[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

docs/data-processing/apis/fastapi/example.rst

@@ -16,7 +16,6 @@ Erstellt die Datei :file:`main.py` mit diesem Inhalt:
 
     from fastapi import FastAPI
 
-
     app = FastAPI()
 
 
@@ -81,7 +80,6 @@ zu erhalten:
 
     from fastapi import FastAPI
 
-
     app = FastAPI()
 
 

docs/data-processing/postgresql/db-api.rst

@@ -82,12 +82,10 @@ Cursor
     .. code-block:: python
 
         cursor = conn.cursor()
-        cursor.execute(
-            """
+        cursor.execute("""
             SELECT column1, column2
             FROM tableA
-            """
-        )
+            """)
         for column1, column2 in cursor.fetchall():
             print(column1, column2)
 

docs/data-processing/postgresql/sqlalchemy.rst

@@ -44,7 +44,6 @@ Datenbankverbindung
 
     from sqlalchemy import create_engine
 
-
     engine = create_engine("postgresql:///example", echo=True)
 
 Datenmodell
@@ -56,7 +55,6 @@ Datenmodell
     from sqlalchemy.ext.declarative import declarative_base
     from sqlalchemy.orm import relationship
 
-
     Base = declarative_base()
 
 

docs/data-processing/serialisation-formats/pyproject.toml

@@ -14,6 +14,5 @@ lines_between_types = 1
 multi_line_output = 3
 not_skip = "__init__.py"
 use_parentheses = true
-
 known_first_party = "jupyter-tutorial"
 known_third_party = [ "mpi4py", "numpy", "requests" ]

docs/data-processing/serialisation-formats/toml/index.rst

@@ -66,7 +66,6 @@ Beispiel
 
     import toml
 
-
     config = toml.load("pyproject.toml")
 
 .. seealso::

docs/data-processing/serialisation-formats/toml/pyproject.toml

@@ -14,6 +14,5 @@ lines_between_types = 1
 multi_line_output = 3
 not_skip = "__init__.py"
 use_parentheses = true
-
 known_first_party = [ "MY_FIRST_MODULE", "MY_SECOND_MODULE" ]
 known_third_party = [ "mpi4py", "numpy", "requests" ]

docs/performance/index.rst

@@ -55,7 +55,6 @@ Beispieldaten können wir uns erstellen mit:
 
     from sklearn.datasets import make_blobs
 
-
     points, labels_true = make_blobs(
         n_samples=1000, centers=3, random_state=0, cluster_std=0.60
     )
@@ -128,7 +127,6 @@ k-Means-Algorithmus gibt es sogar gleich zwei Implementierungen:
 
      from sklearn.cluster import KMeans
 
-
      KMeans(10).fit_predict(points)
 
 * `dask_ml.cluster.KMeans
@@ -138,7 +136,6 @@ k-Means-Algorithmus gibt es sogar gleich zwei Implementierungen:
 
      from dask_ml.cluster import KMeans
 
-
      KMeans(10).fit(points).predict(points)
 
 Gegen diese bestehenden Lösungen könnte bestenfalls sprechen, dass sie einen

pyproject.toml

@@ -22,8 +22,7 @@ classifiers = [
   "Programming Language :: Python :: 3.13",
   "Programming Language :: Python :: 3.14",
 ]
-dependencies = [  ]
-
+dependencies = []
 urls."Bug Tracker" = "https://github.com/cusyio/Python4DataScience/issues"
 urls."Homepage" = "https://github.com/cusyio/Python4DataScience/"
 
@@ -52,7 +51,7 @@ docs = [
 ]
 
 [tool.setuptools]
-packages = [  ]
+packages = []
 
 [tool.black]
 line-length = 79
@@ -66,7 +65,6 @@ lines_between_types = 1
 multi_line_output = 3
 not_skip = "__init__.py"
 use_parentheses = true
-
 known_first_party = "Python4DataScience"
 known_third_party = [
   "Cython",
@@ -84,5 +82,8 @@ known_third_party = [
 ]
 
 [tool.codespell]
-skip = "*.csv, *.html, *.pdf, *.rst, *.ipynb, ./docs/_build/*, */books.json,  */books.txt, ./styles/*, ./Python4DataScience.egg-info/*"
+skip = """\
+  *.csv, *.html, *.pdf, *.rst, *.ipynb, ./docs/_build/*, */books.json,  */books.txt, ./styles/*, \
+  ./Python4DataScience.egg-info/*\
+  """
 ignore-words-list = "comit"