assertpy2 2.3.7

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

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

Quick start

pip install assertpy2

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")

Composable matchers and structural matching:

from assertpy2 import assert_that, match

# matchers with & | ~ operators
assert_that([3, 7, 12]).contains(match.greater_than(10))
assert_that(42).satisfies(match.greater_than(0) & match.less_than(100))

# validate dict structure declaratively
assert_that(api_response).matches_structure({
    "id": match.is_uuid(),
    "name": match.equal_to("Alice"),
    "status": match.is_non_empty_string(),
})

Structured errors with rich data:

try:
    assert_that({"a": 1, "b": 2}).is_equal_to({"a": 1, "b": 99})
except AssertionError as e:
    e.actual    # {"a": 1, "b": 2}
    e.expected  # {"a": 1, "b": 99}
    e.diff      # DiffResult(kind='dict', entries=[DiffEntry(path='b', actual=2, expected=99)])

The pytest plugin auto-renders this as rich diff sections in failure reports:

FAILED test_example.py::test_comparison
--- AssertionFailure ---
  actual:   {'a': 1, 'b': 2}
  expected: {'a': 1, 'b': 99}

Comparison

	pytest assert	PyHamcrest	assertpy	assertpy2
Type safety	Partial (mypy plugin)	No	No	py.typed, @overload, Self
IDE autocomplete	Generic	Generic	Generic	Type-specific per value
Fluent chaining	No	No	Yes	Yes
Composable matchers	No	Yes (functions)	No	Yes (& | ~ operators)
Structural matching	No	Flat (has_entries)	No	Recursive with matchers
Async assertions	No	No	No	eventually() with polling
Soft assertions	No	No	Yes (not thread-safe)	Yes (thread-safe, async-safe)
Structured errors	Rewrite only	Mismatch string	String only	.actual .expected .diff
Maintained	N/A	Minimal	2020	Active

Why fluent assertions?

# bare assert - passes, but failure message is useless
assert user["age"] >= 18
# AssertionError

# assertpy2 - same check, clear failure message
assert_that(user["age"]).is_greater_than_or_equal_to(18)
# AssertionError: Expected <16> to be greater than or equal to <18>, but was not.

# bare assert - three separate statements
assert isinstance(items, list)
assert len(items) == 3
assert "admin" in items

# assertpy2 - one fluent chain
assert_that(items).is_type_of(list).is_length(3).contains("admin")

Features

• 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.
• Universal negation: .not_ inverts any assertion without dedicated is_not_* methods.
• Collection pipeline: filtered_on(), mapped(), flat_mapped(), first(), last(), element(), single() for transforming collections before assertions.
• Async assertions: eventually() with polling/retry for async and eventual consistency testing.
• Structured errors: AssertionFailure with .actual, .expected, .diff attributes, pytest plugin with rich diff output.
• Typed overloads: assert_that() returns type-specific Protocols, IDE shows only relevant methods per type.
• Type safety: Self return types, py.typed (PEP 561).
• Soft assertions: thread-safe and async-safe via contextvars, collect all failures with soft_assertions(). Group errors with sa.group(), or use assert_all() for inline checks.
• JSON assertions: JSONPath navigation (at_json_path, has_json_path) and JSON Schema validation (matches_json_schema).
• Fluent chaining: write assertions as readable one-liners that chain naturally.
• Dynamic assertions: has_<name>() for any attribute, property, or zero-argument method on objects and dicts.
• Dict comparison: is_equal_to() with ignore and include for selective key matching.
• Extracting: flatten collections on attributes with filter and sort support.
• File assertions: exists(), is_file(), is_readable(), is_writable(), is_executable() with pathlib.Path support.
• Snapshot testing: store and compare data structures in JSON format, inspired by Jest.
• Allure integration: auto-attach structured diff and actual/expected data to Allure reports.
• Behave step matchers: ready-made parameter types (PositiveInt, BoolLike, etc.) for Behave step definitions.
• Custom matchers: register domain-specific matchers via register_matcher(), composable with &, |, ~.
• Regex group extraction: extracting_group() and matches_with_groups() to assert on regex captures fluently.
• Extensions: add custom assertions via add_extension().
• Strings, numbers, lists, tuples, sets, dicts, dates, booleans, objects, exceptions.

Composable matchers

Matchers are objects that describe conditions. Combine them with & (and), | (or), ~ (not):

from assertpy2 import assert_that, match

# check a value against a composed condition
assert_that(42).satisfies(match.greater_than(0) & match.less_than(100))

# matchers inside contains - find element by condition
assert_that([3, 7, 12]).contains(match.greater_than(10))

# check every element in a collection
assert_that([18, 25, 30]).each(match.between(18, 120))

# invert with ~
assert_that("hello").satisfies(~match.equal_to("world"))

# combine freely
assert_that(150).satisfies(match.is_negative() | match.greater_than(100))

Matchers also support == directly, so you can use them with plain assert or mix into dicts and lists:

from assertpy2 import match

assert 42 == match.is_positive()
assert {"id": 5, "name": "Alice"} == {"id": match.is_positive(), "name": match.is_non_empty_string()}

Available matchers: equal_to, greater_than, greater_than_or_equal_to, less_than, less_than_or_equal_to, between, close_to, is_none, is_not_none, is_instance_of, has_length, is_empty, is_not_empty, is_positive, is_negative, is_zero, is_even, is_odd, is_divisible_by, is_callable, is_in, has_property, contains_string, matches_regex, is_uuid, is_non_empty_string, ignore, each_item, structure.

Structural matching

Validate dict structure declaratively, even when values are dynamic (UUIDs, timestamps):

from assertpy2 import assert_that, match

assert_that(api_response).matches_structure({
    "id": match.is_uuid(),
    "name": match.equal_to("Alice"),
    "created_at": match.is_non_empty_string(),
    "metadata": match.structure({
        "version": match.greater_than(0),
        "tags": match.each_item(match.is_instance_of(str)),
    }),
    "debug_info": match.ignore(),
})

Async assertions

Poll a callable until the assertion passes or timeout is reached:

from assertpy2 import assert_that

async def test_eventual_consistency():
    await assert_that(get_status).eventually().within(5).every(0.5).is_equal_to("ready")

    # works with async callables
    await assert_that(async_get_count).eventually().within(10).is_greater_than(100)

Any assertion method is available after eventually(). Only AssertionError is retried, other exceptions propagate immediately.

Soft assertions

Collect all failures instead of stopping at the first one:

from assertpy2 import assert_that, soft_assertions

def test_user_profile():
    with soft_assertions():
        assert_that(user.name).is_equal_to("Alice")
        assert_that(user.age).is_greater_than(0)
        assert_that(user.email).contains("@")

All failures are reported at the end of the block:

AssertionError: soft assertion failures:
1. Expected <Bob> to be equal to <Alice>, but was not.
2. Expected <-1> to be greater than <0>, but was not.
3. Expected <invalid> to contain <@>, but did not.

Use soft_fail("message") inside the block for non-halting explicit failures (unlike fail(), which stops immediately).

Soft assertions are thread-safe and async-safe: each thread and each asyncio task gets independent state via contextvars.

Structured errors

When assertions fail, AssertionFailure carries structured data alongside the human-readable message:

try:
    assert_that(1).is_equal_to(2)
except AssertionError as e:
    e.actual    # 1
    e.expected  # 2

For dict comparisons, a DiffResult with per-key diff entries is available:

try:
    assert_that({"a": 1, "b": 2}).is_equal_to({"a": 1, "b": 99})
except AssertionError as e:
    e.diff  # DiffResult(kind='dict', entries=[DiffEntry(path='b', actual=2, expected=99)])

AssertionFailure is a subclass of AssertionError, so all existing except AssertionError handlers work unchanged.

The pytest plugin (auto-registered, no configuration needed) renders structured data as extra sections in failure reports:

FAILED test_example.py::test_comparison
--- AssertionFailure ---
  actual:   {'a': 1, 'b': 2}
  expected: {'a': 1, 'b': 99}
--- Structured Diff ---
DiffResult(kind='dict', entries=[DiffEntry(path='b', actual=2, expected=99)])

More features

Universal negation

Invert any assertion with .not_:

assert_that(5).not_.is_none()
assert_that("abc123").not_.is_alpha()
assert_that([3, 1, 2]).not_.is_sorted()
assert_that(value).described_as("check").not_.is_none().is_positive()

Works with soft assertions and warn mode.

Collection pipeline

Transform collections before asserting:

orders = [Order("DONE", 100), Order("FAILED", 50), Order("DONE", 200)]

assert_that(orders).filtered_on(lambda o: o.status == "FAILED").is_length(1)
assert_that(orders).mapped(lambda o: o.total).contains(100, 200)
assert_that(orders).first().has_status("DONE")
assert_that(orders).element(1).has_status("FAILED")
assert_that([42]).single().is_equal_to(42)

# chaining pipeline steps
assert_that(items).filtered_on(match.is_positive()).mapped(str).contains("1")

Available methods: filtered_on(), mapped(), flat_mapped(), first(), last(), element(), single().

Grouped soft assertions

with soft_assertions() as sa:
    with sa.group("Headers"):
        assert_that(headers["Content-Type"]).is_equal_to("application/json")
    with sa.group("Body"):
        assert_that(body["status"]).is_equal_to("ok")
        assert_that(body["items"]).is_not_empty()

# or inline with assert_all
assert_all(
    lambda: assert_that(x).is_positive(),
    lambda: assert_that(y).is_not_none(),
)

JSON path and schema validation

Requires pip install assertpy2[json].

data = {"users": [{"name": "Alice"}, {"name": "Bob"}], "meta": {"total": 2}}

assert_that(data).at_json_path("$.users[0].name").is_equal_to("Alice")
assert_that(data).has_json_path("$.meta.total")
assert_that(data).does_not_have_json_path("$.error")
assert_that(data).matches_json_schema({"type": "object", "required": ["users"]})

Dict comparison with ignore/include

assert_that({"a": 1, "b": 2, "c": 3}).is_equal_to({"a": 1}, ignore=["b", "c"])
assert_that({"a": 1, "b": {"c": 2, "d": 3}}).is_equal_to({"b": {"d": 3}}, include=("b", "d"))

Extracting with filter and sort

users = [
    {"user": "Fred", "age": 36, "active": True},
    {"user": "Bob", "age": 40, "active": False},
    {"user": "Johnny", "age": 13, "active": True},
]

assert_that(users).extracting("user", filter="active").is_equal_to(["Fred", "Johnny"])
assert_that(users).extracting("user", sort="age").is_equal_to(["Johnny", "Fred", "Bob"])

Expected exceptions

assert_that(some_func).raises(RuntimeError).when_called_with("bad_arg")\
    .is_length(8).starts_with("some").is_equal_to("some err")

Dynamic assertions

fred = {"first_name": "Fred", "last_name": "Smith", "shoe_size": 12}

assert_that(fred).has_first_name("Fred")
assert_that(fred).has_last_name("Smith")
assert_that(fred).has_shoe_size(12)

Snapshot testing

assert_that({"a": 1, "b": 2, "c": 3}).snapshot()

Custom matchers

Register domain-specific matchers on the match namespace with register_matcher():

from assertpy2 import assert_that, match, register_matcher

@register_matcher("is_valid_email")
def is_valid_email():
    return match.matches_regex(r"^[\w.-]+@[\w.-]+\.\w+$")

# parametrised matchers
@register_matcher("has_status")
def has_status(expected: str):
    return match.has_property("status", match.equal_to(expected))

# use everywhere matchers are accepted
assert_that("[e-mail]").satisfies(match.is_valid_email())
assert_that(users).extracting("email").each(match.is_valid_email())
assert_that(data).matches_structure({"email": match.is_valid_email()})

# composition works automatically
assert_that(email).satisfies(match.is_valid_email() & match.contains_string("@company.com"))

Remove with unregister_matcher("is_valid_email").

Regex group extraction

Extract regex groups and continue the fluent chain:

log = "2024-01-15 ERROR status=500 path=/api/users"

# extract a positional group
assert_that(log).extracting_group(r"status=(\d+)", 1).is_equal_to("500")

# extract a named group
assert_that(log).extracting_group(r"(?P<level>\w+) status", "level").is_equal_to("ERROR")

# get all groups as a tuple or dict (named groups)
assert_that("key=value").matches_with_groups(r"(?P<k>\w+)=(?P<v>\w+)") \
    .contains_entry({"k": "key"}).contains_entry({"v": "value"})

Extensions

from assertpy2 import add_extension

def is_5(self):
    if self.val != 5:
        return self.error(f'{self.val} is NOT 5!')
    return self

add_extension(is_5)

assert_that(5).is_5()

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

Allure integration

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 step matchers

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.

Migration from assertpy

assertpy2 is a drop-in replacement for Python 3.10+. Change the import, everything else works:

# before
from assertpy import assert_that, soft_assertions

# after
from assertpy2 import assert_that, soft_assertions

See the comparison table above for feature differences with other libraries.

Contributing

Contributions are welcome. See CONTRIBUTING.md for details.