diff --git a/.github/workflows/unit-tests.yml b/.github/workflows/unit-tests.yml
index c25eb04..8aba954 100644
--- a/.github/workflows/unit-tests.yml
+++ b/.github/workflows/unit-tests.yml
@@ -10,15 +10,15 @@ jobs:
steps:
- name: Checkout
uses: actions/checkout@v3
- - name: Set up Python 3.11
- uses: actions/setup-python@v4
- with:
- python-version: '3.11'
+ - name: Install uv
+ uses: astral-sh/setup-uv@v5
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install -r requirements-dev.lock
- name: Run tests
- run: python -m coverage run -m pytest
+ run: uv run coverage run -m pytest
- name: Check coverage
- run: python -m coverage report --fail-under=100
+ run: uv run coverage report --fail-under=100
+ - name: Check types
+ run: uv run ty check wimsey
diff --git a/docs/generate_possible_tests.py b/docs/generate_possible_tests.py
index 22cd95f..e2c1318 100644
--- a/docs/generate_possible_tests.py
+++ b/docs/generate_possible_tests.py
@@ -18,6 +18,8 @@
"be": ["column_a", "column_b"],
"not_be": ["column_a", "column_b", "column_c"],
"be_one_of": ["int64", "float64"],
+ "not_be_one_of": ["a", "b"],
+ "match_regex": "at$",
}
@@ -27,7 +29,16 @@ def generate_doc(name: str, doc_string: str, annotations: dict) -> str:
examples["be"] = "int64"
examples["not_be"] = "string"
annotations.pop("return")
- dict_eg = {k: examples[k] for k in annotations}
+ try:
+ dict_eg = {k: examples[k] for k in annotations}
+ except KeyError:
+ msg = (
+ f"Argument for function {name} can't be generated because it is "
+ "not in examples within generate_possible_tests arg_examples, consider"
+ " adding.\n For reference here is the list of attempted examples: "
+ f"{annotations}"
+ )
+ raise KeyError(msg) from KeyError
yaml_eg = yaml.dump({"test": name} | dict_eg)
json_eg = json.dumps({"test": name} | dict_eg, indent=2)
python_eg = f"""
@@ -71,11 +82,15 @@ def generate_doc(name: str, doc_string: str, annotations: dict) -> str:
"with the exception of where `column` is required."
)
for test_name, test_generator in possible_tests.items():
+ test_doc_string = test_generator.__doc__
+ assert isinstance(test_doc_string, str), (
+ f"{test_generator.__name__} has no docstring, this is required for doc generation"
+ )
doc = generate_doc(
test_name,
" ".join(test_generator.__doc__.replace("\n", "").split()),
test_generator.__annotations__,
)
file_doc += doc
- with open("docs/possible-tests.md", "wt") as file:
+ with open("docs/possible-tests.md", "w") as file:
file.write(file_doc)
diff --git a/docs/possible-tests.md b/docs/possible-tests.md
index d72f89e..07671d3 100644
--- a/docs/possible-tests.md
+++ b/docs/possible-tests.md
@@ -2,7 +2,7 @@
This documentation is intended as an exaustive list of possible tests within Wimsey. Note that examples given intentionally use all possible keywords for demonstrative purposes. This isn't required, and you can give as many or as few keywords as you like with the exception of where `column` is required.
## mean_should
-Test that column mean is within designated range
+Test that column metric is within designated range
=== "yaml"
```yaml
@@ -50,7 +50,7 @@ Test that column mean is within designated range
## min_should
-Test that column min is within designated range
+Test that column metric is within designated range
=== "yaml"
```yaml
@@ -98,7 +98,7 @@ Test that column min is within designated range
## max_should
-Test that column max is within designated range
+Test that column metric is within designated range
=== "yaml"
```yaml
@@ -146,7 +146,7 @@ Test that column max is within designated range
## std_should
-Test that column std is within designated range
+Test that column metric is within designated range
=== "yaml"
```yaml
@@ -192,9 +192,9 @@ Test that column std is within designated range
-## null_count_should
+## count_should
-Test that column null_count is within designated range
+Test that column metric is within designated range
=== "yaml"
```yaml
@@ -204,13 +204,13 @@ Test that column null_count is within designated range
be_less_than: 500
be_less_than_or_equal_to: 300
column: column_a
- test: null_count_should
+ test: count_should
```
=== "json"
```json
{
- "test": "null_count_should",
+ "test": "count_should",
"column": "column_a",
"be_exactly": 300,
"be_less_than": 500,
@@ -223,7 +223,7 @@ Test that column null_count is within designated range
```python
from wimsey import test
- from wimsey.tests import null_count_should
+ from wimsey.tests import count_should
keywords = {
"column": "column_a",
@@ -234,15 +234,60 @@ Test that column null_count is within designated range
"be_greater_than_or_equal_to": 500
}
- result = test(df, contract=[null_count_should(**keywords)])
+ result = test(df, contract=[count_should(**keywords)])
```
-## count_should
+## row_count_should
+
+Test that dataframe row count is within designated range
+
+=== "yaml"
+ ```yaml
+ be_exactly: 300
+ be_greater_than: 500
+ be_greater_than_or_equal_to: 500
+ be_less_than: 500
+ be_less_than_or_equal_to: 300
+ test: row_count_should
+
+ ```
+=== "json"
+ ```json
+ {
+ "test": "row_count_should",
+ "be_less_than": 500,
+ "be_less_than_or_equal_to": 300,
+ "be_greater_than": 500,
+ "be_greater_than_or_equal_to": 500,
+ "be_exactly": 300
+ }
+ ```
+=== "python"
+ ```python
+
+ from wimsey import test
+ from wimsey.tests import row_count_should
+
+ keywords = {
+ "be_less_than": 500,
+ "be_less_than_or_equal_to": 300,
+ "be_greater_than": 500,
+ "be_greater_than_or_equal_to": 500,
+ "be_exactly": 300
+ }
+
+ result = test(df, contract=[row_count_should(**keywords)])
+
+ ```
+
+
+
+## average_difference_from_other_column_should
-Test that column count is within designated range
+Test that the average difference between column and other column are within designated bounds.
=== "yaml"
```yaml
@@ -252,14 +297,16 @@ Test that column count is within designated range
be_less_than: 500
be_less_than_or_equal_to: 300
column: column_a
- test: count_should
+ other_column: column_b
+ test: average_difference_from_other_column_should
```
=== "json"
```json
{
- "test": "count_should",
+ "test": "average_difference_from_other_column_should",
"column": "column_a",
+ "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -271,10 +318,11 @@ Test that column count is within designated range
```python
from wimsey import test
- from wimsey.tests import count_should
+ from wimsey.tests import average_difference_from_other_column_should
keywords = {
"column": "column_a",
+ "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -282,15 +330,15 @@ Test that column count is within designated range
"be_greater_than_or_equal_to": 500
}
- result = test(df, contract=[count_should(**keywords)])
+ result = test(df, contract=[average_difference_from_other_column_should(**keywords)])
```
-## null_percentage_should
+## average_ratio_to_other_column_should
-Test that column null_percentage is within designated range
+Test that the average ratio between column and other column are within designated bounds (for instance, a value of 1 has a ratio of 0.1 to a value of 10)
=== "yaml"
```yaml
@@ -300,14 +348,16 @@ Test that column null_percentage is within designated range
be_less_than: 500
be_less_than_or_equal_to: 300
column: column_a
- test: null_percentage_should
+ other_column: column_b
+ test: average_ratio_to_other_column_should
```
=== "json"
```json
{
- "test": "null_percentage_should",
+ "test": "average_ratio_to_other_column_should",
"column": "column_a",
+ "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -319,10 +369,11 @@ Test that column null_percentage is within designated range
```python
from wimsey import test
- from wimsey.tests import null_percentage_should
+ from wimsey.tests import average_ratio_to_other_column_should
keywords = {
"column": "column_a",
+ "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -330,64 +381,113 @@ Test that column null_percentage is within designated range
"be_greater_than_or_equal_to": 500
}
- result = test(df, contract=[null_percentage_should(**keywords)])
+ result = test(df, contract=[average_ratio_to_other_column_should(**keywords)])
```
-## columns_should
+## max_string_length_should
-Test column names match up with expected values
+Test that the maximum string length iswithin expected bounds
=== "yaml"
```yaml
- be:
- - column_a
- - column_b
- have:
- - column_a
- not_have:
- - column_c
- test: columns_should
+ be_exactly: 300
+ be_greater_than: 500
+ be_greater_than_or_equal_to: 500
+ be_less_than: 500
+ be_less_than_or_equal_to: 300
+ column: column_a
+ test: max_string_length_should
```
=== "json"
```json
{
- "test": "columns_should",
- "have": [
- "column_a"
+ "test": "max_string_length_should",
+ "column": "column_a",
+ "be_exactly": 300,
+ "be_less_than": 500,
+ "be_less_than_or_equal_to": 300,
+ "be_greater_than": 500,
+ "be_greater_than_or_equal_to": 500
+ }
+ ```
+=== "python"
+ ```python
+
+ from wimsey import test
+ from wimsey.tests import max_string_length_should
+
+ keywords = {
+ "column": "column_a",
+ "be_exactly": 300,
+ "be_less_than": 500,
+ "be_less_than_or_equal_to": 300,
+ "be_greater_than": 500,
+ "be_greater_than_or_equal_to": 500
+ }
+
+ result = test(df, contract=[max_string_length_should(**keywords)])
+
+ ```
+
+
+
+## all_values_should
+
+Test all unique values within a column are within expected group
+
+=== "yaml"
+ ```yaml
+ be_one_of:
+ - int64
+ - float64
+ column: column_a
+ match_regex: at$
+ not_be_one_of:
+ - a
+ - b
+ test: all_values_should
+
+ ```
+=== "json"
+ ```json
+ {
+ "test": "all_values_should",
+ "column": "column_a",
+ "be_one_of": [
+ "int64",
+ "float64"
],
- "not_have": [
- "column_c"
+ "not_be_one_of": [
+ "a",
+ "b"
],
- "be": [
- "column_a",
- "column_b"
- ]
+ "match_regex": "at$"
}
```
=== "python"
```python
from wimsey import test
- from wimsey.tests import columns_should
+ from wimsey.tests import all_values_should
keywords = {
- "have": [
- "column_a"
+ "column": "column_a",
+ "be_one_of": [
+ "int64",
+ "float64"
],
- "not_have": [
- "column_c"
+ "not_be_one_of": [
+ "a",
+ "b"
],
- "be": [
- "column_a",
- "column_b"
- ]
+ "match_regex": "at$"
}
- result = test(df, contract=[columns_should(**keywords)])
+ result = test(df, contract=[all_values_should(**keywords)])
```
@@ -395,7 +495,7 @@ Test column names match up with expected values
## type_should
-Test column type matches up with expected value. Note that this will expect *polars* style types, although does not require that they be case sensitive. For example, if testing a pandas dataframe for integer type, specify "int64" rather than, say, "int64[pyarrow]" or otherwise.
+Check that type of column meets expected criteria. Note that because Wimsey is a dataframe agnostic tool, this should be of *Narwhals* expected types, such as Float64, Int64, String, etc. See Narwhals' documentation for more details: https://narwhals-dev.github.io/narwhals/api-reference/dtypes/
=== "yaml"
```yaml
@@ -443,54 +543,58 @@ Test column type matches up with expected value. Note that this will expect *pol
-## row_count_should
+## columns_should
-Test that dataframe row count is within designated range
+Check that expected columns are present / non-present within dataframe
=== "yaml"
```yaml
- be_exactly: 300
- be_greater_than: 500
- be_greater_than_or_equal_to: 500
- be_less_than: 500
- be_less_than_or_equal_to: 300
- test: row_count_should
+ be: int64
+ have:
+ - column_a
+ not_have:
+ - column_c
+ test: columns_should
```
=== "json"
```json
{
- "test": "row_count_should",
- "be_less_than": 500,
- "be_less_than_or_equal_to": 300,
- "be_greater_than": 500,
- "be_greater_than_or_equal_to": 500,
- "be_exactly": 300
+ "test": "columns_should",
+ "have": [
+ "column_a"
+ ],
+ "not_have": [
+ "column_c"
+ ],
+ "be": "int64"
}
```
=== "python"
```python
from wimsey import test
- from wimsey.tests import row_count_should
+ from wimsey.tests import columns_should
keywords = {
- "be_less_than": 500,
- "be_less_than_or_equal_to": 300,
- "be_greater_than": 500,
- "be_greater_than_or_equal_to": 500,
- "be_exactly": 300
+ "have": [
+ "column_a"
+ ],
+ "not_have": [
+ "column_c"
+ ],
+ "be": "int64"
}
- result = test(df, contract=[row_count_should(**keywords)])
+ result = test(df, contract=[columns_should(**keywords)])
```
-## average_difference_from_other_column_should
+## null_count_should
-Test that the average difference between column and other column are within designated bounds.
+Check that null count of column meets expected criteria.
=== "yaml"
```yaml
@@ -500,16 +604,14 @@ Test that the average difference between column and other column are within desi
be_less_than: 500
be_less_than_or_equal_to: 300
column: column_a
- other_column: column_b
- test: average_difference_from_other_column_should
+ test: null_count_should
```
=== "json"
```json
{
- "test": "average_difference_from_other_column_should",
+ "test": "null_count_should",
"column": "column_a",
- "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -521,11 +623,10 @@ Test that the average difference between column and other column are within desi
```python
from wimsey import test
- from wimsey.tests import average_difference_from_other_column_should
+ from wimsey.tests import null_count_should
keywords = {
"column": "column_a",
- "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -533,15 +634,15 @@ Test that the average difference between column and other column are within desi
"be_greater_than_or_equal_to": 500
}
- result = test(df, contract=[average_difference_from_other_column_should(**keywords)])
+ result = test(df, contract=[null_count_should(**keywords)])
```
-## average_ratio_to_other_column_should
+## null_percentage_should
-Test that the average ratio between column and other column are within designated bounds (for instance, a value of 1 has a ratio of 0.1 to a value of 10)
+Check that null percentage of column meets expected criteria.
=== "yaml"
```yaml
@@ -551,16 +652,14 @@ Test that the average ratio between column and other column are within designate
be_less_than: 500
be_less_than_or_equal_to: 300
column: column_a
- other_column: column_b
- test: average_ratio_to_other_column_should
+ test: null_percentage_should
```
=== "json"
```json
{
- "test": "average_ratio_to_other_column_should",
+ "test": "null_percentage_should",
"column": "column_a",
- "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -572,11 +671,10 @@ Test that the average ratio between column and other column are within designate
```python
from wimsey import test
- from wimsey.tests import average_ratio_to_other_column_should
+ from wimsey.tests import null_percentage_should
keywords = {
"column": "column_a",
- "other_column": "column_b",
"be_exactly": 300,
"be_less_than": 500,
"be_less_than_or_equal_to": 300,
@@ -584,7 +682,7 @@ Test that the average ratio between column and other column are within designate
"be_greater_than_or_equal_to": 500
}
- result = test(df, contract=[average_ratio_to_other_column_should(**keywords)])
+ result = test(df, contract=[null_percentage_should(**keywords)])
```
diff --git a/pyproject.toml b/pyproject.toml
index 4dfdc3a..0f37896 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
[project]
name = "wimsey"
dynamic = ["version"]
-description = "A lightweight data contracts framework"
+description = "A lightweight data contracts library"
authors = [
{ name = "Ben", email = "benrutter@posteo.net" }
]
@@ -16,15 +16,19 @@ requires-python = ">= 3.10"
requires = ["hatchling"]
build-backend = "hatchling.build"
-[tool.rye]
-managed = true
-dev-dependencies = [
+[tool.hatch.metadata]
+allow-direct-references = true
+
+[tool.hatch.version]
+path = "wimsey/_version.py"
+
+[dependency-groups]
+dev = [
"ipdb>=0.13.13",
"ipython>=8.21.0",
"pandas>=2.2.3",
"pyyaml>=6.0.2",
"polars>=1.10.0",
- "mypy>=1.13.0",
"types-pyyaml>=6.0.12.20240917",
"dask>=2024.10.0",
"dask-expr>=1.1.16",
@@ -34,11 +38,75 @@ dev-dependencies = [
"grip>=4.6.2",
"mkdocs-charts-plugin>=0.0.12",
"coverage>=7.6.10",
- "pylint>=3.3.3",
+ "ty>=0.0.1a11",
+ "ruff>=0.12.0",
]
-[tool.hatch.metadata]
-allow-direct-references = true
-[tool.hatch.version]
-path = "wimsey/_version.py"
+[tool.ruff]
+line-length = 100
+fix = true
+include = ["wimsey/**.py"]
+target-version = "py39"
+
+[tool.ruff.lint]
+preview = true
+explicit-preview-rules = true
+typing-modules = ["narwhals._typing_compat"]
+
+extend-safe-fixes = [
+ "C419", # unnecessary-comprehension-in-call
+ "C420", # unnecessary-dict-comprehension-for-iterable
+ "PLR6201", # literal-membership
+ "TC", # flake8-type-checking
+ "UP", # pyupgrade
+]
+extend-select = [
+ "PLR0914", # too-many-locals
+ "PLR0916", # too-many-boolean-expressions
+ "PLR1702", # too-many-nested-blocks
+ "PLR6201", # literal-membership
+]
+
+select = [
+ "W",
+ "E",
+ "A",
+ "F",
+ "D",
+ "C4",
+ "COM",
+ "ERA",
+ "ICN",
+ "PIE",
+ "RET",
+ "SIM",
+ "ARG",
+ "I",
+ "NPY",
+ "N",
+ "UP",
+ "FURB",
+ "FIX001",
+ "FIX002",
+ "TC001",
+ "TC002",
+ "TC003",
+ "TC004",
+ "ANN001",
+ "ANN205",
+ "ANN204",
+]
+
+ignore = [
+ "E501", # max-line-length-80
+ "D203", # blank-line-before-class-docstring
+ "D213", # multi-line-summary-second-line
+]
+
+[tool.coverage.report]
+exclude_also = [
+ 'if TYPE_CHECKING:',
+ '^\s*\.\.\.\s*$', # <- line of "..." (no code)
+ '^\s*pass\s*$', # <- line of "pass"
+]
diff --git a/requirements-dev.lock b/requirements-dev.lock
index d1ce20c..f3537c5 100644
--- a/requirements-dev.lock
+++ b/requirements-dev.lock
@@ -183,6 +183,7 @@ toolz==1.0.0
traitlets==5.14.3
# via ipython
# via matplotlib-inline
+ty==0.0.1a11
types-pyyaml==6.0.12.20241230
typing-extensions==4.12.2
# via astroid
diff --git a/tests/test_config.py b/tests/test_config.py
index 58eafb6..9b5fe89 100644
--- a/tests/test_config.py
+++ b/tests/test_config.py
@@ -1,10 +1,13 @@
import json
from collections.abc import Callable
+from typing import NoReturn
+import narwhals.stable.v1 as nw
import pytest
import yaml
from wimsey import config
+from wimsey.types import MagicExpr
@pytest.fixture
@@ -15,34 +18,38 @@ def test_suite():
]
-def throw_import_error(*args, **kwargs):
+def throw_import_error(*args, **kwargs) -> NoReturn:
raise ImportError
-def test_collect_tests_returns_list_of_callable_functions(test_suite):
+def test_collect_tests_returns_list_of_tuples_with_expressions_and_callables(
+ test_suite,
+) -> None:
actual = config.collect_tests(test_suite)
- assert all(isinstance(i, Callable) for i in actual)
+ for expr, callable in actual:
+ assert isinstance(expr, (nw.Expr, MagicExpr))
+ assert isinstance(callable, Callable)
-def test_collect_tests_returns_friendly_error_when_required_value_not_give(test_suite):
+def test_collect_tests_returns_friendly_error_when_required_value_not_give(test_suite) -> None:
test_suite[0].pop("column")
with pytest.raises(TypeError, match="column"):
config.collect_tests(test_suite)
-def test_collect_tests_returns_friendly_error_when_no_test_is_given(test_suite):
+def test_collect_tests_returns_friendly_error_when_no_test_is_given(test_suite) -> None:
test_suite[1].pop("test")
with pytest.raises(ValueError, match="test"):
config.collect_tests(test_suite)
-def test_collect_tests_returns_input_when_input_is_already_test_functions(test_suite):
+def test_collect_tests_returns_input_when_input_is_already_test_functions(test_suite) -> None:
initial = config.collect_tests(test_suite)
actual = config.collect_tests(initial)
assert actual == initial
-def test_read_config_parses_yaml(monkeypatch, test_suite):
+def test_read_config_parses_yaml(monkeypatch, test_suite) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
@@ -57,10 +64,12 @@ def open_file_patch(*args, **kwargs):
monkeypatch.setattr(config.fsspec, "open", open_file_patch)
actual = config.read_config("file.yaml")
- assert all(isinstance(i, Callable) for i in actual)
+ for expr, callable in actual:
+ assert isinstance(expr, (nw.Expr, MagicExpr))
+ assert isinstance(callable, Callable)
-def test_read_config_parses_yaml_with_test_section(monkeypatch, test_suite):
+def test_read_config_parses_yaml_with_test_section(monkeypatch, test_suite) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
@@ -75,10 +84,12 @@ def open_file_patch(*args, **kwargs):
monkeypatch.setattr(config.fsspec, "open", open_file_patch)
actual = config.read_config("file.yaml")
- assert all(isinstance(i, Callable) for i in actual)
+ for expr, callable in actual:
+ assert isinstance(expr, (nw.Expr, MagicExpr))
+ assert isinstance(callable, Callable)
-def test_read_config_parses_yaml_with_only_one_test(monkeypatch, test_suite):
+def test_read_config_parses_yaml_with_only_one_test(monkeypatch, test_suite) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
@@ -93,10 +104,12 @@ def open_file_patch(*args, **kwargs):
monkeypatch.setattr(config.fsspec, "open", open_file_patch)
actual = config.read_config("file.yaml")
- assert all(isinstance(i, Callable) for i in actual)
+ for expr, callable in actual:
+ assert isinstance(expr, (nw.Expr, MagicExpr))
+ assert isinstance(callable, Callable)
-def test_read_config_parses_json(monkeypatch, test_suite):
+def test_read_config_parses_json(monkeypatch, test_suite) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
@@ -111,10 +124,12 @@ def open_file_patch(*args, **kwargs):
monkeypatch.setattr(config.fsspec, "open", open_file_patch)
actual = config.read_config("file.json")
- assert all(isinstance(i, Callable) for i in actual)
+ for expr, callable in actual:
+ assert isinstance(expr, (nw.Expr, MagicExpr))
+ assert isinstance(callable, Callable)
-def test_friendly_message_is_raised_when_yaml_is_unimportable(test_suite, monkeypatch):
+def test_friendly_message_is_raised_when_yaml_is_unimportable(test_suite, monkeypatch) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
@@ -134,15 +149,15 @@ def open_file_patch(*args, **kwargs):
def test_friendly_message_is_raised_when_yaml_does_not_return_contents(
- test_suite, monkeypatch
-):
+ test_suite, monkeypatch,
+) -> None:
class DummyOpenFile:
def __enter__(self, *args, **kwargs):
return self
def __exit__(self, *args, **kwargs): ...
- def read(self, *args, **kwargs):
+ def read(self, *args, **kwargs) -> str:
return "dsafasdfasdf"
def open_file_patch(*args, **kwargs):
diff --git a/tests/test_dataframe.py b/tests/test_dataframe.py
index 25af3db..1f57839 100644
--- a/tests/test_dataframe.py
+++ b/tests/test_dataframe.py
@@ -1,4 +1,5 @@
import polars as pl
+import pytest
from wimsey import dataframe
@@ -23,37 +24,11 @@ def test_that_describe_returns_expected_dictionary_for_lazy_frame() -> None:
assert actual["columns"] == "a_^&^_b"
-def test_that_describe_excludes_non_specified_columns() -> None:
- df = pl.DataFrame({"a": [1.2, 1.3, 1.4], "b": ["one", "two", None]})
- actual = dataframe.describe(df, columns=["a"])
- assert "mean_a" in actual
- assert "mean_b" not in actual
-
-
-def test_that_describe_excludes_non_specified_metrics() -> None:
- df = pl.DataFrame({"a": [1.2, 1.3, 1.4], "b": ["one", "two", None]})
- actual = dataframe.describe(df, metrics=["mean", "max"])
- assert "mean_a" in actual
- assert "mean_b" in actual
- assert "max_a" in actual
- assert "max_b" in actual
- assert "std_a" not in actual
- assert "std_b" not in actual
-
-
-def test_that_describe_excludes_non_specified_column_and_metric_combos() -> None:
- df = pl.DataFrame({"a": [1.2, 1.3, 1.4], "b": ["one", "two", None]})
- actual = dataframe.describe(df, columns=["a"], metrics=["count"])
- assert "count_a" in actual
- assert "count_b" not in actual
- assert "min_a" not in actual
-
-
def test_that_profile_by_sampling_returns_list_of_dicts_of_expected_length() -> None:
df = pl.DataFrame({"a": [1.2, 1.3, 1.4], "b": ["one", "two", None]})
actual = dataframe.profile_from_sampling(df, samples=10, n=1)
assert len(actual) == 10
- assert actual[0]["mean_a"] in [1.2, 1.3, 1.4]
+ assert actual[0]["mean_a"] in {1.2, 1.3, 1.4}
assert actual[4]["columns"] == "a_^&^_b"
@@ -68,7 +43,6 @@ def test_that_profile_from_samples_returns_list_of_dicts_of_expected_length() ->
assert actual[4]["columns"] == "a_^&^_b"
-def test_that_describe_returns_empty_dict_for_empty_dataframe() -> None:
- actual = dataframe.describe(pl.DataFrame())
- assert isinstance(actual, dict)
- assert len(actual) == 0
+def test_that_describe_raises_error_for_empty_dataframe_with_no_columns() -> None:
+ with pytest.raises(TypeError):
+ dataframe.describe(pl.DataFrame())
diff --git a/tests/test_executions.py b/tests/test_executions.py
index 09f2fa7..3b2505a 100644
--- a/tests/test_executions.py
+++ b/tests/test_executions.py
@@ -1,11 +1,10 @@
import polars as pl
import pytest
-from wimsey import execution
-from wimsey import tests
+from wimsey import execution, tests
-def test_run_all_tests_produces_expected_result_object():
+def test_run_all_tests_produces_expected_result_object() -> None:
tests_to_carry_out = [
tests.max_should(column="a", be_less_than=10),
tests.std_should(column="a", be_greater_than=0),
@@ -18,7 +17,7 @@ def test_run_all_tests_produces_expected_result_object():
assert result.success is True
-def test_validate_carries_out_tests_then_returns_object_if_passing():
+def test_validate_carries_out_tests_then_returns_object_if_passing() -> None:
tests_to_carry_out = [
tests.max_should(column="a", be_less_than=10),
tests.std_should(column="a", be_greater_than=0),
@@ -29,18 +28,18 @@ def test_validate_carries_out_tests_then_returns_object_if_passing():
assert isinstance(actual, pl.DataFrame)
-def test_validate_raises_error_if_tests_fail():
+def test_validate_raises_error_if_tests_fail() -> None:
tests_to_carry_out = [
- tests.max_should(column="a", be_less_than=0),
+ tests.max_should(column="a", be_exactly=0),
tests.std_should(column="a", be_greater_than=10),
tests.type_should(column="b", be_one_of=["string", "int64"]),
]
df = pl.DataFrame({"a": [1, 2, 3], "b": ["hat", "bat", "cat"]})
- with pytest.raises(execution.DataValidationException):
+ with pytest.raises(execution.DataValidationError):
execution.validate(df, tests_to_carry_out)
-def test_row_count_expectations_pass_when_expected():
+def test_row_count_expectations_pass_when_expected() -> None:
tests_to_carry_out = [
tests.row_count_should(
be_less_than=3.1,
@@ -48,7 +47,7 @@ def test_row_count_expectations_pass_when_expected():
be_greater_than=-2.343,
be_greater_than_or_equal_to=0.3,
be_exactly=3,
- )
+ ),
]
df = pl.DataFrame({"a": [1, 2, 3], "b": ["hat", "bat", "cat"]})
actual = execution.run_all_tests(df, tests_to_carry_out)
@@ -57,20 +56,20 @@ def test_row_count_expectations_pass_when_expected():
assert result.success is True
-def test_columns_should_have_expectations_fail_when_expected():
+def test_columns_should_have_expectations_fail_when_expected() -> None:
tests_to_carry_out = [
tests.columns_should(
have="c",
not_have="a",
be=["b", "c"],
- )
+ ),
]
df = pl.DataFrame({"a": [1, 2, 3], "b": ["hat", "bat", "cat"]})
actual = execution.run_all_tests(df, tests_to_carry_out)
assert actual.success is False
-def test_column_type_tests_pass_when_expected():
+def test_column_type_tests_pass_when_expected() -> None:
tests_to_carry_out = [
tests.type_should(column="a", be="int64"),
tests.type_should(column="a", be_one_of=["int64", "float64"]),
@@ -84,7 +83,7 @@ def test_column_type_tests_pass_when_expected():
assert result.success is True
-def test_average_column_difference_tests_pass_when_expected():
+def test_average_column_difference_tests_pass_when_expected() -> None:
tests_to_carry_out = [
tests.average_difference_from_other_column_should(
column="a",
@@ -103,7 +102,7 @@ def test_average_column_difference_tests_pass_when_expected():
assert result.success is True
-def test_average_column_ratio_tests_pass_when_expected():
+def test_average_column_ratio_tests_pass_when_expected() -> None:
tests_to_carry_out = [
tests.average_ratio_to_other_column_should(
column="b",
@@ -122,10 +121,15 @@ def test_average_column_ratio_tests_pass_when_expected():
assert result.success is True
-def test_max_string_length_tests_fail_when_expected():
+def test_max_string_length_tests_fail_when_expected() -> None:
tests_to_carry_out = [
tests.max_string_length_should(
- column="a", be_less_than=4, be_greater_than=2, be_exactly=3.0
+ column="a",
+ be_less_than=4,
+ be_less_than_or_equal_to=4,
+ be_greater_than=2,
+ be_greater_than_or_equal_to=2,
+ be_exactly=3.0,
),
]
df = pl.DataFrame({"a": ["cat", "bat", "ssssssssssssssnake"]})
@@ -133,14 +137,14 @@ def test_max_string_length_tests_fail_when_expected():
assert actual.success is False
-def test_all_values_tests_succeed_when_expected():
+def test_all_values_tests_succeed_when_expected() -> None:
tests_to_carry_out = [
tests.all_values_should(
column="a",
be_one_of=["cat", "bat", "mat"],
not_be_one_of=["hat"],
match_regex="at$",
- )
+ ),
]
df = pl.DataFrame({"a": ["cat", "bat"]})
actual = execution.run_all_tests(df, tests_to_carry_out)
diff --git a/tests/test_profile.py b/tests/test_profile.py
index c5e8a04..17d0255 100644
--- a/tests/test_profile.py
+++ b/tests/test_profile.py
@@ -1,14 +1,13 @@
-from typing import Callable, Type
+from typing import Callable
import polars as pl
import pytest
-from wimsey import profile
-from wimsey import execution
+from wimsey import execution, profiling
-def raise_exception_patch(exception_type: Type[Exception]) -> Callable:
- """Creates a patch that will throw an error"""
+def raise_exception_patch(exception_type: type[Exception]) -> Callable:
+ """Create a patch that will throw an error."""
def raise_exception(*args, **kwargs) -> None:
raise exception_type
@@ -16,15 +15,18 @@ def raise_exception(*args, **kwargs) -> None:
return raise_exception
+def do_nothing(*args, **kwargs) -> None: ...
+
+
def test_starter_tests_from_sampling_returns_passing_test() -> None:
df = pl.DataFrame(
{
"a": [1, 2, 3, 4, 5],
"b": ["hat", "bat", "cat", "mat", "sat"],
"c": [0.2, 0.4, 0.2, 0.56, 0.1],
- }
+ },
)
- starter_test = profile.starter_tests_from_sampling(df, samples=100, n=5)
+ starter_test = profiling.starter_tests_from_sampling(df, samples=100, n=5)
result = execution.test(df, starter_test)
assert result.success
@@ -35,10 +37,10 @@ def test_starter_tests_from_samples_returns_passing_test() -> None:
"a": [1, 2, 3, 4, 5],
"b": ["hat", "bat", "cat", "mat", "sat"],
"c": [0.2, 0.4, 0.2, 0.56, 0.1],
- }
+ },
)
- starter_test = profile.starter_tests_from_samples(
- [df.sample(fraction=0.5) for _ in range(100)]
+ starter_test = profiling.starter_tests_from_samples(
+ [df.sample(fraction=0.5) for _ in range(100)],
)
result = execution.test(df, starter_test)
assert result.success
@@ -50,12 +52,12 @@ def test_margin_works_as_anticipated() -> None:
"a": [1, 2, 3, 4, 5],
"b": ["hat", "bat", "cat", "mat", "sat"],
"c": [0.2, 0.4, 0.2, 0.56, 0.1],
- }
+ },
)
- starter_test = profile.starter_tests_from_sampling(df, n=5, margin=50)
+ starter_test = profiling.starter_tests_from_sampling(df, n=5, margin=50)
result = execution.test(df, starter_test)
assert result.success
- impossible_test = profile.starter_tests_from_sampling(df, n=5, margin=-500)
+ impossible_test = profiling.starter_tests_from_sampling(df, n=5, margin=-500)
result = execution.test(df, impossible_test)
assert not result.success
@@ -66,10 +68,13 @@ def test_save_tests_from_sampling_creates_expected_and_runnable_file(tmp_path) -
"a": [1, 2, 3, 4, 5],
"b": ["hat", "bat", "cat", "mat", "sat"],
"c": [0.2, 0.4, 0.2, 0.56, 0.1],
- }
+ },
)
- profile.save_starter_tests_from_sampling(
- str(tmp_path / "cool.yaml"), df, n=5, margin=1
+ profiling.save_starter_tests_from_sampling(
+ str(tmp_path / "cool.yaml"),
+ df,
+ n=5,
+ margin=1,
)
result = execution.test(df, str(tmp_path / "cool.yaml"))
assert result.success
@@ -81,9 +86,9 @@ def test_save_tests_from_samples_creates_expected_and_runnable_file(tmp_path) ->
"a": [1, 2, 3, 4, 5],
"b": ["hat", "bat", "cat", "mat", "sat"],
"c": [0.2, 0.4, 0.2, 0.56, 0.1],
- }
+ },
)
- profile.save_starter_tests_from_samples(
+ profiling.save_starter_tests_from_samples(
str(tmp_path / "cool.json"),
[df.sample(fraction=0.5) for _ in range(10)],
)
@@ -94,11 +99,11 @@ def test_save_tests_from_samples_creates_expected_and_runnable_file(tmp_path) ->
def test_validate_or_build_falls_back_to_save_starter_tests_from_sampling_if_validate_crashes(
monkeypatch,
) -> None:
- monkeypatch.setattr(profile, "validate", raise_exception_patch(FileNotFoundError))
+ monkeypatch.setattr(profiling, "validate", raise_exception_patch(FileNotFoundError))
monkeypatch.setattr(
- profile,
+ profiling,
"save_starter_tests_from_sampling",
- raise_exception_patch(ZeroDivisionError),
+ do_nothing,
)
- with pytest.raises(ZeroDivisionError):
- profile.validate_or_build(None, None)
+ actual = profiling.validate_or_build(3, "cool.json")
+ assert actual == 3
diff --git a/tests/test_tests.py b/tests/test_tests.py
index cfebce1..5ebfb79 100644
--- a/tests/test_tests.py
+++ b/tests/test_tests.py
@@ -1,10 +1,14 @@
from typing import Callable
+import polars as pl
+
from wimsey import tests
+from wimsey.execution import test as _test # so pytest won't think this is test
def test_that_all_possible_tests_are_functions_that_return_partials() -> None:
for test_name, actual_test in tests.possible_tests.items():
+ assert isinstance(test_name, str)
assert isinstance(
actual_test(
column="anything",
@@ -12,7 +16,7 @@ def test_that_all_possible_tests_are_functions_that_return_partials() -> None:
be_less_than=3,
be_one_of=[1, 2, 3],
),
- Callable,
+ tuple,
)
@@ -21,14 +25,57 @@ def test_all_possible_tests_exposed_as_variables_of_the_same_name_in_module() ->
assert isinstance(getattr(tests, test_name), Callable)
-def test_average_ratio_to_other_column_should_matches_expected() -> None:
- test = tests.average_ratio_to_other_column_should(
- "a",
- "b",
- be_greater_than=0.09,
- be_less_than=0.11,
+def test_null_count_passes_and_fails_when_expected() -> None:
+ df = pl.DataFrame({"a": [1, None, 2]})
+ should_pass = _test(
+ df,
+ tests.null_count_should(
+ column="a",
+ be_exactly=1,
+ be_less_than=2,
+ be_less_than_or_equal_to=1,
+ be_greater_than=0,
+ be_greater_than_or_equal_to=1,
+ ),
+ )
+ should_fail = _test(
+ df,
+ tests.null_count_should(
+ column="a",
+ be_exactly=2,
+ be_less_than=1,
+ be_less_than_or_equal_to=0,
+ be_greater_than=1,
+ be_greater_than_or_equal_to=0,
+ ),
+ )
+ assert should_pass.success
+ assert not should_fail.success
+
+
+def test_null_percentage_passes_and_fails_when_expected() -> None:
+ df = pl.DataFrame({"a": [1, None, 2, None]})
+ should_pass = _test(
+ df,
+ tests.null_percentage_should(
+ column="a",
+ be_exactly=0.5,
+ be_less_than=1,
+ be_less_than_or_equal_to=0.5,
+ be_greater_than=0,
+ be_greater_than_or_equal_to=0.5,
+ ),
+ )
+ should_fail = _test(
+ df,
+ tests.null_percentage_should(
+ column="a",
+ be_exactly=1,
+ be_less_than=0,
+ be_less_than_or_equal_to=0,
+ be_greater_than=1,
+ be_greater_than_or_equal_to=1,
+ ),
)
- passing_result = test({"mean_a": 13, "mean_b": 130})
- failing_result = test({"mean_a": 13, "mean_b": 160})
- assert passing_result.success
- assert not failing_result.success
+ assert should_pass.success
+ assert not should_fail.success
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..dd053e8
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1385 @@
+version = 1
+requires-python = ">=3.10"
+resolution-markers = [
+ "python_full_version >= '3.12'",
+ "python_full_version == '3.11.*'",
+ "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "asttokens"
+version = "3.0.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/4a/e7/82da0a03e7ba5141f05cce0d302e6eed121ae055e0456ca228bf693984bc/asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7", size = 61978 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/25/8a/c46dcc25341b5bce5472c718902eb3d38600a903b14fa6aeecef3f21a46f/asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2", size = 26918 },
+]
+
+[[package]]
+name = "babel"
+version = "2.17.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/7d/6b/d52e42361e1aa00709585ecc30b3f9684b3ab62530771402248b1b1d6240/babel-2.17.0.tar.gz", hash = "sha256:0c54cffb19f690cdcc52a3b50bcbf71e07a808d1c80d549f2459b9d2cf0afb9d", size = 9951852 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/b7/b8/3fe70c75fe32afc4bb507f75563d39bc5642255d1d94f1f23604725780bf/babel-2.17.0-py3-none-any.whl", hash = "sha256:4d0b53093fdfb4b21c92b5213dba5a1b23885afa8383709427046b21c366e5f2", size = 10182537 },
+]
+
+[[package]]
+name = "backrefs"
+version = "5.9"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/eb/a7/312f673df6a79003279e1f55619abbe7daebbb87c17c976ddc0345c04c7b/backrefs-5.9.tar.gz", hash = "sha256:808548cb708d66b82ee231f962cb36faaf4f2baab032f2fbb783e9c2fdddaa59", size = 5765857 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/19/4d/798dc1f30468134906575156c089c492cf79b5a5fd373f07fe26c4d046bf/backrefs-5.9-py310-none-any.whl", hash = "sha256:db8e8ba0e9de81fcd635f440deab5ae5f2591b54ac1ebe0550a2ca063488cd9f", size = 380267 },
+ { url = "https://files.pythonhosted.org/packages/55/07/f0b3375bf0d06014e9787797e6b7cc02b38ac9ff9726ccfe834d94e9991e/backrefs-5.9-py311-none-any.whl", hash = "sha256:6907635edebbe9b2dc3de3a2befff44d74f30a4562adbb8b36f21252ea19c5cf", size = 392072 },
+ { url = "https://files.pythonhosted.org/packages/9d/12/4f345407259dd60a0997107758ba3f221cf89a9b5a0f8ed5b961aef97253/backrefs-5.9-py312-none-any.whl", hash = "sha256:7fdf9771f63e6028d7fee7e0c497c81abda597ea45d6b8f89e8ad76994f5befa", size = 397947 },
+ { url = "https://files.pythonhosted.org/packages/10/bf/fa31834dc27a7f05e5290eae47c82690edc3a7b37d58f7fb35a1bdbf355b/backrefs-5.9-py313-none-any.whl", hash = "sha256:cc37b19fa219e93ff825ed1fed8879e47b4d89aa7a1884860e2db64ccd7c676b", size = 399843 },
+ { url = "https://files.pythonhosted.org/packages/fc/24/b29af34b2c9c41645a9f4ff117bae860291780d73880f449e0b5d948c070/backrefs-5.9-py314-none-any.whl", hash = "sha256:df5e169836cc8acb5e440ebae9aad4bf9d15e226d3bad049cf3f6a5c20cc8dc9", size = 411762 },
+ { url = "https://files.pythonhosted.org/packages/41/ff/392bff89415399a979be4a65357a41d92729ae8580a66073d8ec8d810f98/backrefs-5.9-py39-none-any.whl", hash = "sha256:f48ee18f6252b8f5777a22a00a09a85de0ca931658f1dd96d4406a34f3748c60", size = 380265 },
+]
+
+[[package]]
+name = "blinker"
+version = "1.9.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/21/28/9b3f50ce0e048515135495f198351908d99540d69bfdc8c1d15b73dc55ce/blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf", size = 22460 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/10/cb/f2ad4230dc2eb1a74edf38f1a38b9b52277f75bef262d8908e60d957e13c/blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc", size = 8458 },
+]
+
+[[package]]
+name = "certifi"
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+ { name = "mkdocs-material", specifier = ">=9.5.42" },
+ { name = "pandas", specifier = ">=2.2.3" },
+ { name = "polars", specifier = ">=1.10.0" },
+ { name = "pytest", specifier = ">=8.3.3" },
+ { name = "pyyaml", specifier = ">=6.0.2" },
+ { name = "ruff", specifier = ">=0.12.0" },
+ { name = "ty", specifier = ">=0.0.1a11" },
+ { name = "types-pyyaml", specifier = ">=6.0.12.20240917" },
+]
+
+[[package]]
+name = "zipp"
+version = "3.23.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166", size = 25547 }
+wheels = [
+ { url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e", size = 10276 },
+]
diff --git a/wimsey/__init__.py b/wimsey/__init__.py
index e03d341..ed1a268 100644
--- a/wimsey/__init__.py
+++ b/wimsey/__init__.py
@@ -1,2 +1,2 @@
-from wimsey.execution import DataValidationException, test, validate # noqa
+from wimsey.execution import DataValidationError, test, validate # noqa
from wimsey._version import __version__ # noqa
diff --git a/wimsey/_version.py b/wimsey/_version.py
index 906d362..49e0fc1 100644
--- a/wimsey/_version.py
+++ b/wimsey/_version.py
@@ -1 +1 @@
-__version__ = "0.6.0"
+__version__ = "0.7.0"
diff --git a/wimsey/config.py b/wimsey/config.py
index c6fcdc1..5e1c0b0 100644
--- a/wimsey/config.py
+++ b/wimsey/config.py
@@ -1,45 +1,46 @@
+"""Configuration loading/saving functions."""
+
+from __future__ import annotations
+
import json
-from typing import Any, Callable
+from typing import Any, Callable, cast
import fsspec
+import narwhals.stable.v1 as nw
from wimsey.tests import possible_tests
-def collect_tests(config: list[dict] | dict | list[Callable]) -> list[Callable]:
- """
- Take a configuration, and build out tests
- """
- list_config: list[dict] | list[Callable] = (
- config if isinstance(config, list) else [config]
- )
- if isinstance(list_config[0], Callable):
- return list_config
- tests: list[Callable] = []
- for item in list_config:
+def collect_tests(config: list[dict] | dict | list[tuple]) -> list[tuple]:
+ """Take a configuration, and build out tests."""
+ list_config: list[dict] | list[tuple] = config if isinstance(config, list) else [config]
+ if isinstance(list_config[0], tuple) and isinstance(list_config[0][0], nw.Expr):
+ return cast("list[tuple]", list_config)
+ dict_list: list[dict] = list_config # type: ignore[assignment]
+ tests: list[tuple] = []
+ for item in dict_list:
test_name: str | None = item.get("test")
test: Callable | None = None
if test_name:
- test = possible_tests.get(item.get("test"))(**item) # type: ignore[arg-type]
+ test = possible_tests.get(item.get("test")) # type: ignore[arg-type]
if test is None:
msg = (
"Issue reading configuration, for at least one test, either no "
- "test is named, or a mispelt/unimplemented test is given"
+ "test is named, or a mispelt/unimplemented test is given.\n"
+ f"Specifically, could not find: {test_name!s}"
)
raise ValueError(msg)
- tests.append(test)
+ tests.append(test(**item))
return tests
-def read_config(path: str, storage_options: dict | None = None) -> list[Callable]:
- """
- Read a json or yaml configuration, and return list of test callables
- """
+def read_config(path: str, storage_options: dict | None = None) -> list[tuple]:
+ """Read a json or yaml configuration, and return list of test callables."""
storage_options_dict: dict = storage_options or {}
- config: dict
+ config: dict | list[dict]
with fsspec.open(path, "rt", **storage_options_dict) as file:
contents = file.read()
- if path.endswith(".yaml") or path.endswith(".yml"):
+ if path.endswith((".yaml", ".yml")):
try:
import yaml
@@ -53,15 +54,16 @@ def read_config(path: str, storage_options: dict | None = None) -> list[Callable
)
raise ImportError(msg) from exception
config = parse_contents(json.loads(contents))
- return collect_tests(config) # type: ignore[arg-type]
+ return collect_tests(config)
def parse_contents(contents: Any) -> list[dict] | dict:
+ """Parse contents of loaded json/yaml into list of tests."""
if isinstance(contents, list):
return contents
if isinstance(contents, dict):
if isinstance(contents.get("tests"), list):
- return contents.get("tests")
+ return cast("dict | list[dict]", contents.get("tests"))
return contents
msg = (
"It looks like the json/yaml file parsed in is either invalid "
diff --git a/wimsey/dataframe.py b/wimsey/dataframe.py
index bd59337..1846579 100644
--- a/wimsey/dataframe.py
+++ b/wimsey/dataframe.py
@@ -1,118 +1,133 @@
+"""Functions for dataframe interactions, like sampling and executing expressions."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any
+
import narwhals.stable.v1 as nw
-from narwhals.stable.v1.typing import FrameT
+if TYPE_CHECKING:
+ from collections.abc import Iterable
-@nw.narwhalify
-def describe(
- df: FrameT,
- columns: list[str] | None = None,
- metrics: list[str] | None = None,
-) -> dict[str, float]:
- """
- Outputs a dictionary for use in testing, mimicking polars 'describe' method.
+ from narwhals.stable.v1 import NwDataFrame
+ from narwhals.stable.v1.typing import IntoFrame
- Note this code is adapted from polars own descrip function.
+
+def _narwhals_to_dict(df: NwDataFrame) -> dict:
+ """Convert Narwhals datframe to dict.
+
+ Private function to convert Narwhals' FrameT to dict,
+ note that this will *only* present the first row because
+ it is built for specific evaluation of aggergate values.
+
+ As such it's not suitable for general use.
+
+ Also note, this will *force computation* for lazy frames,
+ this is needed in order for Wimsey to carry out certain
+ tests.
"""
+ try:
+ return {k: v[0] for k, v in df.to_dict(as_series=False).items()} # type: ignore[union-attr]
+ except AttributeError:
+ return {
+ k: v[0]
+ for k, v in df.collect().to_dict(as_series=False).items() # type: ignore[union-attr]
+ }
+
+
+def _validate_df_has_columns(df: NwDataFrame) -> NwDataFrame:
if not df.columns:
- return {}
- columns = columns or df.columns
- columns_to_check = [i for i in columns if i in df.columns]
- metrics = metrics or [
- "mean",
- "std",
- "min",
- "max",
- "type",
- "count",
- "null",
- "null_percentage",
- "length",
- ]
+ msg = "Wimsey cannot build a profile of this dataframe because it has no columns"
+ raise TypeError(msg)
+ return df
+
+
+def evaluate(
+ df: IntoFrame,
+ metrics: list[nw.Expr],
+) -> dict[str, Any]:
+ """Execute a list of scalar expressions and return dictionary of results."""
+ metrics += [nw.lit(str(df.schema[c])).alias(f"type_{c}") for c in df.columns]
+
+ evaluation_df = nw.from_native(df).pipe(_validate_df_has_columns).select(*metrics)
+ evaluation_dict = _narwhals_to_dict(evaluation_df)
+ type_evals: list = [i for i in evaluation_dict if i.startswith("type")]
+ schema_dict: dict = {i.replace("type_", ""): evaluation_dict.pop(i) for i in type_evals}
+ return evaluation_dict | {"schema": schema_dict}
+
+
+def describe(
+ df: IntoFrame,
+) -> dict[str, Any]:
+ """Retrieve dictionary discribing dataframe.
+
+ Mimics polars 'describe' method, but returns dict.
+ """
+ nw_df: NwDataFrame = nw.from_native(df)
+ _validate_df_has_columns(nw_df)
+ columns_to_check = nw_df.columns
# Determine which columns should get std/mean/percentile statistics
- stat_cols = {c for c, dt in df.schema.items() if dt.is_numeric()}
+ stat_cols = {c for c, dt in nw_df.schema.items() if dt.is_numeric()}
required_exprs: list = [
- nw.lit("_^&^_".join(df.columns)).alias("columns"),
+ nw.lit("_^&^_".join(nw_df.columns)).alias("columns"),
]
post_exprs: list = []
- for metric_expr in metrics:
- if not isinstance(metric_expr, nw.Expr):
- continue
- required_exprs += [metric_expr]
- if "mean" in metrics:
- required_exprs += [
- (nw.col(c).mean() if c in stat_cols else nw.lit(None)).alias(f"mean_{c}")
- for c in columns_to_check
- ]
- if "std" in metrics:
- required_exprs += [
- (nw.col(c).std() if c in stat_cols else nw.lit(None)).alias(f"std_{c}")
- for c in columns_to_check
- ]
- if "min" in metrics:
- required_exprs += [
- (nw.col(c).min() if c in stat_cols else nw.lit(None)).alias(f"min_{c}")
- for c in columns_to_check
- ]
- if "max" in metrics:
- required_exprs += [
- (nw.col(c).max() if c in stat_cols else nw.lit(None)).alias(f"max_{c}")
- for c in columns_to_check
- ]
- if "type" in metrics:
- required_exprs += [
- nw.lit(str(df.schema[c])).alias(f"type_{c}") for c in columns_to_check
- ]
- if (
- "count" in metrics
- or "null" in metrics
- or "length" in metrics
- or "null_percentage" in metrics
- ):
- required_exprs += [nw.col(*columns_to_check).count().name.prefix("count_")]
- if "null" in metrics or "length" in metrics or "null_percentage" in metrics:
- required_exprs += [
- nw.col(*columns_to_check).null_count().name.prefix("null_count_")
- ]
- post_exprs += [
- (
- nw.col(f"null_count_{c}")
- / (nw.col(f"count_{c}") + nw.col(f"null_count_{c}"))
- ).alias(f"null_percentage_{c}")
- for c in columns_to_check
- ]
- post_exprs += [
- (
- nw.col(f"count_{columns_to_check[0]}")
- + nw.col(f"null_count_{columns_to_check[0]}")
- ).alias("length")
- ]
- df_metrics = df.select(
+ required_exprs += [
+ (nw.col(c).mean() if c in stat_cols else nw.lit(None)).alias(f"mean_{c}")
+ for c in columns_to_check
+ ]
+ required_exprs += [
+ (nw.col(c).std() if c in stat_cols else nw.lit(None)).alias(f"std_{c}")
+ for c in columns_to_check
+ ]
+ required_exprs += [
+ (nw.col(c).min() if c in stat_cols else nw.lit(None)).alias(f"min_{c}")
+ for c in columns_to_check
+ ]
+ required_exprs += [
+ (nw.col(c).max() if c in stat_cols else nw.lit(None)).alias(f"max_{c}")
+ for c in columns_to_check
+ ]
+ required_exprs += [nw.lit(str(nw_df.schema[c])).alias(f"type_{c}") for c in columns_to_check]
+ required_exprs += [nw.col(*columns_to_check).count().name.prefix("count_")]
+ required_exprs += [
+ nw.col(*columns_to_check).null_count().name.prefix("null_count_"),
+ ]
+ post_exprs += [
+ (nw.col(f"null_count_{c}") / (nw.col(f"count_{c}") + nw.col(f"null_count_{c}"))).alias(
+ f"null_percentage_{c}",
+ )
+ for c in columns_to_check
+ ]
+ post_exprs += [
+ (
+ nw.col(f"count_{columns_to_check[0]}") + nw.col(f"null_count_{columns_to_check[0]}")
+ ).alias("length"),
+ ]
+ df_metrics = nw_df.select(
*required_exprs,
).with_columns(*post_exprs)
- try:
- return {k: v[0] for k, v in df_metrics.to_dict(as_series=False).items()} # type: ignore[union-attr]
- except AttributeError:
- return {
- k: v[0]
- for k, v in df_metrics.collect().to_dict(as_series=False).items() # type: ignore[union-attr]
- }
+ return _narwhals_to_dict(df_metrics)
def profile_from_sampling(
- df: FrameT,
+ df: IntoFrame,
samples: int = 100,
n: int | None = None,
fraction: int | None = None,
) -> list[dict[str, float]]:
+ """Return profiles from sampling a single dataframe."""
+ nw_df = nw.from_native(df)
return [
- describe(df.sample(n=n, fraction=fraction, with_replacement=True))
+ describe(nw_df.sample(n=n, fraction=fraction, with_replacement=True)) # type: ignore[union-attr]
for _ in range(samples)
- ]
+ ] # type: ignore[union-attr]
def profile_from_samples(
- samples: list[FrameT],
-) -> list[dict[str, float]]:
+ samples: Iterable[IntoFrame],
+) -> list[dict[str, Any]]:
+ """Return profiles from individual samples."""
return [describe(i) for i in samples]
diff --git a/wimsey/execution.py b/wimsey/execution.py
index 14083e4..54dfc16 100644
--- a/wimsey/execution.py
+++ b/wimsey/execution.py
@@ -1,48 +1,41 @@
-from typing import Callable, Any
-from dataclasses import dataclass
+"""Functions relating to execution of defined tests."""
-from narwhals.typing import FrameT
+from __future__ import annotations
-from wimsey.dataframe import describe
-from wimsey.tests import Result
-from wimsey.config import read_config, collect_tests
+from typing import TYPE_CHECKING, Any
+import narwhals.stable.v1 as nw
-@dataclass
-class FinalResult:
- success: bool
- results: list[Result]
+from wimsey.config import collect_tests, read_config
+from wimsey.dataframe import evaluate
+from wimsey.types import DataValidationError, FinalResult
+if TYPE_CHECKING:
+ from narwhals.stable.v1.typing import FrameT
-class DataValidationException(Exception): ...
+ from wimsey.tests import Result
+ from wimsey.types import GeneratedTest, MagicExpr
-def _as_set(val: Any) -> set:
- """
- Internal function, if val is none, return empty set,
- otherwise return set of just val
- """
- return {val} if val is not None else set()
-
-
-def run_all_tests(df: FrameT, tests: list[Callable[[Any], Result]]) -> FinalResult:
- """
- Run all given tests on a dataframe. Will return a `FinalResult` object
- """
- columns: set[str] | None = set()
- metrics: set[str] | None = []
- for test in tests:
- metrics += test.required_metrics
- columns |= _as_set(test.keywords.get("column"))
- columns |= _as_set(test.keywords.get("other_column"))
- description: dict[str, Any] = describe(
+def run_all_tests(df: FrameT, tests: list[GeneratedTest]) -> FinalResult:
+ """Run all given tests on a dataframe. Will return a `FinalResult` object."""
+ metrics: list[nw.Expr | MagicExpr] = []
+ for i, expr_tuple in enumerate(tests):
+ expr, _ = expr_tuple
+ if isinstance(expr, nw.Expr):
+ metrics.append(expr.alias(str(i)))
+ evaluation: dict[str, Any] = evaluate(
df,
- columns=list(columns),
metrics=metrics,
)
results: list[Result] = []
- for i_test in tests:
- results.append(i_test(description))
+ for i, expr_tuple in enumerate(tests):
+ expr, check = expr_tuple
+ results.append(
+ check(
+ evaluation[str(i)] if isinstance(expr, nw.Expr) else evaluation[expr.expr_name],
+ ),
+ )
return FinalResult(
success=all(i.success for i in results),
results=results,
@@ -50,9 +43,12 @@ def run_all_tests(df: FrameT, tests: list[Callable[[Any], Result]]) -> FinalResu
def test(
- df: FrameT, contract: str | list[dict] | dict, storage_options: dict | None = None
+ df: FrameT,
+ contract: str | list[dict] | dict,
+ storage_options: dict | None = None,
) -> FinalResult:
- """
+ """Test a dataframe against a data contract.
+
Carry out tests on dataframe and return results. This will *not* raise
an exception on test failure, and will instead return a 'final_result'
object, with a boolean 'success' field, and a detailed list of individual
@@ -74,7 +70,8 @@ def validate(
contract: str | list[dict] | dict,
storage_options: dict | None = None,
) -> FrameT:
- """
+ """Validate a dataframe against a data contract.
+
Carry out tests on dataframe, returning original dataframe if tests are
successful, and raising a DataValidationException in case of failure.
"""
@@ -85,11 +82,9 @@ def validate(
)
if not results.success:
failures: list[str] = [
- f"{i.name} (unexpected: {i.unexpected})"
- for i in results.results
- if not i.success
+ f"{i.name} (unexpected: {i.unexpected})" for i in results.results if not i.success
]
newline = "\n - "
msg = f"At least one test failed:\n - {newline.join(failures)}"
- raise DataValidationException(msg)
+ raise DataValidationError(msg)
return df
diff --git a/wimsey/profile.py b/wimsey/profiling.py
similarity index 82%
rename from wimsey/profile.py
rename to wimsey/profiling.py
index 770e12f..e03aec3 100644
--- a/wimsey/profile.py
+++ b/wimsey/profiling.py
@@ -1,17 +1,25 @@
+"""Functions for profiling and building tests."""
+
+from __future__ import annotations
+
import json
from enum import Enum, auto
-from typing import Iterable
from statistics import stdev
+from typing import TYPE_CHECKING, Any
import fsspec
-from narwhals.stable.v1.typing import FrameT
-from wimsey.dataframe import profile_from_sampling, profile_from_samples
+from wimsey.dataframe import profile_from_samples, profile_from_sampling
from wimsey.execution import validate
+if TYPE_CHECKING:
+ from collections.abc import Iterable
+
+ from narwhals.stable.v1.typing import FrameT
+
class _StarterTestStatus(Enum):
- """Internal class to mark out column consistency for use"""
+ """Internal class to mark out column consistency for use."""
UNSET = auto()
SET = auto()
@@ -22,8 +30,7 @@ def starter_tests_from_samples(
samples: Iterable[FrameT],
margin: float = 1,
) -> list[dict]:
- """
- From a list of supported dataframes, produce a list of passing tests.
+ """From a list of supported dataframes, produce a list of passing tests.
Margin is worth explaining here, as it's the amount of *extra margin* tests
are given, based on standard deviation. If three dataframes are given, with
@@ -34,7 +41,7 @@ def starter_tests_from_samples(
the above example, Wimsey will test for a maximum for 4. This can be tuned with
the 'margin' keyword.
"""
- df_samples = profile_from_samples(samples)
+ df_samples: list[dict[str, Any]] = profile_from_samples(samples)
return _starter_tests_from_sample_describes(df_samples, margin)
@@ -44,7 +51,8 @@ def save_starter_tests_from_samples(
margin: float = 1,
storage_options: dict | None = None,
) -> None:
- """
+ """Build and save starter tests from iterable of dataframe.
+
See starter_tests_from_samples for more information, will additionally
save tests as yaml or json dependend on path extension.
"""
@@ -62,7 +70,8 @@ def save_starter_tests_from_sampling(
margin: float = 1,
storage_options: dict | None = None,
) -> None:
- """
+ """Create and save starter tests from dataframe.
+
See starter_tests_from_sampling for more information, will additionally
save tests as yaml or json dependend on path extension.
"""
@@ -82,7 +91,7 @@ def _save_starter_tests(
tests: list[dict],
storage_options: dict | None = None,
) -> None:
- if path.endswith(".yaml") or path.endswith(".yml"):
+ if path.endswith((".yaml", ".yml")):
try:
import yaml
except ImportError as exception: # pragma: no cover
@@ -106,7 +115,8 @@ def starter_tests_from_sampling(
fraction: int | None = None,
margin: float = 1,
) -> list[dict]:
- """
+ """Build out starter tests from a dataframe.
+
From a supported dataframe, produce a list of passing tests by sampling the
dataframe. Note n *or* fraction should be given, but not both. Keyword `samples`
relates to the *number of samples* to take, whereas `n` relates to the *size of
@@ -128,11 +138,8 @@ def starter_tests_from_sampling(
def _starter_tests_from_sample_describes(
samples: list[dict],
margin: float = 1,
-) -> dict:
- """
- Internal function doing the main body of work for building out tests once sample
- describes have been taken.
- """
+) -> list[dict]:
+ """Build out starter tests from `describe` dictionary returs."""
column_test: dict = {"test": "columns_should", "status": _StarterTestStatus.UNSET}
for sample in samples:
column_test = _update_column_starter_test(column_test, sample)
@@ -153,11 +160,11 @@ def _starter_tests_from_sample_describes(
column_test["be"],
margin=margin,
)
- return column_tests + [column_test]
+ return [*column_tests, column_test]
def _update_column_starter_test(starter: dict, sample: dict) -> dict:
- """Internal function to update columns for a given iteration."""
+ """Update column checks for a single sample."""
sample_columns = set(sample["columns"].split("_^&^_"))
if starter["status"] is _StarterTestStatus.UNSET:
starter["be"] = list(sample_columns)
@@ -165,7 +172,7 @@ def _update_column_starter_test(starter: dict, sample: dict) -> dict:
elif starter["status"] is _StarterTestStatus.CANCELLED: # pragma: no cover
pass
elif starter.get("be") is not None and set(starter["be"]) != set(
- sample_columns
+ sample_columns,
): # pragma: no cover
old_be = starter.pop("be")
new_have = set(old_be) & set(sample_columns)
@@ -182,12 +189,10 @@ def _stat_starter_tests(
columns: list[str],
margin: float,
) -> list[dict]:
- """Internal function to build statistical starter tests from sample describes"""
+ """Build out statistical starter tests."""
tests: list[dict] = []
for column in columns:
- values = [
- i[f"{stat}_{column}"] for i in samples if i[f"{stat}_{column}"] is not None
- ]
+ values = [i[f"{stat}_{column}"] for i in samples if i[f"{stat}_{column}"] is not None]
if len(values) == 0:
continue
absolute_margin = stdev(values) * margin
@@ -212,13 +217,13 @@ def _type_starter_tests(
samples: list[dict],
columns: list[str],
) -> list[dict]:
- """Internal function to build 'column x should be type y' tests from sample describes"""
+ """Build out type based starter tests."""
tests: list[dict] = []
for column in columns:
- types = set(i[f"type_{column}"] for i in samples)
- test = {"column": column, "test": "type_should"}
+ types: set[str] = {i[f"type_{column}"] for i in samples}
+ test: dict[str, Any] = {"column": column, "test": "type_should"}
if len(types) == 1:
- test |= {"be": list(types)[0]}
+ test |= {"be": next(iter(types))}
else: # pragma: no cover
test |= {"be_one_of": list(types)}
tests.append(test)
@@ -234,16 +239,16 @@ def validate_or_build(
margin: float = 1,
storage_options: dict | None = None,
) -> FrameT:
- """
- Will attempt to validate based on a given contract, but if that contract does not exist yet
- will generate one from sampling the dataset.
+ """Validate dataframe if test file exists, otherwise make one.
+
+ Test file will be generated from multple samples of dataset.
Will fall back to starter_tests_from_sampling (a list samples is not possible with
only one dataframe), see *starter_tests_from_sampling* and *save_starter_tests_from_sampling*
for more details on use of keyword arguments aside from df, contract and storage_options.
"""
try:
- validate(df, contract=contract, storage_options=storage_options)
+ return validate(df, contract=contract, storage_options=storage_options)
except FileNotFoundError:
save_starter_tests_from_sampling(
path=contract,
@@ -253,3 +258,4 @@ def validate_or_build(
fraction=fraction,
margin=margin,
)
+ return df
diff --git a/wimsey/tests.py b/wimsey/tests.py
index 5b0da7c..c57d494 100644
--- a/wimsey/tests.py
+++ b/wimsey/tests.py
@@ -1,101 +1,76 @@
-from functools import partial
-from typing import Any, Callable
-from dataclasses import dataclass
+"""Wimsey test definitions.
+Definitions for test functions that can be ran against a dataframe.
+"""
-import narwhals.stable.v1 as nw
+from __future__ import annotations
+from typing import Any, Callable
-@dataclass
-class Result:
- name: str
- success: bool
- unexpected: Any = None
+import narwhals.stable.v1 as nw
+from wimsey.types import GeneratedTest, Result, schema
-def _range_check(metric: str) -> Callable:
- """
- Factory function for generated tests of the form "x should be within range"
+
+def _range_check(
+ aggregation: Callable[[str], nw.Expr],
+ metric_name: str,
+) -> Callable:
+ """Generate tests of the form "x should be within range".
Tests are also factories in themselves, they'll generate functions to take
only a "describe" object.
"""
- def should(
- describe: dict[str, Any],
+ def check_aggregation_is_in_range(
column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- ) -> Result:
- """Test that column metric is within designated range"""
- checks: list[bool] = []
- value: Any = describe[f"{metric}_{column}"]
- if be_exactly is not None:
- checks.append(value == be_exactly)
- if be_less_than is not None:
- checks.append(value < be_less_than)
- if be_greater_than is not None:
- checks.append(value > be_greater_than)
- if be_less_than_or_equal_to is not None:
- checks.append(value <= be_less_than_or_equal_to)
- if be_greater_than_or_equal_to is not None:
- checks.append(value >= be_greater_than_or_equal_to)
- return Result(
- name=f"{metric}-of-{column}",
- success=all(checks),
- unexpected=value if not all(checks) else None,
- )
-
- def should_be_partial(
- column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- **kwargs,
- ) -> Callable:
- """Test that column {metric} is within designated range"""
- return_partial = partial(
- should,
- column=column,
- be_exactly=be_exactly,
- be_less_than=be_less_than,
- be_less_than_or_equal_to=be_less_than_or_equal_to,
- be_greater_than=be_greater_than,
- be_greater_than_or_equal_to=be_greater_than_or_equal_to,
- )
- return_partial.required_metrics = [metric]
- return return_partial
-
- should_be_partial.__doc__ = should_be_partial.__doc__.replace("{metric}", metric)
- return should_be_partial
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+ ) -> GeneratedTest:
+ """Test that column metric is within designated range."""
+ del kwargs
+
+ def _check(value: float) -> Result:
+ checks = []
+ if be_exactly is not None:
+ checks.append(value == be_exactly)
+ if be_less_than is not None:
+ checks.append(value < be_less_than)
+ if be_greater_than is not None:
+ checks.append(value > be_greater_than)
+ if be_less_than_or_equal_to is not None:
+ checks.append(value <= be_less_than_or_equal_to)
+ if be_greater_than_or_equal_to is not None:
+ checks.append(value >= be_greater_than_or_equal_to)
+ return Result(
+ name=f"{metric_name}-of-{column}",
+ success=all(checks),
+ unexpected=value if not all(checks) else None,
+ )
+
+ return aggregation(column), _check
+
+ return check_aggregation_is_in_range
def row_count_should(
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- be_exactly: float | int | None = None,
- **kwargs,
-) -> Callable:
- """Test that dataframe row count is within designated range"""
-
- def row_count_should_be(
- description: dict,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- be_exactly: float | int | None = None,
- ) -> Result:
- """Test that dataframe row count is within designated range"""
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ be_exactly: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Test that dataframe row count is within designated range."""
+ del kwargs
+
+ def _check(value: float) -> Result:
+ """Test that dataframe row count is within designated range."""
checks: list[bool] = []
- value: float | int = description["length"]
if be_exactly is not None:
checks.append(value == be_exactly)
if be_less_than is not None:
@@ -112,146 +87,28 @@ def row_count_should_be(
unexpected=value if not all(checks) else None,
)
- should_be_partial = partial(
- row_count_should_be,
- be_less_than=be_less_than,
- be_less_than_or_equal_to=be_less_than_or_equal_to,
- be_greater_than=be_greater_than,
- be_greater_than_or_equal_to=be_greater_than_or_equal_to,
- be_exactly=be_exactly,
- )
- should_be_partial.required_metrics = ["length"]
- return should_be_partial
-
-
-def columns_should(
- have: list[str] | str | None = None,
- not_have: list[str] | str | None = None,
- be: list[str] | str | None = None,
- **kwargs,
-) -> Callable:
- """Test column names match up with expected values"""
-
- def should_have(
- description: dict,
- have: list[str] | str | None = None,
- not_have: list[str] | str | None = None,
- be: list[str] | str | None = None,
- **kwargs,
- ) -> Result:
- """Test column names match up with expected values"""
- have = list(have) if isinstance(have, str) else have
- not_have = list(not_have) if isinstance(not_have, str) else not_have
- be = list(be) if isinstance(be, str) else be
- checks: list[bool] = []
- present_columns = description["columns"].split("_^&^_")
- if have is not None:
- for col in have:
- checks.append(col in present_columns)
- if not_have is not None:
- for col in not_have:
- checks.append(col not in present_columns)
- if be is not None:
- checks.append(set(present_columns) == set(be))
- checks.append(len(present_columns) == len(be))
- return Result(
- name="columns",
- success=all(checks),
- unexpected=present_columns if not all(checks) else None,
- )
-
- should_have_partial = partial(should_have, have=have, not_have=not_have, be=be)
- should_have_partial.required_metrics = []
- return should_have_partial
-
-
-def type_should(
- column: str,
- be: str | None = None,
- not_be: str | None = None,
- be_one_of: list[str] | None = None,
- **kwargs,
-) -> Callable:
- """
- Test column type matches up with expected value.
-
- Note that this will expect *polars* style types, although does not require
- that they be case sensitive. For example, if testing a pandas dataframe for
- integer type, specify "int64" rather than, say, "int64[pyarrow]" or otherwise.
- """
-
- def should_be(
- description: dict,
- column: str,
- be: str | None,
- not_be: str | None,
- be_one_of: str | None,
- **kwargs,
- ) -> Result:
- """
- Test column type matches up with expected value.
-
- Note that this will expect *polars* style types, although does not require
- that they be case sensitive. For example, if testing a pandas dataframe for
- integer type, specify "int64" rather than, say, "int64[pyarrow]" or otherwise.
- """
- checks: list[bool] = []
- col_type = description[f"type_{column}"]
- if be is not None:
- checks.append(be.lower() == col_type.lower())
- if not_be is not None:
- checks.append(not_be.lower() != col_type.lower())
- if be_one_of is not None:
- checks.append(col_type.lower() in [i.lower() for i in be_one_of])
- return Result(
- name=f"type-of-{column}",
- success=all(checks),
- unexpected=col_type if not all(checks) else None,
- )
-
- should_be_partial = partial(
- should_be,
- column=column,
- be=be,
- not_be=not_be,
- be_one_of=be_one_of,
- )
- should_be_partial.required_metrics = ["type"]
- return should_be_partial
+ return nw.len(), _check
def average_difference_from_other_column_should(
column: str,
other_column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- **kwargs,
-) -> Callable:
- """
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Compare difference between two columns.
+
Test that the average difference between column and other column are
within designated bounds.
"""
+ del kwargs
- def should(
- description: dict,
- column: str,
- other_column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- **kwargs,
- ) -> Result:
- """
- Test that the average difference between column and other column are
- within designated bounds.
- """
+ def _check(difference: float) -> Result:
checks: list[bool] = []
- difference = description[f"mean_{column}"] - description[f"mean_{other_column}"]
if be_exactly is not None:
checks.append(difference == be_exactly)
if be_less_than is not None:
@@ -268,54 +125,29 @@ def should(
unexpected=difference if not all(checks) else None,
)
- should_partial = partial(
- should,
- column=column,
- other_column=other_column,
- be_exactly=be_exactly,
- be_less_than=be_less_than,
- be_less_than_or_equal_to=be_less_than_or_equal_to,
- be_greater_than=be_greater_than,
- be_greater_than_or_equal_to=be_greater_than_or_equal_to,
- )
- should_partial.required_metrics = ["mean"]
- return should_partial
+ return (nw.col(column) - nw.col(other_column)).mean(), _check
def average_ratio_to_other_column_should(
column: str,
other_column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- **kwargs,
-) -> Callable:
- """
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Compare ratio between two columns.
+
Test that the average ratio between column and other column are
within designated bounds (for instance, a value of 1 has a ratio
- of 0.1 to a value of 10)
+ of 0.1 to a value of 10).
"""
+ del kwargs
- def should(
- description: dict,
- column: str,
- other_column: str,
- be_exactly: float | int | None = None,
- be_less_than: float | int | None = None,
- be_less_than_or_equal_to: float | int | None = None,
- be_greater_than: float | int | None = None,
- be_greater_than_or_equal_to: float | int | None = None,
- **kwargs,
- ) -> Result:
- """
- Test that the average ratio between column and other column are
- within designated bounds (for instance, a value of 1 has a ratio
- of 0.1 to a value of 10)
- """
+ def _check(ratio: float) -> Result:
checks: list[bool] = []
- ratio = description[f"mean_{column}"] / description[f"mean_{other_column}"]
if be_exactly is not None:
checks.append(ratio == be_exactly)
if be_less_than is not None:
@@ -332,56 +164,40 @@ def should(
unexpected=ratio if not all(checks) else None,
)
- should_partial = partial(
- should,
- column=column,
- other_column=other_column,
- be_exactly=be_exactly,
- be_less_than=be_less_than,
- be_less_than_or_equal_to=be_less_than_or_equal_to,
- be_greater_than=be_greater_than,
- be_greater_than_or_equal_to=be_greater_than_or_equal_to,
- )
- should_partial.required_metrics = ["mean"]
- return should_partial
+ return (nw.col(column) / nw.col(other_column)).mean(), _check
def max_string_length_should(
column: str,
- be_less_than: int | float | None = None,
- be_greater_than: int | float | None = None,
- be_exactly: int | float | None = None,
- **kwargs,
-) -> Callable:
- def should(
- description: dict,
- column: str,
- **kwargs,
- ) -> Result:
- success = description[f"max_string_length_of_{column}"]
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Test that the maximum string length is within expected bounds."""
+ del kwargs
+
+ def _check(max_length: float) -> Result:
+ checks: list[bool] = []
+ if be_exactly is not None:
+ checks.append(max_length == be_exactly)
+ if be_less_than is not None:
+ checks.append(max_length < be_less_than)
+ if be_less_than_or_equal_to is not None:
+ checks.append(max_length <= be_less_than_or_equal_to)
+ if be_greater_than is not None:
+ checks.append(max_length > be_greater_than)
+ if be_greater_than_or_equal_to is not None:
+ checks.append(max_length >= be_greater_than_or_equal_to)
return Result(
- name=f"max-string-length-of-{column}",
- success=success,
- unexpected=None
- if success
- else "Length of column values did not meet bounds",
+ name=f"average-max-string-length-of-{column}",
+ success=all(checks),
+ unexpected=max_length if not all(checks) else None,
)
- expressions: list[nw.Expr] = []
- if be_less_than:
- expr: nw.Expr = nw.col(column).str.len_chars().max() < nw.lit(be_less_than)
- expressions.append(expr)
- if be_greater_than:
- expr: nw.Expr = nw.col(column).str.len_chars().max() > nw.lit(be_greater_than)
- expressions.append(expr)
- if be_exactly:
- expr: nw.Expr = nw.col(column).str.len_chars().max() == nw.lit(be_exactly)
- expressions.append(expr)
- partial_should = partial(should, column=column)
- partial_should.required_metrics = [
- nw.all_horizontal(*expressions).alias(f"max_string_length_of_{column}")
- ]
- return partial_should
+ return nw.col(column).str.len_chars().max(), _check
def all_values_should(
@@ -389,13 +205,14 @@ def all_values_should(
be_one_of: list[str] | None = None,
not_be_one_of: list[str] | None = None,
match_regex: str | None = None,
- **kwargs,
-) -> None:
- def should(
- description: dict,
- **kwargs,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Test all unique values within a column are within expected group."""
+ del kwargs
+
+ def _check(
+ success: bool,
) -> Result:
- success = description[f"all_values_of_{column}"]
return Result(
name=f"all-values-of-{column}",
success=success,
@@ -409,28 +226,155 @@ def should(
expressions.append(~(nw.col(column).is_in(not_be_one_of)).max())
if match_regex:
expressions.append(nw.col(column).str.contains(match_regex))
- partial_should = partial(should)
- partial_should.required_metrics = [
- nw.all_horizontal(*expressions).alias(f"all_values_of_{column}")
- ]
- return partial_should
+ return nw.all_horizontal(*expressions), _check
+
+
+def type_should(
+ column: str,
+ be: str | None = None,
+ not_be: str | None = None,
+ be_one_of: list[str] | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Check for type of column.
+
+ Check that type of column meets expected criteria. Note that because Wimsey
+ is a dataframe agnostic tool, this should be of *Narwhals* expected types,
+ such as Float64, Int64, String, etc.
+
+ See Narwhals' documentation for more details:
+ https://narwhals-dev.github.io/narwhals/api-reference/dtypes/
+ """
+ del kwargs
+
+ def _check(schema_dict: dict) -> Result:
+ col_type = schema_dict[column]
+ checks: list[bool] = []
+ if be is not None:
+ checks.append(be.lower() == col_type.lower())
+ if not_be is not None:
+ checks.append(not_be.lower() != col_type.lower())
+ if be_one_of is not None:
+ checks.append(col_type.lower() in [i.lower() for i in be_one_of])
+ return Result(
+ name=f"type-of-{column}",
+ success=all(checks),
+ unexpected=col_type if not all(checks) else None,
+ )
+
+ return schema, _check
+
+
+def columns_should(
+ have: list[str] | str | None = None,
+ not_have: list[str] | str | None = None,
+ be: list[str] | str | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Check that expected columns are present / non-present within dataframe."""
+ del kwargs
+
+ def _check(schema_dict: dict) -> Result:
+ _have = list(have) if isinstance(have, str) else have
+ _not_have = list(not_have) if isinstance(not_have, str) else not_have
+ _be = list(be) if isinstance(be, str) else be
+ checks: list[bool] = []
+ present_columns = list(schema_dict)
+ if _have is not None:
+ checks.extend(col in present_columns for col in _have)
+ if _not_have is not None:
+ checks.extend(col not in present_columns for col in _not_have)
+ if _be is not None:
+ checks.append(set(present_columns) == set(_be))
+ checks.append(len(present_columns) == len(_be))
+ return Result(
+ name="columns",
+ success=all(checks),
+ unexpected=present_columns if not all(checks) else None,
+ )
+
+ return schema, _check
+
+
+def null_count_should(
+ column: str,
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Check that null count of column meets expected criteria."""
+ del kwargs
+
+ def _check(null_count: float) -> Result:
+ checks = []
+ if be_exactly is not None:
+ checks.append(null_count == be_exactly)
+ if be_less_than is not None:
+ checks.append(null_count < be_less_than)
+ if be_greater_than is not None:
+ checks.append(null_count > be_greater_than)
+ if be_less_than_or_equal_to is not None:
+ checks.append(null_count <= be_less_than_or_equal_to)
+ if be_greater_than_or_equal_to is not None:
+ checks.append(null_count >= be_greater_than_or_equal_to)
+ return Result(
+ name=f"null-count-of-{column}",
+ success=all(checks),
+ unexpected=null_count if not all(checks) else None,
+ )
+
+ return nw.col(column).null_count(), _check
+
+
+def null_percentage_should(
+ column: str,
+ be_exactly: float | None = None,
+ be_less_than: float | None = None,
+ be_less_than_or_equal_to: float | None = None,
+ be_greater_than: float | None = None,
+ be_greater_than_or_equal_to: float | None = None,
+ **kwargs: Any,
+) -> GeneratedTest:
+ """Check that null percentage of column meets expected criteria."""
+ del kwargs
+
+ def _check(null_percentage: float) -> Result:
+ checks = []
+ if be_exactly is not None:
+ checks.append(null_percentage == be_exactly)
+ if be_less_than is not None:
+ checks.append(null_percentage < be_less_than)
+ if be_greater_than is not None:
+ checks.append(null_percentage > be_greater_than)
+ if be_less_than_or_equal_to is not None:
+ checks.append(null_percentage <= be_less_than_or_equal_to)
+ if be_greater_than_or_equal_to is not None:
+ checks.append(null_percentage >= be_greater_than_or_equal_to)
+ return Result(
+ name=f"null-percentage-of-{column}",
+ success=all(checks),
+ unexpected=null_percentage if not all(checks) else None,
+ )
+
+ return (nw.col(column).null_count() / nw.col(column).len()), _check
possible_tests: dict[str, Callable] = {
- "mean_should": (mean_should := _range_check("mean")),
- "min_should": (min_should := _range_check("min")),
- "max_should": (max_should := _range_check("max")),
- "std_should": (std_should := _range_check("std")),
- "null_count_should": (null_count_should := _range_check("null_count")),
- "count_should": (count_should := _range_check("count")),
- "null_percentage_should": (
- null_percentage_should := _range_check("null_percentage")
- ),
- "columns_should": columns_should,
- "type_should": type_should,
+ "mean_should": (mean_should := _range_check(nw.mean, "mean")),
+ "min_should": (min_should := _range_check(nw.min, "min")),
+ "max_should": (max_should := _range_check(nw.max, "max")),
+ "std_should": (std_should := _range_check(lambda col: nw.col(col).std(), "stdev")),
+ "count_should": (count_should := _range_check(lambda col: nw.col(col).count(), "count")),
"row_count_should": row_count_should,
"average_difference_from_other_column_should": average_difference_from_other_column_should,
"average_ratio_to_other_column_should": average_ratio_to_other_column_should,
"max_string_length_should": max_string_length_should,
"all_values_should": all_values_should,
+ "type_should": type_should,
+ "columns_should": columns_should,
+ "null_count_should": null_count_should,
+ "null_percentage_should": null_percentage_should,
}
diff --git a/wimsey/types.py b/wimsey/types.py
new file mode 100644
index 0000000..7023a87
--- /dev/null
+++ b/wimsey/types.py
@@ -0,0 +1,44 @@
+"""Types (mostly data based) for use in Wimsey module."""
+
+from dataclasses import dataclass
+from typing import Any, Callable, TypeAlias
+
+import narwhals.stable.v1 as nw
+
+
+@dataclass
+class MagicExpr:
+ """Type for use in place of nw.Expr.
+
+ Special Wimsey object for special data such as column names that aren't
+ possible via expressions directly.
+ """
+
+ expr_name: str
+
+
+schema = MagicExpr("schema")
+
+
+class DataValidationError(Exception):
+ """Exception class for raising against invalid data."""
+
+
+@dataclass
+class Result:
+ """Dataclass for storing test result information."""
+
+ name: str
+ success: bool
+ unexpected: Any = None
+
+
+@dataclass
+class FinalResult:
+ """Dataclass for storing test result information."""
+
+ success: bool
+ results: list[Result]
+
+
+GeneratedTest: TypeAlias = tuple[nw.Expr | MagicExpr, Callable[[Any], Result]]