[pre-commit.ci] pre-commit autoupdate (#562)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tobias Raabe <raabe@posteo.de>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -24,18 +24,6 @@ repos:
     -   id: python-check-mock-methods
     -   id: python-no-log-warn
     -   id: text-unicode-replacement-char
--   repo: https://github.com/asottile/reorder-python-imports
-    rev: v3.12.0
-    hooks:
-    -   id: reorder-python-imports
-        args: [--py38-plus, --add-import, 'from __future__ import annotations']
-        exclude: ^(docs_src/)
--   repo: https://github.com/asottile/reorder-python-imports
-    rev: v3.12.0
-    hooks:
-    -   id: reorder-python-imports
-        args: [--py38-plus]
-        files: ^(docs_src/)
 # -   repo: https://github.com/tox-dev/pyproject-fmt
 #     rev: 1.2.0
 #     hooks:
@@ -45,7 +33,7 @@ repos:
     hooks:
     -   id: sort-all
 -   repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.0
+    rev: v0.3.1
     hooks:
       - id: ruff-format
       - id: ruff
@@ -90,7 +78,7 @@ repos:
         ]
         files: (docs/.)
 -   repo: https://github.com/nbQA-dev/nbQA
-    rev: 1.7.1
+    rev: 1.8.4
     hooks:
     -   id: nbqa-black
     -   id: nbqa-isort

docs/source/conf.py

@@ -4,6 +4,7 @@
 documentation: https://www.sphinx-doc.org/en/master/usage/configuration.html
 
 """
+
 from __future__ import annotations
 
 import inspect

docs/source/tutorials/making_tasks_persist.md

@@ -13,7 +13,7 @@ in the database such that the subsequent execution will skip the task successful
 
 ## When is this useful?
 
-- You ran a formatter like Black on the files in your project and want to prevent the
+- You ran a formatter like ruff on the files in your project and want to prevent the
   longest-running tasks from being rerun.
 - You extend a repetition of a task function but do not want to rerun all tasks.
 - You want to integrate a task that you have already run elsewhere. Copy over the

docs/source/tutorials/repeating_tasks_with_different_inputs.md

@@ -93,17 +93,17 @@ auto-generated ids are used.
 
 ### Auto-generated ids
 
-pytask construct ids by extending the task name with representations of the values used
+pytask constructs ids by extending the task name with representations of the values used
 for each iteration. Booleans, floats, integers, and strings enter the task id directly.
 For example, a task function that receives four arguments, `True`, `1.0`, `2`, and
-`"hello"`, one of each dtype, has the following id.
+`"hello"`, one of each data type, has the following id.
 
 ```
 task_data_preparation.py::task_create_random_data[True-1.0-2-hello]
 ```
 
-Arguments with other dtypes cannot be converted to strings and, thus, are replaced with
-a combination of the argument name and the iteration counter.
+Arguments with other data types cannot be converted to strings and, thus, are replaced
+with a combination of the argument name and the iteration counter.
 
 For example, the following function is parametrized with tuples.
 
@@ -136,7 +136,7 @@ For example, the following function is parametrized with tuples.
 
 Since the tuples are not converted to strings, the ids of the two tasks are
 
-```
+```text
 task_data_preparation.py::task_create_random_data[seed0]
 task_data_preparation.py::task_create_random_data[seed1]
 ```
@@ -177,7 +177,7 @@ a unique name for the iteration.
 
 produces these ids
 
-```
+```text
 task_data_preparation.py::task_create_random_data[first]
 task_data_preparation.py::task_create_random_data[second]
 ```

docs/source/tutorials/using_a_data_catalog.md

@@ -61,7 +61,7 @@ The following tabs show you how to use the data catalog given the interface you
 ````{tab-item} Python 3.10+
 :sync: python310plus
 
-Use `data_catalog["key"]` as an default argument to access the
+Use `data_catalog["data"]` as an default argument to access the
 {class}`~pytask.PickleNode` within the task. When you are done transforming your
 {class}`~pandas.DataFrame`, save it with {meth}`~pytask.PickleNode.save`.
 
@@ -74,7 +74,7 @@ Use `data_catalog["key"]` as an default argument to access the
 ````{tab-item} Python 3.8+
 :sync: python38plus
 
-Use `data_catalog["key"]` as an default argument to access the
+Use `data_catalog["data"]` as an default argument to access the
 {class}`~pytask.PickleNode` within the task. When you are done transforming your
 {class}`~pandas.DataFrame`, save it with {meth}`~pytask.PickleNode.save`.
 
@@ -87,7 +87,7 @@ Use `data_catalog["key"]` as an default argument to access the
 ````{tab-item} ​produces
 :sync: produces
 
-Use `data_catalog["key"]` as an default argument to access the
+Use `data_catalog["data"]` as an default argument to access the
 {class}`~pytask.PickleNode` within the task. When you are done transforming your
 {class}`~pandas.DataFrame`, save it with {meth}`~pytask.PickleNode.save`.
 
@@ -125,7 +125,7 @@ Following one of the interfaces gives you immediate access to the
 ````{tab-item} Python 3.10+
 :sync: python310plus
 
-Use `data_catalog["key"]` as an default argument to access the
+Use `data_catalog["data"]` as an default argument to access the
 {class}`~pytask.PickleNode` within the task. When you are done transforming your
 {class}`~pandas.DataFrame`, save it with {meth}`~pytask.PickleNode.save`.
 
@@ -138,7 +138,7 @@ Use `data_catalog["key"]` as an default argument to access the
 ````{tab-item} Python 3.8+
 :sync: python38plus
 
-Use `data_catalog["key"]` as an default argument to access the
+Use `data_catalog["data"]` as an default argument to access the
 {class}`~pytask.PickleNode` within the task. When you are done transforming your
 {class}`~pandas.DataFrame`, save it with {meth}`~pytask.PickleNode.save`.
 

docs_src/how_to_guides/bp_scaling_tasks_1.py

@@ -4,7 +4,6 @@
 from my_project.config import BLD
 from my_project.config import SRC
 
-
 DATA = {
     "data_0": {"subset": "subset_1"},
     "data_1": {"subset": "subset_2"},

docs_src/how_to_guides/bp_scaling_tasks_3.py

@@ -4,7 +4,6 @@
 from my_project.config import BLD
 from my_project.data_preparation.config import DATA
 
-
 _MODELS = ["linear_probability", "logistic_model", "decision_tree"]
 
 

docs_src/how_to_guides/bp_structure_of_task_files.py

@@ -26,9 +26,7 @@ def task_prepare_census_data(
     df.to_pickle(path_to_census)
 
 
-def _clean_data(df: pd.DataFrame) -> None:
-    ...
+def _clean_data(df: pd.DataFrame) -> None: ...
 
 
-def _create_new_variables(df: pd.DataFrame) -> None:
-    ...
+def _create_new_variables(df: pd.DataFrame) -> None: ...

docs_src/how_to_guides/interfaces/dependencies_annotation.py

@@ -4,5 +4,4 @@
 from pytask import PathNode
 
 
-def task_example(path: Annotated[Path, PathNode(path=Path("input.txt"))]) -> None:
-    ...
+def task_example(path: Annotated[Path, PathNode(path=Path("input.txt"))]) -> None: ...

docs_src/how_to_guides/interfaces/dependencies_default.py

@@ -1,5 +1,4 @@
 from pathlib import Path
 
 
-def task_example(path: Path = Path("input.txt")) -> None:
-    ...
+def task_example(path: Path = Path("input.txt")) -> None: ...