diff --git a/.github/workflows/assign.yml b/.github/workflows/assign.yml
index 9f87b8fa7e0f9..fa3b6f95a5e95 100644
--- a/.github/workflows/assign.yml
+++ b/.github/workflows/assign.yml
@@ -20,5 +20,8 @@ jobs:
     steps:
       - run: |
           echo "Assigning issue ${{ github.event.issue.number }} to ${{ github.event.comment.user.login }}"
-          curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" -d '{"assignees": ["${{ github.event.comment.user.login }}"]}' https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/assignees
-          curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" -X "DELETE" https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/labels/help%20wanted
+          gh issue edit $ISSUE --add-assignee ${{ github.event.comment.user.login }}
+          gh issue edit $ISSUE --remove-label "help wanted"
+        env:
+          GH_TOKEN: ${{ github.token }}
+          ISSUE: ${{ github.event.issue.html_url }}
diff --git a/.github/workflows/unassign.yml b/.github/workflows/unassign.yml
index c73b854530ff7..94a50d49839d6 100644
--- a/.github/workflows/unassign.yml
+++ b/.github/workflows/unassign.yml
@@ -18,4 +18,7 @@ jobs:
         if: github.event.issue.state == 'open'
         run: |
           echo "Marking issue ${{ github.event.issue.number }} as help wanted"
-          curl -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" -d '{"labels": ["help wanted"]}' https://api.github.com/repos/${{ github.repository }}/issues/${{ github.event.issue.number }}/labels
+          gh issue edit $ISSUE --add-label "help wanted"
+        env:
+          GH_TOKEN: ${{ github.token }}
+          ISSUE: ${{ github.event.issue.html_url }}
diff --git a/.gitignore b/.gitignore
index 199c2bd85d997..770f0b84f074a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,6 +13,7 @@ sklearn/**/*.html
 
 dist/
 MANIFEST
+doc/sg_execution_times.rst
 doc/_build/
 doc/auto_examples/
 doc/modules/generated/
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 458e3ee395f62..588083ba2ac57 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -231,7 +231,7 @@ jobs:
         not(contains(dependencies['git_commit']['outputs']['commit.message'], '[ci skip]'))
       )
     matrix:
-      # Linux + Python 3.9 build with OpenBLAS
+      # Linux + Python 3.9 build with OpenBLAS and without pandas
       pymin_conda_defaults_openblas:
         DISTRIB: 'conda'
         LOCK_FILE: './build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock'
diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt
index 02577734f5601..02b9100e3dd6b 100644
--- a/build_tools/azure/debian_atlas_32bit_lock.txt
+++ b/build_tools/azure/debian_atlas_32bit_lock.txt
@@ -4,9 +4,9 @@
 #
 #    pip-compile --output-file=build_tools/azure/debian_atlas_32bit_lock.txt build_tools/azure/debian_atlas_32bit_requirements.txt
 #
-attrs==23.1.0
+attrs==23.2.0
     # via pytest
-coverage==7.3.2
+coverage==7.4.0
     # via pytest-cov
 cython==0.29.33
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
index 01448879fd3d9..4171e34d5b5d1 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 7aa55d66dfbd0f6267a9aff8c750d1e9f42cd339726c8f9c4d1299341b064849
+# input_hash: 0e751f4212c4e51710aad471314a8b385a5e12fe3536c2a766f949da61eabb88
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda#01ffc8d36f9eba0ce0b3c1955fa780ee
@@ -11,7 +11,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.co
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda#937eaed008f6bf2191c5fe76f87755e9
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
@@ -20,7 +20,7 @@ https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.10-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
-https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.23.0-hd590300_0.conda#d459949bc10f64dee1595c176c2e6291
+https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.24.0-hd590300_0.conda#f5842b88e9cbfa177abfaeacd457a45d
 https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz2#14947d8770185e5153fdd04d4673ed37
 https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc
 https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h58526e2_1001.tar.bz2#8c54672728e8ec6aa6db90cf2806d220
@@ -36,7 +36,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172b
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_3.conda#c714d905cdfa0e70200f68b80cc04764
-https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_1.conda#4b06b43d0eca61db2899e4d7a289c302
+https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e
 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8
 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7
 https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.16-h0b41bf4_1.conda#28bfe2cb11357ccc5be21101a6b7ce86
@@ -45,13 +45,14 @@ https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-h166bdaf_0.tar.bz2#ede4266dc02e875fe1ea77b25dd43747
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b
 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_0.conda#30de3fd9b3b602f7473f30e684eeea8c
+https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0
 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.3-h59595ed_0.conda#bdadff838d5437aea83607ced8b37f75
 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-h59595ed_2.conda#7dbaa197d7ba6032caf7ae7f32c1efa0
 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1
 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.0-hd590300_1.conda#603827b39ea2b835268adb8c821b8570
-https://conda.anaconda.org/conda-forge/linux-64/pixman-0.42.2-h59595ed_0.conda#700edd63ccd5fc66b70b1c028cea9a68
+https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.0-h59595ed_0.conda#6b4b43013628634b6cfdee6b74fd696b
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100
 https://conda.anaconda.org/conda-forge/linux-64/re2-2023.03.02-h8c504da_0.conda#206f8fa808748f6e90599c3368a1114e
@@ -112,9 +113,9 @@ https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.18.1-h8fd135c_2.cond
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-ha9c0a0a_2.conda#55ed21669b2015f77c180feb1dd41930
 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-17.0.6-h4dfa4b3_0.conda#c1665f9c1c9f6c93d8b4e492a6a39056
 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.33-hca2cd23_6.conda#e87530d1b12dd7f4e0f856dc07358d60
-https://conda.anaconda.org/conda-forge/linux-64/nss-3.95-h1d7d5a4_0.conda#d3a8067adcc45a923f4b1987c91d69da
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.96-h1d7d5a4_0.conda#1c8f8b8eb041ecd54053fc4b6ad57957
 https://conda.anaconda.org/conda-forge/linux-64/orc-1.9.0-h2f23424_1.conda#9571eb3eb0f7fe8b59956a7786babbcd
-https://conda.anaconda.org/conda-forge/linux-64/python-3.11.6-hab00c5b_0_cpython.conda#b0dfbe2fcbfdb097d321bfd50ecddab1
+https://conda.anaconda.org/conda-forge/linux-64/python-3.11.7-hab00c5b_1_cpython.conda#27cf681282c11dba7b0b1fd266e8f289
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-hd590300_1.conda#9bfac7ccd94d54fd21a0501296d60424
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_1.conda#632413adcd8bc16b515cab87a2932913
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd590300_1.conda#e995b155d938b6779da6ace6c6b13816
@@ -128,7 +129,7 @@ https://conda.anaconda.org/conda-forge/linux-64/ccache-4.8.1-h1fcd64f_0.conda#fd
 https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.conda#2011bcf45376341dd1d690263fdbc789
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
-https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.6-py311hb755f60_0.conda#88cc84238dda72e11285d9cfcbe43e51
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.7-py311hb755f60_0.conda#97b12677eec6c2fd23c7867db1c7a87d
 https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2#ecfff944ba3960ecb334b9a2663d708d
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9
@@ -147,11 +148,10 @@ https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.0-h488ebb8_3.conda#
 https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#79002079284aa895f883c6b7f3f88fd6
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.3.0-pyhd8ed1ab_0.conda#2390bd10bed1f3fdc7a537fb5a447d8d
 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-py_1.tar.bz2#7205635cd71531943440fbfe3b6b5727
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
-https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.3-pyhd8ed1ab_0.conda#2590495f608a63625e165915fb4e2e34
+https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.4-pyhd8ed1ab_0.conda#c79cacf8a06a51552fc651652f170208
 https://conda.anaconda.org/conda-forge/noarch/pytz-2023.3.post1-pyhd8ed1ab_0.conda#c93346b446cd08c169d843ae5fc0da97
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
 https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.11.0-h00ab1b0_0.conda#fde515afbbe6e36eb4564965c20b1058
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
@@ -166,39 +166,38 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.3-h28f7589_1.conda#97503d3e565004697f1651753aa95b9e
 https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.9.3-hb447be9_1.conda#c520669eb0be9269a5f0d8ef62531882
 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e
-https://conda.anaconda.org/conda-forge/linux-64/coverage-7.3.2-py311h459d7ec_0.conda#7b3145fed7adc7c63a0e08f6f29f5480
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.46.0-py311h459d7ec_0.conda#a14114f70e23f7fd5ab9941fec45b095
+https://conda.anaconda.org/conda-forge/linux-64/coverage-7.4.0-py311h459d7ec_0.conda#bbaf0376ed2f153a90f167ad908da3d0
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.47.0-py311h459d7ec_0.conda#f7ec87c448f714f53519fe9c87ba1747
 https://conda.anaconda.org/conda-forge/linux-64/glib-2.78.3-hfc55251_0.conda#e08e51acc7d1ae8dbe13255e7b4c64ac
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
 https://conda.anaconda.org/conda-forge/linux-64/libclang-15.0.7-default_hb11cfb5_4.conda#c90f4cbb57839c98fef8f830e4b9972f
 https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.12.0-hac9eb74_1.conda#0dee716254497604762957076ac76540
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.6.0-h5d7e998_0.conda#d8edd0e29db6fb6b6988e1a28d35d994
 https://conda.anaconda.org/conda-forge/linux-64/mkl-2022.2.1-h84fe81f_16997.conda#a7ce56d5757f5b57e7daabe703ade5bb
-https://conda.anaconda.org/conda-forge/linux-64/pillow-10.1.0-py311ha6c5da5_0.conda#83a988daf5c49e57f7d2086fb6781fe8
+https://conda.anaconda.org/conda-forge/linux-64/pillow-10.2.0-py311ha6c5da5_0.conda#a5ccd7f2271f28b7d2de0b02b64e3796
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-16.1-hb77b528_5.conda#ac902ff3c1c6d750dd0dfc93a974ab74
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
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 https://conda.anaconda.org/conda-forge/linux-64/blas-1.0-mkl.tar.bz2#349aef876b1d8c9dccae01de20d5b385
-https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.22.7-h98fc4e7_1.conda#a8d71f6705ed1f70d7099a6bd1c078ac
+https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.22.8-h98fc4e7_1.conda#1b52a89485ab573a5bb83a5225ff706e
 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.3.0-h3d44ed6_0.conda#5a6f6c00ef982a9bc83558d9ac8f64a0
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diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
index 07ec7bb7ff206..107ad5b3d6f8b 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
+++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pytest-cov
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_environment.yml
deleted file mode 100644
index 02392a4e05aa8..0000000000000
--- a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_environment.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-# DO NOT EDIT: this file is generated from the specification found in the
-# following script to centralize the configuration for CI builds:
-# build_tools/update_environments_and_lock_files.py
-channels:
-  - conda-forge
-dependencies:
-  - python
-  - numpy
-  - blas[build=mkl]
-  - scipy
-  - cython
-  - joblib
-  - threadpoolctl
-  - matplotlib
-  - pandas
-  - pyamg
-  - pytest
-  - pytest-xdist=2.5.0
-  - pillow
-  - setuptools
-  - ccache
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock
deleted file mode 100644
index 23ab9fe212c16..0000000000000
--- a/build_tools/azure/pylatest_conda_forge_mkl_no_coverage_linux-64_conda.lock
+++ /dev/null
@@ -1,168 +0,0 @@
-# Generated by conda-lock.
-# platform: linux-64
-# input_hash: 223cf367742008b437f38ff4642c0e70494f665cf9434d4da5c6483c757397fd
-@EXPLICIT
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diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
index 10bb23b3c31ee..e7fda548f5985 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: osx-64
-# input_hash: 02abef27514db5e5119c3cdc253e84a06374c1b308495298b46bdb14dcc52ae9
+# input_hash: 8d19b3cb048dd1e254e00f21d81841feddd52c98a15661153cb472e9903b5cb3
 @EXPLICIT
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-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
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+https://conda.anaconda.org/conda-forge/osx-64/libxml2-2.12.3-hc0ae0f7_0.conda#959e8dad65f624a362546d96005e47f6
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+https://conda.anaconda.org/conda-forge/osx-64/libclang-cpp16-16.0.6-default_h6b1ee41_4.conda#0eea849d8d0b489bae1b9ae8656b62fb
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+https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.7-py312hede676d_0.conda#89a76a23df8d704d26a3f27e0a1c372d
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9
 https://conda.anaconda.org/conda-forge/osx-64/gfortran_impl_osx-64-12.3.0-h54fd467_1.conda#5f4d40236e204c6e62cd0a316244f316
@@ -77,15 +77,14 @@ https://conda.anaconda.org/conda-forge/osx-64/kiwisolver-1.4.5-py312h49ebfd2_1.c
 https://conda.anaconda.org/conda-forge/osx-64/ld64-609-ha02d983_15.conda#1bd5c0a940ecc8946dbe2a5b84290049
 https://conda.anaconda.org/conda-forge/osx-64/liblapacke-3.9.0-20_osx64_mkl.conda#124ae8e384268a8da66f1d64114a1eda
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
-https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.2-py312hfd3bce2_0.conda#aba72e40976485051b7567b567336319
+https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.3-py312he3a82b2_0.conda#cc7cfa90fc5c70a62b788daa71b782ef
 https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#79002079284aa895f883c6b7f3f88fd6
-https://conda.anaconda.org/conda-forge/osx-64/pillow-10.1.0-py312h0c70c2f_0.conda#50fc3446a464ff986aa4496e1eebf60b
+https://conda.anaconda.org/conda-forge/osx-64/pillow-10.2.0-py312h0c70c2f_0.conda#0cc3674239ad12c6836cb4174f106c92
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.3.0-pyhd8ed1ab_0.conda#2390bd10bed1f3fdc7a537fb5a447d8d
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
-https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.3-pyhd8ed1ab_0.conda#2590495f608a63625e165915fb4e2e34
+https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.4-pyhd8ed1ab_0.conda#c79cacf8a06a51552fc651652f170208
 https://conda.anaconda.org/conda-forge/noarch/pytz-2023.3.post1-pyhd8ed1ab_0.conda#c93346b446cd08c169d843ae5fc0da97
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
@@ -93,31 +92,30 @@ https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5
 https://conda.anaconda.org/conda-forge/osx-64/tornado-6.3.3-py312h104f124_1.conda#6835d4940d6fbd41e1a32d58dfae8f06
 https://conda.anaconda.org/conda-forge/osx-64/blas-devel-3.9.0-20_osx64_mkl.conda#cc3260179093918b801e373c6e888e02
 https://conda.anaconda.org/conda-forge/osx-64/cctools-973.0.1-h40f6528_15.conda#bc85aa6ab5eea61c47f39015dbe34a88
-https://conda.anaconda.org/conda-forge/osx-64/clang-16.0.6-hac416ee_3.conda#b143a7f213c0d25ced055089a2baef46
+https://conda.anaconda.org/conda-forge/osx-64/clang-16.0.6-hac416ee_4.conda#8c9109ae105a10984b9077899100167a
 https://conda.anaconda.org/conda-forge/osx-64/contourpy-1.2.0-py312hbf0bb39_0.conda#74190e06053cda7139a0cb71f3e618fd
-https://conda.anaconda.org/conda-forge/osx-64/coverage-7.3.2-py312h104f124_0.conda#1e98139a6dc6e29569dff47a1895a40c
-https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.46.0-py312h41838bb_0.conda#d5cc686fe3a5971312ac3ff9fd4f1557
+https://conda.anaconda.org/conda-forge/osx-64/coverage-7.4.0-py312h41838bb_0.conda#8fdd619940b64e33b0702cb46d701f6e
+https://conda.anaconda.org/conda-forge/osx-64/fonttools-4.47.0-py312h41838bb_0.conda#73605f0b5026ee8445b68fceafb53941
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
 https://conda.anaconda.org/conda-forge/osx-64/scipy-1.11.4-py312heccc6a5_0.conda#b7b422b49ae2e5c8276bffd05f3ba63c
 https://conda.anaconda.org/conda-forge/osx-64/blas-2.120-mkl.conda#b041a7677a412f3d925d8208936cb1e2
-https://conda.anaconda.org/conda-forge/osx-64/clangxx-16.0.6-default_h6b1ee41_3.conda#0cd1aaa751aa374141fa4c802b88674a
+https://conda.anaconda.org/conda-forge/osx-64/clangxx-16.0.6-default_h6b1ee41_4.conda#c5ed5a7857f12a3b8117f743e081286f
 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-base-3.8.2-py312h302682c_0.conda#6a3b7c29d663a9cda13afb8f2638cc46
 https://conda.anaconda.org/conda-forge/osx-64/pandas-2.1.4-py312haf8ecfc_0.conda#cb889a75192ef98a17c3f431f6518dd2
 https://conda.anaconda.org/conda-forge/osx-64/pyamg-5.0.1-py312h674694f_1.conda#e5b9c0f8b5c367467425ff34353ef761
 https://conda.anaconda.org/conda-forge/noarch/pytest-cov-4.1.0-pyhd8ed1ab_0.conda#06eb685a3a0b146347a58dda979485da
-https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-16.0.6-ha38d28d_2.conda#7a46507edc35c6c8818db0adaf8d787f
 https://conda.anaconda.org/conda-forge/osx-64/matplotlib-3.8.2-py312hb401068_0.conda#926f479dcab7d6d26bba7fe39f67e3b2
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-16.0.6-ha38d28d_2.conda#3b9e8c5c63b8e86234f499490acd85c2
-https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_6.conda#878ac85d82296525ee4684a70783912a
-https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_6.conda#6b39c2b8566c12a838dde6c1d401d7f4
+https://conda.anaconda.org/conda-forge/osx-64/clang_impl_osx-64-16.0.6-h8787910_8.conda#2e694b8880599d19aec8e489eb01580f
+https://conda.anaconda.org/conda-forge/osx-64/clang_osx-64-16.0.6-hb91bd55_8.conda#831779e455d39ed7e8911be6e7d02814
 https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.7.0-h282daa2_0.conda#4652f33fe8d895f61177e2783b289377
-https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h1b7723c_6.conda#58c76521e83274278e43d49f9ed31377
+https://conda.anaconda.org/conda-forge/osx-64/clangxx_impl_osx-64-16.0.6-h6d92fbe_8.conda#f2f85938b8d78c2380657efd92194490
 https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda#436af2384c47aedb94af78a128e174f1
-https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-h2fff7d5_6.conda#e42585e3b359f9fe112cc5016c649624
+https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-16.0.6-hb91bd55_8.conda#abc99f4ac92e65c4f829e4320ea200f8
 https://conda.anaconda.org/conda-forge/osx-64/gfortran-12.3.0-h2c809b3_1.conda#c48adbaa8944234b80ef287c37e329b0
 https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.7.0-h7728843_0.conda#8abaa2694c1fba2b6bd3753d00a60415
 https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.7.0-h6c2ab21_0.conda#2c11db8b46df0a547997116f0fd54b8e
diff --git a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
index 4ddb80c7cae3d..8535baec11c4d 100644
--- a/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
+++ b/build_tools/azure/pylatest_conda_forge_mkl_osx-64_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pytest-cov
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
index 64a33fe7d7522..6bc77eef6ed64 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pytest-cov
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
index 0eb965b9bd634..9bdd868dbf1f9 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
@@ -1,10 +1,10 @@
 # Generated by conda-lock.
 # platform: osx-64
-# input_hash: 03f7604aefb9752d2367c457bdf4e4923158be96db35ac0dd1d5dc60a9981cd1
+# input_hash: 9eaf961c53a9a025d43e8f2e3c17586b0ff793daddfbde53625c4b098de328ff
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a
 https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h1de35cc_0.conda#19fcb113b170fe2a0be96b47801fed7d
-https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2023.08.22-hecd8cb5_0.conda#62e40f0ed4b9adcf54eb2da76acbaf63
+https://repo.anaconda.com/pkgs/main/osx-64/ca-certificates-2023.12.12-hecd8cb5_0.conda#1f885715539fba0c408ab58d1bda6c8e
 https://repo.anaconda.com/pkgs/main/osx-64/giflib-5.2.1-h6c40b1e_3.conda#a5ab49bdb6fdc875fb965221241e3bcf
 https://repo.anaconda.com/pkgs/main/osx-64/jpeg-9e-h6c40b1e_1.conda#fc3e61fa41309946c9283fe8737d7f41
 https://repo.anaconda.com/pkgs/main/osx-64/libbrotlicommon-1.0.9-hca72f7f_7.conda#6c865b9e76fa2fad0c8ac32aa0f01f75
@@ -14,7 +14,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libffi-3.4.4-hecd8cb5_0.conda#c20b268
 https://repo.anaconda.com/pkgs/main/osx-64/libwebp-base-1.3.2-h6c40b1e_0.conda#d8fd9f599dd4e012694e69d119016442
 https://repo.anaconda.com/pkgs/main/osx-64/llvm-openmp-14.0.6-h0dcd299_0.conda#b5804d32b87dc61ca94561ade33d5f2d
 https://repo.anaconda.com/pkgs/main/osx-64/ncurses-6.4-hcec6c5f_0.conda#0214d1ee980e217fabc695f1e40662aa
-https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd
+https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023d-h04d1e81_0.conda#fdb319536f351b2b828a350ffd1a35a1
 https://repo.anaconda.com/pkgs/main/osx-64/xz-5.4.5-h6c40b1e_0.conda#351c5d33fe551018a2068e7a2ca8a6c1
 https://repo.anaconda.com/pkgs/main/osx-64/zlib-1.2.13-h4dc903c_0.conda#d0202dd912bfb45d3422786531717882
 https://repo.anaconda.com/pkgs/main/osx-64/ccache-3.7.9-hf120daa_0.conda#a01515a32e721c51d631283f991bc8ea
@@ -37,10 +37,10 @@ https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.41.2-h6c40b1e_0.conda#6947a5
 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_0.conda#5e0b7ddb1b7dc6b630e1f9a03499c19c
 https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-hca72f7f_7.conda#68e54d12ec67591deb2ffd70348fb00f
 https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.1-hcec6c5f_0.conda#e127a800ffd9d300ed7d5e1b026944ec
-https://repo.anaconda.com/pkgs/main/osx-64/python-3.11.5-hf27a42d_0.conda#f088169d190325a14aaa0dcb53a9864f
+https://repo.anaconda.com/pkgs/main/osx-64/python-3.11.7-hf27a42d_0.conda#fe0cfacb8965d0a06f8098464d5a8402
 https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py311h6c40b1e_0.conda#e15605553450156cf75c3ae38a920475
 https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab
-https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.0-py311h6c40b1e_0.conda#f1831f4c643b4653ecb777477763f9cc
+https://repo.anaconda.com/pkgs/main/osx-64/cython-3.0.6-py311h6c40b1e_0.conda#6c8a140209eb4814de054f52627f543c
 https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513
 https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507
 https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.2.0-py311hecd8cb5_0.conda#af8c1fcd4e8e0c6fa2a4f4ecda261dc9
@@ -52,11 +52,10 @@ https://repo.anaconda.com/pkgs/main/noarch/munkres-1.1.4-py_0.conda#148362ba07f9
 https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h66ea3da_0.conda#882833bd7befc5e60e6fba9c518c1b79
 https://repo.anaconda.com/pkgs/main/osx-64/packaging-23.1-py311hecd8cb5_0.conda#4f5c491cd2de9d61f61c0ea3340ab46a
 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py311hecd8cb5_1.conda#98e4da64cd934965a0caf4136280ff35
-https://repo.anaconda.com/pkgs/main/noarch/py-1.11.0-pyhd3eb1b0_0.conda#7205a898ed2abbf6e9b903dff6abe08e
 https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py311hecd8cb5_0.conda#a4262f849ecc82af69f58da0cbcaaf04
 https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda#479c037de0186d114b9911158427624e
 https://repo.anaconda.com/pkgs/main/osx-64/pytz-2023.3.post1-py311hecd8cb5_0.conda#32d107281d133e3935dfb6935153e438
-https://repo.anaconda.com/pkgs/main/osx-64/setuptools-68.0.0-py311hecd8cb5_0.conda#ad594daf4f91ef9b89b10b0f4b2c9e10
+https://repo.anaconda.com/pkgs/main/osx-64/setuptools-68.2.2-py311hecd8cb5_0.conda#c5f526775f35d920e9d6099fa146d7a1
 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0
 https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a
@@ -67,8 +66,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/pillow-10.0.1-py311h7d39338_0.conda#0
 https://repo.anaconda.com/pkgs/main/osx-64/pytest-7.4.0-py311hecd8cb5_0.conda#8c5496a4a1f36160ac5556495faa4a24
 https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9
 https://repo.anaconda.com/pkgs/main/osx-64/pytest-cov-4.1.0-py311hecd8cb5_1.conda#b1e41a8eda3f119b39b13f3a4d0c5bf5
-https://repo.anaconda.com/pkgs/main/osx-64/pytest-forked-1.6.0-py311hecd8cb5_0.conda#b1154a9887bee381b3405ec37f8b13f3
-https://repo.anaconda.com/pkgs/main/noarch/pytest-xdist-2.5.0-pyhd3eb1b0_0.conda#d15cdc4207bcf8ca920822597f1d138d
+https://repo.anaconda.com/pkgs/main/osx-64/pytest-xdist-3.5.0-py311hecd8cb5_0.conda#e892e4359ea4f0987e8268f7e7869680
 https://repo.anaconda.com/pkgs/main/osx-64/bottleneck-1.3.5-py311hb9e55a9_0.conda#5aa1b58b421d4608b16184f8468253ef
 https://repo.anaconda.com/pkgs/main/osx-64/contourpy-1.2.0-py311ha357a0b_0.conda#c9189b40e5b4be360aef22be336a4838
 https://repo.anaconda.com/pkgs/main/osx-64/matplotlib-3.8.0-py311hecd8cb5_0.conda#f720f09a9d1bb976aa92a13180cf7133
diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
index ddbc75c1d9110..6167ca6e63748 100644
--- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
+++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml
@@ -17,7 +17,7 @@ dependencies:
     - pandas
     - pyamg
     - pytest
-    - pytest-xdist==2.5.0
+    - pytest-xdist
     - pillow
     - setuptools
     - pytest-cov
diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
index ff6980c051d83..593c5571ece8b 100644
--- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
@@ -1,11 +1,11 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: d01d23bd27bcd50d2b3643492f966c8e390822d72b69f31bf66c2fe98a265a4c
+# input_hash: 11d8952d04302b85207df163f6a5b20d8680e2eb067f9fb492d381a2b74c3a8f
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
-https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.08.22-h06a4308_0.conda#243d5065a09a3e85ab888c05f5b6445a
+https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.12.12-h06a4308_0.conda#12bf7315c3f5ca50300e8b48d1b4ef2e
 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b
-https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd
+https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023d-h04d1e81_0.conda#fdb319536f351b2b828a350ffd1a35a1
 https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
@@ -20,19 +20,19 @@ https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be421
 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
 https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.18-h955ad1f_0.conda#65fb745edecf85675ed0487fc54316b5
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py39h06a4308_0.conda#0af0f107fd4cfe0b3b46ce9fe0471873
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de
 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py39h06a4308_0.conda#ec1b8213c3585defaa6042ed2f95861d
 https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685007e3dae59d211620f19926577bd6
-# pip alabaster @ https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl#sha256=1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3
+# pip alabaster @ https://files.pythonhosted.org/packages/a8/11/a3159174442867ea12826e60a9f1d6f6299c2ae3f896d2a47566ab826686/alabaster-0.7.15-py3-none-any.whl#sha256=d99c6fd0f7a86fca68ecc5231c9de45227991c10ee6facfb894cf6afb953b142
 # pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287
 # pip certifi @ https://files.pythonhosted.org/packages/64/62/428ef076be88fa93716b576e4a01f919d25968913e817077a386fcbe4f42/certifi-2023.11.17-py3-none-any.whl#sha256=e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/98/69/5d8751b4b670d623aa7a47bef061d69c279e9f922f6705147983aa76c3ce/charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796
 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30
-# pip cython @ https://files.pythonhosted.org/packages/4e/0c/c796b64bb889e980a9b066249f65da5105110e4fbaf53885180313012ad3/Cython-3.0.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1074e84752cd0daf3226823ddbc37cca8bc45f61c94a1db2a34e641f2b9b0797
+# pip cython @ https://files.pythonhosted.org/packages/32/63/b947d620e99250ab9b920d3bfdbeab305124e9d39afbe260a85906943e59/Cython-3.0.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b9d0dae6dccd349b8ccf197c10ef2d05c711ca36a649c7eddbab1de2c90b63a1
 # pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6
 # pip exceptiongroup @ https://files.pythonhosted.org/packages/b8/9a/5028fd52db10e600f1c4674441b968cf2ea4959085bfb5b99fb1250e5f68/exceptiongroup-1.2.0-py3-none-any.whl#sha256=4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14
 # pip execnet @ https://files.pythonhosted.org/packages/e8/9c/a079946da30fac4924d92dbc617e5367d454954494cf1e71567bcc4e00ee/execnet-2.0.2-py3-none-any.whl#sha256=88256416ae766bc9e8895c76a87928c0012183da3cc4fc18016e6f050e025f41
-# pip fonttools @ https://files.pythonhosted.org/packages/ad/94/6cc0d252b4e8e6c61c971a8c50e38229c34a61147a059aafd308d1587b9f/fonttools-4.46.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=d00fc63131dcac6b25f50a5a129758438317e54e3ce5587163f7058de4b0e933
+# pip fonttools @ https://files.pythonhosted.org/packages/55/a7/f08f063c6ff1b2d3abd68cc4a6872143fbc0f99a83cc44b96944ff11f817/fonttools-4.47.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=253bb46bab970e8aae254cebf2ae3db98a4ef6bd034707aa68a239027d2b198d
 # pip idna @ https://files.pythonhosted.org/packages/c2/e7/a82b05cf63a603df6e68d59ae6a68bf5064484a0718ea5033660af4b54a9/idna-3.6-py3-none-any.whl#sha256=c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
 # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374
@@ -41,11 +41,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685
 # pip lazy-loader @ https://files.pythonhosted.org/packages/a1/c3/65b3814e155836acacf720e5be3b5757130346670ac454fee29d3eda1381/lazy_loader-0.3-py3-none-any.whl#sha256=1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554
 # pip markupsafe @ https://files.pythonhosted.org/packages/de/63/cb7e71984e9159ec5f45b5e81e896c8bdd0e45fe3fc6ce02ab497f0d790e/MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e
 # pip networkx @ https://files.pythonhosted.org/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl#sha256=f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2
-# pip numpy @ https://files.pythonhosted.org/packages/2f/75/f007cc0e6a373207818bef17f463d3305e9dd380a70db0e523e7660bf21f/numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=baf8aab04a2c0e859da118f0b38617e5ee65d75b83795055fb66c0d5e9e9b818
+# pip numpy @ https://files.pythonhosted.org/packages/ea/ee/7a93594b78d7834d14ff49e74ba79e3f26b85604a542a790db81b1dd2326/numpy-1.26.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b4d362e17bcb0011738c2d83e0a65ea8ce627057b2fdda37678f4374a382a137
 # pip packaging @ https://files.pythonhosted.org/packages/ec/1a/610693ac4ee14fcdf2d9bf3c493370e4f2ef7ae2e19217d7a237ff42367d/packaging-23.2-py3-none-any.whl#sha256=8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7
-# pip pillow @ https://files.pythonhosted.org/packages/5c/dc/acccca38a87272cb2eed372f112595439418dfb6119770b04dc06d3b78bd/Pillow-10.1.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=b4005fee46ed9be0b8fb42be0c20e79411533d1fd58edabebc0dd24626882cfd
+# pip pillow @ https://files.pythonhosted.org/packages/87/0d/8f5136a5481731c342a901ff155c587ce7804114db069345e1894ab4978a/pillow-10.2.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=b6f491cdf80ae540738859d9766783e3b3c8e5bd37f5dfa0b76abdecc5081f13
 # pip pluggy @ https://files.pythonhosted.org/packages/05/b8/42ed91898d4784546c5f06c60506400548db3f7a4b3fb441cba4e5c17952/pluggy-1.3.0-py3-none-any.whl#sha256=d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7
-# pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378
 # pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c
 # pip pyparsing @ https://files.pythonhosted.org/packages/39/92/8486ede85fcc088f1b3dba4ce92dd29d126fd96b0008ea213167940a2475/pyparsing-3.1.1-py3-none-any.whl#sha256=32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb
 # pip pytz @ https://files.pythonhosted.org/packages/32/4d/aaf7eff5deb402fd9a24a1449a8119f00d74ae9c2efa79f8ef9994261fc2/pytz-2023.3.post1-py2.py3-none-any.whl#sha256=ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7
@@ -55,28 +54,27 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py39h06a4308_0.conda#685
 # pip tabulate @ https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl#sha256=024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f
 # pip threadpoolctl @ https://files.pythonhosted.org/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl#sha256=2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032
 # pip tomli @ https://files.pythonhosted.org/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl#sha256=939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc
-# pip tzdata @ https://files.pythonhosted.org/packages/d5/fb/a79efcab32b8a1f1ddca7f35109a50e4a80d42ac1c9187ab46522b2407d7/tzdata-2023.3-py2.py3-none-any.whl#sha256=7e65763eef3120314099b6939b5546db7adce1e7d6f2e179e3df563c70511eda
+# pip tzdata @ https://files.pythonhosted.org/packages/a3/fb/52b62131e21b24ee297e4e95ed41eba29647dad0e0051a92bb66b43c70ff/tzdata-2023.4-py2.py3-none-any.whl#sha256=aa3ace4329eeacda5b7beb7ea08ece826c28d761cda36e747cfbf97996d39bf3
 # pip urllib3 @ https://files.pythonhosted.org/packages/96/94/c31f58c7a7f470d5665935262ebd7455c7e4c7782eb525658d3dbf4b9403/urllib3-2.1.0-py3-none-any.whl#sha256=55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3
 # pip zipp @ https://files.pythonhosted.org/packages/d9/66/48866fc6b158c81cc2bfecc04c480f105c6040e8b077bc54c634b4a67926/zipp-3.17.0-py3-none-any.whl#sha256=0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31
 # pip contourpy @ https://files.pythonhosted.org/packages/a9/ba/d8fd1380876f1e9114157606302e3644c85f6d116aeba354c212ee13edc7/contourpy-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=11f8d2554e52f459918f7b8e6aa20ec2a3bce35ce95c1f0ef4ba36fbda306df5
-# pip coverage @ https://files.pythonhosted.org/packages/f1/e7/6d778d717d178c8c73103e2c467f3c8d8ebc9cacb825ebe3f3cf05e7c6df/coverage-7.3.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=149de1d2401ae4655c436a3dced6dd153f4c3309f599c3d4bd97ab172eaf02d9
+# pip coverage @ https://files.pythonhosted.org/packages/dc/9a/825705f435ef469c780045746c725f974ca8b059380df28b6331995a2ae1/coverage-7.4.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bf635a52fc1ea401baf88843ae8708591aa4adff875e5c23220de43b1ccf575c
 # pip imageio @ https://files.pythonhosted.org/packages/c0/69/3aaa69cb0748e33e644fda114c9abd3186ce369edd4fca11107e9f39c6a7/imageio-2.33.1-py3-none-any.whl#sha256=c5094c48ccf6b2e6da8b4061cd95e1209380afafcbeae4a4e280938cce227e1d
-# pip importlib-metadata @ https://files.pythonhosted.org/packages/73/26/9777cfe0cdc8181a32eaf542f4a2a435e5aba5dd38f41cfc0a532dc51027/importlib_metadata-7.0.0-py3-none-any.whl#sha256=d97503976bb81f40a193d41ee6570868479c69d5068651eb039c40d850c59d67
+# pip importlib-metadata @ https://files.pythonhosted.org/packages/c0/8b/d8427f023c081a8303e6ac7209c16e6878f2765d5b59667f3903fbcfd365/importlib_metadata-7.0.1-py3-none-any.whl#sha256=4805911c3a4ec7c3966410053e9ec6a1fecd629117df5adee56dfc9432a1081e
 # pip importlib-resources @ https://files.pythonhosted.org/packages/93/e8/facde510585869b5ec694e8e0363ffe4eba067cb357a8398a55f6a1f8023/importlib_resources-6.1.1-py3-none-any.whl#sha256=e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6
 # pip jinja2 @ https://files.pythonhosted.org/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl#sha256=6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
-# pip pytest @ https://files.pythonhosted.org/packages/f3/8c/f16efd81ca8e293b2cc78f111190a79ee539d0d5d36ccd49975cb3beac60/pytest-7.4.3-py3-none-any.whl#sha256=0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac
+# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8
 # pip python-dateutil @ https://files.pythonhosted.org/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl#sha256=961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
 # pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f
 # pip scipy @ https://files.pythonhosted.org/packages/db/86/bf3f01f003224c00dd94d9443d676023ed65d63ea2e34356888dc7fa8f48/scipy-1.11.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=91af76a68eeae0064887a48e25c4e616fa519fa0d38602eda7e0f97d65d57937
 # pip tifffile @ https://files.pythonhosted.org/packages/54/a4/569fc717831969cf48bced350bdaf070cdeab06918d179429899e144358d/tifffile-2023.12.9-py3-none-any.whl#sha256=9b066e4b1a900891ea42ffd33dab8ba34c537935618b9893ddef42d7d422692f
-# pip lightgbm @ https://files.pythonhosted.org/packages/b8/9d/1ce80cee7c5ef60f2fcc7e9fa97f29f7a8de3dc5a08922b3b2f1e9106481/lightgbm-4.1.0-py3-none-manylinux_2_28_x86_64.whl#sha256=47578cff4bc8116b62adc02437bf2b49dcc7ad4e8e3dd8dad3fe88e694d74d93
+# pip lightgbm @ https://files.pythonhosted.org/packages/a6/11/5171f6a1ecf7f008648fef6ef780d92414763ff5ba50a796657b9275dc1e/lightgbm-4.2.0-py3-none-manylinux_2_28_x86_64.whl#sha256=4a767795253ea5872abc7cc4e0892120af9b48a10e151c03cd62116bc2f099ab
 # pip matplotlib @ https://files.pythonhosted.org/packages/53/1f/653d60d2ec81a6095fa3e571cf2de57742bab8a51a5c01de26730ce3dc53/matplotlib-3.8.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5864bdd7da445e4e5e011b199bb67168cdad10b501750367c496420f2ad00843
 # pip pandas @ https://files.pythonhosted.org/packages/bc/f8/2aa75ae200bdb9dc6967712f26628a06bf45d3ad94cbbf6fb4962ada15a3/pandas-2.1.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1ebfd771110b50055712b3b711b51bee5d50135429364d0498e1213a7adc2be8
 # pip pyamg @ https://files.pythonhosted.org/packages/35/1c/8b2aa6fbb2bae258ab6cdb35b09635bf50865ac2bcdaf220db3d972cc0d8/pyamg-5.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=1332acec6d5ede9440c8ced0ef20952f5b766387116f254b79880ce29fdecee7
 # pip pytest-cov @ https://files.pythonhosted.org/packages/a7/4b/8b78d126e275efa2379b1c2e09dc52cf70df16fc3b90613ef82531499d73/pytest_cov-4.1.0-py3-none-any.whl#sha256=6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a
-# pip pytest-forked @ https://files.pythonhosted.org/packages/f4/af/9c0bda43e486a3c9bf1e0f876d0f241bc3f229d7d65d09331a0868db9629/pytest_forked-1.6.0-py3-none-any.whl#sha256=810958f66a91afb1a1e2ae83089d8dc1cd2437ac96b12963042fbb9fb4d16af0
+# pip pytest-xdist @ https://files.pythonhosted.org/packages/50/37/125fe5ec459321e2d48a0c38672cfc2419ad87d580196fd894e5f25230b0/pytest_xdist-3.5.0-py3-none-any.whl#sha256=d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24
 # pip scikit-image @ https://files.pythonhosted.org/packages/a3/7e/4cd853a855ac34b4ef3ef6a5c3d1c2e96eaca1154fc6be75db55ffa87393/scikit_image-0.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3b7a6c89e8d6252332121b58f50e1625c35f7d6a85489c0b6b7ee4f5155d547a
-# pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65
 # pip numpydoc @ https://files.pythonhosted.org/packages/9c/94/09c437fd4a5fb5adf0468c0865c781dbc11d399544b55f1163d5d4414afb/numpydoc-1.6.0-py3-none-any.whl#sha256=b6ddaa654a52bdf967763c1e773be41f1c3ae3da39ee0de973f2680048acafaa
 # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/c0/0c/261c0949083c0ac635853528bb0070c89e927841d4e533ba0b5563365c06/sphinxcontrib_applehelp-1.0.7-py3-none-any.whl#sha256=094c4d56209d1734e7d252f6e0b3ccc090bd52ee56807a5d9315b19c122ab15d
 # pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/c0/03/010ac733ec7b7f71c1dc88e7115743ee466560d6d85373b56fb9916e4586/sphinxcontrib_devhelp-1.0.5-py3-none-any.whl#sha256=fe8009aed765188f08fcaadbb3ea0d90ce8ae2d76710b7e29ea7d047177dae2f
diff --git a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
index 2d3de7b1e1ed4..63987809e6ddd 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
+++ b/build_tools/azure/pylatest_pip_scipy_dev_environment.yml
@@ -10,7 +10,7 @@ dependencies:
   - pip:
     - threadpoolctl
     - pytest
-    - pytest-xdist==2.5.0
+    - pytest-xdist
     - setuptools
     - pytest-cov
     - coverage
diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
index 0f98f987d6851..a3c3af5613906 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
@@ -1,11 +1,11 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 28ec764eefc982520846833c9ea571cf6ea5a0593dee76d7a7560b34e341e35b
+# input_hash: 4ef027bae3f3dd18c4b010f99e6cc898037a9e17722412580463a65b352072ea
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
-https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.08.22-h06a4308_0.conda#243d5065a09a3e85ab888c05f5b6445a
+https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.12.12-h06a4308_0.conda#12bf7315c3f5ca50300e8b48d1b4ef2e
 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b
-https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd
+https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023d-h04d1e81_0.conda#fdb319536f351b2b828a350ffd1a35a1
 https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
@@ -21,15 +21,15 @@ https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6f
 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb
 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
-https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.5-h955ad1f_0.conda#3fd62f043c124c7aad747122e3a9edf2
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py311h06a4308_0.conda#eae51c7be37e9fc2b6f708114b9f2e8d
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.11.7-h955ad1f_0.conda#721e0e84035214979d06e677d5afa9f4
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py311h06a4308_0.conda#264aaac990aa82ff86442ad8249787a3
 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py311h06a4308_0.conda#2d4ff85d3dfb7749ae0485ee148d4ea5
 https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py311h06a4308_0.conda#6fdb2a3c731f093b0014450a071c7f7f
-# pip alabaster @ https://files.pythonhosted.org/packages/64/88/c7083fc61120ab661c5d0b82cb77079fc1429d3f913a456c1c82cf4658f7/alabaster-0.7.13-py3-none-any.whl#sha256=1ee19aca801bbabb5ba3f5f258e4422dfa86f82f3e9cefb0859b283cdd7f62a3
+# pip alabaster @ https://files.pythonhosted.org/packages/a8/11/a3159174442867ea12826e60a9f1d6f6299c2ae3f896d2a47566ab826686/alabaster-0.7.15-py3-none-any.whl#sha256=d99c6fd0f7a86fca68ecc5231c9de45227991c10ee6facfb894cf6afb953b142
 # pip babel @ https://files.pythonhosted.org/packages/0d/35/4196b21041e29a42dc4f05866d0c94fa26c9da88ce12c38c2265e42c82fb/Babel-2.14.0-py3-none-any.whl#sha256=efb1a25b7118e67ce3a259bed20545c29cb68be8ad2c784c83689981b7a57287
 # pip certifi @ https://files.pythonhosted.org/packages/64/62/428ef076be88fa93716b576e4a01f919d25968913e817077a386fcbe4f42/certifi-2023.11.17-py3-none-any.whl#sha256=e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/40/26/f35951c45070edc957ba40a5b1db3cf60a9dbb1b350c2d5bef03e01e61de/charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8
-# pip coverage @ https://files.pythonhosted.org/packages/bc/01/bf243cf5c926681b35d0c6aa9a3b33da35ab65323c4a593d386b08a0249e/coverage-7.3.2-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4175e10cc8dda0265653e8714b3174430b07c1dca8957f4966cbd6c2b1b8065a
+# pip coverage @ https://files.pythonhosted.org/packages/3b/35/c5aa0de6a3c40f42b7702298de7b0a67c96bfe0c44ed9d0a953d069b23dc/coverage-7.4.0-cp311-cp311-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=485e9f897cf4856a65a57c7f6ea3dc0d4e6c076c87311d4bc003f82cfe199d25
 # pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6
 # pip execnet @ https://files.pythonhosted.org/packages/e8/9c/a079946da30fac4924d92dbc617e5367d454954494cf1e71567bcc4e00ee/execnet-2.0.2-py3-none-any.whl#sha256=88256416ae766bc9e8895c76a87928c0012183da3cc4fc18016e6f050e025f41
 # pip idna @ https://files.pythonhosted.org/packages/c2/e7/a82b05cf63a603df6e68d59ae6a68bf5064484a0718ea5033660af4b54a9/idna-3.6-py3-none-any.whl#sha256=c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f
@@ -39,7 +39,6 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py311h06a4308_0.conda#6f
 # pip packaging @ https://files.pythonhosted.org/packages/ec/1a/610693ac4ee14fcdf2d9bf3c493370e4f2ef7ae2e19217d7a237ff42367d/packaging-23.2-py3-none-any.whl#sha256=8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7
 # pip platformdirs @ https://files.pythonhosted.org/packages/be/53/42fe5eab4a09d251a76d0043e018172db324a23fcdac70f77a551c11f618/platformdirs-4.1.0-py3-none-any.whl#sha256=11c8f37bcca40db96d8144522d925583bdb7a31f7b0e37e3ed4318400a8e2380
 # pip pluggy @ https://files.pythonhosted.org/packages/05/b8/42ed91898d4784546c5f06c60506400548db3f7a4b3fb441cba4e5c17952/pluggy-1.3.0-py3-none-any.whl#sha256=d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7
-# pip py @ https://files.pythonhosted.org/packages/f6/f0/10642828a8dfb741e5f3fbaac830550a518a775c7fff6f04a007259b0548/py-1.11.0-py2.py3-none-any.whl#sha256=607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378
 # pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c
 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a
@@ -48,13 +47,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py311h06a4308_0.conda#6f
 # pip threadpoolctl @ https://files.pythonhosted.org/packages/81/12/fd4dea011af9d69e1cad05c75f3f7202cdcbeac9b712eea58ca779a72865/threadpoolctl-3.2.0-py3-none-any.whl#sha256=2b7818516e423bdaebb97c723f86a7c6b0a83d3f3b0970328d66f4d9104dc032
 # pip urllib3 @ https://files.pythonhosted.org/packages/96/94/c31f58c7a7f470d5665935262ebd7455c7e4c7782eb525658d3dbf4b9403/urllib3-2.1.0-py3-none-any.whl#sha256=55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3
 # pip jinja2 @ https://files.pythonhosted.org/packages/bc/c3/f068337a370801f372f2f8f6bad74a5c140f6fda3d9de154052708dd3c65/Jinja2-3.1.2-py3-none-any.whl#sha256=6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61
-# pip pytest @ https://files.pythonhosted.org/packages/f3/8c/f16efd81ca8e293b2cc78f111190a79ee539d0d5d36ccd49975cb3beac60/pytest-7.4.3-py3-none-any.whl#sha256=0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac
+# pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8
 # pip python-dateutil @ https://files.pythonhosted.org/packages/36/7a/87837f39d0296e723bb9b62bbb257d0355c7f6128853c78955f57342a56d/python_dateutil-2.8.2-py2.py3-none-any.whl#sha256=961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9
 # pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f
 # pip pooch @ https://files.pythonhosted.org/packages/1a/a5/5174dac3957ac412e80a00f30b6507031fcab7000afc9ea0ac413bddcff2/pooch-1.8.0-py3-none-any.whl#sha256=1bfba436d9e2ad5199ccad3583cca8c241b8736b5bb23fe67c213d52650dbb66
 # pip pytest-cov @ https://files.pythonhosted.org/packages/a7/4b/8b78d126e275efa2379b1c2e09dc52cf70df16fc3b90613ef82531499d73/pytest_cov-4.1.0-py3-none-any.whl#sha256=6ba70b9e97e69fcc3fb45bfeab2d0a138fb65c4d0d6a41ef33983ad114be8c3a
-# pip pytest-forked @ https://files.pythonhosted.org/packages/f4/af/9c0bda43e486a3c9bf1e0f876d0f241bc3f229d7d65d09331a0868db9629/pytest_forked-1.6.0-py3-none-any.whl#sha256=810958f66a91afb1a1e2ae83089d8dc1cd2437ac96b12963042fbb9fb4d16af0
-# pip pytest-xdist @ https://files.pythonhosted.org/packages/21/08/b1945d4b4986eb1aa10cf84efc5293bba39da80a2f95db3573dd90678408/pytest_xdist-2.5.0-py3-none-any.whl#sha256=6fe5c74fec98906deb8f2d2b616b5c782022744978e7bd4695d39c8f42d0ce65
+# pip pytest-xdist @ https://files.pythonhosted.org/packages/50/37/125fe5ec459321e2d48a0c38672cfc2419ad87d580196fd894e5f25230b0/pytest_xdist-3.5.0-py3-none-any.whl#sha256=d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24
 # pip numpydoc @ https://files.pythonhosted.org/packages/9c/94/09c437fd4a5fb5adf0468c0865c781dbc11d399544b55f1163d5d4414afb/numpydoc-1.6.0-py3-none-any.whl#sha256=b6ddaa654a52bdf967763c1e773be41f1c3ae3da39ee0de973f2680048acafaa
 # pip sphinxcontrib-applehelp @ https://files.pythonhosted.org/packages/c0/0c/261c0949083c0ac635853528bb0070c89e927841d4e533ba0b5563365c06/sphinxcontrib_applehelp-1.0.7-py3-none-any.whl#sha256=094c4d56209d1734e7d252f6e0b3ccc090bd52ee56807a5d9315b19c122ab15d
 # pip sphinxcontrib-devhelp @ https://files.pythonhosted.org/packages/c0/03/010ac733ec7b7f71c1dc88e7115743ee466560d6d85373b56fb9916e4586/sphinxcontrib_devhelp-1.0.5-py3-none-any.whl#sha256=fe8009aed765188f08fcaadbb3ea0d90ce8ae2d76710b7e29ea7d047177dae2f
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
index 9f6a649249cbe..a93498d23e537 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
+++ b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
@@ -12,10 +12,9 @@ dependencies:
   - joblib
   - threadpoolctl=2.2.0
   - matplotlib=3.3.4  # min
-  - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pytest-cov
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
index 32eeb4b9c9118..4543307280a3b 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
@@ -1,13 +1,13 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 9e735ba6c65ff977fbfdb3bade1b172aca761f7e774bf4b2814dc2efb8b9fa3b
+# input_hash: 82d3fc4a221c5788b1501ed52f4700a43ac387e29dba2eccc9f2fd6521c878ff
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8
-https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.08.22-h06a4308_0.conda#243d5065a09a3e85ab888c05f5b6445a
+https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2023.12.12-h06a4308_0.conda#12bf7315c3f5ca50300e8b48d1b4ef2e
 https://repo.anaconda.com/pkgs/main/linux-64/ld_impl_linux-64-2.38-h1181459_1.conda#68eedfd9c06f2b0e6888d8db345b7f5b
 https://repo.anaconda.com/pkgs/main/linux-64/libgfortran5-11.2.0-h1234567_1.conda#36a01a8c30e0cadf0d3e842c50b73f3b
-https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023c-h04d1e81_0.conda#29db02adf8808f7c64642cead3e28acd
+https://repo.anaconda.com/pkgs/main/noarch/tzdata-2023d-h04d1e81_0.conda#fdb319536f351b2b828a350ffd1a35a1
 https://repo.anaconda.com/pkgs/main/linux-64/libgfortran-ng-11.2.0-h00389a5_1.conda#7429b67ab7b1d7cb99b9d1f3ddaec6e3
 https://repo.anaconda.com/pkgs/main/linux-64/libgomp-11.2.0-h1234567_1.conda#b372c0eea9b60732fdae4b817a63c8cd
 https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.conda#57623d10a70e09e1d048c2b2b6f4e2dd
@@ -34,7 +34,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_0.conda#333e31
 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e
 https://repo.anaconda.com/pkgs/main/linux-64/glib-2.69.1-he621ea3_2.conda#51cf1899782b3f3744aedd143fbc07f3
 https://repo.anaconda.com/pkgs/main/linux-64/libcups-2.4.2-h2d74bed_1.conda#3f265c2172a9e8c90a74037b6fa13685
-https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20221030-h5eee18b_0.conda#7c724a17739aceaf9d1633ff06962137
+https://repo.anaconda.com/pkgs/main/linux-64/libedit-3.1.20230828-h5eee18b_0.conda#850eb5a9d2d7d3c66cce12e84406ca08
 https://repo.anaconda.com/pkgs/main/linux-64/libllvm14-14.0.6-hdb19cb5_3.conda#aefea2b45cf32f12b4f1ffaa70aa3201
 https://repo.anaconda.com/pkgs/main/linux-64/libpng-1.6.39-h5eee18b_0.conda#f6aee38184512eb05b06c2e94d39ab22
 https://repo.anaconda.com/pkgs/main/linux-64/libxml2-2.10.4-hf1b16e4_1.conda#e87849ce513f9968794f20bba620e6a4
@@ -72,11 +72,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/packaging-23.1-py39h06a4308_0.conda
 https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.0.1-py39ha6cbd5a_0.conda#a16f050efc583049a46accd497525967
 https://repo.anaconda.com/pkgs/main/linux-64/pluggy-1.0.0-py39h06a4308_1.conda#fb4fed11ed43cf727dbd51883cc1d9fa
 https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py39h06a4308_0.conda#6c89bf6d2fdf6d24126e34cb83fd10f1
-https://repo.anaconda.com/pkgs/main/noarch/py-1.11.0-pyhd3eb1b0_0.conda#7205a898ed2abbf6e9b903dff6abe08e
 https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py39h06a4308_0.conda#3a0537468e59760404f63b4f04369828
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py39h5eee18b_0.conda#256840c3841b52346ea5743be8490ede
-https://repo.anaconda.com/pkgs/main/linux-64/pytz-2023.3.post1-py39h06a4308_0.conda#0089cedc881d263480200b4e26377352
-https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.0.0-py39h06a4308_0.conda#0af0f107fd4cfe0b3b46ce9fe0471873
+https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de
 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0
 https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a
@@ -88,14 +86,10 @@ https://repo.anaconda.com/pkgs/main/linux-64/pytest-7.4.0-py39h06a4308_0.conda#9
 https://repo.anaconda.com/pkgs/main/noarch/python-dateutil-2.8.2-pyhd3eb1b0_0.conda#211ee00320b08a1ac9fea6677649f6c9
 https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd0c79e82df6323f638bdcb871891b61
 https://repo.anaconda.com/pkgs/main/linux-64/sip-6.7.12-py39h6a678d5_0.conda#6988a3e12fcacfedcac523c1e4c3167c
-https://repo.anaconda.com/pkgs/main/linux-64/bottleneck-1.3.5-py39h7deecbd_0.conda#c07c855de7bcef6b409ef1460dea7438
 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-base-3.3.4-py39h62a2d02_0.conda#dbab28222c740af8e21a3e5e2882c178
-https://repo.anaconda.com/pkgs/main/linux-64/numexpr-2.8.7-py39h286c3b5_0.conda#de217418aa5b86ad59ecace62c11494e
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2
 https://repo.anaconda.com/pkgs/main/linux-64/pytest-cov-4.1.0-py39h06a4308_1.conda#8f41fce21670b120bf7fa8a7883380d9
-https://repo.anaconda.com/pkgs/main/linux-64/pytest-forked-1.6.0-py39h06a4308_0.conda#f0a6e858c06dc4d2ae5c9644630a6a83
+https://repo.anaconda.com/pkgs/main/linux-64/pytest-xdist-3.5.0-py39h06a4308_0.conda#e1d7ffcb1ee2ed9a84800f5c4bbbd7ae
 https://repo.anaconda.com/pkgs/main/linux-64/scipy-1.7.3-py39hf838250_2.conda#0667ea5ac14d35e26da19a0f068739da
 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a
-https://repo.anaconda.com/pkgs/main/linux-64/pandas-1.5.3-py39h417a72b_0.conda#d4d4678ada21d96c49ee4583de1a7b3a
 https://repo.anaconda.com/pkgs/main/linux-64/pyamg-4.2.3-py39h79cecc1_0.conda#afc634da8b81dc504179d53d334e6e55
-https://repo.anaconda.com/pkgs/main/noarch/pytest-xdist-2.5.0-pyhd3eb1b0_0.conda#d15cdc4207bcf8ca920822597f1d138d
diff --git a/build_tools/azure/pymin_conda_forge_mkl_environment.yml b/build_tools/azure/pymin_conda_forge_mkl_environment.yml
index 125c169ddc95f..a3b8b75363a46 100644
--- a/build_tools/azure/pymin_conda_forge_mkl_environment.yml
+++ b/build_tools/azure/pymin_conda_forge_mkl_environment.yml
@@ -13,7 +13,7 @@ dependencies:
   - threadpoolctl
   - matplotlib
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pytest-cov
diff --git a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
index 73dee95cc4ab7..e709d540b60d6 100644
--- a/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_mkl_win-64_conda.lock
@@ -1,27 +1,27 @@
 # Generated by conda-lock.
 # platform: win-64
-# input_hash: af544b6135127d0b6abf1eedcc8ba32a4d5e2e1d2904d4592abc7f3dba338569
+# input_hash: 2f4b1d16d553e6307f97867b796d97131fd60899af1e29931840dbbc1b00d7b9
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/win-64/ca-certificates-2023.11.17-h56e8100_0.conda#1163114b483f26761f993c709e65271f
 https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2023.2.0-h57928b3_50497.conda#a401f3cae152deb75bbed766a90a6312
 https://conda.anaconda.org/conda-forge/win-64/mkl-include-2023.2.0-h6a75c08_50497.conda#02fd1f15c56cc902aeaf3df3497cf266
 https://conda.anaconda.org/conda-forge/win-64/msys2-conda-epoch-20160418-1.tar.bz2#b0309b72560df66f71a9d5e34a5efdfa
 https://conda.anaconda.org/conda-forge/win-64/python_abi-3.9-4_cp39.conda#948b0d93d4ab1372d8fd45e1560afd47
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_0.tar.bz2#72608f6cd3e5898229c3ea16deb1ac43
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gmp-6.1.0-2.tar.bz2#53a1c73e1e3d185516d7e3af177596d9
 https://conda.anaconda.org/conda-forge/win-64/m2w64-libwinpthread-git-5.0.0.4634.697f757-2.tar.bz2#774130a326dee16f1ceb05cc687ee4f0
-https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.36.32532-hdcecf7f_17.conda#d0de20f2f3fc806a81b44fcdd941aaf7
+https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.38.33130-h82b7239_18.conda#8be79fdd2725ddf7bbf8a27a4c1f79ba
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-core-5.3.0-7.tar.bz2#4289d80fb4d272f1f3b56cfe87ac90bd
-https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h64f974e_17.conda#67ff6791f235bb606659bf2a5c169191
-https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.36.32532-h05e6639_17.conda#4618046c39f7c81861e53ded842e738a
+https://conda.anaconda.org/conda-forge/win-64/vc-14.3-hcf57466_18.conda#20e1e652a4c740fa719002a8449994a2
+https://conda.anaconda.org/conda-forge/win-64/vs2015_runtime-14.38.33130-hcb4865c_18.conda#10d42885e3ed84e575b454db30f1aa93
 https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-hcfcfb64_5.conda#26eb8ca6ea332b675e11704cce84a3be
 https://conda.anaconda.org/conda-forge/win-64/icu-73.2-h63175ca_0.conda#0f47d9e3192d9e09ae300da0d28e0f56
 https://conda.anaconda.org/conda-forge/win-64/lerc-4.0.0-h63175ca_0.tar.bz2#1900cb3cab5055833cfddb0ba233b074
 https://conda.anaconda.org/conda-forge/win-64/libbrotlicommon-1.1.0-hcfcfb64_1.conda#f77f319fb82980166569e1280d5b2864
 https://conda.anaconda.org/conda-forge/win-64/libdeflate-1.19-hcfcfb64_0.conda#002b1b723b44dbd286b9e3708762433c
 https://conda.anaconda.org/conda-forge/win-64/libffi-3.4.2-h8ffe710_5.tar.bz2#2c96d1b6915b408893f9472569dee135
-https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-hcfcfb64_1.conda#38d2d9078f2d6d3366fe7db635bf9de6
+https://conda.anaconda.org/conda-forge/win-64/libiconv-1.17-hcfcfb64_2.conda#e1eb10b1cca179f2baa3601e4efc8712
 https://conda.anaconda.org/conda-forge/win-64/libjpeg-turbo-3.0.0-hcfcfb64_1.conda#3f1b948619c45b1ca714d60c7389092c
 https://conda.anaconda.org/conda-forge/win-64/libogg-1.3.4-h8ffe710_1.tar.bz2#04286d905a0dcb7f7d4a12bdfe02516d
 https://conda.anaconda.org/conda-forge/win-64/libsqlite-3.44.2-hcfcfb64_0.conda#4a5f5ab56cbf3ccd08d71a1168061213
@@ -42,13 +42,13 @@ https://conda.anaconda.org/conda-forge/win-64/libvorbis-1.3.7-h0e60522_0.tar.bz2
 https://conda.anaconda.org/conda-forge/win-64/libxml2-2.11.6-hc3477c8_0.conda#08ffbb4c22dd3622e122058368f8b708
 https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libs-5.3.0-7.tar.bz2#fe759119b8b3bfa720b8762c6fdc35de
 https://conda.anaconda.org/conda-forge/win-64/pcre2-10.42-h17e33f8_0.conda#59610c61da3af020289a806ec9c6a7fd
-https://conda.anaconda.org/conda-forge/win-64/python-3.9.18-h4de0772_0_cpython.conda#ab83d6883a06de9c783c9aba765226c9
+https://conda.anaconda.org/conda-forge/win-64/python-3.9.18-h4de0772_1_cpython.conda#c0bc0080c5ec044edae6dbfa97ab337f
 https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.5-h12be248_0.conda#792bb5da68bf0a6cac6a6072ecb8dbeb
 https://conda.anaconda.org/conda-forge/win-64/brotli-bin-1.1.0-hcfcfb64_1.conda#0105229d7c5fabaa840043a86c10ec64
 https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.conda#2011bcf45376341dd1d690263fdbc789
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
-https://conda.anaconda.org/conda-forge/win-64/cython-3.0.6-py39h99910a6_0.conda#eff4ff92d5839706ca82770ffdd12c36
+https://conda.anaconda.org/conda-forge/win-64/cython-3.0.7-py39h99910a6_0.conda#1b2dc7e2a329356c29d63f655c7b0c56
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9
 https://conda.anaconda.org/conda-forge/win-64/freetype-2.12.1-hdaf720e_2.conda#3761b23693f768dc75a8fd0a73ca053f
@@ -63,9 +63,8 @@ https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.3.0-pyhd8ed1ab_0.conda#2390bd10bed1f3fdc7a537fb5a447d8d
 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-py_1.tar.bz2#7205635cd71531943440fbfe3b6b5727
 https://conda.anaconda.org/conda-forge/win-64/pthread-stubs-0.4-hcd874cb_1001.tar.bz2#a1f820480193ea83582b13249a7e7bd9
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
 https://conda.anaconda.org/conda-forge/noarch/toml-0.10.2-pyhd8ed1ab_0.tar.bz2#f832c45a477c78bebd107098db465095
@@ -77,35 +76,34 @@ https://conda.anaconda.org/conda-forge/win-64/xorg-libxau-1.0.11-hcd874cb_0.cond
 https://conda.anaconda.org/conda-forge/win-64/xorg-libxdmcp-1.1.3-hcd874cb_0.tar.bz2#46878ebb6b9cbd8afcf8088d7ef00ece
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
 https://conda.anaconda.org/conda-forge/win-64/brotli-1.1.0-hcfcfb64_1.conda#f47f6db2528e38321fb00ae31674c133
-https://conda.anaconda.org/conda-forge/win-64/coverage-7.3.2-py39ha55989b_0.conda#c1fc093d65ac2cedefa2e1e8e45b891e
+https://conda.anaconda.org/conda-forge/win-64/coverage-7.4.0-py39ha55989b_0.conda#ba8293a942069b021cbbef98f8df62ea
 https://conda.anaconda.org/conda-forge/win-64/glib-tools-2.78.3-h12be248_0.conda#03c45e65dbac2ba6c247dfd4896b664c
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.1.1-pyhd8ed1ab_0.conda#3d5fa25cf42f3f32a12b2d874ace8574
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
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+https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.3-py39hddb5d58_0.conda#5cd2960dafe35dbaf816b7c79d6c8178
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diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
index de366a19e740d..51fe4e3308868 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
+++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - sphinx
diff --git a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
index 1a77242a74fc3..55ed5154a3d12 100644
--- a/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_forge_openblas_ubuntu_2204_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: d70964a380150a9fdd34471eab9c13547ec7744156a6719ec0e4b97fc7d298fa
+# input_hash: c5b0ca4d81a3951a78ce653cf958c09f523e7579537cf5f6f0c709eb3691bc3d
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda#01ffc8d36f9eba0ce0b3c1955fa780ee
@@ -11,7 +11,7 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_1.co
 https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda#7aca3059a1729aa76c597603f10b0dd3
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-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
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@@ -30,20 +30,21 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda
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-https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_1.conda#4b06b43d0eca61db2899e4d7a289c302
+https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e
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+https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc
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-https://conda.anaconda.org/conda-forge/linux-64/pixman-0.42.2-h59595ed_0.conda#700edd63ccd5fc66b70b1c028cea9a68
+https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.0-h59595ed_0.conda#6b4b43013628634b6cfdee6b74fd696b
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
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@@ -67,7 +68,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.39-h753d276_0.conda#e
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-https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.2-h232c23b_0.conda#1917ed337979482731e8ac8c1bedf9dd
+https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.3-h232c23b_0.conda#bc6ac4c0cea148d924f621985bc3892b
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@@ -87,8 +88,8 @@ https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.cond
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-https://conda.anaconda.org/conda-forge/linux-64/nss-3.95-h1d7d5a4_0.conda#d3a8067adcc45a923f4b1987c91d69da
-https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-h0755675_0_cpython.conda#3ede353bc605068d9677e700b1847382
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.96-h1d7d5a4_0.conda#1c8f8b8eb041ecd54053fc4b6ad57957
+https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-h0755675_1_cpython.conda#255a7002aeec7a067ff19b545aca6328
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@@ -102,7 +103,7 @@ https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.co
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-https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.6-py39h3d6467e_0.conda#bfde3cf098e298b81d1c1cbc9c79ab59
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.7-py39h3d6467e_0.conda#04866e62ce30cff8f6f9c2ea9460eb09
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@@ -126,13 +127,12 @@ https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.0-h488ebb8_3.conda#
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-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
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-https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.3-pyhd8ed1ab_0.conda#2590495f608a63625e165915fb4e2e34
+https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2023.4-pyhd8ed1ab_0.conda#c79cacf8a06a51552fc651652f170208
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-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
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@@ -147,11 +147,11 @@ https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.40-hd590300_0
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-https://conda.anaconda.org/conda-forge/noarch/babel-2.13.1-pyhd8ed1ab_0.conda#3ccff479c246692468f604df9c85ef26
+https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e
 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.46.0-py39hd1e30aa_0.conda#9b58e5973dd3d786253f4ca9534b1aba
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.47.0-py39hd1e30aa_0.conda#01eba09d574310de928abf121f89b116
 https://conda.anaconda.org/conda-forge/linux-64/glib-2.78.3-hfc55251_0.conda#e08e51acc7d1ae8dbe13255e7b4c64ac
-https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.0.0-pyha770c72_0.conda#a941237cd06538837b25cd245fcd25d8
+https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.0.1-pyha770c72_0.conda#746623a787e06191d80a2133e5daff17
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.1.1-pyhd8ed1ab_0.conda#3d5fa25cf42f3f32a12b2d874ace8574
 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2#c8490ed5c70966d232fdd389d0dbed37
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
@@ -159,25 +159,24 @@ https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openbl
 https://conda.anaconda.org/conda-forge/linux-64/libclang-15.0.7-default_hb11cfb5_4.conda#c90f4cbb57839c98fef8f830e4b9972f
 https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda#6fabc51f5e647d09cc010c40061557e0
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.6.0-hd429924_1.conda#1dbcc04604fdf1e526e6d1b0b6938396
-https://conda.anaconda.org/conda-forge/linux-64/pillow-10.1.0-py39had0adad_0.conda#eeaa413fddccecb2ab7f747bdb55b07f
+https://conda.anaconda.org/conda-forge/linux-64/pillow-10.2.0-py39had0adad_0.conda#2972754dc054bb079d1d121918b5126f
 https://conda.anaconda.org/conda-forge/linux-64/pulseaudio-client-16.1-hb77b528_5.conda#ac902ff3c1c6d750dd0dfc93a974ab74
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
 https://conda.anaconda.org/conda-forge/linux-64/sip-6.7.12-py39h3d6467e_0.conda#e667a3ab0df62c54e60e1843d2e6defb
 https://conda.anaconda.org/conda-forge/noarch/urllib3-2.1.0-pyhd8ed1ab_0.conda#f8ced8ee63830dec7ecc1be048d1470a
-https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.22.7-h98fc4e7_1.conda#a8d71f6705ed1f70d7099a6bd1c078ac
+https://conda.anaconda.org/conda-forge/linux-64/gstreamer-1.22.8-h98fc4e7_1.conda#1b52a89485ab573a5bb83a5225ff706e
 https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-8.3.0-h3d44ed6_0.conda#5a6f6c00ef982a9bc83558d9ac8f64a0
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.1.1-pyhd8ed1ab_0.conda#d04bd1b5bed9177dd7c3cef15e2b6710
 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_openblas.conda#05c5862c7dc25e65ba6c471d96429dae
-https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.2-py39h474f0d3_0.conda#459a58eda3e74dd5e3d596c618e7f20a
+https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.3-py39h474f0d3_0.conda#a1f1ad2d8ebf63f13f45fb21b7f49dfb
 https://conda.anaconda.org/conda-forge/linux-64/pyqt5-sip-12.12.2-py39h3d6467e_5.conda#93aff412f3e49fdb43361c0215cbd72d
-https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/noarch/requests-2.31.0-pyhd8ed1ab_0.conda#a30144e4156cdbb236f99ebb49828f8b
 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-20_linux64_openblas.conda#9932a1d4e9ecf2d35fb19475446e361e
 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.0-py39h7633fee_0.conda#ed71ad3e30eb03da363fb797419cce98
-https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.22.7-h8e1006c_1.conda#89cd9374d5fc7371db238e4ef5c5f258
+https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.22.8-h8e1006c_1.conda#3926dab94fe06d88ade0e716d77b8cf8
 https://conda.anaconda.org/conda-forge/linux-64/pandas-2.1.4-py39hddac248_0.conda#dcfd2f15c6f8f0bbf234412b18a2a5d0
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.4-py39h474f0d3_0.conda#4b401c1516417b4b14aa1249d2f7929d
 https://conda.anaconda.org/conda-forge/linux-64/blas-2.120-openblas.conda#c8f6916a81a340650078171b1d852574
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.2-py39he9076e7_0.conda#6085411aa2f0b2b801d3b46e1d3b83c5
diff --git a/build_tools/azure/pypy3_environment.yml b/build_tools/azure/pypy3_environment.yml
index d4f0d22e96042..45a0d0e8ffebb 100644
--- a/build_tools/azure/pypy3_environment.yml
+++ b/build_tools/azure/pypy3_environment.yml
@@ -15,6 +15,6 @@ dependencies:
   - matplotlib
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - setuptools
   - ccache
diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock
index 7446b1acce459..136b85b5395b8 100644
--- a/build_tools/azure/pypy3_linux-64_conda.lock
+++ b/build_tools/azure/pypy3_linux-64_conda.lock
@@ -1,12 +1,12 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 296e0e62aa19cfbc6aa6d615c86db2d06be56b4b5f76bf148152aff936fcddf5
+# input_hash: 231e6765d0906ea65daa71dd10e672c1afde9ae87cba2e958a8744a6a38a4e7b
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda#01ffc8d36f9eba0ce0b3c1955fa780ee
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda#937eaed008f6bf2191c5fe76f87755e9
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_pypy39_pp73.conda#c1b2f29111681a4036ed21eaa3f44620
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
 https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_3.conda#23fdf1fef05baeb7eadc2aed5fb0011f
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
@@ -61,21 +61,20 @@ https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-0_73_pypy.conda#aa
 https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.conda#2011bcf45376341dd1d690263fdbc789
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
-https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.6-py39hc10206b_0.conda#5fc69db5e035852a013acfa22dd8e18b
+https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.7-py39hc10206b_0.conda#4068d9f575989a3482032d526cf42d5a
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
 https://conda.anaconda.org/conda-forge/noarch/execnet-2.0.2-pyhd8ed1ab_0.conda#67de0d8241e1060a479e3c37793e26f9
 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5
 https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.5-py39ha90811c_1.conda#25edffabcb0760fc1821597c4ce920db
 https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.9.0-20_linux64_openblas.conda#05c5862c7dc25e65ba6c471d96429dae
 https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2#2ba8498c1018c1e9c61eb99b973dfe19
-https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.2-py39h6dedee3_0.conda#be8411b206cee82a218fd8fc219d1ae9
+https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.3-py39h6dedee3_0.conda#fcab766baac334344078d0aaf0945ec4
 https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#79002079284aa895f883c6b7f3f88fd6
-https://conda.anaconda.org/conda-forge/linux-64/pillow-10.1.0-py39hcf8a34e_0.conda#2bcde78b6e284e4266eee50ed5d0897d
+https://conda.anaconda.org/conda-forge/linux-64/pillow-10.2.0-py39hcf8a34e_0.conda#8a406ee5a979c2591f4c734d6fe4a958
 https://conda.anaconda.org/conda-forge/noarch/pluggy-1.3.0-pyhd8ed1ab_0.conda#2390bd10bed1f3fdc7a537fb5a447d8d
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
 https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.13-0_pypy39.conda#0973de0664d1bd004c1bc64a7aab8f2e
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
@@ -84,16 +83,15 @@ https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-15.1.0-py39hf860d4a
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
 https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.9.0-20_linux64_openblas.conda#9932a1d4e9ecf2d35fb19475446e361e
 https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.2.0-py39ha90811c_0.conda#f3b2afc64bf0cbe901a9b00d44611c61
-https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.46.0-py39hf860d4a_0.conda#05d7d08eaa9678ac1dd33d1fc3e1c6dd
+https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.47.0-py39hf860d4a_0.conda#ebe895da6a30d81da5433696f008389d
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.1.1-pyhd8ed1ab_0.conda#3d5fa25cf42f3f32a12b2d874ace8574
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.11.4-py39h6dedee3_0.conda#066da96b1c7587d85b572f97d631ce1a
 https://conda.anaconda.org/conda-forge/linux-64/blas-2.120-openblas.conda#c8f6916a81a340650078171b1d852574
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.1.1-pyhd8ed1ab_0.conda#d04bd1b5bed9177dd7c3cef15e2b6710
 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.0.1-py39h5fd064f_1.conda#e364cfb3ffb590ccef24b5a92389e751
-https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.2-py39h4e7d633_0.conda#a60f8c577d2db485f0b92bef480d6277
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.8.2-py39h4162558_0.conda#24444011be733e7bde8617eb8fe725e1
diff --git a/build_tools/azure/test_script.sh b/build_tools/azure/test_script.sh
index a45fa3dd49842..0b378675eebde 100755
--- a/build_tools/azure/test_script.sh
+++ b/build_tools/azure/test_script.sh
@@ -51,17 +51,15 @@ fi
 if [[ -n "$CHECK_WARNINGS" ]]; then
     TEST_CMD="$TEST_CMD -Werror::DeprecationWarning -Werror::FutureWarning -Werror::sklearn.utils.fixes.VisibleDeprecationWarning"
 
-    # numpy's 1.19.0's tostring() deprecation is ignored until scipy and joblib
-    # removes its usage
-    TEST_CMD="$TEST_CMD -Wignore:tostring:DeprecationWarning"
-
-    # Ignore distutils deprecation warning, used by joblib internally
-    TEST_CMD="$TEST_CMD -Wignore:distutils\ Version\ classes\ are\ deprecated:DeprecationWarning"
-
     # Ignore pkg_resources deprecation warnings triggered by pyamg
     TEST_CMD="$TEST_CMD -W 'ignore:pkg_resources is deprecated as an API:DeprecationWarning'"
     TEST_CMD="$TEST_CMD -W 'ignore:Deprecated call to \`pkg_resources:DeprecationWarning'"
 
+    # pytest-cov issue https://github.com/pytest-dev/pytest-cov/issues/557 not
+    # fixed although it has been closed. https://github.com/pytest-dev/pytest-cov/pull/623
+    # would probably fix it.
+    TEST_CMD="$TEST_CMD -W 'ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning'"
+
     # In some case, exceptions are raised (by bug) in tests, and captured by pytest,
     # but not raised again. This is for instance the case when Cython directives are
     # activated: IndexErrors (which aren't fatal) are raised on out-of-bound accesses.
diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt
index 65ffa33bd87ff..42e63264193af 100644
--- a/build_tools/azure/ubuntu_atlas_lock.txt
+++ b/build_tools/azure/ubuntu_atlas_lock.txt
@@ -18,16 +18,11 @@ packaging==23.2
     # via pytest
 pluggy==1.3.0
     # via pytest
-py==1.11.0
-    # via pytest-forked
-pytest==7.4.3
+pytest==7.4.4
     # via
     #   -r build_tools/azure/ubuntu_atlas_requirements.txt
-    #   pytest-forked
     #   pytest-xdist
-pytest-forked==1.6.0
-    # via pytest-xdist
-pytest-xdist==2.5.0
+pytest-xdist==3.5.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 threadpoolctl==2.0.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt
index b4fad825466a7..7bca99cc63cf2 100644
--- a/build_tools/azure/ubuntu_atlas_requirements.txt
+++ b/build_tools/azure/ubuntu_atlas_requirements.txt
@@ -5,4 +5,4 @@ cython==0.29.33  # min
 joblib==1.2.0  # min
 threadpoolctl==2.0.0  # min
 pytest
-pytest-xdist==2.5.0
+pytest-xdist
diff --git a/build_tools/circle/doc_environment.yml b/build_tools/circle/doc_environment.yml
index b12ebf12f254d..22400c45091bb 100644
--- a/build_tools/circle/doc_environment.yml
+++ b/build_tools/circle/doc_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - scikit-image
@@ -28,6 +28,7 @@ dependencies:
   - numpydoc
   - sphinx-prompt
   - plotly
+  - polars
   - pooch
   - sphinxext-opengraph
   - pip
diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock
index 57700c0a0835f..77565ab07e476 100644
--- a/build_tools/circle/doc_linux-64_conda.lock
+++ b/build_tools/circle/doc_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 74e9e451b651d0b84d1c066a106b93d1a0f711e6aa6c5a48d2169af2e01f4d90
+# input_hash: e9ce7b66471a75e2156a32c83078c9688bbda241cd62e3d881989eae546ee2e9
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
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@@ -14,7 +14,7 @@ https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.3.0-h8bca
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@@ -34,7 +34,7 @@ https://conda.anaconda.org/conda-forge/linux-64/gettext-0.21.1-h27087fc_0.tar.bz
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@@ -44,7 +44,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda
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@@ -52,6 +52,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
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+https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.1-pyhd8ed1ab_0.conda#c1c0e175f993a4677c3163b26652b96c
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-https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.0-hd8ed1ab_0.conda#ebd31a95a7008b7e164dad9dbbb5bb5a
+https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.1-hd8ed1ab_0.conda#8d9b6f5e94b7840210b2b9ed235068c7
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-# pip attrs @ https://files.pythonhosted.org/packages/f0/eb/fcb708c7bf5056045e9e98f62b93bd7467eb718b0202e7698eb11d66416c/attrs-23.1.0-py3-none-any.whl#sha256=1f28b4522cdc2fb4256ac1a020c78acf9cba2c6b461ccd2c126f3aa8e8335d04
+https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1ab_0.conda#286283e05a1eff606f55e7cd70f6d7f7
+# pip attrs @ https://files.pythonhosted.org/packages/e0/44/827b2a91a5816512fcaf3cc4ebc465ccd5d598c45cefa6703fcf4a79018f/attrs-23.2.0-py3-none-any.whl#sha256=99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1
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-# pip fastjsonschema @ https://files.pythonhosted.org/packages/63/e9/d3dca06ea6b8e58e65716973bc7d9bee9bc39ce233595aa04d04e89a1089/fastjsonschema-2.19.0-py3-none-any.whl#sha256=b9fd1a2dd6971dbc7fee280a95bd199ae0dd9ce22beb91cc75e9c1c528a5170e
+# pip fastjsonschema @ https://files.pythonhosted.org/packages/9c/b9/79691036d4a8f9857e74d1728b23f34f583b81350a27492edda58d5604e1/fastjsonschema-2.19.1-py3-none-any.whl#sha256=3672b47bc94178c9f23dbb654bf47440155d4db9df5f7bc47643315f9c405cd0
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@@ -269,32 +270,32 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.0-pyhd8ed1
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+# pip rpds-py @ https://files.pythonhosted.org/packages/5e/e3/8a2d5cfb6c77c5897e72793b6bdc769fd55e4ce349569a4faf8e076eb775/rpds_py-0.16.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=80443fe2f7b3ea3934c5d75fb0e04a5dbb4a8e943e5ff2de0dec059202b70a8b
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-# pip traitlets @ https://files.pythonhosted.org/packages/a7/1d/7d07e1b152b419a8a9c7f812eeefd408a0610d869489ee2e86973486713f/traitlets-5.14.0-py3-none-any.whl#sha256=f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33
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+# pip traitlets @ https://files.pythonhosted.org/packages/45/34/5dc77fdc7bb4bd198317eea5679edf9cc0a186438b5b19dbb9062fb0f4d5/traitlets-5.14.1-py3-none-any.whl#sha256=2e5a030e6eff91737c643231bfcf04a65b0132078dad75e4936700b213652e74
+# pip types-python-dateutil @ https://files.pythonhosted.org/packages/28/50/8ed67814241e2684369f4b8b881c7d31a0816e76c8690ea8518017a35b7e/types_python_dateutil-2.8.19.20240106-py3-none-any.whl#sha256=efbbdc54590d0f16152fa103c9879c7d4a00e82078f6e2cf01769042165acaa2
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-# pip anyio @ https://files.pythonhosted.org/packages/85/4f/d010eca6914703d8e6be222165d02c3e708ed909cdb2b7af3743667f302e/anyio-4.1.0-py3-none-any.whl#sha256=56a415fbc462291813a94528a779597226619c8e78af7de0507333f700011e5f
+# pip anyio @ https://files.pythonhosted.org/packages/bf/cd/d6d9bb1dadf73e7af02d18225cbd2c93f8552e13130484f1c8dcfece292b/anyio-4.2.0-py3-none-any.whl#sha256=745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee
 # pip arrow @ https://files.pythonhosted.org/packages/f8/ed/e97229a566617f2ae958a6b13e7cc0f585470eac730a73e9e82c32a3cdd2/arrow-1.3.0-py3-none-any.whl#sha256=c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80
 # pip beautifulsoup4 @ https://files.pythonhosted.org/packages/57/f4/a69c20ee4f660081a7dedb1ac57f29be9378e04edfcb90c526b923d4bebc/beautifulsoup4-4.12.2-py3-none-any.whl#sha256=bd2520ca0d9d7d12694a53d44ac482d181b4ec1888909b035a3dbf40d0f57d4a
 # pip bleach @ https://files.pythonhosted.org/packages/ea/63/da7237f805089ecc28a3f36bca6a21c31fcbc2eb380f3b8f1be3312abd14/bleach-6.1.0-py3-none-any.whl#sha256=3225f354cfc436b9789c66c4ee030194bee0568fbf9cbdad3bc8b5c26c5f12b6
 # pip cffi @ https://files.pythonhosted.org/packages/ea/ac/e9e77bc385729035143e54cc8c4785bd480eaca9df17565963556b0b7a93/cffi-1.16.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=8f8e709127c6c77446a8c0a8c8bf3c8ee706a06cd44b1e827c3e6a2ee6b8c098
 # pip doit @ https://files.pythonhosted.org/packages/44/83/a2960d2c975836daa629a73995134fd86520c101412578c57da3d2aa71ee/doit-0.36.0-py3-none-any.whl#sha256=ebc285f6666871b5300091c26eafdff3de968a6bd60ea35dd1e3fc6f2e32479a
-# pip jupyter-core @ https://files.pythonhosted.org/packages/ab/ea/af6508f71d2bcbf4db538940120cc3d3f10287f62105e756bd315aa345b5/jupyter_core-5.5.0-py3-none-any.whl#sha256=e11e02cd8ae0a9de5c6c44abf5727df9f2581055afe00b22183f621ba3585805
-# pip referencing @ https://files.pythonhosted.org/packages/b4/11/d121780c173336c9bc3a5b8240ed31f518957cc22f6311c76259cb0fcf32/referencing-0.32.0-py3-none-any.whl#sha256=bdcd3efb936f82ff86f993093f6da7435c7de69a3b3a5a06678a6050184bee99
+# pip jupyter-core @ https://files.pythonhosted.org/packages/4f/64/c15b7ac8915f7cae6c64718a6ffbb5e75fd398cda05d0a8aca2f570f0ed5/jupyter_core-5.7.0-py3-none-any.whl#sha256=16eea462f7dad23ba9f86542bdf17f830804e2028eb48d609b6134d91681e983
+# pip referencing @ https://files.pythonhosted.org/packages/14/2a/0a9f649354cd2d40f6c4f16eadabd9727377e3b9bc2ccec6cb630d9a6765/referencing-0.32.1-py3-none-any.whl#sha256=7e4dc12271d8e15612bfe35792f5ea1c40970dadf8624602e33db2758f7ee554
 # pip rfc3339-validator @ https://files.pythonhosted.org/packages/7b/44/4e421b96b67b2daff264473f7465db72fbdf36a07e05494f50300cc7b0c6/rfc3339_validator-0.1.4-py2.py3-none-any.whl#sha256=24f6ec1eda14ef823da9e36ec7113124b39c04d50a4d3d3a3c2859577e7791fa
 # pip terminado @ https://files.pythonhosted.org/packages/69/df/deebc9fb14a49062a3330f673e80b100e665b54d998163b3f62620b6240c/terminado-0.18.0-py3-none-any.whl#sha256=87b0d96642d0fe5f5abd7783857b9cab167f221a39ff98e3b9619a788a3c0f2e
 # pip tinycss2 @ https://files.pythonhosted.org/packages/da/99/fd23634d6962c2791fb8cb6ccae1f05dcbfc39bce36bba8b1c9a8d92eae8/tinycss2-1.2.1-py3-none-any.whl#sha256=2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847
 # pip argon2-cffi-bindings @ https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae
 # pip isoduration @ https://files.pythonhosted.org/packages/7b/55/e5326141505c5d5e34c5e0935d2908a74e4561eca44108fbfb9c13d2911a/isoduration-20.11.0-py3-none-any.whl#sha256=b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042
-# pip jsonschema-specifications @ https://files.pythonhosted.org/packages/d7/48/b62ccba8f4ac91817d6a11b340e63806175dafb10234a8cf7140bd389da5/jsonschema_specifications-2023.11.2-py3-none-any.whl#sha256=e74ba7c0a65e8cb49dc26837d6cfe576557084a8b423ed16a420984228104f93
-# pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/63/9a/98d252b7977ac3aa0aa4152b87b356e2048d4b193f38840c0e00dd85fadc/jupyter_server_terminals-0.5.0-py3-none-any.whl#sha256=2fc0692c883bfd891f4fba0c4b4a684a37234b0ba472f2e97ed0a3888f46e1e4
-# pip jupyterlite-core @ https://files.pythonhosted.org/packages/2f/0b/58eb568cbce3bbaa8702c6ce297870402828b222598a1db10e23e7190f52/jupyterlite_core-0.2.1-py3-none-any.whl#sha256=3f6161c4ad609bca913a42598005ff577611daae8dce448292fbb2c15db6b393
+# pip jsonschema-specifications @ https://files.pythonhosted.org/packages/ee/07/44bd408781594c4d0a027666ef27fab1e441b109dc3b76b4f836f8fd04fe/jsonschema_specifications-2023.12.1-py3-none-any.whl#sha256=87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c
+# pip jupyter-server-terminals @ https://files.pythonhosted.org/packages/13/50/9e4688558eb1a20d16e99171af9026be27d31a8b212c241595241736811a/jupyter_server_terminals-0.5.1-py3-none-any.whl#sha256=5e63e947ddd97bb2832db5ef837a258d9ccd4192cd608c1270850ad947ae5dd7
+# pip jupyterlite-core @ https://files.pythonhosted.org/packages/93/62/4387ca1578447027560863e8a4ebabd5d919ac990c99dc124a45a45846b2/jupyterlite_core-0.2.2-py3-none-any.whl#sha256=1f1babdbe630d429f631a508f0e3b3ffb4dfa005aeb748831e854c24025e766f
 # pip pyzmq @ https://files.pythonhosted.org/packages/76/8b/6fca99e22c6316917de32b17be299dea431544209d619da16b6d9ec85c83/pyzmq-25.1.2-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=c0b5ca88a8928147b7b1e2dfa09f3b6c256bc1135a1338536cbc9ea13d3b7add
 # pip argon2-cffi @ https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl#sha256=c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea
 # pip jsonschema @ https://files.pythonhosted.org/packages/0f/ed/0058234d8dd2b1fc6beeea8eab945191a05e9d391a63202f49fe23327586/jsonschema-4.20.0-py3-none-any.whl#sha256=ed6231f0429ecf966f5bc8dfef245998220549cbbcf140f913b7464c52c3b6b3
@@ -303,7 +304,7 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.0-pyhd8ed1
 # pip jupyter-events @ https://files.pythonhosted.org/packages/e3/55/0c1aa72f4317e826a471dc4adc3036acd11d496ded68c4bbac2a88551519/jupyter_events-0.9.0-py3-none-any.whl#sha256=d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf
 # pip nbformat @ https://files.pythonhosted.org/packages/f4/e7/ef30a90b70eba39e675689b9eaaa92530a71d7435ab8f9cae520814e0caf/nbformat-5.9.2-py3-none-any.whl#sha256=1c5172d786a41b82bcfd0c23f9e6b6f072e8fb49c39250219e4acfff1efe89e9
 # pip nbclient @ https://files.pythonhosted.org/packages/6b/3a/607149974149f847125c38a62b9ea2b8267eb74823bbf8d8c54ae0212a00/nbclient-0.9.0-py3-none-any.whl#sha256=a3a1ddfb34d4a9d17fc744d655962714a866639acd30130e9be84191cd97cd15
-# pip nbconvert @ https://files.pythonhosted.org/packages/f4/c8/b2b201d67d8fbe6e33865bf32b84104a77e6ace7f1e12614d686a1130033/nbconvert-7.12.0-py3-none-any.whl#sha256=5b6c848194d270cc55fb691169202620d7b52a12fec259508d142ecbe4219310
-# pip jupyter-server @ https://files.pythonhosted.org/packages/ed/20/2437a3865083360103b0218e82a910c4c35f3bf7248c5cdae6934ba4d01c/jupyter_server-2.12.1-py3-none-any.whl#sha256=fd030dd7be1ca572e4598203f718df6630c12bd28a599d7f1791c4d7938e1010
+# pip nbconvert @ https://files.pythonhosted.org/packages/7f/ba/3a8a9870a8b42e63e8f5e770adedd191d5adc2348f3097fc0e7c83a39439/nbconvert-7.14.0-py3-none-any.whl#sha256=483dde47facdaa4875903d651305ad53cd76e2255ae3c61efe412a95f2d22a24
+# pip jupyter-server @ https://files.pythonhosted.org/packages/0c/3b/24a511c81b580a038aca06c91fc89df0464815903044bae1c85145cdf03c/jupyter_server-2.12.2-py3-none-any.whl#sha256=abcfa33f98a959f908c8733aa2d9fa0101d26941cbd49b148f4cef4d3046fc61
 # pip jupyterlab-server @ https://files.pythonhosted.org/packages/a2/97/abbbe35fc67b6f9423309988f2e411f7cb117b08321866d3d8b720f4c0d4/jupyterlab_server-2.25.2-py3-none-any.whl#sha256=5b1798c9cc6a44f65c757de9f97fc06fc3d42535afbf47d2ace5e964ab447aaf
-# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/fa/f9/ad6d7164eca7ab9d523fc9b8c8a4a5508b424ee051f44a01797be224aeaa/jupyterlite_sphinx-0.10.0-py3-none-any.whl#sha256=72f332bf2748902802b719fbce598234e27facfcdc9aec020bf8cf025b12ba62
+# pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/9c/bd/1695eebeb376315c9fc5cbd41c54fb84bb69c68e69651bfc6f03aa4fe659/jupyterlite_sphinx-0.11.0-py3-none-any.whl#sha256=2a0762167e89ec6acd267c73bb90b528728fdba5e30390ea4fe37ddcec277191
diff --git a/build_tools/circle/doc_min_dependencies_environment.yml b/build_tools/circle/doc_min_dependencies_environment.yml
index 18c52146da5ff..3a8320a7f8dd0 100644
--- a/build_tools/circle/doc_min_dependencies_environment.yml
+++ b/build_tools/circle/doc_min_dependencies_environment.yml
@@ -15,7 +15,7 @@ dependencies:
   - pandas=1.1.5  # min
   - pyamg
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - scikit-image=0.17.2  # min
@@ -28,6 +28,7 @@ dependencies:
   - numpydoc=1.2.0  # min
   - sphinx-prompt=1.3.0  # min
   - plotly=5.14.0  # min
+  - polars=0.19.12  # min
   - pooch
   - pip
   - pip:
diff --git a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
index cd5716a795079..b0848d8fbea6f 100644
--- a/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
+++ b/build_tools/circle/doc_min_dependencies_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 35f943b65f19232746bf1ac103664d9fa08c9fce0bcc39d7ee2ecf873d996bff
+# input_hash: a58a98732e5815c15757bc1def8ddc0d87f20f11edcf6e7b408594bf948cbb3e
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda#01ffc8d36f9eba0ce0b3c1955fa780ee
@@ -14,7 +14,7 @@ https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.3.0-h8bca
 https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.3.0-h8bca6fd_103.conda#3f784d2c059e960156d1ab3858cbf200
 https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda#937eaed008f6bf2191c5fe76f87755e9
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_cp39.conda#bfe4b3259a8ac6cdf0037752904da6a7
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29
 https://conda.anaconda.org/conda-forge/linux-64/libgomp-13.2.0-h807b86a_3.conda#7124cbb46b13d395bdde68f2d215c989
 https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.12-he073ed8_16.conda#071ea8dceff4d30ac511f4a2f8437cd1
@@ -37,7 +37,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.19-hd590300_0.conda
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.5.0-hcb278e6_1.conda#6305a3dd2752c76335295da4e581f2fd
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
 https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_3.conda#c714d905cdfa0e70200f68b80cc04764
-https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_1.conda#4b06b43d0eca61db2899e4d7a289c302
+https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e
 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8
 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7
 https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2#6e8cc2173440d77708196c5b93771680
@@ -45,13 +45,14 @@ https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.3.0-h0f45ef3_3.conda#eda05ab0db8f8490945fd99244183e3a
 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b
 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_0.conda#30de3fd9b3b602f7473f30e684eeea8c
+https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad
 https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0
 https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.3-h59595ed_0.conda#bdadff838d5437aea83607ced8b37f75
 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4-h59595ed_2.conda#7dbaa197d7ba6032caf7ae7f32c1efa0
 https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1
 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.0-hd590300_1.conda#603827b39ea2b835268adb8c821b8570
-https://conda.anaconda.org/conda-forge/linux-64/pixman-0.42.2-h59595ed_0.conda#700edd63ccd5fc66b70b1c028cea9a68
+https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.0-h59595ed_0.conda#6b4b43013628634b6cfdee6b74fd696b
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.1-hd590300_0.conda#b462a33c0be1421532f28bfe8f4a7514
@@ -75,7 +76,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.39-h753d276_0.conda#e
 https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.44.2-h2797004_0.conda#3b6a9f225c3dbe0d24f4fedd4625c5bf
 https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2#309dec04b70a3cc0f1e84a4013683bc0
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c
-https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.2-h232c23b_0.conda#1917ed337979482731e8ac8c1bedf9dd
+https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.3-h232c23b_0.conda#bc6ac4c0cea148d924f621985bc3892b
 https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.0.33-hf1915f5_6.conda#80bf3b277c120dd294b51d404b931a75
 https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.42-hcad00b1_0.conda#679c8961826aa4b50653bce17ee52abe
 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
@@ -97,8 +98,8 @@ https://conda.anaconda.org/conda-forge/linux-64/libsndfile-1.2.2-hc60ed4a_1.cond
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-ha9c0a0a_2.conda#55ed21669b2015f77c180feb1dd41930
 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-17.0.6-h4dfa4b3_0.conda#c1665f9c1c9f6c93d8b4e492a6a39056
 https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.33-hca2cd23_6.conda#e87530d1b12dd7f4e0f856dc07358d60
-https://conda.anaconda.org/conda-forge/linux-64/nss-3.95-h1d7d5a4_0.conda#d3a8067adcc45a923f4b1987c91d69da
-https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-h0755675_0_cpython.conda#3ede353bc605068d9677e700b1847382
+https://conda.anaconda.org/conda-forge/linux-64/nss-3.96-h1d7d5a4_0.conda#1c8f8b8eb041ecd54053fc4b6ad57957
+https://conda.anaconda.org/conda-forge/linux-64/python-3.9.18-h0755675_1_cpython.conda#255a7002aeec7a067ff19b545aca6328
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-hd590300_1.conda#9bfac7ccd94d54fd21a0501296d60424
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h8ee46fc_1.conda#632413adcd8bc16b515cab87a2932913
 https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-hd590300_1.conda#e995b155d938b6779da6ace6c6b13816
@@ -144,8 +145,7 @@ https://conda.anaconda.org/conda-forge/noarch/packaging-23.2-pyhd8ed1ab_0.conda#
 https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.1.0-pyhd8ed1ab_0.conda#45a5065664da0d1dfa8f8cd2eaf05ab9
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 https://conda.anaconda.org/conda-forge/noarch/ply-3.11-py_1.tar.bz2#7205635cd71531943440fbfe3b6b5727
-https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.5-py39hd1e30aa_1.conda#c2e412b0f11e5983bcfc35d9beb91ecb
-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
+https://conda.anaconda.org/conda-forge/linux-64/psutil-5.9.7-py39hd1e30aa_0.conda#34d2731732bc7de6269657d5d9fd6e79
 https://conda.anaconda.org/conda-forge/noarch/pygments-2.17.2-pyhd8ed1ab_0.conda#140a7f159396547e9799aa98f9f0742e
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
 https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha2e5f31_6.tar.bz2#2a7de29fb590ca14b5243c4c812c8025
@@ -168,13 +168,13 @@ https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.40-hd590300_0
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h0b41bf4_2.conda#82b6df12252e6f32402b96dacc656fec
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.11-hd590300_0.conda#ed67c36f215b310412b2af935bf3e530
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
-https://conda.anaconda.org/conda-forge/noarch/babel-2.13.1-pyhd8ed1ab_0.conda#3ccff479c246692468f604df9c85ef26
+https://conda.anaconda.org/conda-forge/noarch/babel-2.14.0-pyhd8ed1ab_0.conda#9669586875baeced8fc30c0826c3270e
 https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.0-h3faef2a_0.conda#f907bb958910dc404647326ca80c263e
 https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_0.conda#b4537c98cb59f8725b0e1e65816b4a28
 https://conda.anaconda.org/conda-forge/linux-64/cytoolz-0.12.2-py39hd1e30aa_1.conda#e5b62f0c1f96413116f16d33973f1a44
 https://conda.anaconda.org/conda-forge/linux-64/fortran-compiler-1.7.0-heb67821_0.conda#7ef7c0f111dad1c8006504a0f1ccd820
 https://conda.anaconda.org/conda-forge/linux-64/glib-2.78.3-hfc55251_0.conda#e08e51acc7d1ae8dbe13255e7b4c64ac
-https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.0.0-pyha770c72_0.conda#a941237cd06538837b25cd245fcd25d8
+https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-7.0.1-pyha770c72_0.conda#746623a787e06191d80a2133e5daff17
 https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2#c8490ed5c70966d232fdd389d0dbed37
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
 https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda#36d486d72ab64ffea932329a1d3729a3
@@ -183,33 +183,33 @@ https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openb
 https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.6.0-hd429924_1.conda#1dbcc04604fdf1e526e6d1b0b6938396
 https://conda.anaconda.org/conda-forge/noarch/memory_profiler-0.61.0-pyhd8ed1ab_0.tar.bz2#8b45f9f2b2f7a98b0ec179c8991a4a9b
 https://conda.anaconda.org/conda-forge/noarch/partd-1.4.1-pyhd8ed1ab_0.conda#acf4b7c0bcd5fa3b0e05801c4d2accd6
-https://conda.anaconda.org/conda-forge/linux-64/pillow-10.1.0-py39had0adad_0.conda#eeaa413fddccecb2ab7f747bdb55b07f
-https://conda.anaconda.org/conda-forge/noarch/pip-23.3.1-pyhd8ed1ab_0.conda#2400c0b86889f43aa52067161e1fb108
+https://conda.anaconda.org/conda-forge/linux-64/pillow-10.2.0-py39had0adad_0.conda#2972754dc054bb079d1d121918b5126f
+https://conda.anaconda.org/conda-forge/noarch/pip-23.3.2-pyhd8ed1ab_0.conda#8591c748f98dcc02253003533bc2e4b1
 https://conda.anaconda.org/conda-forge/noarch/plotly-5.14.0-pyhd8ed1ab_0.conda#6a7bcc42ef58dd6cf3da9333ea102433
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-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
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-https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-7.0.0-hd8ed1ab_0.conda#12aff14f84c337be5e5636bf612f4140
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-https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.4-pyhd8ed1ab_0.conda#1184267eddebb57e47f8e1419c225595
+https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.6-pyhd8ed1ab_0.conda#a5b55d1cb110cdcedc748b5c3e16e687
+https://conda.anaconda.org/conda-forge/linux-64/polars-0.19.12-py39h90d8ae4_0.conda#191828961c95f8d59fa2b86a590f9905
 https://conda.anaconda.org/conda-forge/noarch/pooch-1.8.0-pyhd8ed1ab_0.conda#134b2b57b7865d2316a7cce1915a51ed
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.3.0-py39hd257fcd_1.tar.bz2#c4b698994b2d8d2e659ae02202e6abe4
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diff --git a/build_tools/cirrus/pymin_conda_forge_environment.yml b/build_tools/cirrus/pymin_conda_forge_environment.yml
index 70aedd73bf883..67a163d2bd46b 100644
--- a/build_tools/cirrus/pymin_conda_forge_environment.yml
+++ b/build_tools/cirrus/pymin_conda_forge_environment.yml
@@ -13,7 +13,7 @@ dependencies:
   - threadpoolctl
   - matplotlib
   - pytest
-  - pytest-xdist=2.5.0
+  - pytest-xdist
   - pillow
   - setuptools
   - pip
diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
index 6a370e8e00abc..fa842def2d8d2 100644
--- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
+++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock
@@ -1,12 +1,12 @@
 # Generated by conda-lock.
 # platform: linux-aarch64
-# input_hash: 26cb8d771d4d1ecc00c0fc477f3a4b364e4bd7558f3d18ecd50c0d1b440ffe7f
+# input_hash: dc7e28d3993d445e2d092c8e0962c7c7b4861c3413f40ab9e1f017be338abb90
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2023.11.17-hcefe29a_0.conda#695a28440b58e3ba920bcac4ac7c73c6
 https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h2d8c526_0.conda#16246d69e945d0b1969a6099e7c5d457
 https://conda.anaconda.org/conda-forge/linux-aarch64/libstdcxx-ng-13.2.0-h9a76618_3.conda#7ad2164936c4975d94ca883d34809c0f
 https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878
-https://conda.anaconda.org/conda-forge/noarch/tzdata-2023c-h71feb2d_0.conda#939e3e74d8be4dac89ce83b20de2492a
+https://conda.anaconda.org/conda-forge/noarch/tzdata-2023d-h0c530f3_0.conda#8dee24b8be2d9ff81e7bd4d7d97ff1b0
 https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#98a1185182fec3c434069fa74e6473d6
 https://conda.anaconda.org/conda-forge/linux-aarch64/libgcc-ng-13.2.0-hf8544c7_3.conda#00f021ee1a24c798ae53c87ee79597f1
 https://conda.anaconda.org/conda-forge/linux-aarch64/bzip2-1.0.8-h31becfc_5.conda#a64e35f01e0b7a2a152eca87d33b9c87
@@ -19,6 +19,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libjpeg-turbo-3.0.0-h31becf
 https://conda.anaconda.org/conda-forge/linux-aarch64/libnsl-2.0.1-h31becfc_0.conda#c14f32510f694e3185704d89967ec422
 https://conda.anaconda.org/conda-forge/linux-aarch64/libuuid-2.38.1-hb4cce97_0.conda#000e30b09db0b7c775b21695dff30969
 https://conda.anaconda.org/conda-forge/linux-aarch64/libwebp-base-1.3.2-h31becfc_0.conda#1490de434d2a2c06a98af27641a2ffff
+https://conda.anaconda.org/conda-forge/linux-aarch64/libxcrypt-4.4.36-h31becfc_1.conda#b4df5d7d4b63579d081fd3a4cf99740e
 https://conda.anaconda.org/conda-forge/linux-aarch64/libzlib-1.2.13-h31becfc_5.conda#b213aa87eea9491ef7b129179322e955
 https://conda.anaconda.org/conda-forge/linux-aarch64/ncurses-6.4-h0425590_2.conda#4ff0a396150dedad4269e16e5810f769
 https://conda.anaconda.org/conda-forge/linux-aarch64/openssl-3.2.0-h31becfc_1.conda#b24247441ed7ce138382de2ec51200e4
@@ -41,13 +42,13 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libhiredis-1.0.2-h05efe27_0
 https://conda.anaconda.org/conda-forge/linux-aarch64/libopenblas-0.3.25-pthreads_h5a5ec62_0.conda#60e86bc93e3f213278dc5081115fb63b
 https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-h1708d11_2.conda#d5638e110e7f22e2602a8edd20656720
 https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-17.0.6-h8b0cb96_0.conda#48337e980ec89cd22dd87ced0a0aa878
-https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.18-h4ac3b42_0_cpython.conda#4d36e157278470ac06508579c6d36555
+https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.18-h4ac3b42_1_cpython.conda#6ba2858e603df9b6ab7ad172b15be15f
 https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f
 https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.8.1-h6552966_0.conda#5b436a19e818f05fe0c9ab4f5ac61233
 https://conda.anaconda.org/conda-forge/noarch/certifi-2023.11.17-pyhd8ed1ab_0.conda#2011bcf45376341dd1d690263fdbc789
 https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99
 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441
-https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.6-py39h387a81e_0.conda#49d46c249d4e1d6ccc302059537c9ef9
+https://conda.anaconda.org/conda-forge/linux-aarch64/cython-3.0.7-py39h387a81e_0.conda#e5495f92998c2dca45221dbe10c49999
 https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.0-pyhd8ed1ab_0.conda#f6c211fee3c98229652b60a9a42ef363
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 https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_0.conda#f800d2da156d08e289b14e87e43c1ae5
@@ -59,9 +60,8 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/openblas-0.3.25-pthreads_h3
 https://conda.anaconda.org/conda-forge/linux-aarch64/openjpeg-2.5.0-h0d9d63b_3.conda#123f5df3bc7f0e23c6950fddb97d1f43
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-https://conda.anaconda.org/conda-forge/noarch/py-1.11.0-pyh6c4a22f_0.tar.bz2#b4613d7e7a493916d867842a6a148054
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.1-pyhd8ed1ab_0.conda#176f7d56f0cfe9008bdf1bccd7de02fb
-https://conda.anaconda.org/conda-forge/noarch/setuptools-68.2.2-pyhd8ed1ab_0.conda#fc2166155db840c634a1291a5c35a709
+https://conda.anaconda.org/conda-forge/noarch/setuptools-69.0.3-pyhd8ed1ab_0.conda#40695fdfd15a92121ed2922900d0308b
 https://conda.anaconda.org/conda-forge/noarch/six-1.16.0-pyh6c4a22f_0.tar.bz2#e5f25f8dbc060e9a8d912e432202afc2
 https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.2.0-pyha21a80b_0.conda#978d03388b62173b8e6f79162cf52b86
 https://conda.anaconda.org/conda-forge/noarch/tomli-2.0.1-pyhd8ed1ab_0.tar.bz2#5844808ffab9ebdb694585b50ba02a96
@@ -69,22 +69,21 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.3.3-py39h7cc1d5f_
 https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851
 https://conda.anaconda.org/conda-forge/noarch/wheel-0.42.0-pyhd8ed1ab_0.conda#1cdea58981c5cbc17b51973bcaddcea7
 https://conda.anaconda.org/conda-forge/noarch/zipp-3.17.0-pyhd8ed1ab_0.conda#2e4d6bc0b14e10f895fc6791a7d9b26a
-https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.46.0-py39h898b7ef_0.conda#515b31b7bba3302949c9be091b1945e2
+https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.47.0-py39h898b7ef_0.conda#c1104ffe473cef5d35af62e0b6351de3
 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.1.1-pyhd8ed1ab_0.conda#3d5fa25cf42f3f32a12b2d874ace8574
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.3.2-pyhd8ed1ab_0.conda#4da50d410f553db77e62ab62ffaa1abc
 https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-20_linuxaarch64_openblas.conda#b41e55ae2cb9d3518da2cbe3677b3b3b
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-20_linuxaarch64_openblas.conda#e7412a592d9ee7c92026eb1189687271
-https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.1.0-py39h8ce38d7_0.conda#afedc0abb518dac535cb861f24585160
-https://conda.anaconda.org/conda-forge/noarch/pip-23.3.1-pyhd8ed1ab_0.conda#2400c0b86889f43aa52067161e1fb108
-https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.3-pyhd8ed1ab_0.conda#5bdca0aca30b0ee62bb84854e027eae0
+https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.2.0-py39h8ce38d7_0.conda#cf4745fb7f7cb5d0b90c476116c7d8ac
+https://conda.anaconda.org/conda-forge/noarch/pip-23.3.2-pyhd8ed1ab_0.conda#8591c748f98dcc02253003533bc2e4b1
+https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.8.2-pyhd8ed1ab_0.tar.bz2#dd999d1cc9f79e67dbb855c8924c7984
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.1.1-pyhd8ed1ab_0.conda#d04bd1b5bed9177dd7c3cef15e2b6710
 https://conda.anaconda.org/conda-forge/linux-aarch64/liblapacke-3.9.0-20_linuxaarch64_openblas.conda#1b8192f036a2dc41fec67700bb8bacef
-https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.2-py39h91c28bb_0.conda#fc8077e28f5f86b80f6f0d86263ce72d
-https://conda.anaconda.org/conda-forge/noarch/pytest-forked-1.6.0-pyhd8ed1ab_0.conda#a46947638b6e005b63d2d6271da529b0
+https://conda.anaconda.org/conda-forge/linux-aarch64/numpy-1.26.3-py39h91c28bb_0.conda#9e10c6f9e309c2ada0d41c945e0f9b56
+https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-devel-3.9.0-20_linuxaarch64_openblas.conda#211c74d7600d8d1dec226daf5e28e2dc
 https://conda.anaconda.org/conda-forge/linux-aarch64/contourpy-1.2.0-py39hd16970a_0.conda#dc11a4a2e020d1d71350baa7cb4980e4
-https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-2.5.0-pyhd8ed1ab_0.tar.bz2#1fdd1f3baccf0deb647385c677a1a48e
 https://conda.anaconda.org/conda-forge/linux-aarch64/scipy-1.11.3-py39h91c28bb_1.conda#216b118cdb919665ad7d9d2faff412df
 https://conda.anaconda.org/conda-forge/linux-aarch64/blas-2.120-openblas.conda#4354e2978d15f5b29b1557792e5c5c63
 https://conda.anaconda.org/conda-forge/linux-aarch64/matplotlib-base-3.8.2-py39h8e43113_0.conda#0dd681b8d2a93b799954714481761fe0
diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index 93c5f45397692..6625c88affe29 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -5,8 +5,11 @@
 
 Two scenarios where this script can be useful:
 - make sure that the latest versions of all the dependencies are used in the CI.
-  We can run this script regularly and open a PR with the changes to the lock
-  files. This workflow will eventually be automated with a bot in the future.
+  There is a scheduled workflow that does this, see
+  .github/workflows/update-lock-files.yml. This is still useful to run this
+  script when when the automated PR fails and for example some packages need to
+  be pinned. You can add the pins to this script, run it, and open a PR with
+  the changes.
 - bump minimum dependencies in sklearn/_min_dependencies.py. Running this
   script will update both the CI environment files and associated lock files.
   You can then open a PR with the changes.
@@ -78,11 +81,7 @@
 
 docstring_test_dependencies = ["sphinx", "numpydoc"]
 
-default_package_constraints = {
-    # XXX: pin pytest-xdist to workaround:
-    # https://github.com/pytest-dev/pytest-xdist/issues/840
-    "pytest-xdist": "2.5.0",
-}
+default_package_constraints = {}
 
 
 def remove_from(alist, to_remove):
@@ -136,22 +135,12 @@ def remove_from(alist, to_remove):
             "numpy": "<1.25",
         },
     },
-    {
-        "build_name": "pylatest_conda_forge_mkl_no_coverage",
-        "folder": "build_tools/azure",
-        "platform": "linux-64",
-        "channel": "conda-forge",
-        "conda_dependencies": common_dependencies_without_coverage + ["ccache"],
-        "package_constraints": {
-            "blas": "[build=mkl]",
-        },
-    },
     {
         "build_name": "pymin_conda_defaults_openblas",
         "folder": "build_tools/azure",
         "platform": "linux-64",
         "channel": "defaults",
-        "conda_dependencies": common_dependencies + ["ccache"],
+        "conda_dependencies": remove_from(common_dependencies, ["pandas"]) + ["ccache"],
         "package_constraints": {
             "python": "3.9",
             "blas": "[build=openblas]",
@@ -276,6 +265,7 @@ def remove_from(alist, to_remove):
             "numpydoc",
             "sphinx-prompt",
             "plotly",
+            "polars",
             "pooch",
         ],
         "pip_dependencies": ["sphinxext-opengraph"],
@@ -294,6 +284,7 @@ def remove_from(alist, to_remove):
             "sphinx-prompt": "min",
             "sphinxext-opengraph": "min",
             "plotly": "min",
+            "polars": "min",
         },
     },
     {
@@ -312,6 +303,7 @@ def remove_from(alist, to_remove):
             "numpydoc",
             "sphinx-prompt",
             "plotly",
+            "polars",
             "pooch",
             "sphinxext-opengraph",
         ],
diff --git a/doc/conf.py b/doc/conf.py
index c5e87442abe1f..20181c0a84769 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -303,6 +303,9 @@
     "auto_examples/ensemble/plot_adaboost_hastie_10_2": (
         "auto_examples/ensemble/plot_adaboost_multiclass"
     ),
+    "auto_examples/decomposition/plot_pca_3d": (
+        "auto_examples/decomposition/plot_pca_iris"
+    ),
 }
 html_context["redirects"] = redirects
 for old_link in redirects:
diff --git a/doc/developers/contributing.rst b/doc/developers/contributing.rst
index 6aecc524a9a30..02e02eb485e8a 100644
--- a/doc/developers/contributing.rst
+++ b/doc/developers/contributing.rst
@@ -971,7 +971,7 @@ To build the PDF manual, run:
    versions of Sphinx as possible, the different versions tend to
    behave slightly differently. To get the best results, you should
    use the same version as the one we used on CircleCI. Look at this
-   `GitHub search <https://github.com/search?q=repo%3Ascikit-learn%2Fscikit-learn+sphinx+path%3Abuild_tools%2Fcircle%2Fdoc_environment.yml&type=code>`_
+   `GitHub search <https://github.com/search?q=repo%3Ascikit-learn%2Fscikit-learn+%2F%5C%2Fsphinx-%5B0-9.%5D%2B%2F+path%3Abuild_tools%2Fcircle%2Fdoc_linux-64_conda.lock&type=code>`_
    to know the exact version.
 
 
diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst
index b922b0640c083..4cd86a0bf70c1 100644
--- a/doc/modules/clustering.rst
+++ b/doc/modules/clustering.rst
@@ -182,6 +182,10 @@ It suffers from various drawbacks:
    :align: center
    :scale: 50
 
+For more detailed descriptions of the issues shown above and how to address them,
+refer to the examples :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`
+and :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.
+
 K-means is often referred to as Lloyd's algorithm. In basic terms, the
 algorithm has three steps. The first step chooses the initial centroids, with
 the most basic method being to choose :math:`k` samples from the dataset
@@ -218,7 +222,9 @@ initializations of the centroids. One method to help address this issue is the
 k-means++ initialization scheme, which has been implemented in scikit-learn
 (use the ``init='k-means++'`` parameter). This initializes the centroids to be
 (generally) distant from each other, leading to probably better results than
-random initialization, as shown in the reference.
+random initialization, as shown in the reference. For a detailed example of
+comaparing different initialization schemes, refer to
+:ref:`sphx_glr_auto_examples_cluster_plot_kmeans_digits.py`.
 
 K-means++ can also be called independently to select seeds for other
 clustering algorithms, see :func:`sklearn.cluster.kmeans_plusplus` for details
@@ -231,7 +237,17 @@ weight of 2 to a sample is equivalent to adding a duplicate of that sample
 to the dataset :math:`X`.
 
 K-means can be used for vector quantization. This is achieved using the
-transform method of a trained model of :class:`KMeans`.
+``transform`` method of a trained model of :class:`KMeans`. For an example of
+performing vector quantization on an image refer to
+:ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`.
+
+.. topic:: Examples:
+
+ * :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`: Example usage of
+   :class:`KMeans` using the iris dataset
+
+ * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
+   using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
 
 Low-level parallelism
 ---------------------
@@ -291,11 +307,11 @@ small, as shown in the example and cited reference.
 
 .. topic:: Examples:
 
- * :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of KMeans and
-   MiniBatchKMeans
+ * :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`: Comparison of
+   :class:`KMeans` and :class:`MiniBatchKMeans`
 
- * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering using sparse
-   MiniBatchKMeans
+ * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`: Document clustering
+   using :class:`KMeans` and :class:`MiniBatchKMeans` based on sparse data
 
  * :ref:`sphx_glr_auto_examples_cluster_plot_dict_face_patches.py`
 
diff --git a/doc/modules/decomposition.rst b/doc/modules/decomposition.rst
index c1e317d2ff7d3..223985c6579f0 100644
--- a/doc/modules/decomposition.rst
+++ b/doc/modules/decomposition.rst
@@ -53,6 +53,7 @@ data based on the amount of variance it explains. As such it implements a
 
 .. topic:: Examples:
 
+    * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`
     * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_lda.py`
     * :ref:`sphx_glr_auto_examples_decomposition_plot_pca_vs_fa_model_selection.py`
 
@@ -319,6 +320,11 @@ is eigendecomposed in the Kernel PCA fitting process has an effective rank that
 is much smaller than its size. This is a situation where approximate
 eigensolvers can provide speedup with very low precision loss.
 
+
+|details-start|
+**Eigensolvers**
+|details-split|
+
 The optional parameter ``eigen_solver='randomized'`` can be used to
 *significantly* reduce the computation time when the number of requested
 ``n_components`` is small compared with the number of samples. It relies on
@@ -343,6 +349,7 @@ is extremely small. It is enabled by default when the desired number of
 components is less than 10 (strict) and the number of samples is more than 200
 (strict). See :class:`KernelPCA` for details.
 
+
 .. topic:: References:
 
     * *dense* solver:
@@ -365,6 +372,8 @@ components is less than 10 (strict) and the number of samples is more than 200
       <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.linalg.eigsh.html>`_
       R. B. Lehoucq, D. C. Sorensen, and C. Yang, (1998)
 
+|details-end|
+
 
 .. _LSA:
 
@@ -375,6 +384,16 @@ Truncated singular value decomposition and latent semantic analysis
 (SVD) that only computes the :math:`k` largest singular values,
 where :math:`k` is a user-specified parameter.
 
+:class:`TruncatedSVD` is very similar to :class:`PCA`, but differs
+in that the matrix :math:`X` does not need to be centered.
+When the columnwise (per-feature) means of :math:`X`
+are subtracted from the feature values,
+truncated SVD on the resulting matrix is equivalent to PCA.
+
+|details-start|
+**About truncated SVD and latent semantic analysis (LSA)**
+|details-split|
+
 When truncated SVD is applied to term-document matrices
 (as returned by :class:`~sklearn.feature_extraction.text.CountVectorizer` or
 :class:`~sklearn.feature_extraction.text.TfidfVectorizer`),
@@ -415,11 +434,6 @@ To also transform a test set :math:`X`, we multiply it with :math:`V_k`:
     We present LSA in a different way that matches the scikit-learn API better,
     but the singular values found are the same.
 
-:class:`TruncatedSVD` is very similar to :class:`PCA`, but differs
-in that the matrix :math:`X` does not need to be centered.
-When the columnwise (per-feature) means of :math:`X`
-are subtracted from the feature values,
-truncated SVD on the resulting matrix is equivalent to PCA.
 
 While the :class:`TruncatedSVD` transformer
 works with any feature matrix,
@@ -430,6 +444,8 @@ should be turned on (``sublinear_tf=True, use_idf=True``)
 to bring the feature values closer to a Gaussian distribution,
 compensating for LSA's erroneous assumptions about textual data.
 
+|details-end|
+
 .. topic:: Examples:
 
    * :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`
@@ -442,6 +458,7 @@ compensating for LSA's erroneous assumptions about textual data.
     <https://nlp.stanford.edu/IR-book/pdf/18lsi.pdf>`_
 
 
+
 .. _DictionaryLearning:
 
 Dictionary Learning
@@ -883,6 +900,10 @@ Note that this definition is not valid if :math:`\beta \in (0; 1)`, yet it can
 be continuously extended to the definitions of :math:`d_{KL}` and :math:`d_{IS}`
 respectively.
 
+|details-start|
+**NMF implemented solvers**
+|details-split|
+
 :class:`NMF` implements two solvers, using Coordinate Descent ('cd') [5]_, and
 Multiplicative Update ('mu') [6]_. The 'mu' solver can optimize every
 beta-divergence, including of course the Frobenius norm (:math:`\beta=2`), the
@@ -896,6 +917,8 @@ The 'cd' solver can only optimize the Frobenius norm. Due to the
 underlying non-convexity of NMF, the different solvers may converge to
 different minima, even when optimizing the same distance function.
 
+|details-end|
+
 NMF is best used with the ``fit_transform`` method, which returns the matrix W.
 The matrix H is stored into the fitted model in the ``components_`` attribute;
 the method ``transform`` will decompose a new matrix X_new based on these
@@ -910,6 +933,8 @@ stored components::
     >>> X_new = np.array([[1, 0], [1, 6.1], [1, 0], [1, 4], [3.2, 1], [0, 4]])
     >>> W_new = model.transform(X_new)
 
+
+
 .. topic:: Examples:
 
     * :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
@@ -996,6 +1021,10 @@ of topics in the corpus and the distribution of words in the documents.
 The goal of LDA is to use the observed words to infer the hidden topic
 structure.
 
+|details-start|
+**Details on modeling text corpora**
+|details-split|
+
 When modeling text corpora, the model assumes the following generative process
 for a corpus with :math:`D` documents and :math:`K` topics, with :math:`K`
 corresponding to `n_components` in the API:
@@ -1036,6 +1065,8 @@ Maximizing ELBO is equivalent to minimizing the Kullback-Leibler(KL) divergence
 between :math:`q(z,\theta,\beta)` and the true posterior
 :math:`p(z, \theta, \beta |w, \alpha, \eta)`.
 
+|details-end|
+
 :class:`LatentDirichletAllocation` implements the online variational Bayes
 algorithm and supports both online and batch update methods.
 While the batch method updates variational variables after each full pass through
diff --git a/doc/modules/model_evaluation.rst b/doc/modules/model_evaluation.rst
index a88a92604767e..271e5f6c1c661 100644
--- a/doc/modules/model_evaluation.rst
+++ b/doc/modules/model_evaluation.rst
@@ -886,22 +886,41 @@ following table:
 |                   | Missing result      | Correct absence of result|
 +-------------------+---------------------+--------------------------+
 
-In this context, we can define the notions of precision, recall and F-measure:
+In this context, we can define the notions of precision and recall:
 
 .. math::
 
-   \text{precision} = \frac{tp}{tp + fp},
+   \text{precision} = \frac{\text{tp}}{\text{tp} + \text{fp}},
 
 .. math::
 
-   \text{recall} = \frac{tp}{tp + fn},
+   \text{recall} = \frac{\text{tp}}{\text{tp} + \text{fn}},
 
+(Sometimes recall is also called ''sensitivity'')
+
+F-measure is the weighted harmonic mean of precision and recall, with precision's contribution to the mean weighted by
+some parameter :math:`\beta`:
+F-measure is the weighted harmonic mean of precision and recall, with precision's
+contribution to the mean weighted by some parameter :math:`\beta`:
 .. math::
 
-   F_\beta = (1 + \beta^2) \frac{\text{precision} \times \text{recall}}{\beta^2 \text{precision} + \text{recall}}.
+   F_\beta = (1 + \beta^2) \frac{\text{precision} \times \text{recall}}{\beta^2 \text{precision} + \text{recall}}
+
+To avoid division by zero when precision and recall are zero, Scikit-Learn calculates F-measure with this
+otherwise-equivalent formula:
+To avoid division by zero when precision and recall are zero, we can define the
+F-measure with this otherwise-equivalent formula:
+.. math::
 
-Sometimes recall is also called ''sensitivity''.
+   F_\beta = \frac{(1 + \beta^2) \text{tp}}{(1 + \beta^2) \text{tp} + \text{fp} + \beta^2 \text{fn}}.
 
+Note that this formula is still undefined when there are no true positives, false positives, nor false negatives. By
+default, F-1 for a set of exclusively true negatives is calculated as 0, however this behavior can be changed using the
+`zero_division` parameter.
+Note that this formula is still undefined when there are no true positives, false
+positives, nor false negatives. By default, F-1 for a set of exclusively true negatives
+is calculated as 0, however this behavior can be changed using the `zero_division`
+parameter.
 Here are some small examples in binary classification::
 
   >>> from sklearn import metrics
diff --git a/doc/tutorial/statistical_inference/unsupervised_learning.rst b/doc/tutorial/statistical_inference/unsupervised_learning.rst
index f96ac343a4882..e385eccaf592c 100644
--- a/doc/tutorial/statistical_inference/unsupervised_learning.rst
+++ b/doc/tutorial/statistical_inference/unsupervised_learning.rst
@@ -204,51 +204,57 @@ Decompositions: from a signal to components and loadings
 Principal component analysis: PCA
 -----------------------------------
 
-:ref:`PCA` selects the successive components that
-explain the maximum variance in the signal.
+:ref:`PCA` selects the successive components that explain the maximum variance in the
+signal. Let's create a synthetic 3-dimensional dataset.
 
-.. |pca_3d_axis| image:: /auto_examples/decomposition/images/sphx_glr_plot_pca_3d_001.png
-   :target: ../../auto_examples/decomposition/plot_pca_3d.html
-   :scale: 70
-
-.. |pca_3d_aligned| image:: /auto_examples/decomposition/images/sphx_glr_plot_pca_3d_002.png
-   :target: ../../auto_examples/decomposition/plot_pca_3d.html
-   :scale: 70
+.. np.random.seed(0)
 
-.. rst-class:: centered
+::
 
-   |pca_3d_axis| |pca_3d_aligned|
+    >>> # Create a signal with only 2 useful dimensions
+    >>> x1 = np.random.normal(size=(100, 1))
+    >>> x2 = np.random.normal(size=(100, 1))
+    >>> x3 = x1 + x2
+    >>> X = np.concatenate([x1, x2, x3], axis=1)
 
 The point cloud spanned by the observations above is very flat in one
-direction: one of the three univariate features can almost be exactly
-computed using the other two. PCA finds the directions in which the data is
-not *flat*
+direction: one of the three univariate features (i.e. z-axis) can almost be exactly
+computed using the other two.
 
-When used to *transform* data, PCA can reduce the dimensionality of the
-data by projecting on a principal subspace.
+.. plot::
+   :context: close-figs
+   :align: center
 
-.. np.random.seed(0)
+   >>> import matplotlib.pyplot as plt
+   >>> fig = plt.figure()
+   >>> ax = fig.add_subplot(111, projection='3d')
+   >>> ax.scatter(X[:, 0], X[:, 1], X[:, 2])
+   <...>
+   >>> _ = ax.set(xlabel="x", ylabel="y", zlabel="z")
+
+
+PCA finds the directions in which the data is not *flat*.
 
 ::
 
-    >>> # Create a signal with only 2 useful dimensions
-    >>> x1 = np.random.normal(size=100)
-    >>> x2 = np.random.normal(size=100)
-    >>> x3 = x1 + x2
-    >>> X = np.c_[x1, x2, x3]
-
-    >>> from sklearn import decomposition
-    >>> pca = decomposition.PCA()
-    >>> pca.fit(X)
-    PCA()
-    >>> print(pca.explained_variance_)  # doctest: +SKIP
-    [  2.18565811e+00   1.19346747e+00   8.43026679e-32]
-
-    >>> # As we can see, only the 2 first components are useful
-    >>> pca.n_components = 2
-    >>> X_reduced = pca.fit_transform(X)
-    >>> X_reduced.shape
-    (100, 2)
+   >>> from sklearn import decomposition
+   >>> pca = decomposition.PCA()
+   >>> pca.fit(X)
+   PCA()
+   >>> print(pca.explained_variance_)  # doctest: +SKIP
+   [  2.18565811e+00   1.19346747e+00   8.43026679e-32]
+
+Looking at the explained variance, we see that only the first two components
+are useful. PCA can be used to reduce dimensionality while preserving
+most of the information. It will project the data on the principal subspace.
+
+::
+
+   >>> pca.set_params(n_components=2)
+   PCA(n_components=2)
+   >>> X_reduced = pca.fit_transform(X)
+   >>> X_reduced.shape
+   (100, 2)
 
 .. Eigenfaces here?
 
diff --git a/doc/whats_new.rst b/doc/whats_new.rst
index 210d27cc075e5..8fa4c7007e0fd 100644
--- a/doc/whats_new.rst
+++ b/doc/whats_new.rst
@@ -12,6 +12,7 @@ on libraries.io to be notified when new versions are released.
 .. toctree::
     :maxdepth: 1
 
+    Version 1.5 <whats_new/v1.5.rst>
     Version 1.4 <whats_new/v1.4.rst>
     Version 1.3 <whats_new/v1.3.rst>
     Version 1.2 <whats_new/v1.2.rst>
diff --git a/doc/whats_new/v1.4.rst b/doc/whats_new/v1.4.rst
index 953c4906d3fb2..a932391b732cd 100644
--- a/doc/whats_new/v1.4.rst
+++ b/doc/whats_new/v1.4.rst
@@ -220,6 +220,17 @@ See :ref:`array_api` for more details.
 - :class:`preprocessing.MinMaxScaler` in :pr:`26243` by `Tim Head`_;
 - :class:`preprocessing.Normalizer` in :pr:`27558` by :user:`Edoardo Abati <EdAbati>`.
 
+Private Loss Function Module
+----------------------------
+
+- |FIX| The gradient computation of the binomial log loss is now numerically
+  more stable for very large, in absolute value, input (raw predictions). Before, it
+  could result in `np.nan`. Among the models that profit from this change are
+  :class:`ensemble.GradientBoostingClassifier`,
+  :class:`ensemble.HistGradientBoostingClassifier` and
+  :class:`linear_model.LogisticRegression`.
+  :pr:`28048` by :user:`Christian Lorentzen <lorentzenchr>`.
+
 Changelog
 ---------
 
@@ -547,6 +558,11 @@ Changelog
   type promotion rules of NumPy 2.
   :pr:`27899` by :user:`Olivier Grisel <ogrisel>`.
 
+- |API| The attribute `loss_function_` of :class:`linear_model.SGDClassifier` and
+  :class:`linear_model.SGDOneClassSVM` has been deprecated and will be removed in
+  version 1.6.
+  :pr:`27979` by :user:`Christian Lorentzen <lorentzenchr>`.
+
 :mod:`sklearn.metrics`
 ......................
 
diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst
new file mode 100644
index 0000000000000..f7a521ca4f0d0
--- /dev/null
+++ b/doc/whats_new/v1.5.rst
@@ -0,0 +1,46 @@
+.. include:: _contributors.rst
+
+.. currentmodule:: sklearn
+
+.. _changes_1_5:
+
+Version 1.5.0
+=============
+
+**In Development**
+
+.. include:: changelog_legend.inc
+
+Changelog
+---------
+
+..
+    Entries should be grouped by module (in alphabetic order) and prefixed with
+    one of the labels: |MajorFeature|, |Feature|, |Efficiency|, |Enhancement|,
+    |Fix| or |API| (see whats_new.rst for descriptions).
+    Entries should be ordered by those labels (e.g. |Fix| after |Efficiency|).
+    Changes not specific to a module should be listed under *Multiple Modules*
+    or *Miscellaneous*.
+    Entries should end with:
+    :pr:`123456` by :user:`Joe Bloggs <joeongithub>`.
+    where 123455 is the *pull request* number, not the issue number.
+
+:mod:`sklearn.impute`
+.....................
+- |Enhancement| :class:`impute.SimpleImputer` now supports custom strategies
+  by passing a function in place of a strategy name.
+  :pr:`28053` by :user:`Mark Elliot <mark-thm>`.
+
+Code and Documentation Contributors
+-----------------------------------
+
+Thanks to everyone who has contributed to the maintenance and improvement of
+the project since version 1.4, including:
+
+TODO: update at the time of the release.
+
+:mod:`sklearn.compose`
+......................
+
+- |Feature| A fitted :class:`compose.ColumnTransformer` now implements `__getitem__`
+  which returns the fitted transformers by name. :pr:`27990` by `Thomas Fan`_.
diff --git a/examples/cluster/plot_cluster_iris.py b/examples/cluster/plot_cluster_iris.py
index 79e885be35b86..ad85c0c9910a7 100644
--- a/examples/cluster/plot_cluster_iris.py
+++ b/examples/cluster/plot_cluster_iris.py
@@ -7,13 +7,13 @@
 
 - top left: What a K-means algorithm would yield using 8 clusters.
 
-- top right: What the effect of a bad initialization is
+- top right: What using three clusters would deliver.
+
+- bottom left: What the effect of a bad initialization is
   on the classification process: By setting n_init to only 1
   (default is 10), the amount of times that the algorithm will
   be run with different centroid seeds is reduced.
 
-- bottom left: What using eight clusters would deliver.
-
 - bottom right: The ground truth.
 
 """
@@ -73,8 +73,7 @@
         horizontalalignment="center",
         bbox=dict(alpha=0.2, edgecolor="w", facecolor="w"),
     )
-# Reorder the labels to have colors matching the cluster results
-y = np.choose(y, [1, 2, 0]).astype(float)
+
 ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y, edgecolor="k")
 
 ax.xaxis.set_ticklabels([])
diff --git a/examples/cluster/plot_color_quantization.py b/examples/cluster/plot_color_quantization.py
index eef66be21b104..ec21949466daf 100644
--- a/examples/cluster/plot_color_quantization.py
+++ b/examples/cluster/plot_color_quantization.py
@@ -41,7 +41,7 @@
 china = load_sample_image("china.jpg")
 
 # Convert to floats instead of the default 8 bits integer coding. Dividing by
-# 255 is important so that plt.imshow behaves works well on float data (need to
+# 255 is important so that plt.imshow works well on float data (need to
 # be in the range [0-1])
 china = np.array(china, dtype=np.float64) / 255
 
diff --git a/examples/decomposition/plot_pca_3d.py b/examples/decomposition/plot_pca_3d.py
deleted file mode 100644
index 61ce5dde75c89..0000000000000
--- a/examples/decomposition/plot_pca_3d.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""
-=========================================================
-Principal components analysis (PCA)
-=========================================================
-
-These figures aid in illustrating how a point cloud
-can be very flat in one direction--which is where PCA
-comes in to choose a direction that is not flat.
-
-"""
-
-# Authors: Gael Varoquaux
-#          Jaques Grobler
-#          Kevin Hughes
-# License: BSD 3 clause
-
-# %%
-# Create the data
-# ---------------
-
-import numpy as np
-from scipy import stats
-
-e = np.exp(1)
-np.random.seed(4)
-
-
-def pdf(x):
-    return 0.5 * (stats.norm(scale=0.25 / e).pdf(x) + stats.norm(scale=4 / e).pdf(x))
-
-
-y = np.random.normal(scale=0.5, size=(30000))
-x = np.random.normal(scale=0.5, size=(30000))
-z = np.random.normal(scale=0.1, size=len(x))
-
-density = pdf(x) * pdf(y)
-pdf_z = pdf(5 * z)
-
-density *= pdf_z
-
-a = x + y
-b = 2 * y
-c = a - b + z
-
-norm = np.sqrt(a.var() + b.var())
-a /= norm
-b /= norm
-
-
-# %%
-# Plot the figures
-# ----------------
-
-import matplotlib.pyplot as plt
-
-# unused but required import for doing 3d projections with matplotlib < 3.2
-import mpl_toolkits.mplot3d  # noqa: F401
-
-from sklearn.decomposition import PCA
-
-
-def plot_figs(fig_num, elev, azim):
-    fig = plt.figure(fig_num, figsize=(4, 3))
-    plt.clf()
-    ax = fig.add_subplot(111, projection="3d", elev=elev, azim=azim)
-    ax.set_position([0, 0, 0.95, 1])
-
-    ax.scatter(a[::10], b[::10], c[::10], c=density[::10], marker="+", alpha=0.4)
-    Y = np.c_[a, b, c]
-
-    # Using SciPy's SVD, this would be:
-    # _, pca_score, Vt = scipy.linalg.svd(Y, full_matrices=False)
-
-    pca = PCA(n_components=3)
-    pca.fit(Y)
-    V = pca.components_.T
-
-    x_pca_axis, y_pca_axis, z_pca_axis = 3 * V
-    x_pca_plane = np.r_[x_pca_axis[:2], -x_pca_axis[1::-1]]
-    y_pca_plane = np.r_[y_pca_axis[:2], -y_pca_axis[1::-1]]
-    z_pca_plane = np.r_[z_pca_axis[:2], -z_pca_axis[1::-1]]
-    x_pca_plane.shape = (2, 2)
-    y_pca_plane.shape = (2, 2)
-    z_pca_plane.shape = (2, 2)
-    ax.plot_surface(x_pca_plane, y_pca_plane, z_pca_plane)
-    ax.xaxis.set_ticklabels([])
-    ax.yaxis.set_ticklabels([])
-    ax.zaxis.set_ticklabels([])
-
-
-elev = -40
-azim = -80
-plot_figs(1, elev, azim)
-
-elev = 30
-azim = 20
-plot_figs(2, elev, azim)
-
-plt.show()
diff --git a/examples/linear_model/plot_lasso_and_elasticnet.py b/examples/linear_model/plot_lasso_and_elasticnet.py
index 075d8a50d2f62..78ab9624b64a4 100644
--- a/examples/linear_model/plot_lasso_and_elasticnet.py
+++ b/examples/linear_model/plot_lasso_and_elasticnet.py
@@ -245,4 +245,4 @@
 #
 #   .. [1] :doi:`"Lasso-type recovery of sparse representations for
 #    high-dimensional data" N. Meinshausen, B. Yu - The Annals of Statistics
-#    2009, Vol. 37, No. 1, 246–270 <10.1214/07-AOS582>`
+#    2009, Vol. 37, No. 1, 246-270 <10.1214/07-AOS582>`
diff --git a/examples/model_selection/plot_precision_recall.py b/examples/model_selection/plot_precision_recall.py
index 2e48495f96a16..03b273de66b7f 100644
--- a/examples/model_selection/plot_precision_recall.py
+++ b/examples/model_selection/plot_precision_recall.py
@@ -37,10 +37,11 @@
 
 :math:`R = \\frac{T_p}{T_p + F_n}`
 
-These quantities are also related to the (:math:`F_1`) score, which is defined
-as the harmonic mean of precision and recall.
+These quantities are also related to the :math:`F_1` score, which is the
+harmonic mean of precision and recall. Thus, we can compute the :math:`F_1`
+using the following formula:
 
-:math:`F1 = 2\\frac{P \\times R}{P+R}`
+:math:`F_1 = \\frac{2T_p}{2T_p + F_p + F_n}`
 
 Note that the precision may not decrease with recall. The
 definition of precision (:math:`\\frac{T_p}{T_p + F_p}`) shows that lowering
diff --git a/examples/release_highlights/plot_release_highlights_1_4_0.py b/examples/release_highlights/plot_release_highlights_1_4_0.py
new file mode 100644
index 0000000000000..d8112699e04ed
--- /dev/null
+++ b/examples/release_highlights/plot_release_highlights_1_4_0.py
@@ -0,0 +1,206 @@
+# ruff: noqa
+"""
+=======================================
+Release Highlights for scikit-learn 1.4
+=======================================
+
+.. currentmodule:: sklearn
+
+We are pleased to announce the release of scikit-learn 1.4! Many bug fixes
+and improvements were added, as well as some new key features. We detail
+below a few of the major features of this release. **For an exhaustive list of
+all the changes**, please refer to the :ref:`release notes <changes_1_4>`.
+
+To install the latest version (with pip)::
+
+    pip install --upgrade scikit-learn
+
+or with conda::
+
+    conda install -c conda-forge scikit-learn
+
+"""
+
+# %%
+# HistGradientBoosting Natively Supports Categorical DTypes in DataFrames
+# -----------------------------------------------------------------------
+# :class:`ensemble.HistGradientBoostingClassifier` and
+# :class:`ensemble.HistGradientBoostingRegressor` now directly supports dataframes with
+# categorical features.  Here we have a dataset with a mixture of
+# categorical and numerical features:
+from sklearn.datasets import fetch_openml
+
+X_adult, y_adult = fetch_openml("adult", version=2, return_X_y=True)
+
+# Remove redundant and non-feature columns
+X_adult = X_adult.drop(["education-num", "fnlwgt"], axis="columns")
+X_adult.dtypes
+
+# %%
+# By setting `categorical_features="from_dtype"`, the gradient boosting classifier
+# treats the columns with categorical dtypes as categorical features in the
+# algorithm:
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import roc_auc_score
+
+X_train, X_test, y_train, y_test = train_test_split(X_adult, y_adult, random_state=0)
+hist = HistGradientBoostingClassifier(categorical_features="from_dtype")
+
+hist.fit(X_train, y_train)
+y_decision = hist.decision_function(X_test)
+print(f"ROC AUC score is {roc_auc_score(y_test, y_decision)}")
+
+# %%
+# Polars output in `set_output`
+# -----------------------------
+# scikit-learn's transformers now support polars output with the `set_output` API.
+import polars as pl
+from sklearn.preprocessing import StandardScaler
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.compose import ColumnTransformer
+
+df = pl.DataFrame(
+    {"height": [120, 140, 150, 110, 100], "pet": ["dog", "cat", "dog", "cat", "cat"]}
+)
+preprocessor = ColumnTransformer(
+    [
+        ("numerical", StandardScaler(), ["height"]),
+        ("categorical", OneHotEncoder(sparse_output=False), ["pet"]),
+    ],
+    verbose_feature_names_out=False,
+)
+preprocessor.set_output(transform="polars")
+
+df_out = preprocessor.fit_transform(df)
+print(f"Output type: {type(df_out)}")
+
+# %%
+# Missing value support for Random Forest
+# ---------------------------------------
+# The classes :class:`ensemble.RandomForestClassifier` and
+# :class:`ensemble.RandomForestRegressor` now support missing values. When training
+# every individual tree, the splitter evaluates each potential threshold with the
+# missing values going to the left and right nodes. More details in the
+# :ref:`User Guide <tree_missing_value_support>`.
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+
+X = np.array([0, 1, 6, np.nan]).reshape(-1, 1)
+y = [0, 0, 1, 1]
+
+forest = RandomForestClassifier(random_state=0).fit(X, y)
+forest.predict(X)
+
+# %%
+# Add support for monotonic constraints in tree-based models
+# ----------------------------------------------------------
+# While we added support for monotonic constraints in histogram-based gradient boosting
+# in scikit-learn 0.23, we now support this feature for all other tree-based models as
+# trees, random forests, extra-trees, and exact gradient boosting. Here, we show this
+# feature for random forest on a regression problem.
+import matplotlib.pyplot as plt
+from sklearn.inspection import PartialDependenceDisplay
+from sklearn.ensemble import RandomForestRegressor
+
+n_samples = 500
+rng = np.random.RandomState(0)
+X = rng.randn(n_samples, 2)
+noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
+y = 5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise
+
+rf_no_cst = RandomForestRegressor().fit(X, y)
+rf_cst = RandomForestRegressor(monotonic_cst=[1, 0]).fit(X, y)
+
+disp = PartialDependenceDisplay.from_estimator(
+    rf_no_cst,
+    X,
+    features=[0],
+    feature_names=["feature 0"],
+    line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"},
+)
+PartialDependenceDisplay.from_estimator(
+    rf_cst,
+    X,
+    features=[0],
+    line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"},
+    ax=disp.axes_,
+)
+disp.axes_[0, 0].plot(
+    X[:, 0], y, "o", alpha=0.5, zorder=-1, label="samples", color="tab:green"
+)
+disp.axes_[0, 0].set_ylim(-3, 3)
+disp.axes_[0, 0].set_xlim(-1, 1)
+disp.axes_[0, 0].legend()
+plt.show()
+
+# %%
+# Enriched estimator displays
+# ---------------------------
+# Estimators displays have been enriched: if we look at `forest`, defined above:
+forest
+
+# %%
+# One can access the documentation of the estimator by clicking on the icon "?" on
+# the top right corner of the diagram.
+#
+# In addition, the display changes color, from orange to blue, when the estimator is
+# fitted. You can also get this information by hovering on the icon "i".
+from sklearn.base import clone
+
+clone(forest)  # the clone is not fitted
+
+# %%
+# Metadata Routing Support
+# ------------------------
+# Many meta-estimators and cross-validation routines now support metadata
+# routing, which are listed in the :ref:`user guide
+# <_metadata_routing_models>`. For instance, this is how you can do a nested
+# cross-validation with sample weights and :class:`~model_selection.GroupKFold`:
+import sklearn
+from sklearn.metrics import get_scorer
+from sklearn.datasets import make_regression
+from sklearn.linear_model import Lasso
+from sklearn.model_selection import GridSearchCV, cross_validate, GroupKFold
+
+# For now by default metadata routing is disabled, and need to be explicitly
+# enabled.
+sklearn.set_config(enable_metadata_routing=True)
+
+n_samples = 100
+X, y = make_regression(n_samples=n_samples, n_features=5, noise=0.5)
+rng = np.random.RandomState(7)
+groups = rng.randint(0, 10, size=n_samples)
+sample_weights = rng.rand(n_samples)
+estimator = Lasso().set_fit_request(sample_weight=True)
+hyperparameter_grid = {"alpha": [0.1, 0.5, 1.0, 2.0]}
+scoring_inner_cv = get_scorer("neg_mean_squared_error").set_score_request(
+    sample_weight=True
+)
+inner_cv = GroupKFold(n_splits=5)
+
+grid_search = GridSearchCV(
+    estimator=estimator,
+    param_grid=hyperparameter_grid,
+    cv=inner_cv,
+    scoring=scoring_inner_cv,
+)
+
+outer_cv = GroupKFold(n_splits=5)
+scorers = {
+    "mse": get_scorer("neg_mean_squared_error").set_score_request(sample_weight=True)
+}
+results = cross_validate(
+    grid_search,
+    X,
+    y,
+    cv=outer_cv,
+    scoring=scorers,
+    return_estimator=True,
+    params={"sample_weight": sample_weights, "groups": groups},
+)
+print("cv error on test sets:", results["test_mse"])
+
+# Setting the flag to the default `False` to avoid interference with other
+# scripts.
+sklearn.set_config(enable_metadata_routing=False)
diff --git a/examples/text/plot_document_clustering.py b/examples/text/plot_document_clustering.py
index fa68b8bd312ea..2c3506f4ec32e 100644
--- a/examples/text/plot_document_clustering.py
+++ b/examples/text/plot_document_clustering.py
@@ -99,8 +99,9 @@
 #   assignment have an ARI of 0.0 in expectation.
 #
 # If the ground truth labels are not known, evaluation can only be performed
-# using the model results itself. In that case, the Silhouette Coefficient comes
-# in handy.
+# using the model results itself. In that case, the Silhouette Coefficient comes in
+# handy. See :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`
+# for an example on how to do it.
 #
 # For more reference, see :ref:`clustering_evaluation`.
 
diff --git a/examples/text/plot_hashing_vs_dict_vectorizer.py b/examples/text/plot_hashing_vs_dict_vectorizer.py
index ce2dcc2d13c41..6c08f947e4a2f 100644
--- a/examples/text/plot_hashing_vs_dict_vectorizer.py
+++ b/examples/text/plot_hashing_vs_dict_vectorizer.py
@@ -299,7 +299,7 @@ def n_nonzero_columns(X):
 #
 # Now we make a similar experiment with the
 # :func:`~sklearn.feature_extraction.text.HashingVectorizer`, which is
-# equivalent to combining the “hashing trick” implemented by the
+# equivalent to combining the "hashing trick" implemented by the
 # :func:`~sklearn.feature_extraction.FeatureHasher` class and the text
 # preprocessing and tokenization of the
 # :func:`~sklearn.feature_extraction.text.CountVectorizer`.
@@ -322,15 +322,15 @@ def n_nonzero_columns(X):
 # TfidfVectorizer
 # ---------------
 #
-# In a large text corpus, some words appear with higher frequency (e.g. “the”,
-# “a”, “is” in English) and do not carry meaningful information about the actual
+# In a large text corpus, some words appear with higher frequency (e.g. "the",
+# "a", "is" in English) and do not carry meaningful information about the actual
 # contents of a document. If we were to feed the word count data directly to a
 # classifier, those very common terms would shadow the frequencies of rarer yet
 # more informative terms. In order to re-weight the count features into floating
 # point values suitable for usage by a classifier it is very common to use the
-# tf–idf transform as implemented by the
+# tf-idf transform as implemented by the
 # :func:`~sklearn.feature_extraction.text.TfidfTransformer`. TF stands for
-# "term-frequency" while "tf–idf" means term-frequency times inverse
+# "term-frequency" while "tf-idf" means term-frequency times inverse
 # document-frequency.
 #
 # We now benchmark the :func:`~sklearn.feature_extraction.text.TfidfVectorizer`,
diff --git a/sklearn/__init__.py b/sklearn/__init__.py
index ecb32f9dc0da3..673031649a265 100644
--- a/sklearn/__init__.py
+++ b/sklearn/__init__.py
@@ -42,7 +42,7 @@
 # Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
 # 'X.Y.dev0' is the canonical version of 'X.Y.dev'
 #
-__version__ = "1.4.dev0"
+__version__ = "1.5.dev0"
 
 
 # On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
diff --git a/sklearn/_loss/_loss.pyx.tp b/sklearn/_loss/_loss.pyx.tp
index 0ce653de84310..da974a3c3f4fd 100644
--- a/sklearn/_loss/_loss.pyx.tp
+++ b/sklearn/_loss/_loss.pyx.tp
@@ -695,9 +695,8 @@ cdef inline double cgradient_half_binomial(
     double y_true,
     double raw_prediction
 ) noexcept nogil:
-    # y_pred - y_true = expit(raw_prediction) - y_true
-    # Numerically more stable, see
-    # http://fa.bianp.net/blog/2019/evaluate_logistic/
+    # gradient = y_pred - y_true = expit(raw_prediction) - y_true
+    # Numerically more stable, see http://fa.bianp.net/blog/2019/evaluate_logistic/
     #     if raw_prediction < 0:
     #         exp_tmp = exp(raw_prediction)
     #         return ((1 - y_true) * exp_tmp - y_true) / (1 + exp_tmp)
@@ -708,12 +707,22 @@ cdef inline double cgradient_half_binomial(
     #     return expit(raw_prediction) - y_true
     # i.e. no "if else" and an own inline implementation of expit instead of
     #     from scipy.special.cython_special cimport expit
-    # The case distinction raw_prediction < 0 in the stable implementation
-    # does not provide significant better precision. Therefore we go without
-    # it.
+    # The case distinction raw_prediction < 0 in the stable implementation does not
+    # provide significant better precision apart from protecting overflow of exp(..).
+    # The branch (if else), however, can incur runtime costs of up to 30%.
+    # Instead, we help branch prediction by almost always ending in the first if clause
+    # and making the second branch (else) a bit simpler. This has the exact same
+    # precision but is faster than the stable implementation.
+    # As branching criteria, we use the same cutoff as in log1pexp. Note that the
+    # maximal value to get gradient = -1 with y_true = 1 is -37.439198610162731
+    # (based on mpmath), and scipy.special.logit(np.finfo(float).eps) ~ -36.04365.
     cdef double exp_tmp
-    exp_tmp = exp(-raw_prediction)
-    return ((1 - y_true) - y_true * exp_tmp) / (1 + exp_tmp)
+    if raw_prediction > -37:
+        exp_tmp = exp(-raw_prediction)
+        return ((1 - y_true) - y_true * exp_tmp) / (1 + exp_tmp)
+    else:
+        # expit(raw_prediction) = exp(raw_prediction) for raw_prediction <= -37
+        return exp(raw_prediction) - y_true
 
 
 cdef inline double_pair closs_grad_half_binomial(
@@ -721,21 +730,24 @@ cdef inline double_pair closs_grad_half_binomial(
     double raw_prediction
 ) noexcept nogil:
     cdef double_pair lg
-    if raw_prediction <= 0:
+    # Same if else conditions as in log1pexp.
+    if raw_prediction <= -37:
         lg.val2 = exp(raw_prediction)  # used as temporary
-        if raw_prediction <= -37:
-            lg.val1 = lg.val2 - y_true * raw_prediction              # loss
-        else:
-            lg.val1 = log1p(lg.val2) - y_true * raw_prediction       # loss
+        lg.val1 = lg.val2 - y_true * raw_prediction                  # loss
+        lg.val2 -= y_true                                            # gradient
+    elif raw_prediction <= -2:
+        lg.val2 = exp(raw_prediction)  # used as temporary
+        lg.val1 = log1p(lg.val2) - y_true * raw_prediction           # loss
         lg.val2 = ((1 - y_true) * lg.val2 - y_true) / (1 + lg.val2)  # gradient
+    elif raw_prediction <= 18:
+        lg.val2 = exp(-raw_prediction)  # used as temporary
+        # log1p(exp(x)) = log(1 + exp(x)) = x + log1p(exp(-x))
+        lg.val1 = log1p(lg.val2) + (1 - y_true) * raw_prediction     # loss
+        lg.val2 = ((1 - y_true) - y_true * lg.val2) / (1 + lg.val2)  # gradient
     else:
         lg.val2 = exp(-raw_prediction)  # used as temporary
-        if raw_prediction <= 18:
-            # log1p(exp(x)) = log(1 + exp(x)) = x + log1p(exp(-x))
-            lg.val1 = log1p(lg.val2) + (1 - y_true) * raw_prediction  # loss
-        else:
-            lg.val1 = lg.val2 + (1 - y_true) * raw_prediction         # loss
-        lg.val2 = ((1 - y_true) - y_true * lg.val2) / (1 + lg.val2)   # gradient
+        lg.val1 = lg.val2 + (1 - y_true) * raw_prediction            # loss
+        lg.val2 = ((1 - y_true) - y_true * lg.val2) / (1 + lg.val2)  # gradient
     return lg
 
 
@@ -747,9 +759,15 @@ cdef inline double_pair cgrad_hess_half_binomial(
     # hessian = y_pred * (1 - y_pred) = exp( raw) / (1 + exp( raw))**2
     #                                 = exp(-raw) / (1 + exp(-raw))**2
     cdef double_pair gh
-    gh.val2 = exp(-raw_prediction)  # used as temporary
-    gh.val1 = ((1 - y_true) - y_true * gh.val2) / (1 + gh.val2)  # gradient
-    gh.val2 = gh.val2 / (1 + gh.val2)**2                         # hessian
+    # See comment in cgradient_half_binomial.
+    if raw_prediction > -37:
+        gh.val2 = exp(-raw_prediction)  # used as temporary
+        gh.val1 = ((1 - y_true) - y_true * gh.val2) / (1 + gh.val2)  # gradient
+        gh.val2 = gh.val2 / (1 + gh.val2)**2                         # hessian
+    else:
+        gh.val2 = exp(raw_prediction)
+        gh.val1 = gh.val2 - y_true
+        gh.val2 *= (1 - gh.val2)
     return gh
 
 
diff --git a/sklearn/_loss/tests/test_loss.py b/sklearn/_loss/tests/test_loss.py
index c018bb7147ce9..9c8bba4d717d1 100644
--- a/sklearn/_loss/tests/test_loss.py
+++ b/sklearn/_loss/tests/test_loss.py
@@ -224,48 +224,150 @@ def test_loss_boundary_y_pred(loss, y_pred_success, y_pred_fail):
 
 
 @pytest.mark.parametrize(
-    "loss, y_true, raw_prediction, loss_true",
+    "loss, y_true, raw_prediction, loss_true, gradient_true, hessian_true",
     [
-        (HalfSquaredError(), 1.0, 5.0, 8),
-        (AbsoluteError(), 1.0, 5.0, 4),
-        (PinballLoss(quantile=0.5), 1.0, 5.0, 2),
-        (PinballLoss(quantile=0.25), 1.0, 5.0, 4 * (1 - 0.25)),
-        (PinballLoss(quantile=0.25), 5.0, 1.0, 4 * 0.25),
-        (HuberLoss(quantile=0.5, delta=3), 1.0, 5.0, 3 * (4 - 3 / 2)),
-        (HuberLoss(quantile=0.5, delta=3), 1.0, 3.0, 0.5 * 2**2),
-        (HalfPoissonLoss(), 2.0, np.log(4), 4 - 2 * np.log(4)),
-        (HalfGammaLoss(), 2.0, np.log(4), np.log(4) + 2 / 4),
-        (HalfTweedieLoss(power=3), 2.0, np.log(4), -1 / 4 + 1 / 4**2),
-        (HalfTweedieLossIdentity(power=1), 2.0, 4.0, 2 - 2 * np.log(2)),
-        (HalfTweedieLossIdentity(power=2), 2.0, 4.0, np.log(2) - 1 / 2),
-        (HalfTweedieLossIdentity(power=3), 2.0, 4.0, -1 / 4 + 1 / 4**2 + 1 / 2 / 2),
-        (HalfBinomialLoss(), 0.25, np.log(4), np.log(5) - 0.25 * np.log(4)),
+        (HalfSquaredError(), 1.0, 5.0, 8, 4, 1),
+        (AbsoluteError(), 1.0, 5.0, 4.0, 1.0, None),
+        (PinballLoss(quantile=0.5), 1.0, 5.0, 2, 0.5, None),
+        (PinballLoss(quantile=0.25), 1.0, 5.0, 4 * (1 - 0.25), 1 - 0.25, None),
+        (PinballLoss(quantile=0.25), 5.0, 1.0, 4 * 0.25, -0.25, None),
+        (HuberLoss(quantile=0.5, delta=3), 1.0, 5.0, 3 * (4 - 3 / 2), None, None),
+        (HuberLoss(quantile=0.5, delta=3), 1.0, 3.0, 0.5 * 2**2, None, None),
+        (HalfPoissonLoss(), 2.0, np.log(4), 4 - 2 * np.log(4), 4 - 2, 4),
+        (HalfGammaLoss(), 2.0, np.log(4), np.log(4) + 2 / 4, 1 - 2 / 4, 2 / 4),
+        (HalfTweedieLoss(power=3), 2.0, np.log(4), -1 / 4 + 1 / 4**2, None, None),
+        (HalfTweedieLossIdentity(power=1), 2.0, 4.0, 2 - 2 * np.log(2), None, None),
+        (HalfTweedieLossIdentity(power=2), 2.0, 4.0, np.log(2) - 1 / 2, None, None),
+        (
+            HalfTweedieLossIdentity(power=3),
+            2.0,
+            4.0,
+            -1 / 4 + 1 / 4**2 + 1 / 2 / 2,
+            None,
+            None,
+        ),
+        (
+            HalfBinomialLoss(),
+            0.25,
+            np.log(4),
+            np.log1p(4) - 0.25 * np.log(4),
+            None,
+            None,
+        ),
+        # Extreme log loss cases, checked with mpmath:
+        # import mpmath as mp
+        #
+        # # Stolen from scipy
+        # def mpf2float(x):
+        #     return float(mp.nstr(x, 17, min_fixed=0, max_fixed=0))
+        #
+        # def mp_logloss(y_true, raw):
+        #     with mp.workdps(100):
+        #         y_true, raw = mp.mpf(float(y_true)), mp.mpf(float(raw))
+        #         out = mp.log1p(mp.exp(raw)) - y_true * raw
+        #     return mpf2float(out)
+        #
+        # def mp_gradient(y_true, raw):
+        #     with mp.workdps(100):
+        #         y_true, raw = mp.mpf(float(y_true)), mp.mpf(float(raw))
+        #         out = mp.mpf(1) / (mp.mpf(1) + mp.exp(-raw)) - y_true
+        #     return mpf2float(out)
+        #
+        # def mp_hessian(y_true, raw):
+        #     with mp.workdps(100):
+        #         y_true, raw = mp.mpf(float(y_true)), mp.mpf(float(raw))
+        #         p = mp.mpf(1) / (mp.mpf(1) + mp.exp(-raw))
+        #         out = p * (mp.mpf(1) - p)
+        #     return mpf2float(out)
+        #
+        # y, raw = 0.0, 37.
+        # mp_logloss(y, raw), mp_gradient(y, raw), mp_hessian(y, raw)
+        (HalfBinomialLoss(), 0.0, -1e20, 0, 0, 0),
+        (HalfBinomialLoss(), 1.0, -1e20, 1e20, -1, 0),
+        (HalfBinomialLoss(), 0.0, -1e3, 0, 0, 0),
+        (HalfBinomialLoss(), 1.0, -1e3, 1e3, -1, 0),
+        (HalfBinomialLoss(), 1.0, -37.5, 37.5, -1, 0),
+        (HalfBinomialLoss(), 1.0, -37.0, 37, 1e-16 - 1, 8.533047625744065e-17),
+        (HalfBinomialLoss(), 0.0, -37.0, *[8.533047625744065e-17] * 3),
+        (HalfBinomialLoss(), 1.0, -36.9, 36.9, 1e-16 - 1, 9.430476078526806e-17),
+        (HalfBinomialLoss(), 0.0, -36.9, *[9.430476078526806e-17] * 3),
+        (HalfBinomialLoss(), 0.0, 37.0, 37, 1 - 1e-16, 8.533047625744065e-17),
+        (HalfBinomialLoss(), 1.0, 37.0, *[8.533047625744066e-17] * 3),
+        (HalfBinomialLoss(), 0.0, 37.5, 37.5, 1, 5.175555005801868e-17),
+        (HalfBinomialLoss(), 0.0, 232.8, 232.8, 1, 1.4287342391028437e-101),
+        (HalfBinomialLoss(), 1.0, 1e20, 0, 0, 0),
+        (HalfBinomialLoss(), 0.0, 1e20, 1e20, 1, 0),
+        (
+            HalfBinomialLoss(),
+            1.0,
+            232.8,
+            0,
+            -1.4287342391028437e-101,
+            1.4287342391028437e-101,
+        ),
+        (HalfBinomialLoss(), 1.0, 232.9, 0, 0, 0),
+        (HalfBinomialLoss(), 1.0, 1e3, 0, 0, 0),
+        (HalfBinomialLoss(), 0.0, 1e3, 1e3, 1, 0),
         (
             HalfMultinomialLoss(n_classes=3),
             0.0,
             [0.2, 0.5, 0.3],
             logsumexp([0.2, 0.5, 0.3]) - 0.2,
+            None,
+            None,
         ),
         (
             HalfMultinomialLoss(n_classes=3),
             1.0,
             [0.2, 0.5, 0.3],
             logsumexp([0.2, 0.5, 0.3]) - 0.5,
+            None,
+            None,
         ),
         (
             HalfMultinomialLoss(n_classes=3),
             2.0,
             [0.2, 0.5, 0.3],
             logsumexp([0.2, 0.5, 0.3]) - 0.3,
+            None,
+            None,
+        ),
+        (
+            HalfMultinomialLoss(n_classes=3),
+            2.0,
+            [1e4, 0, 7e-7],
+            logsumexp([1e4, 0, 7e-7]) - (7e-7),
+            None,
+            None,
         ),
     ],
     ids=loss_instance_name,
 )
-def test_loss_on_specific_values(loss, y_true, raw_prediction, loss_true):
-    """Test losses at specific values."""
-    assert loss(
+def test_loss_on_specific_values(
+    loss, y_true, raw_prediction, loss_true, gradient_true, hessian_true
+):
+    """Test losses, gradients and hessians at specific values."""
+    loss1 = loss(y_true=np.array([y_true]), raw_prediction=np.array([raw_prediction]))
+    grad1 = loss.gradient(
+        y_true=np.array([y_true]), raw_prediction=np.array([raw_prediction])
+    )
+    loss2, grad2 = loss.loss_gradient(
+        y_true=np.array([y_true]), raw_prediction=np.array([raw_prediction])
+    )
+    grad3, hess = loss.gradient_hessian(
         y_true=np.array([y_true]), raw_prediction=np.array([raw_prediction])
-    ) == approx(loss_true, rel=1e-11, abs=1e-12)
+    )
+
+    assert loss1 == approx(loss_true, rel=1e-15, abs=1e-15)
+    assert loss2 == approx(loss_true, rel=1e-15, abs=1e-15)
+
+    if gradient_true is not None:
+        assert grad1 == approx(gradient_true, rel=1e-15, abs=1e-15)
+        assert grad2 == approx(gradient_true, rel=1e-15, abs=1e-15)
+        assert grad3 == approx(gradient_true, rel=1e-15, abs=1e-15)
+
+    if hessian_true is not None:
+        assert hess == approx(hessian_true, rel=1e-15, abs=1e-15)
 
 
 @pytest.mark.parametrize("loss", ALL_LOSSES)
diff --git a/sklearn/base.py b/sklearn/base.py
index 56a0ad3233a73..e7361c331617a 100644
--- a/sklearn/base.py
+++ b/sklearn/base.py
@@ -1079,9 +1079,10 @@ def fit_predict(self, X, y=None, **kwargs):
 
 
 class MetaEstimatorMixin:
-    _required_parameters = ["estimator"]
     """Mixin class for all meta estimators in scikit-learn."""
 
+    _required_parameters = ["estimator"]
+
 
 class MultiOutputMixin:
     """Mixin to mark estimators that support multioutput."""
diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
index a7d6b5f7df050..59470aae6c13f 100644
--- a/sklearn/cluster/_kmeans.py
+++ b/sklearn/cluster/_kmeans.py
@@ -1208,6 +1208,9 @@ class KMeans(_BaseKMeans):
         The number of clusters to form as well as the number of
         centroids to generate.
 
+        For an example of how to choose an optimal value for `n_clusters` refer to
+        :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_silhouette_analysis.py`.
+
     init : {'k-means++', 'random'}, callable or array-like of shape \
             (n_clusters, n_features), default='k-means++'
         Method for initialization:
@@ -1364,6 +1367,21 @@ class KMeans(_BaseKMeans):
     >>> kmeans.cluster_centers_
     array([[10.,  2.],
            [ 1.,  2.]])
+
+    For a more detailed example of K-Means using the iris dataset see
+    :ref:`sphx_glr_auto_examples_cluster_plot_cluster_iris.py`.
+
+    For examples of common problems with K-Means and how to address them see
+    :ref:`sphx_glr_auto_examples_cluster_plot_kmeans_assumptions.py`.
+
+    For an example of how to use K-Means to perform color quantization see
+    :ref:`sphx_glr_auto_examples_cluster_plot_color_quantization.py`.
+
+    For a demonstration of how K-Means can be used to cluster text documents see
+    :ref:`sphx_glr_auto_examples_text_plot_document_clustering.py`.
+
+    For a comparison between K-Means and MiniBatchKMeans refer to example
+    :ref:`sphx_glr_auto_examples_cluster_plot_mini_batch_kmeans.py`.
     """
 
     _parameter_constraints: dict = {
diff --git a/sklearn/compose/_column_transformer.py b/sklearn/compose/_column_transformer.py
index a53ad2348fe94..6740bdf4e8993 100644
--- a/sklearn/compose/_column_transformer.py
+++ b/sklearn/compose/_column_transformer.py
@@ -1082,6 +1082,16 @@ def _sk_visual_block_(self):
             "parallel", transformers, names=names, name_details=name_details
         )
 
+    def __getitem__(self, key):
+        try:
+            return self.named_transformers_[key]
+        except AttributeError as e:
+            raise TypeError(
+                "ColumnTransformer is subscriptable after it is fitted"
+            ) from e
+        except KeyError as e:
+            raise KeyError(f"'{key}' is not a valid transformer name") from e
+
     def _get_empty_routing(self):
         """Return empty routing.
 
diff --git a/sklearn/compose/tests/test_column_transformer.py b/sklearn/compose/tests/test_column_transformer.py
index 1ceaad3ec1737..aa7dfe62fc1a8 100644
--- a/sklearn/compose/tests/test_column_transformer.py
+++ b/sklearn/compose/tests/test_column_transformer.py
@@ -2301,6 +2301,24 @@ def test_dataframe_different_dataframe_libraries():
     assert_array_equal(out_pd_in, X_test_np)
 
 
+def test_column_transformer__getitem__():
+    """Check __getitem__ for ColumnTransformer."""
+    X = np.array([[0, 1, 2], [3, 4, 5]])
+    ct = ColumnTransformer([("t1", Trans(), [0, 1]), ("t2", Trans(), [1, 2])])
+
+    msg = "ColumnTransformer is subscriptable after it is fitted"
+    with pytest.raises(TypeError, match=msg):
+        ct["t1"]
+
+    ct.fit(X)
+    assert ct["t1"] is ct.named_transformers_["t1"]
+    assert ct["t2"] is ct.named_transformers_["t2"]
+
+    msg = "'does_not_exist' is not a valid transformer name"
+    with pytest.raises(KeyError, match=msg):
+        ct["does_not_exist"]
+
+
 # Metadata Routing Tests
 # ======================
 
diff --git a/sklearn/datasets/tests/test_svmlight_format.py b/sklearn/datasets/tests/test_svmlight_format.py
index 78a006f8f228b..10b0e29810ef7 100644
--- a/sklearn/datasets/tests/test_svmlight_format.py
+++ b/sklearn/datasets/tests/test_svmlight_format.py
@@ -261,7 +261,7 @@ def test_invalid_filename():
 def test_dump(csr_container):
     X_sparse, y_dense = _load_svmlight_local_test_file(datafile)
     X_dense = X_sparse.toarray()
-    y_sparse = csr_container(y_dense)
+    y_sparse = csr_container(np.atleast_2d(y_dense))
 
     # slicing a csr_matrix can unsort its .indices, so test that we sort
     # those correctly
diff --git a/sklearn/decomposition/_incremental_pca.py b/sklearn/decomposition/_incremental_pca.py
index f05e2dacc66b2..1089b2c54e086 100644
--- a/sklearn/decomposition/_incremental_pca.py
+++ b/sklearn/decomposition/_incremental_pca.py
@@ -39,6 +39,9 @@ class IncrementalPCA(_BasePCA):
     computations to get the principal components, versus 1 large SVD of
     complexity ``O(n_samples * n_features ** 2)`` for PCA.
 
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_incremental_pca.py`.
+
     Read more in the :ref:`User Guide <IncrementalPCA>`.
 
     .. versionadded:: 0.16
diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py
index eb73ced3527c8..8fc4aa26a6dfb 100644
--- a/sklearn/decomposition/_kernel_pca.py
+++ b/sklearn/decomposition/_kernel_pca.py
@@ -41,6 +41,9 @@ class KernelPCA(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator
     components to extract. It can also use a randomized truncated SVD by the
     method proposed in [3]_, see `eigen_solver`.
 
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_kernel_pca.py`.
+
     Read more in the :ref:`User Guide <kernel_PCA>`.
 
     Parameters
diff --git a/sklearn/decomposition/_pca.py b/sklearn/decomposition/_pca.py
index 5fe8d666d8e0b..d121c5e5c186f 100644
--- a/sklearn/decomposition/_pca.py
+++ b/sklearn/decomposition/_pca.py
@@ -136,6 +136,9 @@ class PCA(_BasePCA):
     Notice that this class does not support sparse input. See
     :class:`TruncatedSVD` for an alternative with sparse data.
 
+    For a usage example, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_pca_iris.py`
+
     Read more in the :ref:`User Guide <PCA>`.
 
     Parameters
diff --git a/sklearn/decomposition/_sparse_pca.py b/sklearn/decomposition/_sparse_pca.py
index f544b710fd073..b14df8c5f4d22 100644
--- a/sklearn/decomposition/_sparse_pca.py
+++ b/sklearn/decomposition/_sparse_pca.py
@@ -342,6 +342,9 @@ class MiniBatchSparsePCA(_BaseSparsePCA):
     the data.  The amount of sparseness is controllable by the coefficient
     of the L1 penalty, given by the parameter alpha.
 
+    For an example comparing sparse PCA to PCA, see
+    :ref:`sphx_glr_auto_examples_decomposition_plot_faces_decomposition.py`
+
     Read more in the :ref:`User Guide <SparsePCA>`.
 
     Parameters
diff --git a/sklearn/decomposition/tests/test_pca.py b/sklearn/decomposition/tests/test_pca.py
index c0d1060217fa8..44281b9038697 100644
--- a/sklearn/decomposition/tests/test_pca.py
+++ b/sklearn/decomposition/tests/test_pca.py
@@ -8,7 +8,7 @@
 
 from sklearn import config_context, datasets
 from sklearn.base import clone
-from sklearn.datasets import load_iris
+from sklearn.datasets import load_iris, make_classification
 from sklearn.decomposition import PCA
 from sklearn.decomposition._pca import _assess_dimension, _infer_dimension
 from sklearn.utils._array_api import (
@@ -16,10 +16,10 @@
     _convert_to_numpy,
     yield_namespace_device_dtype_combinations,
 )
+from sklearn.utils._array_api import device as array_device
 from sklearn.utils._testing import _array_api_for_tests, assert_allclose
 from sklearn.utils.estimator_checks import (
     _get_check_estimator_ids,
-    check_array_api_input,
     check_array_api_input_and_values,
 )
 from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
@@ -817,9 +817,9 @@ def test_variance_correctness(copy):
     np.testing.assert_allclose(pca_var, true_var)
 
 
-def check_array_api_get_precision(name, estimator, array_namespace, device, dtype):
+def check_array_api_get_precision(name, estimator, array_namespace, device, dtype_name):
     xp = _array_api_for_tests(array_namespace, device)
-    iris_np = iris.data.astype(dtype)
+    iris_np = iris.data.astype(dtype_name)
     iris_xp = xp.asarray(iris_np, device=device)
 
     estimator.fit(iris_np)
@@ -835,7 +835,7 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
         assert_allclose(
             _convert_to_numpy(precision_xp, xp=xp),
             precision_np,
-            atol=_atol_for_type(dtype),
+            atol=_atol_for_type(dtype_name),
         )
         covariance_xp = estimator_xp.get_covariance()
         assert covariance_xp.shape == (4, 4)
@@ -844,12 +844,12 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
         assert_allclose(
             _convert_to_numpy(covariance_xp, xp=xp),
             covariance_np,
-            atol=_atol_for_type(dtype),
+            atol=_atol_for_type(dtype_name),
         )
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize(
     "check",
@@ -870,31 +870,89 @@ def check_array_api_get_precision(name, estimator, array_namespace, device, dtyp
     ],
     ids=_get_check_estimator_ids,
 )
-def test_pca_array_api_compliance(estimator, check, array_namespace, device, dtype):
+def test_pca_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
     name = estimator.__class__.__name__
-    check(name, estimator, array_namespace, device=device, dtype=dtype)
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize(
     "check",
-    [check_array_api_input, check_array_api_get_precision],
+    [check_array_api_get_precision],
     ids=_get_check_estimator_ids,
 )
 @pytest.mark.parametrize(
     "estimator",
     [
         # PCA with mle cannot use check_array_api_input_and_values because of
-        # rounding errors in the noisy (low variance) components.
+        # rounding errors in the noisy (low variance) components. Even checking
+        # the shape of the `components_` is problematic because the number of
+        # components depends on trimming threshold of the mle algorithm which
+        # can depend on device-specific rounding errors.
         PCA(n_components="mle", svd_solver="full"),
     ],
     ids=_get_check_estimator_ids,
 )
-def test_pca_mle_array_api_compliance(estimator, check, array_namespace, device, dtype):
+def test_pca_mle_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
     name = estimator.__class__.__name__
-    check(name, estimator, array_namespace, device=device, dtype=dtype)
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
+
+    # Simpler variant of the generic check_array_api_input checker tailored for
+    # the specific case of PCA with mle-trimmed components.
+    xp = _array_api_for_tests(array_namespace, device)
+
+    X, y = make_classification(random_state=42)
+    X = X.astype(dtype_name, copy=False)
+    atol = _atol_for_type(X.dtype)
+
+    est = clone(estimator)
+
+    X_xp = xp.asarray(X, device=device)
+    y_xp = xp.asarray(y, device=device)
+
+    est.fit(X, y)
+
+    components_np = est.components_
+    explained_variance_np = est.explained_variance_
+
+    est_xp = clone(est)
+    with config_context(array_api_dispatch=True):
+        est_xp.fit(X_xp, y_xp)
+        components_xp = est_xp.components_
+        assert array_device(components_xp) == array_device(X_xp)
+        components_xp_np = _convert_to_numpy(components_xp, xp=xp)
+
+        explained_variance_xp = est_xp.explained_variance_
+        assert array_device(explained_variance_xp) == array_device(X_xp)
+        explained_variance_xp_np = _convert_to_numpy(explained_variance_xp, xp=xp)
+
+    assert components_xp_np.dtype == components_np.dtype
+    assert components_xp_np.shape[1] == components_np.shape[1]
+    assert explained_variance_xp_np.dtype == explained_variance_np.dtype
+
+    # Check that the explained variance values match for the
+    # common components:
+    min_components = min(components_xp_np.shape[0], components_np.shape[0])
+    assert_allclose(
+        explained_variance_xp_np[:min_components],
+        explained_variance_np[:min_components],
+        atol=atol,
+    )
+
+    # If the number of components differ, check that the explained variance of
+    # the trimmed components is very small.
+    if components_xp_np.shape[0] != components_np.shape[0]:
+        reference_variance = explained_variance_np[-1]
+        extra_variance_np = explained_variance_np[min_components:]
+        extra_variance_xp_np = explained_variance_xp_np[min_components:]
+        assert all(np.abs(extra_variance_np - reference_variance) < atol)
+        assert all(np.abs(extra_variance_xp_np - reference_variance) < atol)
 
 
 def test_array_api_error_and_warnings_on_unsupported_params():
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
index 29146ca857694..46cb96ddd2886 100644
--- a/sklearn/discriminant_analysis.py
+++ b/sklearn/discriminant_analysis.py
@@ -697,7 +697,7 @@ def predict_proba(self, X):
         xp, is_array_api_compliant = get_namespace(X)
         decision = self.decision_function(X)
         if size(self.classes_) == 2:
-            proba = _expit(decision)
+            proba = _expit(decision, xp)
             return xp.stack([1 - proba, proba], axis=1)
         else:
             return softmax(decision)
diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py
index dead7b19592cb..a51d240c87d4e 100644
--- a/sklearn/ensemble/tests/test_forest.py
+++ b/sklearn/ensemble/tests/test_forest.py
@@ -1709,7 +1709,7 @@ def test_max_samples_boundary_classifiers(name):
 @pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
 def test_forest_y_sparse(csr_container):
     X = [[1, 2, 3]]
-    y = csr_container([4, 5, 6])
+    y = csr_container([[4, 5, 6]])
     est = RandomForestClassifier()
     msg = "sparse multilabel-indicator for y is not supported."
     with pytest.raises(ValueError, match=msg):
diff --git a/sklearn/exceptions.py b/sklearn/exceptions.py
index ad7ae08c1fec0..1466ce783ee00 100644
--- a/sklearn/exceptions.py
+++ b/sklearn/exceptions.py
@@ -19,7 +19,7 @@
 
 class UnsetMetadataPassedError(ValueError):
     """Exception class to raise if a metadata is passed which is not explicitly \
-        requested.
+        requested (metadata=True) or not requested (metadata=False).
 
     .. versionadded:: 1.3
 
diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py
index 4202cd5feb799..dff39d4734554 100644
--- a/sklearn/impute/_base.py
+++ b/sklearn/impute/_base.py
@@ -6,6 +6,7 @@
 import warnings
 from collections import Counter
 from functools import partial
+from typing import Callable
 
 import numpy as np
 import numpy.ma as ma
@@ -163,7 +164,7 @@ class SimpleImputer(_BaseImputer):
         nullable integer dtypes with missing values, `missing_values`
         can be set to either `np.nan` or `pd.NA`.
 
-    strategy : str, default='mean'
+    strategy : str or Callable, default='mean'
         The imputation strategy.
 
         - If "mean", then replace missing values using the mean along
@@ -175,10 +176,16 @@ class SimpleImputer(_BaseImputer):
           If there is more than one such value, only the smallest is returned.
         - If "constant", then replace missing values with fill_value. Can be
           used with strings or numeric data.
+        - If an instance of Callable, then replace missing values using the
+          scalar statistic returned by running the callable over a dense 1d
+          array containing non-missing values of each column.
 
         .. versionadded:: 0.20
            strategy="constant" for fixed value imputation.
 
+        .. versionadded:: 1.5
+           strategy=callable for custom value imputation.
+
     fill_value : str or numerical value, default=None
         When strategy == "constant", `fill_value` is used to replace all
         occurrences of missing_values. For string or object data types,
@@ -270,7 +277,10 @@ class SimpleImputer(_BaseImputer):
 
     _parameter_constraints: dict = {
         **_BaseImputer._parameter_constraints,
-        "strategy": [StrOptions({"mean", "median", "most_frequent", "constant"})],
+        "strategy": [
+            StrOptions({"mean", "median", "most_frequent", "constant"}),
+            callable,
+        ],
         "fill_value": "no_validation",  # any object is valid
         "copy": ["boolean"],
     }
@@ -456,6 +466,9 @@ def _sparse_fit(self, X, strategy, missing_values, fill_value):
                     elif strategy == "most_frequent":
                         statistics[i] = _most_frequent(column, 0, n_zeros)
 
+                    elif isinstance(strategy, Callable):
+                        statistics[i] = self.strategy(column)
+
         super()._fit_indicator(missing_mask)
 
         return statistics
@@ -518,6 +531,13 @@ def _dense_fit(self, X, strategy, missing_values, fill_value):
             # fill_value in each column
             return np.full(X.shape[1], fill_value, dtype=X.dtype)
 
+        # Custom
+        elif isinstance(strategy, Callable):
+            statistics = np.empty(masked_X.shape[1])
+            for i in range(masked_X.shape[1]):
+                statistics[i] = self.strategy(masked_X[:, i].compressed())
+            return statistics
+
     def transform(self, X):
         """Impute all missing values in `X`.
 
diff --git a/sklearn/impute/tests/test_impute.py b/sklearn/impute/tests/test_impute.py
index c499dc3b89d32..2128c796e4800 100644
--- a/sklearn/impute/tests/test_impute.py
+++ b/sklearn/impute/tests/test_impute.py
@@ -1710,3 +1710,37 @@ def test_simple_imputer_keep_empty_features(strategy, array_type, keep_empty_fea
             assert_array_equal(constant_feature, 0)
         else:
             assert X_imputed.shape == (X.shape[0], X.shape[1] - 1)
+
+
+@pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
+def test_imputation_custom(csc_container):
+    X = np.array(
+        [
+            [1.1, 1.1, 1.1],
+            [3.9, 1.2, np.nan],
+            [np.nan, 1.3, np.nan],
+            [0.1, 1.4, 1.4],
+            [4.9, 1.5, 1.5],
+            [np.nan, 1.6, 1.6],
+        ]
+    )
+
+    X_true = np.array(
+        [
+            [1.1, 1.1, 1.1],
+            [3.9, 1.2, 1.1],
+            [0.1, 1.3, 1.1],
+            [0.1, 1.4, 1.4],
+            [4.9, 1.5, 1.5],
+            [0.1, 1.6, 1.6],
+        ]
+    )
+
+    imputer = SimpleImputer(missing_values=np.nan, strategy=np.min)
+    X_trans = imputer.fit_transform(X)
+    assert_array_equal(X_trans, X_true)
+
+    # Sparse matrix
+    imputer = SimpleImputer(missing_values=np.nan, strategy=np.min)
+    X_trans = imputer.fit_transform(csc_container(X))
+    assert_array_equal(X_trans.toarray(), X_true)
diff --git a/sklearn/linear_model/_stochastic_gradient.py b/sklearn/linear_model/_stochastic_gradient.py
index 8456b3456291a..aeec7b5588add 100644
--- a/sklearn/linear_model/_stochastic_gradient.py
+++ b/sklearn/linear_model/_stochastic_gradient.py
@@ -22,7 +22,7 @@
 )
 from ..exceptions import ConvergenceWarning
 from ..model_selection import ShuffleSplit, StratifiedShuffleSplit
-from ..utils import check_random_state, compute_class_weight
+from ..utils import check_random_state, compute_class_weight, deprecated
 from ..utils._param_validation import Hidden, Interval, StrOptions
 from ..utils.extmath import safe_sparse_dot
 from ..utils.metaestimators import available_if
@@ -323,6 +323,16 @@ def _make_validation_score_cb(
             classes=classes,
         )
 
+    # TODO(1.6): Remove
+    # mypy error: Decorated property not supported
+    @deprecated(  # type: ignore
+        "Attribute `loss_function_` was deprecated in version 1.4 and will be removed "
+        "in 1.6."
+    )
+    @property
+    def loss_function_(self):
+        return self._loss_function_
+
 
 def _prepare_fit_binary(est, y, i, input_dtye):
     """Initialization for fit_binary.
@@ -455,7 +465,7 @@ def fit_binary(
         intercept,
         average_coef,
         average_intercept,
-        est.loss_function_,
+        est._loss_function_,
         penalty_type,
         alpha,
         C,
@@ -619,7 +629,7 @@ def _partial_fit(
                 % (n_features, self.coef_.shape[-1])
             )
 
-        self.loss_function_ = self._get_loss_function(loss)
+        self._loss_function_ = self._get_loss_function(loss)
         if not hasattr(self, "t_"):
             self.t_ = 1.0
 
@@ -1132,6 +1142,10 @@ class SGDClassifier(BaseSGDClassifier):
 
     loss_function_ : concrete ``LossFunction``
 
+        .. deprecated:: 1.4
+            Attribute `loss_function_` was deprecated in version 1.4 and will be
+            removed in 1.6.
+
     classes_ : array of shape (n_classes,)
 
     t_ : int
@@ -2122,6 +2136,10 @@ class SGDOneClassSVM(BaseSGD, OutlierMixin):
 
     loss_function_ : concrete ``LossFunction``
 
+        .. deprecated:: 1.4
+            ``loss_function_`` was deprecated in version 1.4 and will be removed in
+            1.6.
+
     n_features_in_ : int
         Number of features seen during :term:`fit`.
 
@@ -2260,7 +2278,7 @@ def _fit_one_class(self, X, alpha, C, sample_weight, learning_rate, max_iter):
             intercept[0],
             average_coef,
             average_intercept[0],
-            self.loss_function_,
+            self._loss_function_,
             penalty_type,
             alpha,
             C,
@@ -2354,7 +2372,7 @@ def _partial_fit(
             self._average_coef = np.zeros(n_features, dtype=X.dtype, order="C")
             self._average_intercept = np.zeros(1, dtype=X.dtype, order="C")
 
-        self.loss_function_ = self._get_loss_function(loss)
+        self._loss_function_ = self._get_loss_function(loss)
         if not hasattr(self, "t_"):
             self.t_ = 1.0
 
diff --git a/sklearn/linear_model/tests/test_sgd.py b/sklearn/linear_model/tests/test_sgd.py
index 6edb76d50f738..d1dd1ca960f86 100644
--- a/sklearn/linear_model/tests/test_sgd.py
+++ b/sklearn/linear_model/tests/test_sgd.py
@@ -756,10 +756,13 @@ def test_sgd_proba(klass):
         p = clf.predict_proba([[-1, -1]])
         assert p[0, 1] < 0.5
 
-        p = clf.predict_log_proba([[3, 2]])
-        assert p[0, 1] > p[0, 0]
-        p = clf.predict_log_proba([[-1, -1]])
-        assert p[0, 1] < p[0, 0]
+        # If predict_proba is 0, we get "RuntimeWarning: divide by zero encountered
+        # in log". We avoid it here.
+        with np.errstate(divide="ignore"):
+            p = clf.predict_log_proba([[3, 2]])
+            assert p[0, 1] > p[0, 0]
+            p = clf.predict_log_proba([[-1, -1]])
+            assert p[0, 1] < p[0, 0]
 
     # log loss multiclass probability estimates
     clf = klass(loss="log_loss", alpha=0.01, max_iter=10).fit(X2, Y2)
@@ -2196,3 +2199,16 @@ def test_sgd_numerical_consistency(SGDEstimator):
     sgd_32.fit(X_32, Y_32)
 
     assert_allclose(sgd_64.coef_, sgd_32.coef_)
+
+
+# TODO(1.6): remove
+@pytest.mark.parametrize("Estimator", [SGDClassifier, SGDOneClassSVM])
+def test_loss_attribute_deprecation(Estimator):
+    # Check that we raise the proper deprecation warning if accessing
+    # `loss_function_`.
+    X = np.array([[1, 2], [3, 4]])
+    y = np.array([1, 0])
+    est = Estimator().fit(X, y)
+
+    with pytest.warns(FutureWarning, match="`loss_function_` was deprecated"):
+        est.loss_function_
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index f0a13f8a04830..5b8a024e6e5fc 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -1116,7 +1116,12 @@ def f1_score(
     The relative contribution of precision and recall to the F1 score are
     equal. The formula for the F1 score is::
 
-        F1 = 2 * (precision * recall) / (precision + recall)
+        F1 = 2 * TP / (2 * TP + FN + FP)
+
+    Where "TP" is the number of true positives, "FN" is the number of false
+    negatives, and "FP" is the number of false positives. F1 is by default
+    calculated as 0.0 when there are no true positives, false negatives, nor
+    false positives.
 
     Support beyond term:`binary` targets is achieved by treating :term:`multiclass`
     and :term:`multilabel` data as a collection of binary problems, one for each
@@ -1211,12 +1216,11 @@ def f1_score(
 
     Notes
     -----
-    When ``true positive + false positive == 0``, precision is undefined.
-    When ``true positive + false negative == 0``, recall is undefined.
-    In such cases, by default the metric will be set to 0, as will f-score,
-    and ``UndefinedMetricWarning`` will be raised. This behavior can be
-    modified with ``zero_division``. Note that if `zero_division` is np.nan,
-    scores being `np.nan` will be ignored for averaging.
+    When ``true positive + false positive + false negative == 0`` (i.e. a class
+    is completely absent from both ``y_true`` or ``y_pred``), f-score is
+    undefined. In such cases, by default f-score will be set to 0.0, and
+    ``UndefinedMetricWarning`` will be raised. This behavior can be modified by
+    setting the ``zero_division`` parameter.
 
     References
     ----------
@@ -1404,10 +1408,9 @@ def fbeta_score(
 
     Notes
     -----
-    When ``true positive + false positive == 0`` or
-    ``true positive + false negative == 0``, f-score returns 0 and raises
-    ``UndefinedMetricWarning``. This behavior can be
-    modified with ``zero_division``.
+    When ``true positive + false positive + false negative == 0``, f-score
+    returns 0.0 and raises ``UndefinedMetricWarning``. This behavior can be
+    modified by setting ``zero_division``.
 
     References
     ----------
@@ -1699,10 +1702,11 @@ def precision_recall_fscore_support(
     Notes
     -----
     When ``true positive + false positive == 0``, precision is undefined.
-    When ``true positive + false negative == 0``, recall is undefined.
-    In such cases, by default the metric will be set to 0, as will f-score,
-    and ``UndefinedMetricWarning`` will be raised. This behavior can be
-    modified with ``zero_division``.
+    When ``true positive + false negative == 0``, recall is undefined. When
+    ``true positive + false negative + false positive == 0``, f-score is
+    undefined. In such cases, by default the metric will be set to 0, and
+    ``UndefinedMetricWarning`` will be raised. This behavior can be modified
+    with ``zero_division``.
 
     References
     ----------
diff --git a/sklearn/metrics/cluster/_unsupervised.py b/sklearn/metrics/cluster/_unsupervised.py
index 10749c23dacbe..ccbe473a5f645 100644
--- a/sklearn/metrics/cluster/_unsupervised.py
+++ b/sklearn/metrics/cluster/_unsupervised.py
@@ -14,6 +14,7 @@
 
 from ...preprocessing import LabelEncoder
 from ...utils import _safe_indexing, check_random_state, check_X_y
+from ...utils._array_api import _atol_for_type
 from ...utils._param_validation import (
     Interval,
     StrOptions,
@@ -263,7 +264,8 @@ def silhouette_samples(X, labels, *, metric="euclidean", **kwds):
             "elements on the diagonal. Use np.fill_diagonal(X, 0)."
         )
         if X.dtype.kind == "f":
-            atol = np.finfo(X.dtype).eps * 100
+            atol = _atol_for_type(X.dtype)
+
             if np.any(np.abs(X.diagonal()) > atol):
                 raise error_msg
         elif np.any(X.diagonal() != 0):  # integral dtype
diff --git a/sklearn/metrics/pairwise.py b/sklearn/metrics/pairwise.py
index 5d848dae5b11f..4e5c37dff0091 100644
--- a/sklearn/metrics/pairwise.py
+++ b/sklearn/metrics/pairwise.py
@@ -1084,7 +1084,7 @@ def cosine_distances(X, Y=None):
     if X is Y or Y is None:
         # Ensure that distances between vectors and themselves are set to 0.0.
         # This may not be the case due to floating point rounding errors.
-        S[np.diag_indices_from(S)] = 0.0
+        np.fill_diagonal(S, 0.0)
     return S
 
 
diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py
index 4f5b10a51a4ce..8fad63870e4ac 100644
--- a/sklearn/metrics/tests/test_common.py
+++ b/sklearn/metrics/tests/test_common.py
@@ -1733,67 +1733,87 @@ def test_metrics_pos_label_error_str(metric, y_pred_threshold, dtype_y_str):
 
 
 def check_array_api_metric(
-    metric, array_namespace, device, dtype, y_true_np, y_pred_np, sample_weight=None
+    metric, array_namespace, device, dtype_name, y_true_np, y_pred_np, sample_weight
 ):
     xp = _array_api_for_tests(array_namespace, device)
+
     y_true_xp = xp.asarray(y_true_np, device=device)
     y_pred_xp = xp.asarray(y_pred_np, device=device)
 
     metric_np = metric(y_true_np, y_pred_np, sample_weight=sample_weight)
 
+    if sample_weight is not None:
+        sample_weight = xp.asarray(sample_weight, device=device)
+
     with config_context(array_api_dispatch=True):
-        if sample_weight is not None:
-            sample_weight = xp.asarray(sample_weight, device=device)
         metric_xp = metric(y_true_xp, y_pred_xp, sample_weight=sample_weight)
 
         assert_allclose(
             metric_xp,
             metric_np,
-            atol=_atol_for_type(dtype),
+            atol=_atol_for_type(dtype_name),
         )
 
 
 def check_array_api_binary_classification_metric(
-    metric, array_namespace, device, dtype
+    metric, array_namespace, device, dtype_name
 ):
     y_true_np = np.array([0, 0, 1, 1])
     y_pred_np = np.array([0, 1, 0, 1])
+
     check_array_api_metric(
-        metric, array_namespace, device, dtype, y_true_np=y_true_np, y_pred_np=y_pred_np
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        y_true_np=y_true_np,
+        y_pred_np=y_pred_np,
+        sample_weight=None,
+    )
+
+    sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        y_true_np=y_true_np,
+        y_pred_np=y_pred_np,
+        sample_weight=sample_weight,
     )
-    if "sample_weight" in signature(metric).parameters:
-        check_array_api_metric(
-            metric,
-            array_namespace,
-            device,
-            dtype,
-            y_true_np=y_true_np,
-            y_pred_np=y_pred_np,
-            sample_weight=np.array([0.0, 0.1, 2.0, 1.0]),
-        )
 
 
 def check_array_api_multiclass_classification_metric(
-    metric, array_namespace, device, dtype
+    metric, array_namespace, device, dtype_name
 ):
     y_true_np = np.array([0, 1, 2, 3])
     y_pred_np = np.array([0, 1, 0, 2])
+
     check_array_api_metric(
-        metric, array_namespace, device, dtype, y_true_np=y_true_np, y_pred_np=y_pred_np
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        y_true_np=y_true_np,
+        y_pred_np=y_pred_np,
+        sample_weight=None,
+    )
+
+    sample_weight = np.array([0.0, 0.1, 2.0, 1.0], dtype=dtype_name)
+
+    check_array_api_metric(
+        metric,
+        array_namespace,
+        device,
+        dtype_name,
+        y_true_np=y_true_np,
+        y_pred_np=y_pred_np,
+        sample_weight=sample_weight,
     )
-    if "sample_weight" in signature(metric).parameters:
-        check_array_api_metric(
-            metric,
-            array_namespace,
-            device,
-            dtype,
-            y_true_np=y_true_np,
-            y_pred_np=y_pred_np,
-            sample_weight=np.array([0.0, 0.1, 2.0, 1.0]),
-        )
 
 
-metric_checkers = {
+array_api_metric_checkers = {
     accuracy_score: [
         check_array_api_binary_classification_metric,
         check_array_api_multiclass_classification_metric,
@@ -1805,15 +1825,15 @@ def check_array_api_multiclass_classification_metric(
 }
 
 
-def yield_metric_checker_combinations(metric_checkers=metric_checkers):
+def yield_metric_checker_combinations(metric_checkers=array_api_metric_checkers):
     for metric, checkers in metric_checkers.items():
         for checker in checkers:
             yield metric, checker
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize("metric, check_func", yield_metric_checker_combinations())
-def test_array_api_compliance(metric, array_namespace, device, dtype, check_func):
-    check_func(metric, array_namespace, device, dtype)
+def test_array_api_compliance(metric, array_namespace, device, dtype_name, check_func):
+    check_func(metric, array_namespace, device, dtype_name)
diff --git a/sklearn/metrics/tests/test_dist_metrics.py b/sklearn/metrics/tests/test_dist_metrics.py
index b7b2e04b11396..baaf447d3909b 100644
--- a/sklearn/metrics/tests/test_dist_metrics.py
+++ b/sklearn/metrics/tests/test_dist_metrics.py
@@ -366,7 +366,7 @@ def test_readonly_kwargs():
         (np.array([1, 1.5, np.nan]), ValueError, "w contains NaN"),
         *[
             (
-                csr_container([1, 1.5, 1]),
+                csr_container([[1, 1.5, 1]]),
                 TypeError,
                 "Sparse data was passed for w, but dense data is required",
             )
diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index d3110cb847b4c..07b229b57bf96 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -397,11 +397,16 @@ def cross_validate(
             # `process_routing` code, we pass `fit` as the caller. However,
             # the user is not calling `fit` directly, so we change the message
             # to make it more suitable for this case.
+            unrequested_params = sorted(e.unrequested_params)
             raise UnsetMetadataPassedError(
                 message=(
-                    f"{sorted(e.unrequested_params.keys())} are passed to cross"
-                    " validation but are not explicitly requested or unrequested. See"
-                    " the Metadata Routing User guide"
+                    f"{unrequested_params} are passed to cross validation but are not"
+                    " explicitly set as requested or not requested for cross_validate's"
+                    f" estimator: {estimator.__class__.__name__}. Call"
+                    " `.set_fit_request({{metadata}}=True)` on the estimator for"
+                    f" each metadata in {unrequested_params} that you"
+                    " want to use and `metadata=False` for not using it. See the"
+                    " Metadata Routing User guide"
                     " <https://scikit-learn.org/stable/metadata_routing.html> for more"
                     " information."
                 ),
@@ -1238,13 +1243,17 @@ def cross_val_predict(
             # `process_routing` code, we pass `fit` as the caller. However,
             # the user is not calling `fit` directly, so we change the message
             # to make it more suitable for this case.
+            unrequested_params = sorted(e.unrequested_params)
             raise UnsetMetadataPassedError(
                 message=(
-                    f"{sorted(e.unrequested_params.keys())} are passed to cross"
-                    " validation but are not explicitly requested or unrequested. See"
-                    " the Metadata Routing User guide"
-                    " <https://scikit-learn.org/stable/metadata_routing.html> for more"
-                    " information."
+                    f"{unrequested_params} are passed to `cross_val_predict` but are"
+                    " not explicitly set as requested or not requested for"
+                    f" cross_validate's estimator: {estimator.__class__.__name__} Call"
+                    " `.set_fit_request({{metadata}}=True)` on the estimator for"
+                    f" each metadata in {unrequested_params} that you want to use and"
+                    " `metadata=False` for not using it. See the Metadata Routing User"
+                    " guide <https://scikit-learn.org/stable/metadata_routing.html>"
+                    " for more information."
                 ),
                 unrequested_params=e.unrequested_params,
                 routed_params=e.routed_params,
diff --git a/sklearn/model_selection/tests/test_split.py b/sklearn/model_selection/tests/test_split.py
index 94a33cf1a814b..57bc6b22351b9 100644
--- a/sklearn/model_selection/tests/test_split.py
+++ b/sklearn/model_selection/tests/test_split.py
@@ -1267,7 +1267,7 @@ def test_train_test_split_default_test_size(train_size, exp_train, exp_test):
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize(
     "shuffle,stratify",
@@ -1278,16 +1278,18 @@ def test_train_test_split_default_test_size(train_size, exp_train, exp_test):
         (False, None),
     ),
 )
-def test_array_api_train_test_split(shuffle, stratify, array_namespace, device, dtype):
+def test_array_api_train_test_split(
+    shuffle, stratify, array_namespace, device, dtype_name
+):
     xp = _array_api_for_tests(array_namespace, device)
 
     X = np.arange(100).reshape((10, 10))
     y = np.arange(10)
 
-    X_np = X.astype(dtype)
+    X_np = X.astype(dtype_name)
     X_xp = xp.asarray(X_np, device=device)
 
-    y_np = y.astype(dtype)
+    y_np = y.astype(dtype_name)
     y_xp = xp.asarray(y_np, device=device)
 
     X_train_np, X_test_np, y_train_np, y_test_np = train_test_split(
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index acf4d27e0180e..e1ecfd14f45a3 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -2517,7 +2517,7 @@ def test_groups_with_routing_validation(cv_method):
 def test_passed_unrequested_metadata(cv_method):
     """Check that we raise an error when passing metadata that is not
     requested."""
-    err_msg = re.escape("['metadata'] are passed to cross validation")
+    err_msg = re.escape("but are not explicitly set as requested or not requested")
     with pytest.raises(ValueError, match=err_msg):
         cv_method(
             estimator=ConsumingClassifier(),
diff --git a/sklearn/neighbors/tests/test_neighbors.py b/sklearn/neighbors/tests/test_neighbors.py
index 00c53734c9576..2be0237cd5f7e 100644
--- a/sklearn/neighbors/tests/test_neighbors.py
+++ b/sklearn/neighbors/tests/test_neighbors.py
@@ -476,8 +476,8 @@ def test_is_sorted_by_data(csr_container):
     # _is_sorted_by_data should return True when entries are sorted by data,
     # and False in all other cases.
 
-    # Test with sorted 1D array
-    X = csr_container(np.arange(10))
+    # Test with sorted single row sparse array
+    X = csr_container(np.arange(10).reshape(1, 10))
     assert _is_sorted_by_data(X)
     # Test with unsorted 1D array
     X[0, 2] = 5
diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
index 0bd81a3c57918..4df21618be4ee 100644
--- a/sklearn/pipeline.py
+++ b/sklearn/pipeline.py
@@ -182,6 +182,7 @@ def set_output(self, *, transform=None):
             Configure output of `transform` and `fit_transform`.
 
             - `"default"`: Default output format of a transformer
+            - `"pandas"`: DataFrame output
             - `"polars"`: Polars output
             - `None`: Transform configuration is unchanged
 
diff --git a/sklearn/preprocessing/_data.py b/sklearn/preprocessing/_data.py
index 9120384588ef2..4cbae0e1d3591 100644
--- a/sklearn/preprocessing/_data.py
+++ b/sklearn/preprocessing/_data.py
@@ -483,8 +483,8 @@ def partial_fit(self, X, y=None):
             force_all_finite="allow-nan",
         )
 
-        data_min = _array_api._nanmin(X, axis=0)
-        data_max = _array_api._nanmax(X, axis=0)
+        data_min = _array_api._nanmin(X, axis=0, xp=xp)
+        data_max = _array_api._nanmax(X, axis=0, xp=xp)
 
         if first_pass:
             self.n_samples_seen_ = X.shape[0]
@@ -1234,7 +1234,7 @@ def partial_fit(self, X, y=None):
             mins, maxs = min_max_axis(X, axis=0, ignore_nan=True)
             max_abs = np.maximum(np.abs(mins), np.abs(maxs))
         else:
-            max_abs = _array_api._nanmax(xp.abs(X), axis=0)
+            max_abs = _array_api._nanmax(xp.abs(X), axis=0, xp=xp)
 
         if first_pass:
             self.n_samples_seen_ = X.shape[0]
diff --git a/sklearn/preprocessing/tests/test_data.py b/sklearn/preprocessing/tests/test_data.py
index 5a70c3091a83d..2896e729e970b 100644
--- a/sklearn/preprocessing/tests/test_data.py
+++ b/sklearn/preprocessing/tests/test_data.py
@@ -682,7 +682,7 @@ def test_standard_check_array_of_inverse_transform():
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize(
     "check",
@@ -701,9 +701,11 @@ def test_standard_check_array_of_inverse_transform():
     ],
     ids=_get_check_estimator_ids,
 )
-def test_scaler_array_api_compliance(estimator, check, array_namespace, device, dtype):
+def test_scaler_array_api_compliance(
+    estimator, check, array_namespace, device, dtype_name
+):
     name = estimator.__class__.__name__
-    check(name, estimator, array_namespace, device=device, dtype=dtype)
+    check(name, estimator, array_namespace, device=device, dtype_name=dtype_name)
 
 
 def test_min_max_scaler_iris():
diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py
index 6072c0fab8580..1131cb3560287 100644
--- a/sklearn/utils/_array_api.py
+++ b/sklearn/utils/_array_api.py
@@ -24,7 +24,7 @@ def yield_namespace_device_dtype_combinations():
         The name of the device on which to allocate the arrays. Can be None to
         indicate that the default value should be used.
 
-    dtype : str
+    dtype_name : str
         The name of the data type to use for arrays. Can be None to indicate
         that the default value should be used.
     """
@@ -396,8 +396,9 @@ def get_namespace(*arrays):
     return namespace, is_array_api_compliant
 
 
-def _expit(X):
-    xp, _ = get_namespace(X)
+def _expit(X, xp=None):
+    if xp is None:
+        xp = get_namespace(X)
     if _is_numpy_namespace(xp):
         return xp.asarray(special.expit(numpy.asarray(X)))
 
@@ -444,7 +445,9 @@ def _weighted_sum(sample_score, sample_weight, normalize=False, xp=None):
         sample_score = xp.astype(xp.asarray(sample_score, device="cpu"), xp.float64)
 
     if sample_weight is not None:
-        sample_weight = xp.asarray(sample_weight, dtype=sample_score.dtype)
+        sample_weight = xp.asarray(
+            sample_weight, dtype=sample_score.dtype, device=device(sample_score)
+        )
         if not xp.isdtype(sample_weight.dtype, "real floating"):
             sample_weight = xp.astype(sample_weight, xp.float64)
 
@@ -462,10 +465,11 @@ def _weighted_sum(sample_score, sample_weight, normalize=False, xp=None):
         return float(xp.sum(sample_score))
 
 
-def _nanmin(X, axis=None):
+def _nanmin(X, axis=None, xp=None):
     # TODO: refactor once nan-aware reductions are standardized:
     # https://github.com/data-apis/array-api/issues/621
-    xp, _ = get_namespace(X)
+    if xp is None:
+        xp, _ = get_namespace(X)
     if _is_numpy_namespace(xp):
         return xp.asarray(numpy.nanmin(X, axis=axis))
 
@@ -479,10 +483,11 @@ def _nanmin(X, axis=None):
         return X
 
 
-def _nanmax(X, axis=None):
+def _nanmax(X, axis=None, xp=None):
     # TODO: refactor once nan-aware reductions are standardized:
     # https://github.com/data-apis/array-api/issues/621
-    xp, _ = get_namespace(X)
+    if xp is None:
+        xp, _ = get_namespace(X)
     if _is_numpy_namespace(xp):
         return xp.asarray(numpy.nanmax(X, axis=axis))
 
@@ -569,5 +574,5 @@ def _estimator_with_converted_arrays(estimator, converter):
 
 
 def _atol_for_type(dtype):
-    """Return the absolute tolerance for a given dtype."""
+    """Return the absolute tolerance for a given numpy dtype."""
     return numpy.finfo(dtype).eps * 100
diff --git a/sklearn/utils/_random.pyx b/sklearn/utils/_random.pyx
index a3efa16fa6b63..2d0e512c9b4fb 100644
--- a/sklearn/utils/_random.pyx
+++ b/sklearn/utils/_random.pyx
@@ -230,6 +230,49 @@ cdef _sample_without_replacement(default_int n_population,
                                  random_state=None):
     """Sample integers without replacement.
 
+    Private function for the implementation, see sample_without_replacement
+    documentation for more details.
+    """
+    _sample_without_replacement_check_input(n_population, n_samples)
+
+    all_methods = ("auto", "tracking_selection", "reservoir_sampling", "pool")
+
+    ratio = <double> n_samples / n_population if n_population != 0.0 else 1.0
+
+    # Check ratio and use permutation unless ratio < 0.01 or ratio > 0.99
+    if method == "auto" and ratio > 0.01 and ratio < 0.99:
+        rng = check_random_state(random_state)
+        return rng.permutation(n_population)[:n_samples]
+
+    if method == "auto" or method == "tracking_selection":
+        # TODO the pool based method can also be used.
+        #      however, it requires special benchmark to take into account
+        #      the memory requirement of the array vs the set.
+
+        # The value 0.2 has been determined through benchmarking.
+        if ratio < 0.2:
+            return _sample_without_replacement_with_tracking_selection(
+                n_population, n_samples, random_state)
+        else:
+            return _sample_without_replacement_with_reservoir_sampling(
+                n_population, n_samples, random_state)
+
+    elif method == "reservoir_sampling":
+        return _sample_without_replacement_with_reservoir_sampling(
+            n_population, n_samples, random_state)
+
+    elif method == "pool":
+        return _sample_without_replacement_with_pool(n_population, n_samples,
+                                                     random_state)
+    else:
+        raise ValueError('Expected a method name in %s, got %s. '
+                         % (all_methods, method))
+
+
+def sample_without_replacement(
+        object n_population, object n_samples, method="auto", random_state=None):
+    """Sample integers without replacement.
+
     Select n_samples integers from the set [0, n_population) without
     replacement.
 
@@ -278,44 +321,6 @@ cdef _sample_without_replacement(default_int n_population,
         The sampled subsets of integer. The subset of selected integer might
         not be randomized, see the method argument.
     """
-    _sample_without_replacement_check_input(n_population, n_samples)
-
-    all_methods = ("auto", "tracking_selection", "reservoir_sampling", "pool")
-
-    ratio = <double> n_samples / n_population if n_population != 0.0 else 1.0
-
-    # Check ratio and use permutation unless ratio < 0.01 or ratio > 0.99
-    if method == "auto" and ratio > 0.01 and ratio < 0.99:
-        rng = check_random_state(random_state)
-        return rng.permutation(n_population)[:n_samples]
-
-    if method == "auto" or method == "tracking_selection":
-        # TODO the pool based method can also be used.
-        #      however, it requires special benchmark to take into account
-        #      the memory requirement of the array vs the set.
-
-        # The value 0.2 has been determined through benchmarking.
-        if ratio < 0.2:
-            return _sample_without_replacement_with_tracking_selection(
-                n_population, n_samples, random_state)
-        else:
-            return _sample_without_replacement_with_reservoir_sampling(
-                n_population, n_samples, random_state)
-
-    elif method == "reservoir_sampling":
-        return _sample_without_replacement_with_reservoir_sampling(
-            n_population, n_samples, random_state)
-
-    elif method == "pool":
-        return _sample_without_replacement_with_pool(n_population, n_samples,
-                                                     random_state)
-    else:
-        raise ValueError('Expected a method name in %s, got %s. '
-                         % (all_methods, method))
-
-
-def sample_without_replacement(
-        object n_population, object n_samples, method="auto", random_state=None):
     cdef:
         cnp.intp_t n_pop_intp, n_samples_intp
         long n_pop_long, n_samples_long
diff --git a/sklearn/utils/_testing.py b/sklearn/utils/_testing.py
index a9ecaa8cd2d9d..5411c4dacf766 100644
--- a/sklearn/utils/_testing.py
+++ b/sklearn/utils/_testing.py
@@ -765,7 +765,7 @@ def _convert_container(
     elif constructor_name == "array":
         return np.asarray(container, dtype=dtype)
     elif constructor_name == "sparse":
-        return sp.sparse.csr_matrix(container, dtype=dtype)
+        return sp.sparse.csr_matrix(np.atleast_2d(container), dtype=dtype)
     elif constructor_name in ("pandas", "dataframe"):
         pd = pytest.importorskip("pandas", minversion=minversion)
         result = pd.DataFrame(container, columns=columns_name, dtype=dtype, copy=False)
@@ -803,18 +803,18 @@ def _convert_container(
     elif constructor_name == "slice":
         return slice(container[0], container[1])
     elif constructor_name == "sparse_csr":
-        return sp.sparse.csr_matrix(container, dtype=dtype)
+        return sp.sparse.csr_matrix(np.atleast_2d(container), dtype=dtype)
     elif constructor_name == "sparse_csr_array":
         if sp_version >= parse_version("1.8"):
-            return sp.sparse.csr_array(container, dtype=dtype)
+            return sp.sparse.csr_array(np.atleast_2d(container), dtype=dtype)
         raise ValueError(
             f"sparse_csr_array is only available with scipy>=1.8.0, got {sp_version}"
         )
     elif constructor_name == "sparse_csc":
-        return sp.sparse.csc_matrix(container, dtype=dtype)
+        return sp.sparse.csc_matrix(np.atleast_2d(container), dtype=dtype)
     elif constructor_name == "sparse_csc_array":
         if sp_version >= parse_version("1.8"):
-            return sp.sparse.csc_array(container, dtype=dtype)
+            return sp.sparse.csc_array(np.atleast_2d(container), dtype=dtype)
         raise ValueError(
             f"sparse_csc_array is only available with scipy>=1.8.0, got {sp_version}"
         )
diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py
index 28c099441e1ba..b3135d30b362a 100644
--- a/sklearn/utils/estimator_checks.py
+++ b/sklearn/utils/estimator_checks.py
@@ -51,13 +51,12 @@
 from ..random_projection import BaseRandomProjection
 from ..tree import DecisionTreeClassifier, DecisionTreeRegressor
 from ..utils._array_api import (
+    _atol_for_type,
     _convert_to_numpy,
     get_namespace,
     yield_namespace_device_dtype_combinations,
 )
-from ..utils._array_api import (
-    device as array_device,
-)
+from ..utils._array_api import device as array_device
 from ..utils._param_validation import (
     InvalidParameterError,
     generate_invalid_param_val,
@@ -311,11 +310,15 @@ def _yield_outliers_checks(estimator):
 
 
 def _yield_array_api_checks(estimator):
-    for array_namespace, device, dtype in yield_namespace_device_dtype_combinations():
+    for (
+        array_namespace,
+        device,
+        dtype_name,
+    ) in yield_namespace_device_dtype_combinations():
         yield partial(
             check_array_api_input,
             array_namespace=array_namespace,
-            dtype=dtype,
+            dtype_name=dtype_name,
             device=device,
         )
 
@@ -864,7 +867,7 @@ def check_array_api_input(
     estimator_orig,
     array_namespace,
     device=None,
-    dtype="float64",
+    dtype_name="float64",
     check_values=False,
 ):
     """Check that the estimator can work consistently with the Array API
@@ -878,7 +881,7 @@ def check_array_api_input(
     xp = _array_api_for_tests(array_namespace, device)
 
     X, y = make_classification(random_state=42)
-    X = X.astype(dtype, copy=False)
+    X = X.astype(dtype_name, copy=False)
 
     X = _enforce_estimator_tags_X(estimator_orig, X)
     y = _enforce_estimator_tags_y(estimator_orig, y)
@@ -918,7 +921,7 @@ def check_array_api_input(
                 attribute,
                 est_xp_param_np,
                 err_msg=f"{key} not the same",
-                atol=np.finfo(X.dtype).eps * 100,
+                atol=_atol_for_type(X.dtype),
             )
         else:
             assert attribute.shape == est_xp_param_np.shape
@@ -948,7 +951,7 @@ def check_array_api_input(
             assert isinstance(result, float)
             assert isinstance(result_xp, float)
             if check_values:
-                assert abs(result - result_xp) < np.finfo(X.dtype).eps * 100
+                assert abs(result - result_xp) < _atol_for_type(X.dtype)
             continue
         else:
             result = method(X)
@@ -970,7 +973,7 @@ def check_array_api_input(
                 result,
                 result_xp_np,
                 err_msg=f"{method} did not the return the same result",
-                atol=np.finfo(X.dtype).eps * 100,
+                atol=_atol_for_type(X.dtype),
             )
         else:
             if hasattr(result, "shape"):
@@ -995,7 +998,7 @@ def check_array_api_input(
                     inverse_result,
                     invese_result_xp_np,
                     err_msg="inverse_transform did not the return the same result",
-                    atol=np.finfo(X.dtype).eps * 100,
+                    atol=_atol_for_type(X.dtype),
                 )
             else:
                 assert inverse_result.shape == invese_result_xp_np.shape
@@ -1007,14 +1010,14 @@ def check_array_api_input_and_values(
     estimator_orig,
     array_namespace,
     device=None,
-    dtype="float64",
+    dtype_name="float64",
 ):
     return check_array_api_input(
         name,
         estimator_orig,
         array_namespace=array_namespace,
         device=device,
-        dtype=dtype,
+        dtype_name=dtype_name,
         check_values=True,
     )
 
diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py
index 01b1f2bf1adf8..1df81cf823bd6 100644
--- a/sklearn/utils/tests/test_array_api.py
+++ b/sklearn/utils/tests/test_array_api.py
@@ -129,7 +129,7 @@ def test_asarray_with_order_ignored():
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype", yield_namespace_device_dtype_combinations()
+    "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations()
 )
 @pytest.mark.parametrize(
     "sample_weight, normalize, expected",
@@ -143,20 +143,20 @@ def test_asarray_with_order_ignored():
     ],
 )
 def test_weighted_sum(
-    array_namespace, device, dtype, sample_weight, normalize, expected
+    array_namespace, device, dtype_name, sample_weight, normalize, expected
 ):
     xp = _array_api_for_tests(array_namespace, device)
-    sample_score = numpy.asarray([1, 2, 3, 4], dtype=dtype)
+    sample_score = numpy.asarray([1, 2, 3, 4], dtype=dtype_name)
     sample_score = xp.asarray(sample_score, device=device)
     if sample_weight is not None:
-        sample_weight = numpy.asarray(sample_weight, dtype=dtype)
+        sample_weight = numpy.asarray(sample_weight, dtype=dtype_name)
         sample_weight = xp.asarray(sample_weight, device=device)
 
     with config_context(array_api_dispatch=True):
         result = _weighted_sum(sample_score, sample_weight, normalize)
 
     assert isinstance(result, float)
-    assert_allclose(result, expected, atol=_atol_for_type(dtype))
+    assert_allclose(result, expected, atol=_atol_for_type(dtype_name))
 
 
 @skip_if_array_api_compat_not_configured
diff --git a/sklearn/utils/tests/test_class_weight.py b/sklearn/utils/tests/test_class_weight.py
index d1deeae8ebd20..b98ce6be05658 100644
--- a/sklearn/utils/tests/test_class_weight.py
+++ b/sklearn/utils/tests/test_class_weight.py
@@ -311,6 +311,6 @@ def test_class_weight_does_not_contains_more_classes():
 @pytest.mark.parametrize("csc_container", CSC_CONTAINERS)
 def test_compute_sample_weight_sparse(csc_container):
     """Check that we can compute weight for sparse `y`."""
-    y = csc_container(np.asarray([0, 1, 1])).T
+    y = csc_container(np.asarray([[0], [1], [1]]))
     sample_weight = compute_sample_weight("balanced", y)
     assert_allclose(sample_weight, [1.5, 0.75, 0.75])
diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py
index d7702ba35cf68..6603aca206e66 100644
--- a/sklearn/utils/tests/test_multiclass.py
+++ b/sklearn/utils/tests/test_multiclass.py
@@ -379,17 +379,17 @@ def test_is_multilabel():
 
 
 @pytest.mark.parametrize(
-    "array_namespace, device, dtype",
+    "array_namespace, device, dtype_name",
     yield_namespace_device_dtype_combinations(),
 )
-def test_is_multilabel_array_api_compliance(array_namespace, device, dtype):
+def test_is_multilabel_array_api_compliance(array_namespace, device, dtype_name):
     xp = _array_api_for_tests(array_namespace, device)
 
     for group, group_examples in ARRAY_API_EXAMPLES.items():
         dense_exp = group == "multilabel-indicator"
         for example in group_examples:
             if np.asarray(example).dtype.kind == "f":
-                example = np.asarray(example, dtype=dtype)
+                example = np.asarray(example, dtype=dtype_name)
             else:
                 example = np.asarray(example)
             example = xp.asarray(example, device=device)
diff --git a/sklearn/utils/tests/test_utils.py b/sklearn/utils/tests/test_utils.py
index 89ab73582cefc..5f3fe72c0f7ef 100644
--- a/sklearn/utils/tests/test_utils.py
+++ b/sklearn/utils/tests/test_utils.py
@@ -168,7 +168,7 @@ def test_resample_stratify_sparse_error(csr_container):
     n_samples = 100
     X = rng.normal(size=(n_samples, 2))
     y = rng.randint(0, 2, size=n_samples)
-    stratify = csr_container(y)
+    stratify = csr_container(y.reshape(-1, 1))
     with pytest.raises(TypeError, match="Sparse data was passed"):
         X, y = resample(X, y, n_samples=50, random_state=rng, stratify=stratify)
 
diff --git a/sklearn/utils/tests/test_validation.py b/sklearn/utils/tests/test_validation.py
index 1f847dbd55d62..b627c55a7ef12 100644
--- a/sklearn/utils/tests/test_validation.py
+++ b/sklearn/utils/tests/test_validation.py
@@ -639,9 +639,21 @@ def test_check_array_accept_sparse_no_exception():
 @pytest.fixture(params=["csr", "csc", "coo", "bsr"])
 def X_64bit(request):
     X = sp.rand(20, 10, format=request.param)
-    for attr in ["indices", "indptr", "row", "col"]:
-        if hasattr(X, attr):
-            setattr(X, attr, getattr(X, attr).astype("int64"))
+
+    if request.param == "coo":
+        if hasattr(X, "indices"):
+            # for scipy >= 1.13 .indices is a new attribute and is a tuple. The
+            # .col and .row attributes do not seem to be able to change the
+            # dtype, for more details see https://github.com/scipy/scipy/pull/18530/
+            X.indices = tuple(v.astype("int64") for v in X.indices)
+        else:
+            # scipy < 1.13
+            X.row = X.row.astype("int64")
+            X.col = X.col.astype("int64")
+    else:
+        X.indices = X.indices.astype("int64")
+        X.indptr = X.indptr.astype("int64")
+
     yield X