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[python作业AI毕业设计博客]Tribe of Hackers_ Cybersecurity - 2019 黑客部落：网络安全

已经有成千上万的网络安全专业人员，但是还缺少数十万人。黑客部落希望改变这一点。我们询问了70位网络安全人员的行业，职业和个人建议，他们准备打破障碍并打破天花板。本书可以成为任何人的变革催化剂，从试图进入该行业的初学者到寻求创办自己公司的从业者。 Dragos，Inc。和Duo Security的创始人在创办公司时有哪些提示？您是否需要大学学位或证书才能成为网络安全专家？您的组织可以采取哪些最大的降压行动来改善其网络安全态势？真正的黑客用什么“生活黑客”来让自己的生活更轻松？网络安全中的女性可以利用哪些资源来最大化其潜力？下载地址 https://itbooks.pipipan.com/fs/18113597-335611089 Introduction 101 Marcus J. Carey 602 Ian Anderson 1203 Andrew Bagrin 1804 Zate Berg 2405 Cheryl Biswas 2806 Keirsten Brager 3207 Evan Booth 3808 Kyle Bubp 4209 Lesley Carhart 4810 Lee Carsten 5411 Whitney Champion 6012 Ming Chow 6613 Jim Christy 7214 Ian Coldwater 7815 Dan Cornell 8416 Kim Crawley 9017 Emily Crose 9618 Daniel Crowley 10019 Winnona DeSombre 10420 Ryan Dewhurst 11021 Deidre Diamond 11422 Ben Donnelly 11823 Kimber Dowsett 13024 Ronald Eddings 13625 Justin Elze 14026 Robert Graham 14427 Claudio Guarnieri 15028 Ron Gula 15429 Jennifer Havermann 15830 Teuta Hyseni 16231 Terence Jackson 16832 Ken Johnson 17233 David Kennedy 17834 Michelle Klinger 18635 Marina Krotofil 19236 Sami Laiho 20037 Robert M. Lee 20438 Kelly Lum 20839 Tracy Z. Maleeff 21240 Andy Malone 21841 Jeffrey Man 22442 Jim Manico 23243 Kylie Martonik 23644 Christina Morillo 24045 Kent Nabors 24446 Wendy Nather 25247 Charles Nwatu 25848 Davi Ottenheimer 264 49 Brandon Perry 27450 Bruce Potter 28051 Edward Prevost 28452 Steve Ragan 28853 Stephen A. Ridley 29254 Tony Robinson 30055 David Rook 30656 Guillaume Ross 31457 Brad Schaufenbuel 32058 Chinyere Schwartz 32659 Khalil Sehnaoui 33060 Astha Singhal 33861 Dug Song 34262 Jayson E. Street 35263 Ben Ten 35864 Dan Tentler 36265 Ben Tomhave 36866 Robert "TProphet" Walker 37467 Georgia Weidman 38068 Jake Williams 38469 Robert Willis 39070 Robin Wood 394

2019-02-10

[python作业AI毕业设计博客]人工智能医疗新书下载: Machine Learning and AI for Healthcare ...

你将学到什么深入了解关键机器学习算法及其在更广泛的医疗保健中的使用和实施实施机器学习系统，例如语音识别和增强的深度学习/ AI 选择学习方法/算法和调整以用于医疗保健通过最佳实践，反馈循环和智能代理

2019-02-10

[雪峰磁针石博客]python网络作业：使用python的socket库实现ICMP协议的ping

ICMP ping是您遇到过的最常见的网络扫描类型。打开命令行提示符或终端并输入ping www.google.com非常容易。为什么要在python中实现？很多名牌大学喜欢考试用python的socket库实现ICMP协议的ping 个别环境没有ping 直接上代码： #!/usr/bin/python3 # -*- coding: utf-8 -*- # 技术支持：https://www.jianshu.com/u/69f40328d4f0 # 技术支持 https://china-testing.github.io/ # https://github.com/china-testing/python-api-tesing/blob/master/practices/ping.py # 讨论钉钉免费群21745728 qq群144081101 567351477 # CreateDate: 2018-11-22 import os import argparse import socket import struct import select import time ICMP_ECHO_REQUEST = 8 # Platform specific DEFAULT_TIMEOUT = 2 DEFAULT_COUNT = 4 class Pinger(object): """ Pings to a host -- the Pythonic way""" def __init__(self, target_host, count=DEFAULT_COUNT, timeout=DEFAULT_TIMEOUT): self.target_host = target_host self.count = count self.timeout = timeout def do_checksum(self, source_string): """ Verify the packet integritity """ sum = 0 max_count = (len(source_string)/2)*2 count = 0 while count < max_count: val = source_string[count + 1]*256 + source_string[count] sum = sum + val sum = sum & 0xffffffff count = count + 2 if max_count<len(source_string): sum = sum + ord(source_string[len(source_string) - 1]) sum = sum & 0xffffffff sum = (sum >> 16) + (sum & 0xffff) sum = sum + (sum >> 16) answer = ~sum answer = answer & 0xffff answer = answer >> 8 | (answer << 8 & 0xff00) return answer def receive_pong(self, sock, ID, timeout): """ Receive ping from the socket. """ time_remaining = timeout while True: start_time = time.time() readable = select.select([sock], [], [], time_remaining) time_spent = (time.time() - start_time) if readable[0] == []: # Timeout return time_received = time.time() recv_packet, addr = sock.recvfrom(1024) icmp_header = recv_packet[20:28] type, code, checksum, packet_ID, sequence = struct.unpack( "bbHHh", icmp_header ) if packet_ID == ID: bytes_In_double = struct.calcsize("d") time_sent = struct.unpack("d", recv_packet[28:28 + bytes_In_double])[0] return time_received - time_sent time_remaining = time_remaining - time_spent if time_remaining <= 0: return def send_ping(self, sock, ID): """ Send ping to the target host """ target_addr = socket.gethostbyname(self.target_host) my_checksum = 0 # Create a dummy heder with a 0 checksum. header = struct.pack("bbHHh", ICMP_ECHO_REQUEST, 0, my_checksum, ID, 1) bytes_In_double = struct.calcsize("d") data = (192 - bytes_In_double) * "Q" data = struct.pack("d", time.time()) + bytes(data.encode('utf-8')) # Get the checksum on the data and the dummy header. my_checksum = self.do_checksum(header + data) header = struct.pack( "bbHHh", ICMP_ECHO_REQUEST, 0, socket.htons(my_checksum), ID, 1 ) packet = header + data sock.sendto(packet, (target_addr, 1)) def ping_once(self): """ Returns the delay (in seconds) or none on timeout. """ icmp = socket.getprotobyname("icmp") try: sock = socket.socket(socket.AF_INET, socket.SOCK_RAW, icmp) except socket.error as e: if e.errno == 1: # Not superuser, so operation not permitted e.msg += "ICMP messages can only be sent from root user processes" raise socket.error(e.msg) except Exception as e: print ("Exception: %s" %(e)) my_ID = os.getpid() & 0xFFFF self.send_ping(sock, my_ID) delay = self.receive_pong(sock, my_ID, self.timeout) sock.close() return delay def ping(self): """ Run the ping process """ for i in range(self.count): print ("Ping to %s..." % self.target_host,) try: delay = self.ping_once() except socket.gaierror as e: print ("Ping failed. (socket error: '%s')" % e[1]) break if delay == None: print ("Ping failed. (timeout within %ssec.)" % self.timeout) else: delay = delay * 1000 print ("Get pong in %0.4fms" % delay) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Python ping') parser.add_argument('host', action="store", help=u'主机名') given_args = parser.parse_args() target_host = given_args.host pinger = Pinger(target_host=target_host) pinger.ping() 参考资料本文最新版本地址讨论钉钉群21745728 qq群144081101 567351477 本文涉及的python测试开发库谢谢点赞！本文相关海量书籍下载 python 3.7极速入门教程9最佳python中文工具书籍下载最新代码地址执行注意要有root或管理员权限： # python3 ping.py china-testing.github.io Ping to china-testing.github.io... Get pong in 160.7175ms Ping to china-testing.github.io... Get pong in 160.8465ms Ping to china-testing.github.io... Get pong in 12.0983ms Ping to china-testing.github.io... Get pong in 161.3324ms # python3 ping.py www.so.com Ping to www.so.com... Get pong in 29.0303ms Ping to www.so.com... Get pong in 28.8599ms Ping to www.so.com... Get pong in 28.9860ms Ping to www.so.com... Get pong in 29.0167ms

2018-11-25

[雪峰磁针石博客]web开发工具flask中文英文书籍下载-持续更新

等工具进行Web 开发；如何正确地设计扩展性强的Flask 应用架构和搭建MVC 环境；对于各种NoSQL 数据库的特性，何时应该、何时不应该及如何使用它们；通过使用Flask 扩展快速实现用户的身份系统

2018-11-11

[雪峰磁针石博客]2018最佳人工智能图像处理工具OpenCV书籍下载

计算机视觉是当今最令人兴奋的机器学习应用领域之一，深度学习驱动创新系统，如自动驾驶汽车和谷歌的DeepMind。

2018-10-14

[雪峰磁针石博客]2018最佳人工智能数据采集(爬虫)工具书下载

Python网络数据采集 Python网络数据采集 - 2016.pdf 本书采用简洁强大的Python语言，介绍了网络数据采集，并为采集新式网络中的各种数据类型提供了全面的指导。第 1部分重点介绍网络数据采集的基本原理：如何用Python从网络服务器请求信息，如何对服务器的响应进行基本处理，以及如何以自动化手段与网站进行交互。第二部分介绍如何用网络爬虫测试网站，自动化处理，以及如何通过更多的方式接入网络。 Web Scraping with Python 2nd - 2018.pdf https://github.com/REMitchell/python-scraping 2000左右星精通Python爬虫框架Scrapy Scrapy是使用Python开发的一个快速、高层次的屏幕抓取和Web抓取框架，用于抓Web站点并从页面中提取结构化的数据。《精通Python爬虫框架Scrapy》以Scrapy 1.0版本为基础，讲解了Scrapy的基础知识，以及如何使用Python和三方API提取、整理数据，以满足自己的需求。本书共11章，其内容涵盖了Scrapy基础知识，理解HTML和XPath，安装Scrapy并爬取一个网站，使用爬虫填充数据库并输出到移动应用中，爬虫的强大功能，将爬虫部署到Scrapinghub云服务器，Scrapy的配置与管理，Scrapy编程，管道秘诀，理解Scrapy性能，使用Scrapyd与实时分析进行分布式爬取。本书附录还提供了各种软件的安装与故障排除等内容。本书适合软件开发人员、数据科学家，以及对自然语言处理和机器学习感兴趣的人阅读。源码 github星级 300左右 Learning Scrapy -2016.pdf 另有中文电子版本因为版权已经在CSDN等网站下架，可以在qq群144081101等找到。 python3爬虫基础在线教程 https://github.com/MorvanZhou/easy-scraping-tutorial 200 左右星 First web scraper 教程：https://first-web-scraper.readthedocs.io/en/latest/ https://github.com/ireapps/first-web-scraper/blob/master/docs/index.rst 200 左右星 Practical Web Scraping for Data Science -Best Practices and Examples with Python - 2018.pdf https://github.com/Apress/practical-web-scraping-for-data-science 星级低于100 This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors recommend web scraping as a powerful tool for any data scientist’s arsenal, as many data science projects start by obtaining an appropriate data set. Starting with a brief overview on scraping and real-life use cases, the authors explore the core concepts of HTTP, HTML, and CSS to provide a solid foundation. Along with a quick Python primer, they cover Selenium for JavaScript-heavy sites, and web crawling in detail. The book finishes with a recap of best practices and a collection of examples that bring together everything you've learned and illustrate various data science use cases. 用Python写网络爬虫第2版《用Python写网络爬虫（第 2版》讲解了如何使用Python来编写网络爬虫程序，内容包括网络爬虫简介，从页面中抓取数据的3种方法，提取缓存中的数据，使用多个线程和进程进行并发抓取，抓取动态页面中的内容，与表单进行交互，处理页面中的验证码问题，以及使用Scarpy和Portia进行数据抓取，并在最后介绍了使用本书讲解的数据抓取技术对几个真实的网站进行抓取的实例，旨在帮助读者活学活用书中介绍的技术。《用Python写网络爬虫（第 2版》适合有一定Python编程经验而且对爬虫技术感兴趣的读者阅读。 Python Web Scraping 2nd Edition - 2017.pdf 第一版中文用Python写网络爬虫.pdf https://github.com/kjam/wswp < 100星 Python Web Scraping Cookbook - 2018.pdf 下载 Python Web Scraping Cookbook is a solution-focused book that will teach you techniques to develop high-performance Scrapers, and deal with cookies, hidden form fields, Ajax-based sites and proxies. You'll explore a number of real-world scenarios where every part of the development or product life cycle will be fully covered. You will not only develop the skills to design reliable, high-performing data flows, but also deploy your codebase to Amazon Web Services (AWS). If you are involved in software engineering, product development, or data mining or in building data-driven products, you will find this book useful as each recipe has a clear purpose and objective. Right from extracting data from websites to writing a sophisticated web crawler, the book's independent recipes will be extremely helpful while on the job. This book covers Python libraries, requests, and BeautifulSoup. You will learn about crawling, web spidering, working with AJAX websites, and paginated items. You will also understand to tackle problems such as 403 errors, working with proxy, scraping images, and LXML. By the end of this book, you will be able to scrape websites more efficiently and deploy and operate your scraper in the cloud. https://github.com/PacktPublishing/Python-Web-Scraping-Cookbook < 100星参考资料 https://github.com/lorien/awesome-web-scraping/blob/master/python.md 最好用的Python爬虫推荐 https://www.jianshu.com/p/7da43c16dd87 https://www.zhihu.com/question/41277528

2018-09-09

[雪峰磁针石博客]免费的好用的OCR工具人脸识别等图像识别工具

tesseract -- 推荐 Tesseract 已经有 30 年历史，开始它是惠普实验室的一款专利软件，然后在 2005 年开源，自 2006 年后由 Google 赞助进行后续的开发和维护。在 1995 年 Tesseract 曾是世界前三的 OCR 引擎，而且在现在的免费 OCR 引擎中，其识别精度也仍然是出类拔萃的。因为其免费与较好的效果，许多的个人开发者以及一些较小的团队在使用着 Tesseract ，诸如验证码识别、车牌号识别等应用中，不难见到 Tesseract 的身影。使用参考白描(一天免费5次) 安卓或者IOS APP，使用不如下面的NewOCR方便 NewOCR (www.newocr.com) --强烈推荐不亚于白描之类的的国内收费软件 FreeOCR (www.paperfile.net/index.html) 暂时不支持中文，英文ocr很优秀，效果如下： Free Online OCR (www.i2ocr.com) 需要搭桥才能访问，不做详细介绍 face_recognition -- 推荐 face_recognition使用世界上最简单的人脸识别库，在Python或命令行中识别和操作人脸。使用dlib最先进的人脸识别技术构建而成，并具有深度学习功能。该模型在Labeled Faces in the Wild基准中的准确率为99.38％。另外还提供了face_recognition命令行工具！更多 face_recognition 资料安卓APP行色提供最强悍的植物识别功能。 -- 推荐参考资料讨论qq群144081101 591302926 567351477 钉钉免费群21745728 本文涉及的python测试开发库谢谢点赞！本文相关海量书籍下载

2018-09-02

[雪峰磁针石博客]数据分析工具pandas快速入门教程4-数据汇聚

我们需要的所有信息可能记录在单独的文件和数据帧中。例如，可能有一个公司信息单独表和股票价格表，数据被分成独立的表格以减少冗余信息。连接添加行 4-1.py import pandas as pd df1 = pd.read_csv('data/concat_1.csv') df2 = pd.read_csv('data/concat_2.csv') df3 = pd.read_csv('data/concat_3.csv') print(df1) print(df2) print(df3) row_concat = pd.concat([df1, df2, df3]) print(row_concat) print(row_concat.iloc[3, ]) new_row_series = pd.Series(['n1', 'n2', 'n3', 'n4']) print(pd.concat([df1, new_row_series])) new_row_df = pd.DataFrame([['n1', 'n2', 'n3', 'n4']], columns=['A', 'B', 'C', 'D']) print(new_row_df) print(pd.concat([df1, new_row_df])) print(df1.append(df2)) print(df1.append(new_row_df)) data_dict = {'A': 'n1', 'B': 'n2', 'C': 'n3', 'D': 'n4'} print(df1.append(data_dict, ignore_index=True)) row_concat_i = pd.concat([df1, df2, df3], ignore_index=True) print(row_concat_i) 执行结果 $ python3 4-1.py A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 A B C D 0 a4 b4 c4 d4 1 a5 b5 c5 d5 2 a6 b6 c6 d6 3 a7 b7 c7 d7 A B C D 0 a8 b8 c8 d8 1 a9 b9 c9 d9 2 a10 b10 c10 d10 3 a11 b11 c11 d11 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 0 a4 b4 c4 d4 1 a5 b5 c5 d5 2 a6 b6 c6 d6 3 a7 b7 c7 d7 0 a8 b8 c8 d8 1 a9 b9 c9 d9 2 a10 b10 c10 d10 3 a11 b11 c11 d11 A a3 B b3 C c3 D d3 Name: 3, dtype: object A B C D 0 0 a0 b0 c0 d0 NaN 1 a1 b1 c1 d1 NaN 2 a2 b2 c2 d2 NaN 3 a3 b3 c3 d3 NaN 0 NaN NaN NaN NaN n1 1 NaN NaN NaN NaN n2 2 NaN NaN NaN NaN n3 3 NaN NaN NaN NaN n4 A B C D 0 n1 n2 n3 n4 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 0 n1 n2 n3 n4 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 0 a4 b4 c4 d4 1 a5 b5 c5 d5 2 a6 b6 c6 d6 3 a7 b7 c7 d7 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 0 n1 n2 n3 n4 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 4 n1 n2 n3 n4 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 4 a4 b4 c4 d4 5 a5 b5 c5 d5 6 a6 b6 c6 d6 7 a7 b7 c7 d7 8 a8 b8 c8 d8 9 a9 b9 c9 d9 10 a10 b10 c10 d10 11 a11 b11 c11 d11 添加列 4-2.py In [1]: from numpy import NaN, NAN, nan In [2]: print(NaN == True, NaN == False, NaN == 0, NaN == '', sep='|') False|False|False|False In [3]: print(NaN == NaN, NaN == nan, NaN == NAN, nan == NAN, sep='|') False|False|False|False In [4]: import pandas as pd In [5]: print(pd.isnull(NaN), pd.isnull(nan), pd.isnull(NAN), sep='|') True|True|True In [6]: print(pd.notnull(NaN), pd.notnull(99), pd.notnull("https://china-testing.github.io"), sep='|') False|True|True 执行结果 $ python3 4-2.py A B C D A B C D A B C D 0 a0 b0 c0 d0 a4 b4 c4 d4 a8 b8 c8 d8 1 a1 b1 c1 d1 a5 b5 c5 d5 a9 b9 c9 d9 2 a2 b2 c2 d2 a6 b6 c6 d6 a10 b10 c10 d10 3 a3 b3 c3 d3 a7 b7 c7 d7 a11 b11 c11 d11 A A A 0 a0 a4 a8 1 a1 a5 a9 2 a2 a6 a10 3 a3 a7 a11 A B C D A B C D A B C D new_col_list 0 a0 b0 c0 d0 a4 b4 c4 d4 a8 b8 c8 d8 n1 1 a1 b1 c1 d1 a5 b5 c5 d5 a9 b9 c9 d9 n2 2 a2 b2 c2 d2 a6 b6 c6 d6 a10 b10 c10 d10 n3 3 a3 b3 c3 d3 a7 b7 c7 d7 a11 b11 c11 d11 n4 A B C D A B C D A B C D new_col_list \ 0 a0 b0 c0 d0 a4 b4 c4 d4 a8 b8 c8 d8 n1 1 a1 b1 c1 d1 a5 b5 c5 d5 a9 b9 c9 d9 n2 2 a2 b2 c2 d2 a6 b6 c6 d6 a10 b10 c10 d10 n3 3 a3 b3 c3 d3 a7 b7 c7 d7 a11 b11 c11 d11 n4 new_col_series 0 n1 1 n2 2 n3 3 n4 0 1 2 3 4 5 6 7 8 9 10 11 0 a0 b0 c0 d0 a4 b4 c4 d4 a8 b8 c8 d8 1 a1 b1 c1 d1 a5 b5 c5 d5 a9 b9 c9 d9 2 a2 b2 c2 d2 a6 b6 c6 d6 a10 b10 c10 d10 3 a3 b3 c3 d3 a7 b7 c7 d7 a11 b11 c11 d11 合并不同区间 4-3.py import pandas as pd df1 = pd.read_csv('data/concat_1.csv') df2 = pd.read_csv('data/concat_2.csv') df3 = pd.read_csv('data/concat_3.csv') df1.columns = ['A', 'B', 'C', 'D'] df2.columns = ['E', 'F', 'G', 'H'] df3.columns = ['A', 'C', 'F', 'H'] print(df1) print(df2) print(df3) row_concat = pd.concat([df1, df2, df3]) print(row_concat) print(pd.concat([df1, df2, df3], join='inner')) print(pd.concat([df1,df3], ignore_index=False, join='inner')) df1.index = [0, 1, 2, 3] df2.index = [4, 5, 6, 7] df3.index = [0, 2, 5, 7] print(df1) print(df2) print(df3) col_concat = pd.concat([df1, df2, df3], axis=1) print(col_concat) print(pd.concat([df1, df3], axis=1, join='inner')) 执行结果 $ python3 4-3.py A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 E F G H 0 a4 b4 c4 d4 1 a5 b5 c5 d5 2 a6 b6 c6 d6 3 a7 b7 c7 d7 A C F H 0 a8 b8 c8 d8 1 a9 b9 c9 d9 2 a10 b10 c10 d10 3 a11 b11 c11 d11 A B C D E F G H 0 a0 b0 c0 d0 NaN NaN NaN NaN 1 a1 b1 c1 d1 NaN NaN NaN NaN 2 a2 b2 c2 d2 NaN NaN NaN NaN 3 a3 b3 c3 d3 NaN NaN NaN NaN 0 NaN NaN NaN NaN a4 b4 c4 d4 1 NaN NaN NaN NaN a5 b5 c5 d5 2 NaN NaN NaN NaN a6 b6 c6 d6 3 NaN NaN NaN NaN a7 b7 c7 d7 0 a8 NaN b8 NaN NaN c8 NaN d8 1 a9 NaN b9 NaN NaN c9 NaN d9 2 a10 NaN b10 NaN NaN c10 NaN d10 3 a11 NaN b11 NaN NaN c11 NaN d11 Empty DataFrame Columns: [] Index: [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3] A C 0 a0 c0 1 a1 c1 2 a2 c2 3 a3 c3 0 a8 b8 1 a9 b9 2 a10 b10 3 a11 b11 A B C D 0 a0 b0 c0 d0 1 a1 b1 c1 d1 2 a2 b2 c2 d2 3 a3 b3 c3 d3 E F G H 4 a4 b4 c4 d4 5 a5 b5 c5 d5 6 a6 b6 c6 d6 7 a7 b7 c7 d7 A C F H 0 a8 b8 c8 d8 2 a9 b9 c9 d9 5 a10 b10 c10 d10 7 a11 b11 c11 d11 A B C D E F G H A C F H 0 a0 b0 c0 d0 NaN NaN NaN NaN a8 b8 c8 d8 1 a1 b1 c1 d1 NaN NaN NaN NaN NaN NaN NaN NaN 2 a2 b2 c2 d2 NaN NaN NaN NaN a9 b9 c9 d9 3 a3 b3 c3 d3 NaN NaN NaN NaN NaN NaN NaN NaN 4 NaN NaN NaN NaN a4 b4 c4 d4 NaN NaN NaN NaN 5 NaN NaN NaN NaN a5 b5 c5 d5 a10 b10 c10 d10 6 NaN NaN NaN NaN a6 b6 c6 d6 NaN NaN NaN NaN 7 NaN NaN NaN NaN a7 b7 c7 d7 a11 b11 c11 d11 A B C D A C F H 0 a0 b0 c0 d0 a8 b8 c8 d8 2 a2 b2 c2 d2 a9 b9 c9 d9 合并多个数据集 4-4.py import pandas as pd person = pd.read_csv('data/survey_person.csv') site = pd.read_csv('data/survey_site.csv') survey = pd.read_csv('data/survey_survey.csv') visited = pd.read_csv('data/survey_visited.csv') print(person) print(site) print(survey) print(visited) visited_subset = visited.iloc[[0, 2, 6], ] o2o_merge = site.merge(visited_subset, left_on='name', right_on='site') print(o2o_merge) m2o_merge = site.merge(visited, left_on='name', right_on='site') print(m2o_merge) ps = person.merge(survey, left_on='ident', right_on='person') vs = visited.merge(survey, left_on='ident', right_on='taken') print(ps) print(vs) 执行结果 $ python3 4-4.py ident personal family 0 dyer William Dyer 1 pb Frank Pabodie 2 lake Anderson Lake 3 roe Valentina Roerich 4 danforth Frank Danforth name lat long 0 DR-1 -49.85 -128.57 1 DR-3 -47.15 -126.72 2 MSK-4 -48.87 -123.40 taken person quant reading 0 619 dyer rad 9.82 1 619 dyer sal 0.13 2 622 dyer rad 7.80 3 622 dyer sal 0.09 4 734 pb rad 8.41 5 734 lake sal 0.05 6 734 pb temp -21.50 7 735 pb rad 7.22 8 735 NaN sal 0.06 9 735 NaN temp -26.00 10 751 pb rad 4.35 11 751 pb temp -18.50 12 751 lake sal 0.10 13 752 lake rad 2.19 14 752 lake sal 0.09 15 752 lake temp -16.00 16 752 roe sal 41.60 17 837 lake rad 1.46 18 837 lake sal 0.21 19 837 roe sal 22.50 20 844 roe rad 11.25 ident site dated 0 619 DR-1 1927-02-08 1 622 DR-1 1927-02-10 2 734 DR-3 1939-01-07 3 735 DR-3 1930-01-12 4 751 DR-3 1930-02-26 5 752 DR-3 NaN 6 837 MSK-4 1932-01-14 7 844 DR-1 1932-03-22 name lat long ident site dated 0 DR-1 -49.85 -128.57 619 DR-1 1927-02-08 1 DR-3 -47.15 -126.72 734 DR-3 1939-01-07 2 MSK-4 -48.87 -123.40 837 MSK-4 1932-01-14 name lat long ident site dated 0 DR-1 -49.85 -128.57 619 DR-1 1927-02-08 1 DR-1 -49.85 -128.57 622 DR-1 1927-02-10 2 DR-1 -49.85 -128.57 844 DR-1 1932-03-22 3 DR-3 -47.15 -126.72 734 DR-3 1939-01-07 4 DR-3 -47.15 -126.72 735 DR-3 1930-01-12 5 DR-3 -47.15 -126.72 751 DR-3 1930-02-26 6 DR-3 -47.15 -126.72 752 DR-3 NaN 7 MSK-4 -48.87 -123.40 837 MSK-4 1932-01-14 ident personal family taken person quant reading 0 dyer William Dyer 619 dyer rad 9.82 1 dyer William Dyer 619 dyer sal 0.13 2 dyer William Dyer 622 dyer rad 7.80 3 dyer William Dyer 622 dyer sal 0.09 4 pb Frank Pabodie 734 pb rad 8.41 5 pb Frank Pabodie 734 pb temp -21.50 6 pb Frank Pabodie 735 pb rad 7.22 7 pb Frank Pabodie 751 pb rad 4.35 8 pb Frank Pabodie 751 pb temp -18.50 9 lake Anderson Lake 734 lake sal 0.05 10 lake Anderson Lake 751 lake sal 0.10 11 lake Anderson Lake 752 lake rad 2.19 12 lake Anderson Lake 752 lake sal 0.09 13 lake Anderson Lake 752 lake temp -16.00 14 lake Anderson Lake 837 lake rad 1.46 15 lake Anderson Lake 837 lake sal 0.21 16 roe Valentina Roerich 752 roe sal 41.60 17 roe Valentina Roerich 837 roe sal 22.50 18 roe Valentina Roerich 844 roe rad 11.25 ident site dated taken person quant reading 0 619 DR-1 1927-02-08 619 dyer rad 9.82 1 619 DR-1 1927-02-08 619 dyer sal 0.13 2 622 DR-1 1927-02-10 622 dyer rad 7.80 3 622 DR-1 1927-02-10 622 dyer sal 0.09 4 734 DR-3 1939-01-07 734 pb rad 8.41 5 734 DR-3 1939-01-07 734 lake sal 0.05 6 734 DR-3 1939-01-07 734 pb temp -21.50 7 735 DR-3 1930-01-12 735 pb rad 7.22 8 735 DR-3 1930-01-12 735 NaN sal 0.06 9 735 DR-3 1930-01-12 735 NaN temp -26.00 10 751 DR-3 1930-02-26 751 pb rad 4.35 11 751 DR-3 1930-02-26 751 pb temp -18.50 12 751 DR-3 1930-02-26 751 lake sal 0.10 13 752 DR-3 NaN 752 lake rad 2.19 14 752 DR-3 NaN 752 lake sal 0.09 15 752 DR-3 NaN 752 lake temp -16.00 16 752 DR-3 NaN 752 roe sal 41.60 17 837 MSK-4 1932-01-14 837 lake rad 1.46 18 837 MSK-4 1932-01-14 837 lake sal 0.21 19 837 MSK-4 1932-01-14 837 roe sal 22.50 20 844 DR-1 1932-03-22 844 roe rad 11.25 参考资料技术支持qq群144081101 591302926 567351477 钉钉免费群21745728 本文最新版本地址本文涉及的python测试开发库谢谢点赞！本文相关海量书籍下载源码下载本文英文版书籍下载

2018-08-23

[雪峰磁针石博客]flask构建自动化测试平台2-开始头条项目

2-开始头条项目代码地址我们将创建一个头条应用程序，显示最新的新闻标题，天气信息，和货币汇率。在本章中，我们将介绍RSS源并展示如何自动使用它们从特定出版物中检索最近的新闻报道。在下一章中，我们会讨论如何使用模板来显示检索到的标题和摘要文章给我们的用户。第4章，我们头条页面项目的用户输入，将显示你如何从用户那里获得输入，以便他们可以定制他们的体验，也看看如何将天气和货币数据添加到我们的应用程序。我们会完成在第5章“改善我们头条项目的用户体验”一文中关注项目添加一些CSS样式并查看如何记住用户的偏好。到本章结束时，您将学会如何创建更复杂的Flask应用。我们将从真实世界的新闻故事中提取原始数据并构建HTML格式化将其显示给我们的用户。您还将学习更多关于路由的知识 - 也就是说不同的URL会触发我们应用程序代码的不同部分。在本章中，我们将介绍以下主题： RSS和RSS源简介技术支持QQ群： 144081101 591302926 567351477本文最新版本 RSS和RSS源简介我们需要安装feedparser：feedparser。问题，如何寻找python库? python测试开发库， PYPI pip3 install --user feedparser 主要的RSS有WordPress和Blogger,通常会有如下图标。 BBC news的rss为： http://feeds.bbci.co.uk/news/rss.xml 下面的代码需要对HTML有一定的了解，初学者可以参考：http://www.w3school.com.cn/html/index.asp import feedparser from flask import Flask app = Flask(__name__) RSS_FEEDS = {'bbc': 'http://feeds.bbci.co.uk/news/rss.xml', 'cnn': 'http://rss.cnn.com/rss/edition.rss', 'fox': 'http://feeds.foxnews.com/foxnews/latest', 'iol': 'http://www.iol.co.za/cmlink/1.640'} @app.route("/") @app.route("/<publication>") def get_news(publication="bbc"): feed = feedparser.parse(RSS_FEEDS[publication]) first_article = feed['entries'][0] return """<html> <body> <h1>Headlines </h1> <b>{0}</b> <i>{1}</i> <p>{2}</p> </body> </html>""".format(first_article.get("title"), first_article.get("published"), first_article.get("summary")) if __name__ == "__main__": app.run(host='0.0.0.0',port=8000, debug=True) 执行： $ curl http://172.20.15.200:8000/iol <html> <body> <h1>Headlines </h1> <b>IEC still unable to confirm addresses of 2.8 million citizens</b> <i>Wed, 07 Mar 2018 17:26:00 GMT</i> <p>The Independent Electoral Commission still does not have addresses for 2.8 million South Africans ahead of the 2019 general elections.</p> </body> </html>andrew@andrew-PowerEdge-T630:~/code/Flask-Building-Python-Web-Services/Module 1$ curl http://172.20.15.200:8000/cnn <html> <body> <h1>Headlines </h1> <b>In Lesotho, women say they're finding their abortions on Facebook</b> <i>None</i> <p>None</p> </body> </html>andrew@andrew-PowerEdge-T630:~/code/Flask-Building-Python-Web-Services/Module 1$ curl http://172.20.15.200:8000/test <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd"> <html> <head> <title>KeyError: 'test' // Werkzeug Debugger</title> <link rel="stylesheet" href="?__debugger__=yes&cmd=resource&f=style.css" type="text/css"> <!-- We need to make sure this has a favicon so that the debugger does ... 参考资料本文相关书籍下载讨论钉钉免费群21745728 qq群144081101 567351477 本文涉及的python测试开发库谢谢点赞！本文代码地址

2018-08-21

【全网最全的博客美化系列教程】06.推荐和反对炫酷样式的实现

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2018-07-26

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2018-07-26

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2018-07-16

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2017-11-14

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2017-01-01

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