numpy
diff --git a/.gitignore b/.gitignore
@@ -124,6 +124,7 @@ Thumbs.db
 doc/source/savefig/
 doc/source/**/generated/
 doc/source/release/notes-towncrier.rst
+doc/source/.jupyterlite.doit.db
 
 # Things specific to this project #
 ###################################

diff --git a/doc/Makefile b/doc/Makefile
@@ -50,6 +50,8 @@ help:
 
 clean:
 	-rm -rf build/*
+	-rm -rf source/.jupyterlite.doit.db
+	-rm -rf source/contents/*.ipynb
 	find . -name generated -type d -prune -exec rm -rf "{}" ";"
 
 gitwash-update:

diff --git a/doc/neps/roadmap.rst b/doc/neps/roadmap.rst
@@ -75,7 +75,8 @@ planned improvements. Adding more tutorials is underway in the
 `numpy-tutorials repo <https://github.com/numpy/numpy-tutorials>`__.
 
 We also intend to make all the example code in our documentation interactive -
-work is underway to do so via ``jupyterlite-sphinx`` and Pyodide.
+work is underway to do so via ``jupyterlite-sphinx`` and Pyodide. NumPy 2.3.0
+provides interactive documentation for examples as a pilot for this effort.
 
 Our website (https://numpy.org) is in good shape. Further work on expanding the
 number of languages that the website is translated in is desirable. As are

diff --git a/doc/release/upcoming_changes/26745.highlight.rst b/doc/release/upcoming_changes/26745.highlight.rst
@@ -0,0 +1,10 @@
+Interactive examples in the NumPy documentation
+-----------------------------------------------
+
+The NumPy documentation includes a number of examples that
+can now be run interactively in your browser using WebAssembly
+and Pyodide.
+
+Please note that the examples are currently experimental in
+nature and may not work as expected for all methods in the
+public API.
diff --git a/doc/source/_static/numpy.css b/doc/source/_static/numpy.css
@@ -1,10 +1,11 @@
 @import url('https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;0,900;1,400;1,700;1,900&family=Open+Sans:ital,wght@0,400;0,600;1,400;1,600&display=swap');
 
 .navbar-brand img {
-   height: 75px;
+  height: 75px;
 }
+
 .navbar-brand {
-   height: 75px;
+  height: 75px;
 }
 
 body {
@@ -71,4 +72,43 @@ div.admonition-legacy>.admonition-title::after {
 
 div.admonition-legacy>.admonition-title {
   background-color: var(--pst-color-warning-bg);
-}
+}
+
+/* Buttons for JupyterLite-enabled interactive examples */
+
+.try_examples_button {
+  color: white;
+  background-color: var(--pst-color-info);
+  border: none;
+  padding: 5px 10px;
+  border-radius: 0.25rem;
+  margin-top: 3px;  /* better alignment under admonitions */
+  margin-bottom: 5px !important; /* fix uneven button sizes under admonitions */
+  box-shadow: 0 2px 5px rgba(108, 108, 108, 0.2);
+  font-weight: bold;
+  font-size: small;
+}
+
+/* Use more acccessible colours for text in dark mode */
+[data-theme=dark] .try_examples_button {
+  color: black;
+}
+
+.try_examples_button:hover {
+  transform: scale(1.02);
+  box-shadow: 0 2px 5px rgba(0, 0, 0, 0.2);
+  cursor: pointer;
+}
+
+.try_examples_button_container {
+  display: flex;
+  justify-content: flex-start;
+  gap: 10px;
+  margin-bottom: 20px;
+}
+
+/* Better gaps for examples buttons under admonitions */
+
+.try_examples_outer_iframe {
+  margin-top: 0.4em;
+}
diff --git a/doc/source/conf.py b/doc/source/conf.py
@@ -89,6 +89,7 @@ class PyTypeObject(ctypes.Structure):
     'sphinx_copybutton',
     'sphinx_design',
     'sphinx.ext.imgconverter',
+    'jupyterlite_sphinx',
 ]
 
 skippable_extensions = [
@@ -601,4 +602,17 @@ class NumPyLexer(CLexer):
     ('c:identifier', 'PyHeapTypeObject'),
 ]
 
+# -----------------------------------------------------------------------------
+# Interactive documentation examples via JupyterLite
+# -----------------------------------------------------------------------------
 
+global_enable_try_examples = True
+try_examples_global_button_text = "Try it in your browser!"
+try_examples_global_warning_text = (
+    "NumPy's interactive examples are experimental and may not always work"
+    " as expected, with high load times especially on low-resource platforms,"
+    " and the version of NumPy might not be in sync with the one you are"
+    " browsing the documentation for. If you encounter any issues, please"
+    " report them on the"
+    " [NumPy issue tracker](https://github.com/numpy/numpy/issues)."
+)
diff --git a/doc/source/jupyter_lite_config.json b/doc/source/jupyter_lite_config.json
@@ -0,0 +1,5 @@
+{
+  "LiteBuildConfig": {
+    "no_sourcemaps": true
+  }
+}
diff --git a/doc/source/reference/arrays.classes.rst b/doc/source/reference/arrays.classes.rst
@@ -405,22 +405,28 @@ alias for "matrix "in NumPy.
 
 Example 1: Matrix creation from a string
 
+.. try_examples::
+
   >>> import numpy as np
   >>> a = np.asmatrix('1 2 3; 4 5 3')
   >>> print((a*a.T).I)
     [[ 0.29239766 -0.13450292]
-     [-0.13450292  0.08187135]]
+    [-0.13450292  0.08187135]]
 
 
 Example 2: Matrix creation from a nested sequence
 
+.. try_examples::
+
   >>> import numpy as np
   >>> np.asmatrix([[1,5,10],[1.0,3,4j]])
   matrix([[  1.+0.j,   5.+0.j,  10.+0.j],
           [  1.+0.j,   3.+0.j,   0.+4.j]])
 
 Example 3: Matrix creation from an array
 
+.. try_examples::
+
   >>> import numpy as np
   >>> np.asmatrix(np.random.rand(3,3)).T
   matrix([[4.17022005e-01, 3.02332573e-01, 1.86260211e-01],
@@ -457,6 +463,8 @@ array actually get written to disk.
 
 Example:
 
+.. try_examples::
+
   >>> import numpy as np
 
   >>> a = np.memmap('newfile.dat', dtype=float, mode='w+', shape=1000)
@@ -605,6 +613,8 @@ This default iterator selects a sub-array of dimension :math:`N-1`
 from the array. This can be a useful construct for defining recursive
 algorithms. To loop over the entire array requires :math:`N` for-loops.
 
+.. try_examples::
+
   >>> import numpy as np
   >>> a = np.arange(24).reshape(3,2,4) + 10
   >>> for val in a:
@@ -629,8 +639,9 @@ As mentioned previously, the flat attribute of ndarray objects returns
 an iterator that will cycle over the entire array in C-style
 contiguous order.
 
+.. try_examples::
+
   >>> import numpy as np
-  >>> a = np.arange(24).reshape(3,2,4) + 10
   >>> for i, val in enumerate(a.flat):
   ...     if i%5 == 0: print(i, val)
   0 10
@@ -654,9 +665,12 @@ N-dimensional enumeration
 Sometimes it may be useful to get the N-dimensional index while
 iterating. The ndenumerate iterator can achieve this.
 
+.. try_examples::
+
   >>> import numpy as np
   >>> for i, val in np.ndenumerate(a):
-  ...     if sum(i)%5 == 0: print(i, val)
+  ...     if sum(i)%5 == 0:
+              print(i, val)
   (0, 0, 0) 10
   (1, 1, 3) 25
   (2, 0, 3) 29
@@ -677,6 +691,8 @@ objects as inputs and returns an iterator that returns tuples
 providing each of the input sequence elements in the broadcasted
 result.
 
+.. try_examples::
+
   >>> import numpy as np
   >>> for val in np.broadcast([[1, 0], [2, 3]], [0, 1]):
   ...     print(val)

diff --git a/doc/source/reference/arrays.datetime.rst b/doc/source/reference/arrays.datetime.rst
@@ -57,6 +57,8 @@ letters, for a "Not A Time" value.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     A simple ISO date:
 
     >>> import numpy as np
@@ -95,6 +97,8 @@ datetime type with generic units.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.array(['2007-07-13', '2006-01-13', '2010-08-13'], dtype='datetime64')
@@ -109,6 +113,8 @@ POSIX timestamps with the given unit.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.array([0, 1577836800], dtype='datetime64[s]')
@@ -124,6 +130,8 @@ example :func:`arange` can be used to generate ranges of dates.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     All the dates for one month:
 
     >>> import numpy as np
@@ -146,6 +154,8 @@ because the moment of time is still being represented exactly.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.datetime64('2005') == np.datetime64('2005-01-01')
@@ -175,6 +185,8 @@ data type also accepts the string "NAT" in place of the number for a "Not A Time
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.timedelta64(1, 'D')
@@ -191,6 +203,8 @@ simple datetime calculations.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.datetime64('2009-01-01') - np.datetime64('2008-01-01')
@@ -226,6 +240,8 @@ calculating the averaged values from the 400 year leap-year cycle.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> a = np.timedelta64(1, 'Y')
@@ -307,6 +323,8 @@ specified in business days to datetimes with a unit of 'D' (day).
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.busday_offset('2011-06-23', 1)
@@ -323,6 +341,8 @@ The rules most typically used are 'forward' and 'backward'.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.busday_offset('2011-06-25', 2)
@@ -347,6 +367,8 @@ is necessary to get a desired answer.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     The first business day on or after a date:
 
     >>> import numpy as np
@@ -370,6 +392,8 @@ weekmask.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
@@ -386,6 +410,8 @@ To test a `datetime64` value to see if it is a valid day, use :func:`is_busday`.
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.is_busday(np.datetime64('2011-07-15'))  # a Friday
@@ -405,6 +431,8 @@ dates, use :func:`busday_count`:
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> np.busday_count(np.datetime64('2011-07-11'), np.datetime64('2011-07-18'))
@@ -417,6 +445,8 @@ how many of them are valid dates, you can do this:
 
 .. admonition:: Example
 
+  .. try_examples::
+
     >>> import numpy as np
 
     >>> a = np.arange(np.datetime64('2011-07-11'), np.datetime64('2011-07-18'))
@@ -466,6 +496,8 @@ given below.
     23:59:60.450 UTC" is a valid timestamp which is not parseable by
     `datetime64`:
 
+    .. try_examples::
+
       >>> import numpy as np
 
       >>> np.datetime64("2016-12-31 23:59:60.450")
@@ -481,6 +513,8 @@ given below.
     Compute the number of SI seconds between "2021-01-01 12:56:23.423 UTC" and
     "2001-01-01 00:00:00.000 UTC":
 
+    .. try_examples::
+
       >>> import numpy as np
 
       >>> (
@@ -501,7 +535,8 @@ given below.
      where UT is `universal time
      <https://en.wikipedia.org/wiki/Universal_Time>`_:
 
-
+    .. try_examples::
+
       >>> import numpy as np
 
       >>> a = np.datetime64("0000-01-01", "us")