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Hands-On Enterprise Automation with Python: Automate common administrative and security tasks with the most popular language Python Invent your own Python scripts to automate your infrastructure Hands-On Enterprise Automation with Python starts by covering the set up of a Python environment to perform automation tasks, as well as the modules, libraries, and tools you will be using. We’ll explore examples of network automation tasks using simple Python programs and Ansible. Next, we will walk you through automating administration tasks with Python Fabric, where you will learn to perform server configuration and administration, along with system administration tasks such as user management, database management, and process management. As you progress through this book, you’ll automate several testing services with Python scripts and perform automation tasks on virtual machines and cloud infrastructure with Python. In the concluding chapters, you will cover Python-based offensive security tools and learn how to automate your security tasks. By the end of this book, you will have mastered the skills of automating several system administration tasks with Python. What You Will Learn Understand common automation modules used in Python Develop Python scripts to manage network devices Automate common Linux administration tasks with Ansible and Fabric Managing Linux processes Administrate VMware, OpenStack, and AWS instances with Python Security automation and sharing code on GitHub

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学习时间序列的概念，从传统的技术到最前沿的技术。这本书用全面的例子清楚地说明了时间序列数据的统计方法和分析方法及其在现实世界中的应用。所有的代码都可以在Jupyter中找到。 您将从回顾时间序列的基础知识、时间序列数据的结构、预处理以及如何通过数据争论来构建这些特性开始。接下来，您将使用statmodels和pmdarima等趋势框架来研究传统的时间序列技术，如ARMA、SARIMAX、VAR和VARMA。 该书还解释了使用sktime构建分类模型，并涵盖了先进的基于深度学习的技术，如ANN, CNN, RNN, LSTM, GRU和Autoencoder，以解决使用Tensorflow的时间序列问题。最后介绍了时间序列分析建模的流行框架fbprophet。在使用Python阅读了Hands -On Time Series Analysis之后，您将能够在行业中应用这些新技术，如石油和天然气、机器人、制造业、政府、银行、零售、医疗保健等。 你将学习: 解释时间序列高级概念的基础知识 如何设计、开发、培训和验证时间序列方法 什么是平滑，ARMA, ARIMA, SARIMA,S

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation. What You Will Learn • Understand how to install and manage Anaconda • Read, sort, and map data using NumPy and pandas • Find out how to create and slice data arrays using NumPy • Discover how to subset your DataFrames using pandas • Handle missing data in a pandas DataFrame • Explore hierarchical indexing and plotting with pandas

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动手微服务 通过Spring Boot在本地启动所有微服务 配置服务器环境变量 SPRING_PROFILES_ACTIVE=native; ENCRYPT_KEY=my-very-secure-encrypt-key; SPRING_SECURITY_USER_NAME=dev-usr; SPRING_SECURITY_USER_PASSWORD=dev-pwd; CONFIG_PATH=/local; 尤里卡（Eureka）环境变量 SPRING_CLOUD_CONFIG_URI=http://dev-usr:dev-pwd@localhost:8888 网关环境变量 SPRING_CLOUD_CONFIG_URI=http://dev-usr:dev-pwd@localhost:8888 OAuth环境变量 SPRING_PROFILES_ACTIVE=l

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