Keyword arguments are one of those Python features that often seems a little odd for folks moving to Python from many other programming languages. It doesn’t help that folks learning Python often discover the various features of keyword arguments slowly over time.

When teaching Python, I’ve often wished I had a summary of the various keyword argument-related features that I could link learners to. I hope that this article will accomplish that task.

In this article I’m going to explain what keyword arguments are and why they’re used. I’ll then go over some more advanced uses of them that even long-time Python programmers may have overlooked because quite a few things have changed in recent versions of Python 3. If you’re already an experienced Python programmer, you might want to skip to the end.

What are keyword arguments?

Let’s take a look at what keyword arguments (also called “named arguments”) are.

First let’s take this Python function:

1 2 3 4 5 6 from math import sqrt def quadratic ( a , b , c ): x1 = - b / ( 2 * a ) x2 = sqrt ( b ** 2 - 4 * a * c ) / ( 2 * a ) return ( x1 + x2 ), ( x1 - x2 )

When we call this function, we can pass each of our three arguments in two different ways.

We can pass our arguments as positional arguments like this:

1 2 >>> quadratic ( 31 , 93 , 62 ) (-1.0, -2.0)

Or we can pass our arguments as keyword arguments like this:

1 2 >>> quadratic ( a = 31 , b = 93 , c = 62 ) (-1.0, -2.0)

The order of these arguments matters when they’re passed positionally:

1 2 3 4 >>> quadratic ( 31 , 93 , 62 ) (-1.0, -2.0) >>> quadratic ( 62 , 93 , 31 ) (-0.5, -1.0)

But it doesn’t matter when they’re passed by their name:

1 2 3 4 >>> quadratic ( a = 31 , b = 93 , c = 62 ) (-1.0, -2.0) >>> quadratic ( c = 62 , b = 93 , a = 31 ) (-1.0, -2.0)

When we use keyword/named arguments, it’s the name that matters, not the position:

1 2 3 4 >>> quadratic ( a = 31 , b = 93 , c = 62 ) (-1.0, -2.0) >>> quadratic ( c = 31 , b = 93 , a = 62 ) (-0.5, -1.0)

So unlike many other programming languages, Python knows the names of the arguments our function accepts.

If we ask for help on our function Python will tell us our three arguments by name:

1 2 3 4 >>> help ( quadratic ) Help on function quadratic in module __main__: quadratic(a, b, c)

Note that functions can be called with a mix of positional and named arguments:

1 2 >>> quadratic ( 31 , 93 , c = 62 ) (-1.0, -2.0)

That can come in handy, but with the particular function we’ve written here it’s most clear to use all positional arguments or all keyword arguments.

Why use keyword arguments?

When calling functions in Python, you’ll often have to choose between using keyword arguments or positional arguments. Keyword arguments can often be used to make function calls more explicit.

Take this code:

1 2 3 def write_gzip_file ( output_file , contents ): with GzipFile ( None , 'wt' , 9 , output_file ) as gzip_out : gzip_out . write ( contents )

This takes a file object output_file and contents string and writes a gzipped version of the string to the output file.

This code does the same thing but it uses keyword arguments instead of positional arguments:

1 2 3 def write_gzip_file ( output_file , contents ): with GzipFile ( fileobj = output_file , mode = 'wt' , compresslevel = 9 ) as gzip_out : gzip_out . write ( contents )

Notice that using this keyword argument call style made it more obvious what each of these three arguments represent.

We were also able to leave off an argument here. The first argument that we left off represents a filename and already has a default value of None . We don’t need a filename here because we’re supposed to pass either a file object or a filename to GzipFile , not both.

We’re actually able to leave another argument off though.

Here’s the same code again, but the compress level has been left at its default value of 9 this time:

1 2 3 def write_gzip_file ( output_file , contents ): with GzipFile ( fileobj = output_file , mode = 'wt' ) as gzip_out : gzip_out . write ( contents )

Because we used named arguments, we were able to leave out two arguments and rearrange the remaining 2 arguments in a sensible order (the file object is more important than the “wt” access mode).

When we use keyword arguments:

We can often leave out arguments that have default values We can rearrange arguments in a way that makes them most readable We call arguments by their names to make it more clear what they represent

Where you see keyword arguments

You’ll likely see keyword arguments quite a bit in Python.

Python has a number of functions that take an unlimited number of positional arguments. These functions sometimes have arguments that can be provided to customize their functionality. Those arguments must be provided as named arguments to distinguish them from the unlimited positional arguments.

The built-in print function accepts the optional sep , end , file , and flush attributes as keyword-only arguments:

1 2 >>> print ( 'comma' , 'separated' , 'words' , sep = ', ' ) comma, separated, words

The itertools.zip_longest function also accepts an optional fillvalue attribute (which defaults to None ) exclusively as a keyword argument:

1 2 3 >>> from itertools import zip_longest >>> list ( zip_longest ([ 1 , 2 ], [ 7 , 8 , 9 ], [ 4 , 5 ], fillvalue = 0 )) [(1, 7, 4), (2, 8, 5), (0, 9, 0)]

In fact, some functions in Python force arguments to be named even when they could have been unambiguously specified positionally.

In Python 2, the sorted function accepted all its arguments as either positional or keyword arguments:

1 2 3 4 >>> sorted ([ 4 , 1 , 8 , 2 , 7 ], None , None , True ) [8, 7, 4, 2, 1] >>> sorted ([ 4 , 1 , 8 , 2 , 7 ], reverse = True ) [8, 7, 4, 2, 1]

But Python 3’s sorted function requires all arguments after the provided iterable to be specified as keyword arguments:

1 2 3 4 5 6 >>> sorted ([ 4 , 1 , 8 , 2 , 7 ], None , True ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : must use keyword argument for key function >>> sorted ([ 4 , 1 , 8 , 2 , 7 ], reverse = True ) [8, 7, 4, 2, 1]

Keyword arguments come up quite a bit in Python’s built-in functions as well as in the standard library and third party libraries.

Requiring your arguments be named

You can create a function that accepts any number of positional arguments as well as some keyword-only arguments by using the * operator to capture all the positional arguments and then specify optional keyword-only arguments after the * capture.

Here’s an example:

1 2 3 4 5 def product ( * numbers , initial = 1 ): total = initial for n in numbers : total *= n return total

Note: If you haven’t seen that * syntax before, *numbers captures all positional arguments given to the product function into a tuple which the numbers variable points to.

The initial argument in the above function must be specified as a keyword argument:

1 2 3 4 5 6 >>> product ( 4 , 4 ) 16 >>> product ( 4 , 4 , initial = 1 ) 16 >>> product ( 4 , 5 , 2 , initial = 3 ) 120

Note that while initial has a default value, you can also specify required keyword-only arguments using this syntax:

1 2 3 4 5 6 7 def join ( * iterables , joiner ): if not iterables : return yield from iterables [ 0 ] for iterable in iterables [ 1 :]: yield joiner yield from iterable

That joiner variable doesn’t have a default value, so it must be specified:

1 2 3 4 5 6 7 8 >>> list ( join ([ 1 , 2 , 3 ], [ 4 , 5 ], [ 6 , 7 ], joiner = 0 )) [1, 2, 3, 0, 4, 5, 0, 6, 7] >>> list ( join ([ 1 , 2 , 3 ], [ 4 , 5 ], [ 6 , 7 ], joiner = '-' )) [1, 2, 3, '-', 4, 5, '-', 6, 7] >>> list ( join ([ 1 , 2 , 3 ], [ 4 , 5 ], [ 6 , 7 ])) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : join() missing 1 required keyword-only argument: 'joiner'

Note that this syntax of putting arguments after the * only works in Python 3. There’s no syntactic way in Python 2 to require an argument to be named.

Keyword-only arguments without positional arguments

What if you want to accept keyword-only arguments without also accepting unlimited positional arguments?

If you want to accept keyword-only arguments and you’re not using a * to accept any number of positional arguments, you can use a * without anything after it.

For example here’s a modified version of Django’s django.shortcuts.render function:

1 2 3 def render ( request , template_name , context = None , * , content_type = None , status = None , using = None ): content = loader . render_to_string ( template_name , context , request , using = using ) return HttpResponse ( content , content_type , status )

Unlike Django’s current implementation of render , this version disallows calling render by specifying every argument positionally. The content_type , status , and using arguments must be specified by their name .

1 2 3 4 5 6 >>> render ( request , '500.html' , { 'error' : error }, status = 500 ) <HttpResponse status_code=500, "text/html; charset=utf-8"> >>> render ( request , '500.html' , { 'error' : error }, 500 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : render() takes from 2 to 3 positional arguments but 4 were given

Just like with unlimited positional arguments, these keyword arguments can be required. Here’s a function with four required keyword-only arguments:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 from random import choice , shuffle UPPERCASE = "ABCDEFGHIJKLMNOPQRSTUVWXYZ" LOWERCASE = UPPERCASE . lower () DIGITS = "0123456789" ALL = UPPERCASE + LOWERCASE + DIGITS def random_password ( * , upper , lower , digits , length ): chars = [ * ( choice ( UPPERCASE ) for _ in range ( upper )), * ( choice ( LOWERCASE ) for _ in range ( lower )), * ( choice ( DIGITS ) for _ in range ( digits )), * ( choice ( ALL ) for _ in range ( length - upper - lower - digits )), ] shuffle ( chars ) return "" . join ( chars )

This function requires all of its arguments to be specified using their name:

1 2 3 4 5 6 7 8 >>> random_password ( upper = 1 , lower = 1 , digits = 1 , length = 8 ) 'oNA7rYWI' >>> random_password ( upper = 1 , lower = 1 , digits = 1 , length = 8 ) 'bjonpuM6' >>> random_password ( 1 , 1 , 1 , 8 ) Traceback (most recent call last): File "<stdin>" , line 1 , in <module> TypeError : random_password() takes 0 positional arguments but 4 were given

Requiring arguments to be named can make calls to our function much clearer.

The purpose of this function call:

1 >>> password = random_password ( upper = 1 , lower = 1 , digits = 1 , length = 8 )

Is much more obvious than this one:

1 >>> password = random_password ( 1 , 1 , 1 , 8 )

Again note that this syntax also only works in Python 3.

Capturing arbitrary keyword arguments

What if you want to write a function that captures an arbitrary number of keyword arguments?

For example the string format method accepts any keyword argument you give it:

1 2 >>> "My name is {name} and I like {color}" . format ( name = "Trey" , color = "purple" ) 'My name is Trey and I like purple'

How can you write such a function?

Python allows functions to capture any keyword arguments provided to them using the ** operator when defining the function:

1 2 3 4 5 6 def format_attributes ( ** attributes ): """Return a string of comma-separated key-value pairs.""" return ", " . join ( f "{param}: {value}" for param , value in attributes . items () )

That ** operator will allow our format_attributes function to accept any number of keyword arguments. The given arguments will be stored in a dictionary called attributes .

Here’s an example use of our function:

1 2 >>> format_attributes ( name = "Trey" , website = "http://treyhunner.com" , color = "purple" ) 'name: Trey, website: http://treyhunner.com, color: purple'

Calling functions with arbitrary arguments

Just as you can define functions that take arbitrary keyword arguments, you can also call functions with arbitrary keyword arguments.

By this I mean that you can pass keyword arguments into a function based on items in a dictionary.

Here we’re manually taking every key/value pair from a dictionary and passing them in as keyword arguments:

1 2 3 >>> items = { 'name' : "Trey" , 'website' : "http://treyhunner.com" , 'color' : "purple" } >>> format_attributes ( name = items [ 'name' ], website = items [ 'website' ], color = items [ 'color' ]) 'name: Trey, website: http://treyhunner.com, color: purple'

This approach of hard-coding the keyword arguments in our function call requires that we know every key in the dictionary we’re using at the time our code is written. This won’t work if we have a dictionary with unknown keys.

We can pass arbitrary keyword arguments to our function using the ** operator to unpack our dictionary items into keyword arguments in our function call:

1 2 3 >>> items = { 'name' : "Trey" , 'website' : "http://treyhunner.com" , 'color' : "purple" } >>> format_attributes ( ** items ) 'name: Trey, website: http://treyhunner.com, color: purple'

This ability to pass arbitrary keyword arguments into functions and to accept arbitrary keyword arguments inside functions (as we did before) is seen frequently when using inheritance:

1 2 3 def my_method ( self , * args , ** kwargs ): print ( 'Do something interesting here' ) super () . my_method ( * args , ** kwargs ) # Call parent method with all given arguments

Note: We’re also using the * operator here for the same kind of capturing and unpacking of positional arguments.

Order matters

Since Python 3.6, functions always preserve the order of the keyword arguments passed to them (see PEP 468). This means that when ** is used to capture keyword arguments, the resulting dictionary will have keys in the same order the arguments were passed.

So since Python 3.6, you’ll never see something like this happen:

1 2 >>> format_attributes ( name = "Trey" , website = "http://treyhunner.com" , color = "purple" ) 'website: http://treyhunner.com, color: purple, name: Trey'

Instead, with Python 3.6+, arguments will always maintain the order they were passed in:

1 2 >>> format_attributes ( name = "Trey" , website = "http://treyhunner.com" , color = "purple" ) 'name: Trey, website: http://treyhunner.com, color: purple'

Embrace keyword arguments in Python

An arguments position often doesn’t convey as much meaning as its name. So when calling functions, consider naming arguments that you pass in if it might make their meaning clearer.

When defining a new function, stop to think about which arguments should always be specified as keyword arguments when calling your function. Consider using the * operator to require those arguments be specified as keyword arguments.

And remember that you can accept arbitrary keyword arguments to the functions you define and pass arbitrary keyword arguments to the functions you call by using the ** operator.

Important objects deserve names and you can use keyword arguments to give your objects the names they deserve!

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