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Fluent assertion library for Python with composable matchers, structural matching, and full type safety

Project description

assertpy2

Fluent assertion library for Python with composable matchers, structural matching, and full type safety.
A modern, batteries-included fork of assertpy.

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Quick start

pip install assertpy2  # drop-in replacement for assertpy, just change the import
from assertpy2 import assert_that

def test_user():
    user = {"name": "Alice", "age": 30, "roles": ["viewer", "editor"]}

    assert_that(user).contains_key("name", "age")
    assert_that(user["age"]).is_between(18, 120)
    assert_that(user["roles"]).contains("viewer").does_not_contain("admin")
    assert_that(user).has_name("Alice")

Why fluent assertions?

A fluent chain reads as one intent and replaces several bare asserts - and your IDE offers only the methods that fit the value's type:

# bare - three statements, no autocomplete help
assert isinstance(items, list)
assert len(items) == 3
assert "admin" in items

# assertpy2 - one chain, type-aware autocomplete
assert_that(items).is_type_of(list).is_length(3).contains("admin")

The real difference shows up when a test fails. Here a nested response has two wrong fields. Plain assert dumps both structures and leaves you to find them:

assert response == expected
E   AssertionError: assert {'id': 1, ...} == {'id': 1, ...}
E     Omitting 1 identical items, use -vv to show
E     Differing items:
E     {'user': {'name': 'Alice', 'role': 'superadmin'}} != {'user': {'name': 'Alice', 'role': 'admin'}}
E     {'status': 'active'} != {'status': 'disabled'}

assertpy2 reports the exact path to every difference:

assert_that(response).is_equal_to(expected)
--- Structured Diff ---
diff (dict):
  status:
    - 'active'
    + 'disabled'
  user.role:
    - 'superadmin'
    + 'admin'

Recursive diffs work for dicts, dataclasses, namedtuples, attrs, and Pydantic models. For responses with dynamic fields (IDs, timestamps), validate a subset with matches_structure() instead of exact equality.

Type-aware autocomplete

assert_that() uses @overload to return type-specific Protocols. Your IDE shows only methods relevant to the value you're testing, not all 100+:

  • assert_that("hello"). → string methods: starts_with, matches, is_alpha, ...
  • assert_that(42). → numeric methods: is_positive, is_between, is_close_to, ...
  • assert_that(Path("/tmp")). → path methods: exists, is_file, is_readable, ...
  • assert_that(my_dict). → dict methods: contains_key, contains_entry, has_json_path, ...
  • assert_that(b"\x89PNG"). → bytes methods: starts_with_bytes, is_valid_utf8, decoded_as, ...

9 type-specific Protocols instead of one Any. Works in PyCharm, VS Code, and any LSP-compatible editor.


Features

Fluent API

  • Composable matchers: match.greater_than(5), match.is_uuid(), combine with &, |, ~. Also work with plain assert ==.
  • Structural matching: matches_structure() for declarative dict/API response validation, reporting the exact path to each mismatch on failure.
  • Universal negation: .not_ inverts any assertion without dedicated is_not_* methods.
  • Collection pipeline: filtered_on(), mapped(), flat_mapped(), first(), last(), element(), single().
  • Fluent chaining: write assertions as readable one-liners that chain naturally.

Built-in types

Testing

  • Soft assertions: thread-safe, async-safe via contextvars. Group errors with sa.group(), or use assert_all().
  • Async assertions: eventually() with polling/retry for eventual consistency.
  • Structured errors: AssertionFailure with .actual, .expected, .diff attributes.
  • Rich pytest diffs: recursive structural diffs for lists, sets, strings, dicts, dataclasses, namedtuples, Pydantic models, and matcher-based assertions (matches_structure(), satisfies(), each()). Circular reference protection.
  • Snapshot testing: store and compare data structures in JSON format.

Type safety

Extensibility

  • Custom matchers: register_matcher() for domain-specific matchers, composable with &, |, ~.
  • Regex group extraction: extracting_group() and matches_with_groups() for regex captures.
  • Extensions: add_extension() for custom assertion methods.

Integrations

  • Allure: auto-attach structured diff and actual/expected data to reports. pip install assertpy2[allure].
  • Behave: ready-made parameter types for step definitions. pip install assertpy2[behave].

See the full documentation for all assertion methods, examples, and advanced features.


Integrations

Allure

When allure-pytest is installed, the pytest plugin auto-attaches structured failure data to Allure reports as JSON attachments.

pip install assertpy2[allure]

Three modes controlled via pytest.ini (or pyproject.toml):

Mode What is attached
diff (default) Structured Diff JSON (path-level breakdown)
full Structured Diff + actual/expected JSON
off Nothing
# pyproject.toml
[tool.pytest.ini_options]
assertpy2_allure = "full"

Behave

Ready-made parameter types for Behave step definitions:

pip install assertpy2[behave]
# in environment.py or steps/conftest.py
from assertpy2.behave_matchers import register_assertpy_types
register_assertpy_types()

Then use in step definitions:

@given('a user aged {age:PositiveInt}')
def step_impl(context, age):
    context.age = age  # already validated as int > 0

Available types: PositiveInt, NonNegativeInt, PositiveFloat, NonEmptyString, BoolLike.


BSD 3-Clause License

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