How To Write Beautiful Python Code With PEP 8

Read it in 8 Mins

Last updated on
23rd Feb, 2022
Published
24th Jul, 2019
Views
9,516
How To Write Beautiful Python Code With PEP 8

It gets difficult to understand messed-up handwriting, similarly, an unreadable and unstructured code is not accepted by all. However, you can benefit as a programmer only when you can express better with your code. This is where PEP comes to the rescue. 

Python Enhancement Proposal or PEP is a design document that provides information to the Python community and also describes new features and document aspects, such as style and design for Python.

Python is a multi-paradigm programming language that is easy to learn and has gained popularity in the fields of Data Science and Web Development over a few years and PEP 8 is called the style code of Python. It was written by Guido van Rossum, Barry Warsaw, and Nick Coghlan in the year 2001. It focuses on enhancing Python’s code readability and consistency. Join our python advanced programming bootcamp for beginners and learn PEP 8 skills and various features of Python along with tips and tricks.

A Foolish Consistency is the Hobgoblin of Little Minds

‘A great person does not have to think consistently from one day to the next’ — this is what the statement means.

Consistency is what matters. It is considered as the style guide. You should maintain consistency within a project and mostly within a function or module.

However, there will be situations where you need to make use of your own judgment, where consistency isn’t considered an option. You must know when you need to be inconsistent for example when applying the guideline would make the code less readable or when the code needs to comply with the earlier versions of Python which the style guide doesn’t recommend. 

In simple terms, you cannot break the backward compatibility to follow with PEP.

The Zen of Python

It is a collection of 19 ‘guiding principles’ which was originally written by Tim Peters in the year 1999. It guides the design of the Python Programming Language.

Python was developed with some goals in mind. You can see those when you type the following code and run it:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

The Need for PEP 8

Readability is the key to good code. Writing good code is like an art form that acts as a subjective topic for different developers.

Readability is important in the sense that once you write a code, you need to remember what the code does and why you have written it. You might never write that code again, but you’ll have to read that piece of code again and again while working in a project. 

PEP 8 adds a logical meaning to your code by making sure your variables are named well, sufficient whitespaces are there or not and also by commenting well. If you’re a beginner to the language, A pinch of PEP 8 makes the python programming for the absolute beginner more pleasant.  

Following PEP 8 would also make your task easier if you’re working as a professional developer. People who are unknown to you and have never seen how you style your code will be able to easily read and understand your code only if you follow and recognize a particular guideline where readability is your de facto.

And as Guido van Rossum said— “Code is read much more than it is often written”.

The Code Layout

Your code layout has a huge impact on the readability of your code.

Indentation

The indentation level of line is computed by the leading spaces and tabs at the beginning of a line of logic. It influences the grouping of statements. 

The rules of PEP 8 says to use 4 spaces per indentation level and also spaces should be preferred over tabs.

An example of code to show indentation:

x = 5
if x < 10:
  print('x is less than 10') 

Tabs or Spaces?

Here the print statement is indented which informs Python to execute the statement only if the if statement is true. Indentation also helps Python to know what code it will execute during function calls and also when using classes.

PEP 8 recommends using 4 spaces to show indentation and tabs should only be used to maintain consistency in the code.

Python 3 forbids the mixing of spaces and tabs for indentation. You can either use tabs or spaces and you should maintain consistency while using Python 3. The errors are automatically displayed:

python hello.py
  File "hello.py", line 3
      print(i, j)
                ^

TabError: inconsistent use of tabs and spaces in indentation

However, if you’re working in Python 2, you can check the consistency by using a -t flag in your code which will display the warnings of inconsistencies with the use of spaces and tabs.

You can also use the -tt flag which will show the errors instead of warnings and also the location of inconsistencies in your code. 

Maximum Line Length and Line Breaking

The Python Library is conservative and 79 characters are the maximum required line limit as suggested by PEP 8. This helps to avoid line wrapping.

Since maintaining the limit to 79 characters isn’t always possible, so PEP 8 allows wrapping lines using Python’s implied line continuation with parentheses, brackets, and braces:

def function(argument_1, argument_2,
            argument_3, argument_4):
    return argument_1

Or by using backslashes to break lines:

with open('/path/to/some/file/you/want/to/read') as example_1, \
    open('/path/to/some/file/being/written', 'w') as example_2:
    file_2.write(file_1.read())

When it comes to binary operators, PEP 8 encourages to break lines before the binary operators. This accounts for more readable code.

Let us understand this by comparing two examples:

# Example 1
# Do
total = ( variable_1 + variable_2 - variable_3 )
# Example 2
# Don't
total = ( variable_1 + variable_2 - variable_3 )

In the first example, it is easily understood which variable is added or subtracted, since the operator is just next to the variable to which it is operated. However, in the second example, it is a little difficult to understand which variable is added or subtracted.

Indentation with Line Breaks

Indentation allows a user to differentiate between multiple lines of code and a single line of code that spans multiple lines. It enhances readability too.

The first style of indentation is to adjust the indented block with the delimiter:

def function(argument_one, argument_two,
              argument_three, argument_four):
        return argument_one

You can also improve readability by adding comments:

x = 10
if (x > 5 and
    x < 20):
    # If Both conditions are satisfied
    print(x)

Or by adding extra indentation:

x = 10
if (x > 5 and
      x < 20):
    print(x)

Another type of indentation is the hanging indentation by which you can symbolize a continuation of a line of code visually:

foo = long_function_name(
      variable_one, variable_two,
      variable_three, variable_four)

You can choose any of the methods of indentation, following line breaks, in situations where the 79 character line limit forces you to add line breaks in your code, which will ultimately improve the readability.

Closing Braces in Line Continuations

Closing the braces after line breaks can be easily forgotten, so it is important to put it somewhere where it makes good sense or it can be confusing for a reader.

One way provided by PEP 8 is to put the closing braces with the first white-space character of the last line:

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

Or lining up under the first character of line that initiates the multi-line construct:

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

Remember, you can use any of the two options keeping in mind the consistency of the code.

Blank Lines

Blank lines are also called vertical whitespaces. It is a logical line consisting of spaces, tabs, formfeeds or comments that are basically ignored.

Using blank lines in top-level-functions and classes:

class my_first_class:
    pass
class my_second_class:
    pass
def top_level_function():
    return None

Adding two blank lines between the top-level-functions and classes will have a clear separation and will add more sensibility to the code.

Using blank lines in defining methods inside classes:

class my_class:
    def method_1(self):
        return None

    def method_2(self):
        return None

Here, a single vertical space is enough for a readable code.

You can also use blank spaces inside multi-step functions. It helps the reader to gather the logic of your function and understand it efficiently. A single blank line will work in such case.

An example to illustrate such:

def calculate_average(number_list):
    sum_list = 0
    for number in number_list:
        sum_list = sum_list + number
   
    average = 0
    average = sum_list / len(number_list)

   return average

Above is a function to calculate the average. There is a blank line between each step and also before the return statement.

The use of blank lines can greatly improve the readability of your code and it also allows the reader to understand the separation of the sections of code and the relation between them.

Naming Conventions

Choosing names that are sensible and can be easily understandable, while coding in Python, is very crucial. This will save the time and energy of a reader. Inappropriate names might lead to difficulties when debugging.

Naming Styles

Naming variables, functions, classes or methods must be done very carefully. Here’s a list of the type, naming conventions and examples on how to use them:

TypeNaming ConventionsExamples
VariableUsing short names with CapWords.T, AnyString, My_First_Variable
FunctionUsing a lowercase word or words with underscores to improve readability.function, my_first_function
ClassUsing CapWords and do not use underscores between words.Student, MyFirstClass
MethodUsing lowercase words separated by underscores.Student_method, method
ConstantsUsing all capital letters with underscores separating wordsTOTAL, MY_CONSTANT, MAX_FLOW
ExceptionsUsing CapWords without underscores.IndexError, NameError
ModuleUsing short lower-case letters using underscores.module.py, my_first_module.py
PackageUsing short lowercase words and underscores are discouraged.package, my_first_package

Choosing names

To have readability in your code, choose names that are descriptive and give a clearer sense of what the object represents. A more real-life approach to naming is necessary for a reader to understand the code.

Consider a situation where you want to store the name of a person as a string:

>>> name = 'John William'
>>> first_name, last_name = name.split()
>>> print(first_name, last_name, sep='/ ')
John/ William

Here, you can see, we have chosen variable names like first_name and last_name which are clearer to understand and can be easily remembered. We could have used short names like x, y or z but it is not recommended by PEP 8 since it is difficult to keep track of such short names.

Consider another situation where you want to double a single argument. We can choose an abbreviation like db for the function name:

# Don't
def db(x):
    return x * 2

However, abbreviations might be difficult in situations where you want to return back to the same code after a couple of days and still be able to read and understand. In such cases, it’s better to use a concise name like double_a_variable:

# Do
def double_a_value(x):
    return x * 2

Ultimately, what matters is the readability of your code.

Comments

A comment is a piece of code written in simple English which improves the readability of code without changing the outcome of a program. You can understand the aim of the code much faster just by reading the comments instead of the actual code. It is important in analyzing codes, debugging or making a change in logic. 

Block Comments

Block comments are used while importing data from files or changing a database entry where multiple lines of code are written to focus on a single action. They help in interpreting the aim and functionality of a given block of code.

They start with a hash(#) and a single space and always indent to the same level as the code:

for i in range(0, 10):
    # Loop iterates 10 times and then prints i
    # Newline character
    print(i, '\n')

You can also use multiple paragraphs in a block comment while working on a more technical program. 

Block comments are the most suitable type of comments and you can use it anywhere you like.

Inline Comments

Inline comments are the comments which are placed on the same line as the statement. They are helpful in explaining why a certain line of code is essential.

Example of inline comments:

x = 10  # An inline comment
y = 'JK Rowling' # Author Name

Inline comments are more specific in nature and can easily be used which might lead to clutter. So, PEP 8 basically recommends using block comments for general-purpose coding.

Document Strings

Document strings or docstrings start at the first line of any function, class, file, module or method. These type of comments are enclosed between single quotations ( ''') or double quotations ( """ ).

An example of docstring:

def quadratic_formula(x, y, z, t):
    """Using the quadratic formula"""
    t_1 = (- b+(b**2-4*a*c)**(1/2)) / (2*a)
    t_2 = (- b-(b**2-4*a*c)**(1/2)) / (2*a)

    return t_1, t_2

Whitespaces in Expressions and Statements

In computing, whitespace is any character or sequence of characters that are used for spacing and have an ‘empty’ representation. It is helpful in improving the readability of expressions and statements if used properly.

Whitespace around Binary Operators

When you’re using assignment operators ( =, +=, -=,and so forth ) or comparisons ( ==, !=, >, <. >=, <= ) or booleans ( and, not, or ), it is suggested to use a single whitespace on the either side.

Example of adding whitespace when there is more than one operator in a statement:

# Don't
b = a ** 2 + 10
c = (a + b) * (a - b)

# Do
b = a**2 + 10
c = (a+b) * (a-b)

In such mathematical computations, you should add whitespace around the operators with the least priority since adding spaces around each operator might be confusing for a reader.

Example of adding whitespaces in an if statement with many conditions:

# Don't
if a < 10 and a % 5 == 0:
    print('a is smaller than 10 and is divisible by 5!')

# Do
if a<10 and a%5==0:
    print('a is smaller than 10 and is divisible by 5!')

Here, the and operator has the least priority, so whitespaces have been added around it.

Colons act as binary operators in slices:

ham[3:4]

ham[x+1 : x+2]

ham[3:4:5]

ham[x+1 : x+2 : x+3]

ham[x+1 : x+2 :]

Since colons act as a binary operator, whitespaces are added on either side of the operator with the lowest priority. Colons must have the same amount of spacing in case of an extended slice. An exception is when the slice parameter is omitted, space is also omitted.

Avoiding Whitespaces

Trailing whitespaces are whitespaces placed at the end of a line. These are the most important to avoid. 

You should avoid whitespaces in the following cases—

Inside a parentheses, brackets, or braces:

# Do
list = [1, 2, 3]

# Don't
list = [ 1, 2, 3, ]

Before a comma, a semicolon, or a colon:

x = 2
y = 3

# Do
print(x, y)

# Don't
print(x , y)

Before open parenthesis that initiates the argument list of a function call:

def multiply_by_2(a):
      return a * 2

# Do
multiply_by_2(3)

# Don't
multiply_by_2 (3)

Before an open bracket that begins an index or a slice:

# Do
ham[5]

# Don't
ham [5]

Between a trailing comma and a closing parenthesis:

# Do
spam = (1,)

# Don't
spam = (1, )

To adjust assignment operators:

# Do
variable_1 = 5
variable_2 = 6
my_long_var = 7

# Don't
variable_1    = 5
variable_2    = 6
my_long_var  = 7

Programming Recommendations

PEP 8 guidelines suggest different ways to maintain consistency among multiple implementations of Python like PyPy, Jython or Cython.

An example of comparing boolean values:

# Don't
bool_value = 5 > 4
if bool_value == True:
return '4 is smaller than 5'

# Do
if bool_value:
return '4 is smaller than 5'

Since bool can only accept values True or False, it is useless to use the equivalence operator == in these type of if executions. PEP 8 recommends the second example which will require lesser and simpler coding. 

An example to check whether a list is empty or not:

# Don't
list_value = []
if not len(list_value):
    print('LIST IS EMPTY')

# Do
list_value = []
if not list_value:
    print('LIST IS EMPTY')

Any empty list or string in Python is falsy. So you can write a code to check an empty string without checking the length of the list. The second example is more simple, so PEP encourages to write an if statement in this way.

The expression is not and not ... is are identical in functionality. But the former is more preferable due to its nature of readability:

# Do
if x is not None:
    return 'x has a value'

# Don't
if not x is None:
    return 'x has a value'

String slicing is a type of indexing syntax that extracts substrings from a string. Whenever you want to check if a string is prefixed or suffixed, PEP recommends using .startswith() and .endswith() instead of list slicing. This is because they are cleaner and have lesser chances of error:

# Do
if foo.startswith('cat'):

# Don't
if foo[:3] == 'cat':

An example using .endswith():

# Don't
if file_jpg[-3:] == 'jpg':
    print('It is a JPEG image file')

# Do
if file_jpg.endswith('jpg'):
    print('It is a JPEG image file')

Though there exist multiple ways to execute a particular action, the main agenda of the guidelines laid by PEP 8 is simplicity and readability.

When to Ignore PEP 8

You should never ignore PEP 8. If the guidelines related to PEP8 are followed, you can be confident of writing readable and professional codes. This will also make the lives of your colleagues and other members working on the same project much easier. 

There are some exclusive instances when you may ignore a particular guideline:

  • After following the guidelines, the code becomes less readable, even for a programmer who is comfortable with reading codes that follow PEP 8.
  • If the surrounding code is inconsistent with PEP.
  • Compatible of code with older versions of Python is the priority.

Checking PEP 8 Compliant Code

You can check whether your code actually complies with the rules and regulations of PEP 8 or not. Linters and Autoformatters are two classes of tools used to implement and check PEP 8 compliance.

Linters

It is a program that analyzes your code and detects program errors, syntax errors, bugs and structural problems. They also provide suggestions to correct the errors.

Some of the best linters used for Python code:

  • pycodestyle is a tool to verify the PEP 8 style conventions in your Python code.

You can run the following from the command line to install pycodestyle using pip:

pip install pycodestyle

To display the errors of a program, run pycodestyle in this manner:

pycodestyle my_code.py

my_code.py:1:11: E231 missing whitespace after '{'

my_code.py:3:19: E231 missing whitespace after ')'

my_code.py:4:31: E302 expected 2 blank lines, found 1

  • flake8 is a Python wrapper that verifies PEP 8, pyflakes, and circular complexity.

Type the command to install flake8 using pip:

pip install flake8

Run flake8 from the terminal using the command:

flake8 calc.py 

calc.py:24:3: E111 indentation is not a multiple of two

calc.py:25:3: E111 indentation is not a multiple of two

calc.py:45:9: E225 missing whitespace around operator

You can also use some other good linters like pylint, pyflakes, pychecker and mypy.

Autoformatters

An autoformatter is a tool which will format your code to adapt with PEP 8 automatically.One of the most commonly used autoformatter is black.

To install black using pip, type:

pip install black

Remember, you need to have Python 3.6 or above to install black.

An example of code that doesn’t follow PEP 8:

def add(a, b): return a+b

def multiply(a, b):
      return \
        a   * b

Now run black following the filename from the terminal:

black my_code.py

reformatted my_code.py

All done! 

The reformatted code will look like:

def add(a, b):
    return a + b

def multiply(a, b):
    return a * b

Some other autoformatters include autopep8 and yapf. Their work is similar to black.

Conclusion

Since you have now learned to write a good-quality and readable Python code using PEP 8, you’ll consider it bliss while working on a project. Though it might be too precise in its nature, it will be useful to everyone working on a particular project by making the code more understandable and making changes and debugging easier.

Let us sum up what we’ve learnt so far:

  • What is PEP 8 and what is its importance?
  • Multiple guidelines for writing PEP 8 compliant code.
  • How to check code against PEP 8 using linters and autoformatters.

If you intend to know more about PEP 8 and its book of guidelines, you can refer to pep8.org or simply enroll for the Knowledgehut python programming for the absolute beginner course that you can complete on your own schedule.  

Profile

Priyankur Sarkar

Data Science Enthusiast

Priyankur Sarkar loves to play with data and get insightful results out of it, then turn those data insights and results in business growth. He is an electronics engineer with a versatile experience as an individual contributor and leading teams, and has actively worked towards building Machine Learning capabilities for organizations.