Refactoring Python Applications for Simplicity

Lines of Code

LOC, or Lines of Code, is the crudest measure of complexity. It is debatable whether there is any direct correlation between the lines of code and the complexity of an application, but the indirect correlation is clear. After all, a program with 5 lines is likely simpler than one with 5 million.

When looking at Python metrics, we try to ignore blank lines and lines containing comments.

Lines of code can be calculated using the wc command on Linux and Mac OS, where file.py is the name of the file you want to measure:

$ wc -l file.py

If you want to add the combined lines in a folder by recursively searching for all .py files, you can combine wc with the find command:

$ find . -name \*.py | xargs wc -l

For Windows, PowerShell offers a word count command in Measure-Object and a recursive file search in Get-ChildItem:

PS C:\> Get-ChildItem -Path *.py -Recurse | Measure-Object –Line 

In the response, you will see the total number of lines.

Why are lines of code used to quantify the amount of code in your application? The assumption is that a line of code roughly equates to a statement. Lines is a better measure than characters, which would include whitespace.

In Python, we are encouraged to put a single statement on each line. This example is 9 lines of code:

 1x = 5
 2value = input("Enter a number: ")
 3y = int(value)
 4if x < y:
 5    print(f"{x} is less than {y}")
 6elif x == y:
 7    print(f"{x} is equal to {y}")
 8else:
 9    print(f"{x} is more than {y}")

If you used only lines of code as your measure of complexity, it could encourage the wrong behaviors.

Python code should be easy to read and understand. Taking that last example, you could reduce the number of lines of code to 3:

 1x = 5; y = int(input("Enter a number:"))
 2equality = "is equal to" if x == y else "is less than" if x < y else "is more than"
 3print(f"{x} {equality} {y}")

But the result is hard to read, and PEP 8 has guidelines around maximum line length and line breaking. You can check out How to Write Beautiful Python Code With PEP 8 for more on PEP 8.

This code block uses 2 Python language features to make the code shorter:

• Compound statements: using ;
• Chained conditional or ternary statements: name = value if condition else value if condition2 else value2

We have reduced the number of lines of code but violated one of the fundamental laws of Python:

“Readability counts”

— Tim Peters, Zen of Python

This shortened code is potentially harder to maintain because code maintainers are humans, and this short code is harder to read. We will explore some more advanced and useful metrics for complexity.

Cyclomatic Complexity

Cyclomatic complexity is the measure of how many independent code paths there are through your application. A path is a sequence of statements that the interpreter can follow to get to the end of the application.

One way to think of cyclomatic complexity and code paths is imagine your code is like a railway network.

For a journey, you may need to change trains to reach your destination. The Lisbon Metropolitan railway system in Portugal is simple and easy to navigate. The cyclomatic complexity for any trip is equal to the number of lines you need to travel on:

If you needed to get from Alvalade to Anjos, then you would travel 5 stops on the linha verde (green line):

This trip has a cyclomatic complexity of 1 because you only take 1 train. It’s an easy trip. That train is equivalent in this analogy to a code branch.

If you needed to travel from the Aeroporto (airport) to sample the food in the district of Belém, then it’s a more complicated journey. You would have to change trains at Alameda and Cais do Sodré:

This trip has a cyclomatic complexity of 3, because you take 3 trains. You might be better off taking a taxi!

Seeing as how you’re not navigating Lisbon, but rather writing code, the changes of train line become a branch in execution, like an if statement.

Let’s explore this example:

x = 1

There is only 1 way this code can be executed, so it has a cyclomatic complexity of 1.

If we add a decision, or branch to the code as an if statement, it increases the complexity:

x = 1
if x < 2:
    x += 1

Even though there is only 1 way this code can be executed, as x is a constant, this has a cyclomatic complexity of 2. All of the cyclomatic complexity analyzers will treat an if statement as a branch.

This is also an example of overly complex code. The if statement is useless as x has a fixed value. You could simply refactor this example to the following:

x = 2

That was a toy example, so let’s explore something a little more real.

main() has a cyclomatic complexity of 5. I’ll comment each branch in the code so you can see where they are:

# cyclomatic_example.py
import sys

def main():
    if len(sys.argv) > 1:  # 1
        filepath = sys.argv[1]
    else:
        print("Provide a file path")
        exit(1)
    if filepath:  # 2
        with open(filepath) as fp:  # 3
            for line in fp.readlines():  # 4
                if line != "\n":  # 5
                    print(line, end="")

if __name__ == "__main__":  # Ignored.
    main()

There are certainly ways that code can be refactored into a far simpler alternative. We’ll get to that later.

In the following examples, we will use the radon library from PyPI to calculate metrics. You can install it now:

$ pip install radon

To calculate cyclomatic complexity using radon, you can save the example into a file called cyclomatic_example.py and use radon from the command line.

The radon command takes 2 main arguments:

1. The type of analysis (cc for cyclomatic complexity)
2. A path to the file or folder to analyze

Execute the radon command with the cc analysis against the cyclomatic_example.py file. Adding -s will give the cyclomatic complexity in the output:

$ radon cc cyclomatic_example.py -s
cyclomatic_example.py
    F 4:0 main - B (6)

The output is a little cryptic. Here is what each part means:

• F means function, M means method, and C means class.
• main is the name of the function.
• 4 is the line the function starts on.
• B is the rating from A to F. A is the best grade, meaning the least complexity.
• The number in parentheses, 6, is the cyclomatic complexity of the code.

Halstead Metrics

The Halstead complexity metrics relate to the size of a program’s codebase. They were developed by Maurice H. Halstead in 1977. There are 4 measures in the Halstead equations:

• Operands are values and names of variables.
• Operators are all of the built-in keywords, like if, else, for or while.
• Length (N) is the number of operators plus the number of operands in your program.
• Vocabulary (h) is the number of unique operators plus the number of unique operands in your a program.

There are then 3 additional metrics with those measures:

• Volume (V) represents a product of the length and the vocabulary.
• Difficulty (D) represents a product of half the unique operands and the reuse of operands.
• Effort (E) is the overall metric that is a product of volume and difficulty.

All of this is very abstract, so let’s put it in relative terms:

• The effort of your application is highest if you use a lot of operators and unique operands.
• The effort of your application is lower if you use a few operators and fewer variables.

For the cyclomatic_complexity.py example, operators and operands both occur on the first line:

import sys  # import (operator), sys (operand)

import is an operator, and sys is the name of the module, so it’s an operand.

In a slightly more complex example, there are a number of operators and operands:

if len(sys.argv) > 1:
    ...

There are 5 operators in this example:

1. if
2. (
3. )
4. >
5. :

Furthermore, there are 2 operands:

1. sys.argv
2. 1

Be aware that radon only counts a subset of operators. For example, parentheses are excluded in any calculations.

To calculate the Halstead measures in radon, you can run the following command:

$ radon hal cyclomatic_example.py
cyclomatic_example.py:
    h1: 3
    h2: 6
    N1: 3
    N2: 6
    vocabulary: 9
    length: 9
    calculated_length: 20.264662506490406
    volume: 28.529325012980813
    difficulty: 1.5
    effort: 42.793987519471216
    time: 2.377443751081734
    bugs: 0.009509775004326938

Maintainability Index

The maintainability index brings the McCabe Cyclomatic Complexity and the Halstead Volume measures in a scale roughly between zero and one-hundred.

If you’re interested, the original equation is as follows:

In the equation, V is the Halstead volume metric, C is the cyclomatic complexity, and L is the number of lines of code.

If you’re as baffled as I was when I first saw this equation, here’s it means: it calculates a scale that includes the number of variables, operations, decision paths, and lines of code.

It is used across many tools and languages, so it’s one of the more standard metrics. However, there are numerous revisions of the equation, so the exact number shouldn’t be taken as fact. radon, wily, and Visual Studio cap the number between 0 and 100.

On the maintainability index scale, all you need to be paying attention to is when your code is getting significantly lower (toward 0). The scale considers anything lower than 25 as hard to maintain, and anything over 75 as easy to maintain. The Maintainability Index is also referred to as MI.

The maintainability index can be used as a measure to get the current maintainability of your application and see if you’re making progress as you refactor it.

To calculate the maintainability index from radon, run the following command: