Python language overview

In a nutshell, this book is about making our lives easier with Python. But what is Python and why is it the language of choice by many DevOps engineers? In the words of the Python Foundation Executive Summary (https://www.python.org/doc/essays/blurb/):

"Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level, built-in data structure, combined with dynamic typing and dynamic binding, makes it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy-to-learn syntax emphasizes readability and therefore reduces the cost of program maintenance."

If you are somewhat new to programming, the object-oriented, dynamic semantics probably does not mean much to you. But I think we can all agree on Rapid application development, simple, and easy-to-learn syntax sounds like a good thing. Python as an interpreted language means there is no compilation process required, so the speed of write, test, and edit of the program process is greatly increased. For simple scripts, if your script fails, a few print statement is usually all you need to debug what was going on. Using the interpreter also means that Python is easily ported to different type of operating systems and Python program written on Windows can be used on Linux and Mac.

The object-oriented nature encourages code reuse by breaking into simple reusable form such modules and packages. In fact, all Python files are modules that can be reused or imported in another Python program. This makes it easy to share programs among engineers and encourage code reuse. Python also has a batteries included mantra, which means that for common tasks, you need not download any additional code. In order to achieve this without the code being too bloated, a set of standard libraries is installed when you install the Python interpreter. For common tasks such as regular expression, mathematics functions, and JSON decoding, all you need is the import statement, and the interpreter will move those functions into your program. This is what I would consider one of the killer features of the Python language.

Lastly, the fact that Python code can start in a relatively small-sized script with a few lines of code and grow into a fully production system is very handy for network engineers. As many of us know, the network typically grows organically without a master plan. A language that can grow with your network in size is invaluable. You might be surprised to see a language that was deemed as scripting language by many, which was being used for fully production systems (Organizations using Python, https://wiki.python.org/moin/OrganizationsUsingPython).

If you have ever worked in an environment where you have to switch between working on different vendor platforms, such as Cisco IOS and Juniper Junos, you know how painful it is to switch between syntax and usage when trying to achieve the same task. With Python being flexible enough for large and small programs, there is no such context switching, because it is just Python.

For the rest of the chapter, we will take a high-level tour of the Python language for a bit of a refresher. If you are already familiar with the basics, feel free to quickly scan through it or skip the rest of the chapter.

Python versions

At the time of writing this book in early 2017, Python is going through a transition period of moving from Python version 2 to Python version 3. Unfortunately Python 3 is not backward compatible with Python 2. When I say transition, keep in mind that Python 3 was released back in 2008, over nine years ago with active development with the most recent release of 3.6. The latest Python 2.x release, 2.7, was released over six years ago in mid 2010. Fortunately, both version can coexist on the same machine. Personally, I use Python 2 as my default interpreter when I type in Python at the Command Prompt, and I use Python 3 when I need to use Python 3. More information is given in the next section about invoking Python interpreter, but here is an example of invoking Python 2 and Python 3 on the Ubuntu Linux machine:

    echou@pythonicNeteng:~$ python
    Python 2.7.12 (default, Nov 19 2016, 06:48:10)
    [GCC 5.4.0 20160609] on linux2
    Type "help", "copyright", "credits" or "license" for 
    more information.
    >>> exit()
    echou@pythonicNeteng:~$ python3
    Python 3.5.2 (default, Nov 17 2016, 17:05:23)
    [GCC 5.4.0 20160609] on linux
    Type "help", "copyright", "credits" or "license" for 
    more information.
    >>> exit()

With the 2.7 release being end of life on extended support only for security updates, most Python frameworks are now supporting Python 3. Python 3 also has lots of good features such as asynchronous I/O that can be taken advantage of when we need to optimize our code. This book will use Python 3 for its code examples.

If the particular library or the framework does not support Python 3, such as Ansible (they are actively working on porting to Python 3), it will be pointed out, so you can use Python 2 instead.

Operating system

As mentioned, Python is cross platform. Python programs can be run on Windows, Mac, and Linux. In reality, certain care needs to be taken when you need to ensure cross-platform compatibility, such as taking care of the subtle difference backslashes in Windows filenames. Since this book is for DevOps, systems, and network engineers, Linux is the preferred platform for the intended audience, especially in production. The code in this book will be tested on the Linux Ubuntu 16.06 LTS machine. I will also try my best to make sure the code runs the same on Windows and the Mac platform.

If you are interested in the OS details, they are as follows:

    echou@pythonicNeteng:~$ uname -a
    Linux pythonicNeteng 4.4.0-31-generic #50-Ubuntu SMP 
    Wed Jul 13 00:07:12 UTC 2016 x86_64 x86_64 x86_64 
    GNU/Linux
    echou@pythonicNeteng:~$

Running a Python program

Python programs are executed by an interpreter, which means the code is fed through this interpreter to be executed by the underlying operating system, and results are displayed . There are several different implementation of the interpreter by the Python development community, such as IronPython and Jython. In this book, we will refer to the most common Python interpreter, CPython, which is in use today.

One way you can use Python is by taking the advantage of the interactive prompt. This is useful when you want to quickly test a piece of Python code or concept without writing a whole program. This is typically done by simply typing in the Python keyword:

    Python 3.5.2 (default, Nov 17 2016, 17:05:23)
    [GCC 5.4.0 20160609] on linux
    Type "help", "copyright", "credits" or "license" for 
    more information.
    >>> print("hello world")
    hello world
    >>>

Notice the parentheses around the print statement. In Python 3, the print statement is a function; therefore, it requires the parentheses. In Python 2, you can omit the parentheses.

The interactive mode is one of the Python's most useful features. In the interactive shell, you can type any valid statement or sequence of statements and immediately get a result back. I typically use this to explore around a feature or library that I am not familiar with. Talk about instant gratification!

On Windows, if you do not get a Python shell prompt back, you might not have the program in your system search path. The latest Windows Python installation program provides a check box for adding Python to your system path; make sure that was checked. Or you can add the program in the path manually by going to Environment Settings.

A more common way to run the Python program, however, is to save your Python file and run via the interpreter after. This will save you from typing the same statements over and over again such as in the interactive shell. Python files are just regular text files that are typically saved with the .py extension. In the *Nix world, you can also add the shebang (#!) line on top to specify the interpreter that is used to run the file. The # character can be used to specify comments that will not be executed by the interpreter. The following file, helloworld.py, has the following statements:

    # This is a comment
    print("hello world") 

This can be executed as follows:

    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$ python helloworld.py
    hello world
    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$

Python built-In types

Python has several standard types built into the interpreter:

• None: The Null object
• Numerics: int, long, float, complex, and bool (The subclass of int with True or False value)
• Sequences: str, list, tuple, and range
• Mappings: dict
• Sets: set and frozenset

The None type

The None type denotes an object with no value. This is returned in functions that do not explicitly return anything. The None type is also used in function arguments to error out if the caller does not pass in an actual value.

Numerics

Python numeric objects are basically numbers. With the exception of Boolean, the numeric types of int, long, float, and complex are all signed, meaning they can be positive or negative. Boolean is a subclass of the integer that can be one of two values: 1 for True, and 0 for False. The rest of the numeric types are differentiated by how precise they can represent the number; for example, int are whole numbers with a limited range while long are whole numbers with unlimited range. Float are numbers using the double-precision representation (64-bit) on the machine.

Sequences

Sequences are ordered sets of objects with an index of non-negative integers. In this and the next few sections, we will use the interactive interpreter to illustrate the different types. Please feel free to type along on your own computer.

Sometimes it surprises people that string is actually a sequence type. But if you look closely, strings are series of characters put together. Strings are enclosed by either single, double, or triple quotes. Note in the following examples, the quotes have to match, and triple quotes allow the string to span different lines:

    >>> a = "networking is fun"
    >>> b = 'DevOps is fun too'
    >>> c = """what about coding?
    ... super fun!"""
    >>>

The other two commonly used sequence types are lists and tuples. Lists are the sequence of arbitrary objects. Lists can be created by enclosing the objects in square brackets. Just like string, lists are indexed by non-zero integers that start at zero. The values of lists are retrieved by referencing the index number:

    >>> vendors = ["Cisco", "Arista", "Juniper"]
    >>> vendors[0]
    'Cisco'
    >>> vendors[1]
    'Arista'
    >>> vendors[2]
    'Juniper'

Tuples are similar to lists, created by enclosing the values in parentheses. Like lists, the values in the tuple are retrieved by referencing its index number. Unlike list, the values cannot be modified after creation:

    >>> datacenters = ("SJC1", "LAX1", "SFO1")
    >>> datacenters[0]
    'SJC1'
    >>> datacenters[1]
    'LAX1'
    >>> datacenters[2]
    'SFO1' 

Some operations are common to all sequence types, such as returning an element by index as well as slicing:

    >>> a
    'networking is fun'
    >>> a[1]
    'e'
    >>> vendors
    ['Cisco', 'Arista', 'Juniper']
    >>> vendors[1]
    'Arista'
    >>> datacenters
    ('SJC1', 'LAX1', 'SFO1')
    >>> datacenters[1]
    'LAX1'
    >>>
    >>> a[0:2]
    'ne'
    >>> vendors[0:2]
    ['Cisco', 'Arista']
    >>> datacenters[0:2]
    ('SJC1', 'LAX1')
    >>>

Remember that index starts at 0. Therefore, the index of 1 is actually the second element in the sequence.

There are also common functions that can be applied to sequence types, such as checking the number of elements and minimum and maximum values:

    >>> len(a)
    17
    >>> len(vendors)
    3
    >>> len(datacenters)
    3
    >>>
    >>> b = [1, 2, 3, 4, 5]
    >>> min(b)
    1
    >>> max(b)
    5

It would come as no surprise that there are various methods that apply only to strings. It is worth noting that these methods do not modify the underlying string data itself and always return a new string. If you want to use the new value, you would need to catch the return value and assign it to a different variable:

    >>> a
    'networking is fun'
    >>> a.capitalize()
    'Networking is fun'
    >>> a.upper()
    'NETWORKING IS FUN'
    >>> a
    'networking is fun'
    >>> b = a.upper()
    >>> b
    'NETWORKING IS FUN'
    >>> a.split()
    ['networking', 'is', 'fun']
    >>> a
    'networking is fun'
    >>> b = a.split()
    >>> b
    ['networking', 'is', 'fun']
    >>>

Here are some of the common methods for a list. This list is a very useful structure in terms of putting multiple items in and iterating through them. For example, we can make a list of datacenter spine switches and apply the same access list to all of them by iterating through them one by one. Since a list's value can be modified after creation (unlike tuple), we can also expand and contrast the existing list as we move along the program:

    >>> routers = ['r1', 'r2', 'r3', 'r4', 'r5']
    >>> routers.append('r6')
    >>> routers
    ['r1', 'r2', 'r3', 'r4', 'r5', 'r6']
    >>> routers.insert(2, 'r100')
    >>> routers
    ['r1', 'r2', 'r100', 'r3', 'r4', 'r5', 'r6']
    >>> routers.pop(1)
    'r2'
    >>> routers
    ['r1', 'r100', 'r3', 'r4', 'r5', 'r6']

Mapping

Python provides one mapping type called dictionary. Dictionary is what I think of as a poor man's database, because it contains objects that can be indexed by keys. This is often referred to as the associated array or hashing table in other languages. If you have used any of the dictionary-like objects in other languages, you will know this is a powerful type, because you can refer to the object by a readable key. This key will make more sense for the poor guy who is trying to maintain the code, probably a few months after you wrote the code at 2 am in the morning. The object can also be another data type, such as a list. You can create a dictionary with curly braces:

    >>> datacenter1 = {'spines': ['r1', 'r2', 'r3', 'r4']}
    >>> datacenter1['leafs'] = ['l1', 'l2', 'l3', 'l4']
    >>> datacenter1
    {'leafs': ['l1', 'l2', 'l3', 'l4'], 'spines': ['r1',  
    'r2', 'r3', 'r4']}
    >>> datacenter1['spines']
    ['r1', 'r2', 'r3', 'r4']
    >>> datacenter1['leafs']
    ['l1', 'l2', 'l3', 'l4']

 

Sets

A set is used to contain an unordered collection of objects. Unlike lists and tuples, sets are unordered and cannot be indexed by numbers. But there is one character that makes sets standout and useful: the elements of a set are never duplicated. Imagine if you have a list of IPs that you need to put in an access list of. The only problem in this list of IPs is that they are full of duplicates. Now, think about how you would use a loop to sort it out for the unique items, the set built-in type would allow you to eliminate the duplicate entries with just one line of code. To be honest, I do not use set that much, but when I need it, I am always very thankful this exists. Once the set or sets are created, they can be compared with each other ones using the union, intersection, and differences:

    >>> a = "hello"
    >>> set(a)
    {'h', 'l', 'o', 'e'}
    >>> b = set([1, 1, 2, 2, 3, 3, 4, 4])
    >>> b
    {1, 2, 3, 4}
    >>> b.add(5)
    >>> b
    {1, 2, 3, 4, 5}
    >>> b.update(['a', 'a', 'b', 'b'])
    >>> b
    {1, 2, 3, 4, 5, 'b', 'a'}
    >>> a = set([1, 2, 3, 4, 5])
    >>> b = set([4, 5, 6, 7, 8])
    >>> a.intersection(b)
    {4, 5}
    >>> a.union(b)
    {1, 2, 3, 4, 5, 6, 7, 8}
    >>> 1 *
    {1, 2, 3}
    >>>

Python operators

Python has the some numeric operators that you would expect; note that the truncating division, (//, also known as floor division) truncates the result to an integer and a floating point and return the integer value. The integer value is returned. The modulo (%) operator returns the remainder value in the division:

    >>> 1 + 2
    3
    >>> 2 - 1
    1
    >>> 1 * 5
    5
    >>> 5 / 1
    5.0
    >>> 5 // 2
    2
    >>> 5 % 2
    1

There are also comparison operators:

    >>> a = 1
    >>> b = 2
    >>> a == b
    False
    >>> a > b
    False
    >>> a < b
    True
    >>> a <= b
    True 

We will also see two of the common membership operators to see whether an object is in a sequence type:

    >>> a = 'hello world'
    >>> 'h' in a
    True
    >>> 'z' in a
    False
    >>> 'h' not in a
    False
    >>> 'z' not in a
    True

Python control flow tools

The if, else, and elif statements control conditional code execution. As one would expect, the format of the conditional statement is as follows:

    if expression:
      do something
    elif expression:
      do something if the expression meets
    elif expression:
      do something if the expression meets
    ...
    else:
      statement

Here is a simple example:

    >>> a = 10
    >>> if a > 1:
    ...   print("a is larger than 1")
    ... elif a < 1:
    ...   print("a is smaller than 1")
    ... else:
    ...   print("a is equal to 1")
    ...
    a is larger than 1
    >>>

The while loop will continue to execute until the condition is false, so be careful with this one if you don't want to continue to execute:

    while expression:
      do something
    >>> a = 10
    >>> b = 1
    >>> while b < a:
    ...   print(b)
    ...   b += 1
    ...
    1
    2
    3
    4
    5
    6
    7
    8
    9

The for loop works with any object that supports iteration; this means all the built-in sequence types such as lists, tuples, and strings can be used in a for loop. The letter i in the following for loop is an iterating variable, so you can typically pick something that make sense within the context of your code:

    for i in sequence:
      do something
    >>> a = [100, 200, 300, 400]
    >>> for number in a:
    ...   print(number)
    ...
    100
    200
    300
    400

You can also make your own object that supports the iterator protocol and be able to use the for loop for this object:

Constructing such an object is outside the scope of this chapter, but it is a useful knowledge to have; you can read more about it https://docs.python.org/3/c-api/iter.html.

Python functions

Most of the time when you find yourself reusing some pieces of code, you should break up the code into a self-contained chunk as functions. This practice allows for better modularity, is easier to maintain, and allows for code reuse. Python functions are defined using the def keyword with the function name, followed by the function parameters. The body of the function consists of Python statements that are to be executed. At the end of the function, you can choose to return a value to the function caller, or by default, it will return the None object if you do not specify a return value:

    def name(parameter1, parameter2):
      statements
      return value

We will see a lot more examples of function in the following chapters, so here is a quick example:

    >>> def subtract(a, b):
    ...   c = a - b
    ...   return c
    ...
    >>> result = subtract(10, 5)
    >>> result
    5
    >>>

 

Python classes

Python is an Object-Oriented Programming (OOP) language. The way Python creates objects are with the class keyword. A Python object is most commonly a collection of functions (methods), variables, and attributes (properties). Once a class is defined, you can create instances of such a class. The class serves as the blueprint of the subsequent instances.

The topic of object-oriented programming is outside the scope of this chapter, so here is a simple example of a router object definition:

    >>> class router(object):
    ...   def __init__(self, name, interface_number,
     vendor):
    ...     self.name = name
    ...     self.interface_number = interface_number
    ...     self.vendor = vendor
    ...
    >>>

Once defined, you are able to create as many instances of that class as you'd like:

    >>> r1 = router("SFO1-R1", 64, "Cisco")
    >>> r1.name
    'SFO1-R1'
    >>> r1.interface_number
    64
    >>> r1.vendor
    'Cisco'
    >>>
    >>> r2 = router("LAX-R2", 32, "Juniper")
    >>> r2.name
    'LAX-R2'
    >>> r2.interface_number
    32
    >>> r2.vendor
    'Juniper'
    >>>

Of course, there is a lot more to Python objects and OOP. We will see more examples in the future chapters.

Python modules and packages

Any Python source file can be used as a module, and any functions and classes you define in that source file can be re-used. To load the code, the file referencing the module needs to use the import keyword. Three things happen when the file is imported:

1. The file creates a new namespace for the objects defined in the source file.
2. The caller executes all the code contained in the module.
3. The file creates a name within the caller that refers to the module being imported. The name matches the name of the module.

Remember the subtract() function that you defined using the interactive shell? To reuse the function, we can put it into a file named subtract.py:

    def subtract(a, b):
      c = a - b
      return c

In a file within the same directory of subtract.py, you can start the Python interpreter and import this function:

    Python 2.7.12 (default, Nov 19 2016, 06:48:10)
    [GCC 5.4.0 20160609] on linux2
    Type "help", "copyright", "credits" or "license" for  
    more information.
    >>> import subtract
    >>> result = subtract.subtract(10, 5)
    >>> result
    5

This works because, by default, Python will first search for the current directory for the available modules. If you are in a different directory, you can manually add a search path location using the sys module with sys.path. Remember the standard library that we mentioned a while back? You guessed it, those are just Python files being used as modules.

Packages allow a collection of modules to be grouped together. This further organizes the Python modules into a more namespace protection to further reusability. A package is defined by creating a directory with a name you want to use as the namespace, then yo can place the module source file under that directory. In order for Python to know it as a Python-package, just create a __init__.py file in this directory. In the same example as the subtract.py file, if you were to create a directory called math_stuff and create a __init__.py file:

    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$ mkdir math_stuff
    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$ touch math_stuff/__init__.py
    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$ tree .
    .
    ├── helloworld.py
    └── math_stuff
     ├── __init__.py
     └── subtract.py

    1 directory, 3 files
    echou@pythonicNeteng:~/Master_Python_Networking/
    Chapter1$

The way you will now refer to the module will need to include the package name:

    >>> from math_stuff.subtract import subtract
    >>> result = subtract(10, 5)
    >>> result
    5
    >>>

As you can see, modules and packages are great ways to organize large code files and make sharing Python code a lot easier.

Summary

In this chapter, we covered the OSI model and reviewed the network protocol suites, such as TCP, UDP, and IP. We then did a quick review of the Python language, including built-in types, operators, control flows, functions, classes, modules, and packages.

In the next chapter, we will start to look at using Python to programmatically interact with the network equipment.