Basics of Commenting Code

Comments are created in Python using the pound sign (#) and should be brief statements no longer than a few sentences. Here’s a simple example:

defhello_world():# A simple comment preceding a simple print statementprint("Hello World")

According toPEP 8, comments should have a maximum length of 72 characters. This is true even if your project changes the max line length to be greater than the recommended 80 characters. If a comment is going to be greater than the comment char limit, using multiple lines for the comment is appropriate:

defhello_long_world():# A very long statement that just goes on and on and on and on and# never ends until after it's reached the 80 char limitprint("Hellooooooooooooooooooooooooooooooooooooooooooooooooooooooo World")

Commenting your code servesmultiple purposes, including:

• Planning and Reviewing: When you are developing new portions of your code, it may be appropriate to first use comments as a way of planning or outlining that section of code. Remember to remove these comments once the actual coding has been implemented and reviewed/tested:

  Python

  # First step# Second step# Third step

• Code Description: Comments can be used to explain the intent of specific sections of code:

  Python

  # Attempt a connection based on previous settings. If unsuccessful,# prompt user for new settings.

• Algorithmic Description: When algorithms are used, especially complicated ones, it can be useful to explain how the algorithm works or how it’s implemented within your code. It may also be appropriate to describe why a specific algorithm was selected over another.

  Python

  # Using quick sort for performance gains

• Tagging: The use of tagging can be used to label specific sections of code where known issues or areas of improvement are located. Some examples are:BUG,FIXME, andTODO.

  Python

  # TODO: Add condition for when val is None

Comments to your code should be kept brief and focused. Avoid using long comments when possible. Additionally, you should use the following four essential rules assuggested by Jeff Atwood:

1. Keep comments as close to the code being described as possible. Comments that aren’t near their describing code are frustrating to the reader and easily missed when updates are made.

2. Don’t use complex formatting (such as tables or ASCII figures). Complex formatting leads to distracting content and can be difficult to maintain over time.

3. Don’t include redundant information. Assume the reader of the code has a basic understanding of programming principles and language syntax.

4. Design your code to comment itself. The easiest way to understand code is by reading it. When you design your code using clear, easy-to-understand concepts, the reader will be able to quickly conceptualize your intent.

Remember that comments are designed for the reader, including yourself, to help guide them in understanding the purpose and design of the software.

Commenting Code via Type Hinting (Python 3.5+)

Type hinting was added to Python 3.5 and is an additional form to help the readers of your code. In fact, it takes Jeff’s fourth suggestion from above to the next level. It allows the developer to design and explain portions of their code without commenting. Here’s a quick example:

defhello_name(name:str)->str:return(f"Hello{name}")

From examining the type hinting, you can immediately tell that the function expects the inputname to be of a typestr, orstring. You can also tell that the expected output of the function will be of a typestr, or string, as well. While type hinting helps reduce comments, take into consideration that doing so may also make extra work when you are creating or updating your project documentation.

You can learn more about type hinting and type checking fromthis video created by Dan Bader.

Docstrings Background

Documenting your Python code is all centered on docstrings. These are built-in strings that, when configured correctly, can help your users and yourself with your project’s documentation. Along with docstrings, Python also has the built-in functionhelp() that prints out the objects docstring to the console. Here’s a quick example:

>>>help(str)Help on class str in module builtins:class str(object) |  str(object='') -> str |  str(bytes_or_buffer[, encoding[, errors]]) -> str | |  Create a new string object from the given object. If encoding or |  errors are specified, then the object must expose a data buffer |  that will be decoded using the given encoding and error handler. |  Otherwise, returns the result of object.__str__() (if defined) |  or repr(object). |  encoding defaults to sys.getdefaultencoding(). |  errors defaults to 'strict'. # Truncated for readability

How is this output generated? Since everything in Python is an object, you can examine the directory of the object using thedir() command. Let’s do that and see what find:

>>>dir(str)['__add__', ..., '__doc__', ..., 'zfill'] # Truncated for readability

Within that directory output, there’s an interesting property,__doc__. If you examine that property, you’ll discover this:

>>>print(str.__doc__)str(object='') -> strstr(bytes_or_buffer[, encoding[, errors]]) -> strCreate a new string object from the given object. If encoding orerrors are specified, then the object must expose a data bufferthat will be decoded using the given encoding and error handler.Otherwise, returns the result of object.__str__() (if defined)or repr(object).encoding defaults to sys.getdefaultencoding().errors defaults to 'strict'.

Voilà! You’ve found where docstrings are stored within the object. This means that you can directly manipulate that property. However, there are restrictions for builtins:

>>>str.__doc__="I'm a little string doc! Short and stout; here is my input and print me for my out"Traceback (most recent call last):  File"<stdin>", line1, in<module>TypeError:can't set attributes of built-in/extension type 'str'

Any other custom object can be manipulated:

defsay_hello(name):print(f"Hello{name}, is it me you're looking for?")say_hello.__doc__="A simple function that says hello... Richie style"

>>>help(say_hello)Help on function say_hello in module __main__:say_hello(name)    A simple function that says hello... Richie style

Python has one more feature that simplifies docstring creation. Instead of directly manipulating the__doc__ property, the strategic placement of the string literal directly below the object will automatically set the__doc__ value. Here’s what happens with the same example as above:

defsay_hello(name):"""A simple function that says hello... Richie style"""print(f"Hello{name}, is it me you're looking for?")

>>>help(say_hello)Help on function say_hello in module __main__:say_hello(name)    A simple function that says hello... Richie style

There you go! Now you understand the background of docstrings. Now it’s time to learn about the different types of docstrings and what information they should contain.

Docstring Types

Docstring conventions are described withinPEP 257. Their purpose is to provide your users with a brief overview of the object. They should be kept concise enough to be easy to maintain but still be elaborate enough for new users to understand their purpose and how to use the documented object.

In all cases, the docstrings should use the triple-double quote (""") string format. This should be done whether the docstring is multi-lined or not. At a bare minimum, a docstring should be a quick summary of whatever is it you’re describing and should be contained within a single line:

"""This is a quick summary line used as a description of the object."""

Multi-lined docstrings are used to further elaborate on the object beyond the summary. All multi-lined docstrings have the following parts:

• A one-line summary line
• A blank line proceeding the summary
• Any further elaboration for the docstring
• Another blank line

"""This is the summary lineThis is the further elaboration of the docstring. Within this section,you can elaborate further on details as appropriate for the situation.Notice that the summary and the elaboration is separated by a blank newline."""# Notice the blank line above. Code should continue on this line.

All docstrings should have the same max character length as comments (72 characters). Docstrings can be further broken up into three major categories:

• Class Docstrings: Class and class methods
• Package and Module Docstrings: Package, modules, and functions
• Script Docstrings: Script and functions

classSimpleClass:"""Class docstrings go here."""defsay_hello(self,name:str):"""Class method docstrings go here."""print(f'Hello{name}')

classAnimal:"""    A class used to represent an Animal    ...    Attributes    ----------    says_str : str        a formatted string to print out what the animal says    name : str        the name of the animal    sound : str        the sound that the animal makes    num_legs : int        the number of legs the animal has (default 4)    Methods    -------    says(sound=None)        Prints the animals name and what sound it makes    """says_str="A{name} says{sound}"def__init__(self,name,sound,num_legs=4):"""        Parameters        ----------        name : str            The name of the animal        sound : str            The sound the animal makes        num_legs : int, optional            The number of legs the animal (default is 4)        """self.name=nameself.sound=soundself.num_legs=num_legsdefsays(self,sound=None):"""Prints what the animals name is and what sound it makes.        If the argument `sound` isn't passed in, the default Animal        sound is used.        Parameters        ----------        sound : str, optional            The sound the animal makes (default is None)        Raises        ------        NotImplementedError            If no sound is set for the animal or passed in as a            parameter.        """ifself.soundisNoneandsoundisNone:raiseNotImplementedError("Silent Animals are not supported!")out_sound=self.soundifsoundisNoneelsesoundprint(self.says_str.format(name=self.name,sound=out_sound))

"""Spreadsheet Column PrinterThis script allows the user to print to the console all columns in thespreadsheet. It is assumed that the first row of the spreadsheet is thelocation of the columns.This tool accepts comma separated value files (.csv) as well as excel(.xls, .xlsx) files.This script requires that `pandas` be installed within the Pythonenvironment you are running this script in.This file can also be imported as a module and contains the followingfunctions:    * get_spreadsheet_cols - returns the column headers of the file    * main - the main function of the script"""importargparseimportpandasaspddefget_spreadsheet_cols(file_loc,print_cols=False):"""Gets and prints the spreadsheet's header columns    Parameters    ----------    file_loc : str        The file location of the spreadsheet    print_cols : bool, optional        A flag used to print the columns to the console (default is        False)    Returns    -------    list        a list of strings used that are the header columns    """file_data=pd.read_excel(file_loc)col_headers=list(file_data.columns.values)ifprint_cols:print("\n".join(col_headers))returncol_headersdefmain():parser=argparse.ArgumentParser(description=__doc__)parser.add_argument('input_file',type=str,help="The spreadsheet file to pring the columns of")args=parser.parse_args()get_spreadsheet_cols(args.input_file,print_cols=True)if__name__=="__main__":main()

Docstring Formats

You may have noticed that, throughout the examples given in this tutorial, there has been specific formatting with common elements:Arguments,Returns, andAttributes. There are specific docstrings formats that can be used to help docstring parsers and users have a familiar and known format. The formatting used within the examples in this tutorial are NumPy/SciPy-style docstrings. Some of the most common formats are the following:

The selection of the docstring format is up to you, but you should stick with the same format throughout your document/project. The following are examples of each type to give you an idea of how each documentation format looks.

"""Gets and prints the spreadsheet's header columnsArgs:    file_loc (str): The file location of the spreadsheet    print_cols (bool): A flag used to print the columns to the console        (default is False)Returns:    list: a list of strings representing the header columns"""

"""Gets and prints the spreadsheet's header columns:param file_loc: The file location of the spreadsheet:type file_loc: str:param print_cols: A flag used to print the columns to the console    (default is False):type print_cols: bool:returns: a list of strings representing the header columns:rtype: list"""

"""Gets and prints the spreadsheet's header columnsParameters----------file_loc : str    The file location of the spreadsheetprint_cols : bool, optional    A flag used to print the columns to the console (default is False)Returns-------list    a list of strings representing the header columns"""

"""Gets and prints the spreadsheet's header columns@type file_loc: str@param file_loc: The file location of the spreadsheet@type print_cols: bool@param print_cols: A flag used to print the columns to the console    (default is False)@rtype: list@returns: a list of strings representing the header columns"""

Private Projects

Private projects are projects intended for personal use only and generally aren’t shared with other users or developers. Documentation can be pretty light on these types of projects. There are some recommended parts to add as needed:

• Readme: A brief summary of the project and its purpose. Include any special requirements for installation or operating the project.
• examples.py: A Python script file that gives simple examples of how to use the project.

Remember, even though private projects are intended for you personally, you are also considered a user. Think about anything that may be confusing to you down the road and make sure to capture those in either comments, docstrings, or the readme.

Shared Projects

Shared projects are projects in which you collaborate with a few other people in the development and/or use of the project. The “customer” or user of the project continues to be yourself and those limited few that use the project as well.

Documentation should be a little more rigorous than it needs to be for a private project, mainly to help onboard new members to the project or alert contributors/users of new changes to the project. Some of the recommended parts to add to the project are the following:

• Readme: A brief summary of the project and its purpose. Include any special requirements for installing or operating the project. Additionally, add any major changes since the previous version.
• examples.py: A Python script file that gives simple examples of how to use the projects.
• How to Contribute: This should include how new contributors to the project can start contributing.

Public and Open Source Projects

Public and Open Source projects are projects that are intended to be shared with a large group of users and can involve large development teams. These projects should place as high of a priority on project documentation as the actual development of the project itself. Some of the recommended parts to add to the project are the following:

• Readme: A brief summary of the project and its purpose. Include any special requirements for installing or operating the projects. Additionally, add any major changes since the previous version. Finally, add links to further documentation, bug reporting, and any other important information for the project. Dan Bader has put togethera great tutorial on what all should be included in your readme.

• How to Contribute: This should include how new contributors to the project can help. This includes developing new features, fixing known issues, adding documentation, adding new tests, or reporting issues.

• Code of Conduct: Defines how other contributors should treat each other when developing or using your software. This also states what will happen if this code is broken. If you’re using Github, a Code of Conducttemplate can be generated with recommended wording. For Open Source projects especially, consider adding this.

• License: A plaintext file that describes the license your project is using. For Open Source projects especially, consider adding this.

• docs: A folder that contains further documentation. The next section describes more fully what should be included and how to organize the contents of this folder.

Documentation Tools and Resources

Documenting your code, especially large projects, can be daunting. Thankfully there are some tools out and references to get you started:

Along with these tools, there are some additional tutorials, videos, and articles that can be useful when you are documenting your project:

1. Carol Willing - Practical Sphinx - PyCon 2018
2. Daniele Procida - Documentation-driven development - Lessons from the Django Project - PyCon 2016
3. Eric Holscher - Documenting your project with Sphinx & Read the Docs - PyCon 2016
4. Titus Brown, Luiz Irber - Creating, building, testing, and documenting a Python project: a hands-on HOWTO - PyCon 2016
5. reStructuredText Official Documentation
6. Sphinx’s reStructuredText Primer
7. Documenting Python Projects With Sphinx and Read the Docs
8. Document Your Python Code and Projects With ChatGPT

Sometimes, the best way to learn is to mimic others. Here are some great examples of projects that use documentation well:

• Django:Docs (Source)
• Requests:Docs (Source)
• Click:Docs (Source)
• Pandas:Docs (Source)