06,7229.02,TR7NHqjeKQxGTCi8q8ZY4pL8otSzgjLj6t,success
学习来源Bilibili 根据例子编写出线性回归方程 import tensorflow as tf import pandas as pd data=pd.read_csv('./demo1.csv') x=data.Education y=data.Income model=tf.keras.Sequential(()) model.add(tf.keras.layers.Dense(1,input_shape=(1,))) model.compile(optimizer='adam',loss='mse') history=model.fit(x,y,epochs=10000) res=model.predict(pd.Series([20])) print(res) ,Education,Income 1,10.000000 ,26.658839 2,10.401338 ,27.306435 3,10.842809 ,22.132410 4,11.244147 ,21.169841 5,11.645449 ,15.192634 6,12.086957 ,26.398951 7,12.048829 ,17.435307 8,12.889632 ,25.507885 9,13.290970 ,36.884595 10,13.732441 ,39.666109 11,14.133779 ,34.396281 12,14.635117 ,41.497994 13,14.978589 ,44.981575 14,15.377926 ,47.039595 15,15.779264 ,48.252578 16,16.220736 ,57.034251 17,16.622074 ,51.490919 18,17.023411 ,51.336621 19,17.464883 ,57.681998 20,17.866221 ,68.553714 21,18.267559 ,64.310925 22,18.709030 ,68.959009 23,19.110368 ,74.614639 24,19.511706 ,71.867195 25,19.913043 ,76.098135...
初学第一天主要学习如果搭建环境和快捷安装TensorFlow的扩展 打开miniconda官网 选择合适自己的系统来安装python环境 安装TensorFlow扩展 : pip install tensorflow==2.8.0 可能pip版本不够导致安装失败 easy_install -U pip 安装其他所需的包: pip install pandas matplotlib nodebook pandas 数据分析库 matplotlib python绘图库 nodebook 基于web的开发环境 安装cuda 安装pytorch Q: 运行失败时返回 Could not load dynamic library ‘cudart64_110.dll’; dlerror: cudart64_110.dll not found A: 在截图中找到相应的版本后,打开 nvidia Q: 安装cuda失败时返回 you already have a newer version of the nvdia frameview sdk installed A: 找到C:\Program Files\NVIDIA Corporation\FrameViewSDK文件夹进行卸载 Q: 安装cuda一直安装不成功,nsight visual studio edition提示安装失败 A: 尝试卸载电脑的Visual Studio Community Q: Downgrade the protobuf package to 3....
import sys import threading import queue import traceback import statistics class NoResultsPending(Exception): """All works requests have been processed""" pass class NoWorkersAvailable(Exception): """No worket threads available to process remaining requests.""" pass def _handle_thread_exception(request, exc_info): """默认的异常处理函数，只是简单的打印""" traceback.print_exception(*exc_info) #classes class WorkerThread(threading.Thread): """后台线程，真正的工作线程，从请求队列(requestQueue)中获取work， 并将执行后的结果添加到结果队列(resultQueue)""" def __init__(self,requestQueue,resultQueue,poll_timeout=5,**kwds): threading.Thread.__init__(self,**kwds) '''设置为守护进行''' self.setDaemon(True) self._requestQueue = requestQueue self._resultQueue = resultQueue self._poll_timeout = poll_timeout '''设置一个flag信号，用来表示该线程是否还被dismiss,默认为false''' self._dismissed = threading.Event() self.start() # self.join() def run(self): '''每个线程尽可能多的执行work，所以采用loop， 只要线程可用，并且requestQueue有work未完成，则一直loop''' while True: if self._dismissed.is_set(): break try: ''' Queue....
Selenium是一个用于测试网站的自动化测试工具，支持各种浏览器包括Chrome、Firefox、Safari等主流界面浏览器，同时也支持phantomJS无界面浏览器。 以下是我在实际使用中对于selenium的一些心得和用法 implicitly_wait 等待浏览器的相应时间,与sleep的区别在于implicitly_wait是设置的值或页面超时优先,而sleep则是固定睡眠时间,我在用的时候则是2个都同时使用.确保页面能顺利加载到需要的数据 xpath 如果需要用selenium进行简单的爬虫和模拟浏览,那么xpath的语法是必须要了解明白的,html语言中比起正则,xpath的代码更加清晰和直观 click 对于按钮的点击事件,推荐用 driver.execute_script("arguments[0].click();", nextButton) 据说.click()会导致报错,虽然我还没遇到过
列出指定目录下的全部文件 import os file_array=[] dir_array=[ os.getcwd() + "/directory1/", os.getcwd() + "/directory2/", ] for dir in dir_array: for root, dirs, files in os.walk(dir): for filename in files: file_array.append(dir+filename) Excel操作读写 wb = xlrd.open_workbook(filepath) sh = wb.sheet_by_name('数据明细') # print(sh.nrows) # 有效数据行数 # print(sh.ncols) # 有效数据列数 for index in range(len(sh.row_values(0))): title = sh.row_values(0)[index]