【正版书籍】用于DevOps的Python
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作者NoahGift
出版社东南大学出版社
ISBN9787564194550
出版时间2021-04
装帧平装
开本16开
定价168元
货号J9787564194550
上书时间2024-06-15
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商品简介
过去的十年间，技术发生了很大的变化。数据炙手可热，云计算无处不在，许多组织都需要某种形式的自动化。在这些变化中，Python已经一跃成为世界上***的语言之一。这本实用指南向你展示了如何使用Python配合当今最有用的DevOps工具（包括Docker、kubernetes、Terraform）来完成日常的Linux系统管理任务。
学习如何与Linux打交道和自动化对于数百万专业人士来说是必不可少的。Python使这个过程变得轻松多了。通过本书，你将学会如何使用容器来开发软件和解决问题，以及如何对软件进行监控、检测、负载测试和操作。还在寻找用Python“把活儿搞定”的高效方法？这本就是你的操作指南。
作者简介:
Noah Gift是西北一杜克大学研究生数据科学与工程计划的讲师和顾问。
目录:
Preface
1. Python Essentials for DevOps
Installing and Running Python
The Python Shell
Jupyter Notebooks
Procedural Programming
Variables
Basic Math
Comments
Built-in Functions
Print
Range
Execution Control
if/elif/else
for Loops
while Loops
Handling Exceptions
Built-in Objects
What Is an Object?
Object Methods and Attributes
Sequences
Functions
Anatomy of a Function
Functions as Objects
Anonymous Functions
Using Regular Expressions
Searching
Character Sets
Character Classes
Groups
Named Groups
Find All
Find Iterator
Substitution
Compiling
Lazy Evaluation
Generators
Generator Comprehensions
More IPython Features
Using IPython to Run Unix Shell Commands
Exercises
2. Automating Files and the Filesystem
Reading and Writing Files
Using Regular Expressions to Search Text
Dealing with Large Files
Encrypting Text
Hashing with Hashlib
Encryption with Cryptography
The os Module
Managing Files and Directories Using os.path
Walking Directory Trees Using os.walk
Paths as Objects with Pathlib
3. Working with the Command Line
Working with the Shell
Talking to the Interpreter with the sys Module
Dealing with the Operating System Using the os Module
Spawn Processes with the subprocess Module
Creating Command-Line Tools
Using sys.argv
Using argparse
Using click
……
4. Useful Linux Utilities
5. Package Management
6. Continuous Integration and Continuous Deploymen
7. Monitoring and Logging
9. Cloud Computing
10. Infrastructure as Code
11. Container Technologies: Docker and Docker Compose
12. Container Orchestration: Kubernetes
13. Serverless Technoloaies
14. MLOps and Machine learning Engineering
15. Data Engineering
16. DevOps War Stories and Interviews
Index
目录
Preface
1. Python Essentials for DevOps
Installing and Running Python
The Python Shell
Jupyter Notebooks
Procedural Programming
Variables
Basic Math
Comments
Built-in Functions
Print
Range
Execution Control
if/elif/else
for Loops
while Loops
Handling Exceptions
Built-in Objects
What Is an Object?
Object Methods and Attributes
Sequences
Functions
Anatomy of a Function
Functions as Objects
Anonymous Functions
Using Regular Expressions
Searching
Character Sets
Character Classes
Groups
Named Groups
Find All
Find Iterator
Substitution
Compiling
Lazy Evaluation
Generators
Generator Comprehensions
More IPython Features
Using IPython to Run Unix Shell Commands
Exercises
2. Automating Files and the Filesystem
Reading and Writing Files
Using Regular Expressions to Search Text
Dealing with Large Files
Encrypting Text
Hashing with Hashlib
Encryption with Cryptography
The os Module
Managing Files and Directories Using os.path
Walking Directory Trees Using os.walk
Paths as Objects with Pathlib
3. Working with the Command Line
Working with the Shell
Talking to the Interpreter with the sys Module
Dealing with the Operating System Using the os Module
Spawn Processes with the subprocess Module
Creating Command-Line Tools
Using sys.argv
Using argparse
Using click
……
4. Useful Linux Utilities
5. Package Management
6. Continuous Integration and Continuous Deploymen
7. Monitoring and Logging
9. Cloud Computing
10. Infrastructure as Code
11. Container Technologies: Docker and Docker Compose
12. Container Orchestration: Kubernetes
13. Serverless Technoloaies
14. MLOps and Machine learning Engineering
15. Data Engineering
16. DevOps War Stories and Interviews
Index
内容摘要
过去的十年间，技术发生了很大的变化。数据炙手可热，云计算无处不在，许多组织都需要某种形式的自动化。在这些变化中，Python已经一跃成为世界上很流行的语言之一。这本实用指南向你展示了如何使用Python配合当今很有用的DevOps工具（包括Docker、kubernetes、Terraform）来完成日常的Linux系统管理任务。学习如何与Linux打交道和自动化对于数百万专业人士来说是必不可少的。Python使这个过程变得轻松多了。通过本书，你将学会如何使用容器来开发软件和解决问题，以及如何对软件进行监控、检测、负载测试和操作。还在寻找用Python“把活儿搞定”的高效方法？这本就是你的操作指南。
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