Strict Python function parameters

Published 2022-01-23 by Seth Larson
Reading time: 6 minutes

What do you think about when writing a new function in Python? The function name, parameter names, optional/required parameters, and default arguments are all on the list. Here is a simple Python function that has all these covered:

def process_data(data, encoding="ascii"):
   # Fancy data processing here!

However, there's one aspect many programmers have an opinion about but don't realize can be encoded into the function definition: How should callers specify each argument to the function? For the above function you'd likely document the following usages:


process_data(b"input", encoding="utf-8")

You don't need to specify data= for readers to infer what the first argument is likely to be. The parameter name is hinted by the function name: process_data(). On the other hand the encoding parameter isn't obvious if you only see the argument value. Given this I would recommend using a keyword argument for encoding.

The above decisions make sense to me having written lots of Python, but what about beginners to Python or the library? Function parameters don't explain "how" to pass arguments. Whether an argument is passed as a positional argument or keyword argument is usually up to the caller. Below are all the ways to specify the same parameters, but many are likely not what the author intended:

# All positional parameters, tougher to
# infer the parameter for 'utf-8'.
process_data(b"input", "utf-8")

# Using `data` as keyword argument, but
# not as clean as "data" term is duplicated.
process_data(data=b"input", encoding="utf-8")

# All keyword parameters but encoding
# and data are flip-flopped.
process_data(encoding="utf-8", data=b"input")

If the code is widespread enough you're almost guaranteed that someone is using your code in a way you didn't intend. Let's see two Python features you can use to avoid this problem:

Keyword-only parameters

PEP 3102 introduced this language feature in 2006 for Python 3.0 and later. Despite being in a ecosystem without Python 2 for two years I'm surprised how little I see this feature.

Defining a parameter as being "keyword-only" looks like this:

def process_data(data, *, encoding="ascii"): ...

Notice the * between data and encoding? The asterisk means that all parameters to the right in the function signature can't be passed as positional arguments. These parameters are now "keyword-only".

Now that the encoding parameter is a keyword-only how does the list of potential usages change?

# The way you want users to use the function:
process_data(b"input", encoding="utf-8")

# Raises a TypeError:
process_data(b"input", "utf-8")

# What way can (and will) use the function:
process_data(data=b"input", encoding="utf-8")
process_data(encoding="utf-8", data=b"input")

It's a small improvement but there's more we can do!

Positional-only parameters

PEP 570 introduced another feature for specifying how to pass arguments. This feature landed in Python 3.8 so you may not be able to use it in projects supporting Python 3.7 until mid-2023.

You can define "positional-only" argument in Python like so:

def process_data(data, /, encoding="ascii"): ...

The / in the function signature means that all parameters to the left of the / are positional-only. Positional-only parameters can't be passed a keyword argument:

# This will raise a TypeError:

Many functions in the standard library don't follow the typical rules for parameters. The example used in PEP 570 is the pow() function. When called with keyword arguments pow() will fail because the underlying C implementation only accepts positional arguments:

# The `help()` output for `pow()` used
# the `/` character even before Python
# 3.8 implemented PEP 570:
>>> help(pow)
pow(x, y, z=None, /)

>>> pow(x=5, y=3)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: pow() takes no keyword arguments

Putting it all together

You can use both positional-only and keyword-only arguments together in the same function signature:

def process_data(data, /, *, encoding="ascii"): ...

And now with data being positional-only and encoding keyword-only let's look at how our function can be used:

# The way you want users to use the function:
process_data(b"input", encoding="utf-8")

# Raises a TypeError:
process_data(b"input", "utf-8")
process_data(data=b"input", encoding="utf-8")
process_data(encoding="utf-8", data=b"input")

Success!! 🎉 Your function now only allows specifying arguments as intended.

Why use strict function signatures?

So why go through this extra bit of trouble? You could read the "motivation" sections of PEP 3102 and PEP-570 for some of the reasons why these features are useful. Below are a few reasons that I think are important from an API design perspective:

Less to consider when your function changes

Here's a real-life example I had to handle with the Elasticsearch Python client. We have an API method called get() which fetches a document from Elasticsearch by its ID. The function signature was going to change in v8.0.0 due to the doc_type parameter being deprecated server-side in v7.0.0 and scheduled for removal in v8.0.0.

# Function signature in v7.16.0
def get(index, id, doc_type=None, params=None, ...): ...

# Function signature in v8.0.0
def get(index, id, params=None, ...): ...

If the doc_type parameter were removed without mitigation, code using get() would change between v7.16 and v8.0.0:

client.get("1", "2", "3")

# In 7.16.0 the above arguments will
# be assigned like so:

# {index=1, id=2, doc_type=3}

# In 8.0.0 (if not mitigated) the above
# arguments would be assigned like so:

# {index=1, id=2, params=3} (not good!)

We started emitting a DeprecationWarning whenever doc_type was used, but warnings are opt-in and can be missed. So in addition to deprecating parameters we decided to deprecate using positional arguments and require using only keyword arguments for all Elasticsearch API methods in v8.0.0. Now parameters can be added and removed without considering the parameters' position in previous versions.

This change also meant the API generator logic could be greatly simplified because the generator no longer needed to account for the order parameters were previously generated with.

There's additional API freedoms when using positional-only arguments too. Recall the process_data() function defined above:

def process_data(data, /, *, encoding="utf-8"): ...

If you now wanted the data parameter to accept either a single bytes instance or a list of bytes instances you might want to rename the parameter to better represent the accepted types. If data is a positional-only parameter then you can rename the parameter without breaking anyone. Without being a positional-only argument you risk breaking users specifying data with a keyword argument:

# You can rename 'data' -> 'data_or_list'
# without breaking anyone's code.
def process_data(data_or_list, /, *, encoding="utf-8"): ...

For more information there's an "Empowering Library Authors" section in PEP 570 that details other cases.

Consistency between documentation and usage

Ideally documentation will pick a single way of using each function and be consistent within itself. Why not require users to use functions as they are documented? If urllib3 was being written today the function signature for request() might look like this with method and url being positional-only and all other parameters being keyword-only:

def request(method, url, /, *, headers=None, ...): ...

This function is found everywhere, even across other HTTP client libraries like Requests and aiohttp, so is likely to be understandable to users who have never used urllib3.

# We're used to seeing this everywhere:
request("GET", "https://example.com", headers={...})

# These aren't as immediately recognizable:
request(method="GET", url="https://example.com")
request("GET", "https://example.com", {...})

By having a strict function signature we can ensure code written by users will look recognizable to future readers.

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