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Chapter 1. An Overview of Python Essentials

Devops, the combination of software development with information technology operations, has been a hot field during the last decade.

Traditional boundaries between software development, deployment, maintenance, and quality assurance are broken, enabling more integrated

teams. Python has been a popular language both in traditional IT operations and in the DevOps due to its combination of flexibility, power, and

ease of use. The Python programming language was created in the early 1990s for use in system administration. It has been a great success in

this area and gained wide adoption. It is a general-purpose programming language used in just about every domain. Visual effects and the film

world is another prominent Python success story. More recently, it has become the defacto language in data science and machine learning. It has

been used across industries from avian to bioinformatics. Modern Python has an extensive arsenal of tools to cover the wide-ranging needs of its

users. Learning the whole Python Standard Library (the capabilities that come with any Python installation), would be a daunting task. If you

add to it learning all of the third party packages ( that enliven the Python ecosystem, it would be an immense undertaking. The good news is that

you don’t need to. You can become a powerful DevOps practitioner with only a small subset of Python.

In this chapter, we draw on our decades of Python DevOps experience to teach only the elements of the language that you need. These are the

parts of Python you use daily. They form the essential toolbox to get things done. Once you have these core concepts down, you can add more

complicated tools, and indeed this is touched in more advanced features in later chapters.

Installing and Running Python

If you want to try the code in this overview, you need version Python 3.7 or later installed (the latest release is 3.7.3 as of this writing), and

access to a shell. In Os X, Windows and most Linux distributions, you can open the terminal application to access a shell. To check the version

of Python you are using, open a shell and type python --version.

$ python --version

Python 3.7.3

Python installers can be downloaded directly from the Python.org website. Alternatively, you can use a package manager such as Apt, RPM,

MacPorts, Homebrew, Chocolatey, or many others.

The Python Shell

The simplest way to run Python is to use the built-in interactive interpreter. Just type python in a shell. You can then interactively run Python

statements. Type exit() to exit the shell.

$ python

Python 3.7.0 (default, Sep 23 2018, 09:47:03)

[Clang 9.0.0 (clang-900.0.38)] on darwin

Type "help", "copyright", "credits" or "license" for more information.

>>> 1 + 2

>>> exit()

Python scripts

Python code runs from a file with the py extension.

# This is my first Python script

print("Hello world!")

To invoke the script, in a shell run python followed by the file name.

$ python hello.py

Hello world!

Python scripts are the way most production Python code runs.

IPython

Besides the built-in interactive shell, several third-party interactive shells run Python code. One of the most popular is IPython. IPython offers

introspection (the ability to dynamically get information about objects), syntax highlighting, special magic commands (which we touch on later

in this chapter), and many more features making it a pleasure to use for exploring Python. To install IPython, use the Python package manager,

1. An Overview of Python Essentials

pip, which is installed by the Python installer:

$ pip install ipython

Running is similar to running the built-in an interactive shell of the previous section:

$ ipython

Python 3.7.0 (default, Sep 23 2018, 09:47:03)

Type 'copyright', 'credits' or 'license' for more information

IPython 7.5.0 -- An enhanced Interactive Python. Type '?' for help.

In [1]: print("Hello")

Hello

In [2]: exit()

Jupyter Notebooks

A spin-off from the iPython project, the Jupyter project allows documents containing text, code, and visualizations. These documents are a

powerful tool for combining running code, output, and formatted text. Jupyter enables the delivery of documentation along with the code. It has

achieved widespread popularity, especially in the data science world. To install and run Jupyter notebooks:

$ pip install jupyter

$ jupyter notebook

This command opens a web browser tab showing the current working directory. From here, you can open existing notebooks in the current

project or create new ones.

Procedural Programming

If you’ve been around programming at all, you’ve probably heard terms like Object Oriented Programming (OOPS) and Functional

Programming. These are different approaches to organizing programs. One of the most basic styles, Procedural Programming, is an excellent

place to start. Procedural programming is the issuing instructions to a computer in an ordered sequence:

>>> i = 3

>>> j = i +1

>>> i + j

We can see in this example, there are three statements, which execute in order from the first line to the last. Each statement uses the state

produced by the previous ones. In this case, the first statement assigns the value 3 to a variable named i. In the second statement, this variable’s

value is used to assign a value to a variable named j, and in the third statement, the values from both variables add together. Don’t worry about

the details of these statements yet, notice that they are executed in order and rely on the state left by the previous statements.

Variables

Variables are a name which points to some value. In the previous example, these variables are i and j . Variables in Python can assign to new

values:

>>> dog_name = 'spot'

>>> dog_name

'spot'

>>> dog_name = 'rex'

>>> dog_name

'rex'

>>> dog_name = 't-' + dog_name

>>> dog_name

't-rex'

>>>

Python variables use dynamic typing. In practice, this means reassigned to values of different types or classes:

>>> big = 'large'

1. An Overview of Python Essentials

The print function produces output that a user of a program can view. It is less relevant in interactive environments but is a fundamental tool

when writing Python scripts. In the previous example, the argument to the print function is written as output when the script runs:

# This is my first Python script

print("Hello world!")

$ python hello.py

Hello world!

print can be used to see the value of a variable or to give feedback as to the state of a program. print generally outputs to standard out and

is visible as program output in a shell.

Range

Though range is a built-in function, it is technically not a function at all. It is a type representing a sequence of numbers. When calling the

range() constructor, an object representing a sequence of numbers is returned. Range objects count through a sequence of numbers. The

range function takes up to three integer arguments. If only one argument appears, then the sequence is represented by the numbers from zero

up to, but not including that number. If a second argument appears, it represents the starting point, rather than the default of starting from 0. The

third argument can be used to specify the step distance, and it defaults to 1.

>>> range(10)

range(0, 10)

>>> list(range(10))

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> list(range(5, 10))

[5, 6, 7, 8, 9]

>>> list(range(5, 10, 3))

[5, 8]

>>>

range maintains a small memory footprint, even over extended sequences as it only stores the start, stop, and step values. The range function

can iterate through long sequences of numbers without performance constraints.

Execution Control

Python has many constructs to control the flow of statement execution. You can group statements you wish to run together as a block of code.

These blocks can be run multiple times using for and while loops, or only run under certain conditions using if statements, while loops, or

try-except blocks. Using these constructs is the first step to taking advantage of the power of programming. Different languages demarcate

blocks of code using different conventions. Many languages with syntax similar to the C language (a very influential language used in writing

Unix) use curly brackets around a group of statements to define a block. In Python, indentation is used to indicate a block. Statements that have

the same indentation are run together as a block.

Note

The Python interpreter does not care if you use tabs or spaces to indent, as long as you are consistent. The Python style guide, PEP-8, however,

recommends using four whitespaces for each level of indentation.

if/elif/else

if/elif/else statements are a common way to branch between decisions in code. A block directly after an if statement runs if that

statement evaluates to True:

>>> i = 45

>>> if i == 45:

... print("i is 45")

...

i is 45

1. An Overview of Python Essentials

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