Update pre-commit repos

.git-blame-ignore-revs

@@ -6,3 +6,4 @@ bb473f6ff4a23e4fd5205eacdef713db15decadf
 8d00b416689c428b2bafdaad61ed3ec660ca06c2
 d6b447cab8c40aa47dc675a0f4349ae254e8b3ce
 d8966d848af75751823e825eb76c5bbff58f5c07
+4902d468c9606836b26b393379df4f49208ea847

.pre-commit-config.yaml

@@ -32,14 +32,14 @@ repos:
         args: [--no-pycodestyle, --max-line-length=88]
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: f8a3f8c471fb698229face5ed7640a64900b781e # frozen: v0.4.4
+    rev: 02609a0003fd4903bd7f43852e5dfc82242a96db # frozen: v0.4.9
     hooks:
       - id: ruff
         args: ["--fix", "--show-fixes", "--exit-non-zero-on-fix"]
       - id: ruff-format
 
   - repo: https://github.com/codespell-project/codespell
-    rev: "6e41aba91fb32e9feb741a6258eefeb9c6e4a482" # frozen: v2.2.6
+    rev: "193cd7d27cd571f79358af09a8fb8997e54f8fff" # frozen: v2.3.0
     hooks:
       - id: codespell
         args: ["-w", "-L", "ans,nd,sav,mke,ags,mot,coo,whos,manuel"]

advanced/mathematical_optimization/index.rst

@@ -828,7 +828,7 @@ handy.
 
    See also :func:`scipy.optimize.approx_fprime` to find your errors.
 
-Synthetic exercices
+Synthetic exercises
 -------------------
 
 .. |flat_min_0| image:: auto_examples/images/sphx_glr_plot_exercise_flat_minimum_001.png
@@ -844,7 +844,7 @@ Synthetic exercices
     :target: auto_examples/plot_exercise_ill_conditioned.html
     :align: right
 
-.. topic:: **Exercice: A simple (?) quadratic function**
+.. topic:: **Exercise: A simple (?) quadratic function**
     :class: green
 
     Optimize the following function, using K[0] as a starting point::
@@ -858,7 +858,7 @@ Synthetic exercices
     Time your approach. Find the fastest approach. Why is BFGS not
     working well?
 
-.. topic:: **Exercice: A locally flat minimum**
+.. topic:: **Exercise: A locally flat minimum**
     :class: green
 
     Consider the function `exp(-1/(.1*x**2 + y**2)`. This function admits

intro/numpy/operations.rst

@@ -878,4 +878,4 @@ Summary
 
    The remainder of this chapter is not necessary to follow the rest of
    the intro part. But be sure to come back and finish this chapter, as
-   well as to do some more :ref:`exercices <numpy_exercises>`.
+   well as to do some more :ref:`exercises <numpy_exercises>`.

packages/statistics/examples/plot_wage_data.py

@@ -3,7 +3,7 @@
 =====================================
 
 This example uses seaborn to quickly plot various factors relating wages,
-experience and eduction.
+experience, and education.
 
 Seaborn (https://seaborn.pydata.org) is a library that combines
 visualization and statistical fits to show trends in data.

packages/statistics/index.rst

@@ -824,7 +824,7 @@ lmplot: plotting a univariate regression
    :scale: 60
 
 A regression capturing the relation between one variable and another, eg
-wage and eduction, can be plotted using :func:`seaborn.lmplot`::
+wage, and education, can be plotted using :func:`seaborn.lmplot`::
 
     >>> seaborn.lmplot(y='WAGE', x='EDUCATION', data=data)  # doctest: +SKIP