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Version: 2.15 (deprecated)

Style guide

Some conventions we encourage.

Reminder: running the autoformatters and linters​

Most of Pants' style is enforced via Black, isort, Docformatter, Flake8, and MyPy. You may find it helpful to run these commands before pushing a PR:

$ pants --changed-since=HEAD fmt
$ build-support/githooks/pre-commit
Tip: improving Black's formatting by wrapping in ()

Sometimes, Black will split code over multiple lines awkwardly. For example:

StrOption(
    default="pants",
    help="The name of the script or binary used to invoke pants. "
    "Useful when printing help messages.",
)

Often, you can improve Black's formatting by wrapping the expression in parentheses, then rerunning fmt:

StrOption(
    default="pants",
    help=(
        "The name of the script or binary used to invoke pants. "
        "Useful when printing help messages."
    ),
)

This is not mandatory, only encouraged.

Comments​

Style​

Comments must have a space after the starting #. All comments should be complete sentences and should end with a period.

Good:

# This is a good comment.

Bad:

#Not This

Comment lines should not exceed 100 characters. Black will not auto-format this for you; you must manually format comments.

When to comment​

We strive for self-documenting code. Often, a comment can be better expressed by giving a variable a more descriptive name, adding type information, or writing a helper function.

Further, there is no need to document how typical Python constructs behave, including how type hints work.

Bad:

# Loop 10 times.
for _ in range(10):
  pass

# This stores the user's age in days.
age_in_days = user.age * 365

Instead, comments are helpful to give context that cannot be inferred from reading the code. For example, comments may discuss performance, refer to external documentation / bug links, explain how to use the library, or explain why something was done a particular way.

Good:

def __hash__(self):
    # By overriding __hash__ here, rather than using the default implementation,
    # we get a 10% speedup to `pants list ::` (1000 targets) thanks to more
    # cache hits. This is safe to do because ...
    ...

# See https://github.com/LuminosoInsight/ordered-set for the original implementation.
class OrderedSet:
    ...

TODOs​

When creating a TODO, first create an issue in GitHub. Then, link to the issue # in parantheses and add a brief description.

For example:

# TODO(#5427): Remove this block once we can get rid of the `globs` feature.

Strings​

Use f-strings​

Use f-strings instead of .format() and %.

# Good
f"Hello {name}!"

# Bad
"Hello {}".format(name)
"Hello %s" % name

Conditionals​

Prefer conditional expressions (ternary expressions)​

Similar to most languages' ternary expressions using ?, Python has conditional expressions. Prefer these to explicit if else statements because we generally prefer expressions to statements and they often better express the intent of assigning one of two values based on some predicate.

# Good
x = "hola" if lang == "spanish" else "hello"

# Discouraged, but sometimes appropriate
if lang == "spanish":
   x = "hola"
else:
   x = "hello"

Conditional expressions do not work in more complex situations, such as assigning multiple variables based on the same predicate or wanting to store intermediate values in the branch. In these cases, you can use if else statements.

Prefer early returns in functions​

Often, functions will have branching based on a condition. When you return from a branch, you will exit the function, so you no longer need elif or else in the subsequent branches.

# Good
def safe_divide(dividend: int, divisor: int) -> Optional[int]:
    if divisor == 0:
        return None
    return dividend / divisor

# Discouraged
def safe_divide(dividend: int, divisor: int) -> Optional[int]:
    if divisor == 0:
        return None
    else:
         return dividend / divisor

Why prefer this? It reduces nesting and reduces the cognitive load of readers. See here for more explanation.

Collections​

Use collection literals​

Collection literals are easier to read and have better performance.

We allow the dict constructor because using the constructor will enforce that all the keys are strs. However, usually prefer a literal.

# Good
a_set = {a}
a_tuple = (a, b)
another_tuple = (a,)
a_dict = {"k": v}

# Bad
a_set = set([a])
a_tuple = tuple([a, b])
another_tuple = tuple([a])

# Acceptable
a_dict = dict(k=v)

Prefer merging collections through unpacking​

Python has several ways to merge iterables (e.g. sets, tuples, and lists): using + or |, using mutation like extend(), and using unpacking with the * character. Prefer unpacking because it makes it easier to merge collections with individual elements; it is formatted better by Black; and allows merging different iterable types together, like merging a list and tuple together.

For dictionaries, the only two ways to merge are using mutation like .update() or using ** unpacking (we cannot use PEP 584's | operator yet because we need to support < Python 3.9.). Prefer merging with ** for the same reasons as iterables, in addition to us preferring expressions to mutation.

# Preferred
new_list = [*l1, *l2, "element"]
new_tuple = (*t1, *t2, "element")
new_set = {*s1, *s2, "element"}
new_dict = {**d1, "key": "value"}

# Discouraged
new_list = l1 + l2 + ["element"]
new_tuple = t1 + t2 + ("element",)
new_set = s1 | s2 | {"element"}
new_dict = d1
new_dict["key"] = "value"

Prefer comprehensions​

Comprehensions should generally be preferred to explicit loops and map/filter when creating a new collection. (See https://www.youtube.com/watch?v=ei71YpmfRX4 for a deep dive on comprehensions.)

Why avoid map/filter? Normally, these are fantastic constructs and you'll find them abundantly in the Rust codebase. They are awkward in Python, however, due to poor support for lambdas and because you would typically need to wrap the expression in a call to list() or tuple() to convert it from a generator expression to a concrete collection.

# Good
new_list = [x * 2 for x in xs]
new_dict = {k: v.capitalize() for k, v in d.items()}

# Bad
new_list = []
for x in xs:
    new_list.append(x * 2)

# Discouraged
new_list = list(map(xs, lambda x: x * 2))

There are some exceptions, including, but not limited to:

  • If mutations are involved, use a for loop.
  • If constructing multiple collections by iterating over the same original collection, use a for loop for performance.
  • If the comprehension gets too complex, a for loop may be appropriate. Although, first consider refactoring with a helper function.

Classes​

Prefer dataclasses​

We prefer dataclasses because they are declarative, integrate nicely with MyPy, and generate sensible defaults, such as a sensible repr method.

from dataclasses import dataclass

# Good
@dataclass(frozen=True)
class Example:
    name: str
    age: int = 33

# Bad
class Example:
    def __init__(self, name: str, age: int = 33) -> None:
        self.name = name
        self.age = age

Dataclasses should be marked with frozen=True.

If you want to validate the input, use __post_init__:

@dataclass(frozen=True)
class Example:
    name: str
    age: int = 33

    def __post_init__(self) -> None:
        if self.age < 0:
            raise ValueError(
                f"Invalid age: {self.age}. Must be a positive number."
            )

If you need a custom constructor, such as to transform the parameters, use @frozen_after_init and unsafe_hash=True instead of frozen=True.

from dataclasses import dataclass
from typing import Iterable, Tuple

from pants.util.meta import frozen_after_init

@frozen_after_init
@dataclass(unsafe_hash=True)
class Example:
    values: Tuple[str, ...]

    def __init__(self, values: Iterable[str]) -> None:
        self.values = tuple(values)

Type hints​

Refer to MyPy documentation for an explanation of type hints, including some advanced features you may encounter in our codebase like Protocol and @overload.

Annotate all new code​

All new code should have type hints. Even simple functions like unit tests should have annotations. Why? MyPy will only check the body of functions if they have annotations.

# Good
def test_demo() -> None:
   assert 1 in "abc"  # MyPy will catch this bug.

# Bad
def test_demo():
   assert 1 in "abc"  # MyPy will ignore this.

Precisely, all function definitions should have annotations for their parameters and their return type. MyPy will then tell you which other lines need annotations.

Interacting with legacy code? Consider adding type hints.

Pants did not widely use type hints until the end of 2019. So, a substantial portion of the codebase is still untyped.

If you are working with legacy code, it is often valuable to start by adding type hints. This will both help you to understand that code and to improve the quality of the codebase. Land those type hints as a precursor to your main PR.

Prefer cast() to override annotations​

MyPy will complain when it cannot infer the types of certain lines. You must then either fix the underlying API that MyPy does not understand or explicitly provide an annotation at the call site.

Prefer fixing the underlying API if easy to do, but otherwise, prefer using cast() instead of a variable annotation.

from typing import cast

# Good
x = cast(str, untyped_method()

# Discouraged
x: str = untyped_method()

Why? MyPy will warn if the cast ever becomes redundant, either because MyPy became more powerful or the untyped code became typed.

Use error codes in # type: ignore comments​

# Good
x = "hello"
x = 0  # type: ignore[assignment]

# Bad
y = "hello"
y = 0  # type: ignore

MyPy will output the code at the end of the error message in square brackets.

Prefer Protocols ("duck types") for parameters​

Python type hints use Protocols as a way to express "duck typing". Rather than saying you need a particular class, like a list, you describe which functionality you need and don't care what class is used.

For example, all of these annotations are correct:

from typing import Iterable, List, MutableSequence, Sequence

x: List = []
x: MutableSequence = []
x: Sequence = []
x: Iterable = []

Generally, prefer using a protocol like Iterable, Sequence, or Mapping when annotating function parameters, rather than using concrete types like List and Dict. Why? This often makes call sites much more ergonomic.

# Preferred
def merge_constraints(constraints: Iterable[str]) -> str:
    ...

# Now in call sites, these all work.
merge_constraints([">=3.7", "==3.8"])
merge_constraints({">=3.7", "==3.8"})
merge_constraints((">=3.7", "==3.8"))
merge_constraints(constraint for constraint in all_constraints if constraint.startswith("=="))
# Discouraged, but sometimes appropriate
def merge_constraints(constraints: List[str]) -> str
    ...

# Now in call sites, we would need to wrap in `list()`.
constraints = {">=3.7", "==3.8"}
merge_constraints(list(constraints))
merge_constraints([constraint for constraint in all_constraints if constraint.startswith("==")])

The return type, however, should usually be as precise as possible so that call sites have better type inference.

Tests​

Use Pytest-style instead of unittest​

# Good
def test_demo() -> None:
   assert x is True
   assert y == 2
   assert "hello" in z

# Bad
class TestDemo(unittest.TestCase):
    def test_demo(self) -> None:
        self.assertEqual(y, 2)