Cloud Computing Training Academy, cloud computing python automation DevOps

I forget when I know Python, I am engaged in linux operations, I just know that the OPS must be shell, to do some operations and automation work, such as the implementation of some scheduled backup data Ah, batch execution of an operation Ah, write a monitoring script or something. Later found that the workload is large when the shell began to slow down, to achieve a function using the shell feel overwhelmed, heard that Python can achieve all the functions shell can do, but also the development of high efficiency, fast, slowly on the understanding of Python, more or less something simple.

The most impressive is to spend a few days to read the "Python Concise Handbook" After reading, feel Python is very simple, special two think they will python, embarrassed in the back, found that I just learned a bit of the basis of Python, and now it seems that even fur is not. All say that Python is simple, it may be to professional developers, I as a operation of the programming is really not a cold, found that Python is really profound, very difficult, a lot of things, learn not to finish, but fortunately, we do not engage in the maintenance of engineers so much. Forget it, grumble, not nonsense, on the subject.

About the history of Python, at least should know Guido Bar, called Uncle Turtle, who worked in Google, now Dropbox, a cloud storage company, recently did not know him, maybe change the place, hehe. He is very NB, you think he can not play in the NB. He wants to have a language, like the C language, the full invocation of the computer's functional interface, but also can be like the shell of the easy programming, find a language called ABC, later felt that others write (he also participated in the development of the language does not work well, he wrote a C language.)

Developed in 1991, developed to now in the cloud computing, automation operations, artificial intelligence, and so there is no language to be able to lose it. Whatever the future, Python's history can be read as a novel.

Python features

Object oriented

Object-oriented Programming abstracts out the behavior and properties of objects, leaving the behavior and attributes apart, but also logically organized together. It eliminates object-oriented elements such as protection types, abstract classes, interfaces, and so on, making object-oriented concepts easier to understand.

Simple

There is no semicolon, the code is delimited with indentation, the code is concise, short, and easy to read.

Built-in data structures

Similar to the functionality of a collection class in Java. Python's data structure includes tuples, lists, dictionaries, and so on. Tuples are equivalent to "read-only" arrays, which can be used as variable-length arrays, and dictionaries are equivalent to Hashtable types in Java.

Robust

An exception exit mechanism is provided to catch the exception of the program.

Cross-platform

Python will first compile the binary code associated with the platform and then interpret the execution, which is similar to Java.

Can expand

Python is a language developed in C, so you can use C to extend Python to add new modules, new classes to Python. At the same time, Python can be embedded in the C,c++ language development project, so that the language has scripting language features.

Dynamic

Python does not need to declare variables, directly assign values to create a new variable.

Strongly typed languages

Python determines the data type of a variable based on the contents of an assignment expression. Python has built a mechanism for managing these variables internally, and different types of variables that appear in the same expression require type conversions.

Wide Application

Python has many third-party library support. For example: PIL library is applied to image processing, NumPy library is applied to mathematical calculation, Wxpython library is applied to GUI program design, and Django Library is applied to the development of Web program.

Interpreted language

It's a script.

"The Zen of Python" summarized by the Python core developer Tim Peters

The Zen of Python              Python 之禅    Beautiful is better than ugly.

Beauty is better than ugliness

Explicit is better than implicit.

Better than the hidden

Simple is better than complex.

Jane is better than complex

Complex is better than complicated.

More complex than miscellaneous

Flat is better than nested.

Flat is better than overlapping

Sparse is better than dense.

Sparse is better than secret

Readability counts.

Reading is better than writing

Special cases aren‘t special enough to break the rules.

Rules are better than exceptions.

Although practicality beats purity.

Practicality is better than simplicity

Errors should never pass silently.

Error is better than silence

Unless explicitly silenced.

Silence is better than noise

In the face of ambiguity, refuse the temptation to guess.

Refusal is better than speculation

There should be one-- and preferably only one --obvious way to do it.

The only better than the obvious

Although that way may not be obvious at first unless you‘re Dutch.

Apparently not Holland.

Now is better than never.

Now better than never

Although never is often better than *right* now.

Never better than a hasty quenching

If the implementation is hard to explain, it‘s a bad idea.

Where it is worth saying, it will be easy to say

If the implementation is easy to explain, it may be a good idea.

or vice versa.

Namespaces are one honking great idea -- let‘s do more of those!

Name is name, please always name

Cloud Computing Training Academy, cloud computing python automation DevOps