diff --git a/.github/workflows/testing.yml b/.github/workflows/testing.yml
index b5ffb16d7..92babf3c6 100644
--- a/.github/workflows/testing.yml
+++ b/.github/workflows/testing.yml
@@ -15,14 +15,13 @@ jobs:
     strategy:
       matrix:
         os: [windows-latest, ubuntu-latest, macos-latest]
-        environment: [
-            ci-py309-min-deps,
-            ci-py309-min-optional-deps,
-            ci-py311-transformers,
-            ci-py313-latest-deps,
-            ci-py313-latest-optional-deps,
-            ci-py313-polars-without-pyarrow
-        ]
+        environment:
+          - ci-py309-min-deps
+          - ci-py309-min-optional-deps
+          - ci-py311-transformers
+          - ci-py313-latest-deps
+          - ci-py313-latest-optional-deps
+          - ci-py313-polars-without-pyarrow
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v5
@@ -33,13 +32,10 @@ jobs:
           # we can freeze the environment and manually bump the dependencies to the
           # latest version time to time.
           frozen: true
-
       - name: Run tests
         run: pixi run -e ${{ matrix.environment }} test -n auto
-
       - name: Run user guide doctests
         run: pixi run -e ${{ matrix.environment }} test-user-guide
-
       - name: Upload coverage reports to Codecov
         uses: codecov/codecov-action@v5.5.1
         with:
@@ -55,28 +51,21 @@ jobs:
         with:
           ref: ${{ github.event.pull_request.head.sha }}
       - id: get_head_commit_message
-        name: get head commit message
+        name: Get head commit message
         run: echo "COMMIT_MSG=$(git show -s --format=%s)" >> "$GITHUB_OUTPUT"
 
   test_against_nightly:
     needs: check_run_nightly
     if: ${{ needs.check_run_nightly.outputs.run == 'true' }}
     runs-on: ubuntu-latest
-    name: test against nighlty dependencies
+    name: Test against nightly dependencies
     steps:
       - uses: actions/checkout@v5
-      - uses: actions/setup-python@v6
+      - uses: prefix-dev/setup-pixi@v0.9.1
         with:
-            python-version: "3.11"
-        name: 'Setup python'
-      - shell: bash {0}
-        run: |
-          dev_anaconda_url=https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
-          pip install --pre --upgrade --timeout=60 --extra-index $dev_anaconda_url .
-          dev_arrow=https://pypi.fury.io/arrow-nightlies/
-          pip install --extra-index-url $dev_arrow --prefer-binary --pre .[optional]
-          pip install .[test]
-        name: 'Install skrub and nightly dependencies'
-      - shell: bash {0}
-        run: pytest -vsl --cov=skrub --cov-report=xml skrub -n 3
-        name: 'Run tests'
+          pixi-version: v0.41.3
+          # we can freeze the environment and manually bump the dependencies to the
+          # latest version time to time.
+          frozen: true
+      - name: Run tests
+        run: pixi run --environment ci-nightly-deps pytest -vsl --cov=skrub --cov-report=xml skrub -n 3
diff --git a/CHANGES.rst b/CHANGES.rst
index b2652ce3c..00b739d2e 100644
--- a/CHANGES.rst
+++ b/CHANGES.rst
@@ -43,7 +43,9 @@ Bugfixes
   columns with some dtypes (lists, structs) are sorted. It would not indicate
   Polars columns sorted in descending order. Fixed in :pr:`1673` by
   :user:`Jérôme Dockès <jeromedockes>`.
-
+- Fixed nightly checks and added support for upcoming library versions, including Pandas
+  v3.0. :pr:`1664` by :user:`Auguste Baum <auguste-probabl>` and
+  :user:`Riccardo Cappuzzo <rcap107>`.
 
 Release 0.6.2
 =============
diff --git a/pixi.lock b/pixi.lock
index 2b21835e9..ad732336d 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10,26 +10,26 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-48.1-unix_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/adwaita-icon-theme-49.0-unix_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.14-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-atk-2.38.0-h0630a04_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/at-spi2-core-2.40.3-h0630a04_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/atk-1.0-2.38.0-h04ea711_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb03c661_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb03c661_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py313h7033f15_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py314ha160325_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.8.3-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.10.5-hbd8a1cb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cairo-1.18.4-h3394656_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.8.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py313hf01b4d8_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.3-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py313h7037e92_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.10.5-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py314h4a8dc5f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.4-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py314h9891dd4_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.28-hd9c7081_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h3c4dab8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/epoxy-1.5.10-h166bdaf_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/epoxy-1.5.10-hb03c661_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2
@@ -37,60 +37,63 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.60.0-py313h3dea7bd_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/fonttools-4.60.1-pyh7db6752_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fribidi-1.0.16-hb03c661_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.1-h2b0a6b4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.86.0-hf516916_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.44.4-h2b0a6b4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/glib-tools-2.86.0-hf516916_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphviz-13.1.2-h87b6fe6_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.43-h0c6a113_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.43-h993cebd_6.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.5.0-h15599e2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.1.0-h15599e2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/hicolor-icon-theme-0.17-ha770c72_2.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py313hc8edb43_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.9-py314hc43b2c7_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1423503_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.44-h1aa0949_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h0aef613_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-35_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-37_h4a7cf45_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb03c661_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb03c661_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb03c661_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-35_h0358290_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp21.1-21.1.0-default_h99862b1_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-21.1.0-default_h746c552_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-37_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang-cpp21.1-21.1.4-default_h99862b1_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libclang13-21.1.4-default_h746c552_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcups-2.3.3-hb8b1518_5.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.24-h86f0d12_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdrm-2.4.125-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-1.7.0-ha4b6fd6_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libegl-devel-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.1-hecca717_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h9ec8514_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.1-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.1-h73754d4_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.1.0-h767d61c_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.1.0-h69a702a_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-h767d61c_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgd-2.3.3-h6f5c62b_11.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.1.0-h69a702a_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.1.0-hcea5267_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-hcd61629_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-1.7.0-ha4b6fd6_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.0-h1fed272_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgl-devel-1.7.0-ha4b6fd6_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.0-h32235b2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglvnd-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-1.7.0-ha4b6fd6_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.1.0-h767d61c_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglx-devel-1.7.0-ha4b6fd6_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-h767d61c_7.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.1.0-hb9d3cd8_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-35_h47877c9_openblas.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm21-21.1.0-hecd9e04_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-37_h47877c9_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm21-21.1.4-hf7376ad_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libmpdec-4.0.0-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libntlm-1.8-hb9d3cd8_0.conda
@@ -98,67 +101,70 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpciaccess-0.18-hb9d3cd8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.50-h421ea60_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-17.6-h3675c94_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.58.4-he92a37e_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libpq-18.0-h3675c94_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/librsvg-2.60.0-h61e6d4b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.50.4-h0c1763c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.1.0-h8f9b012_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.1.0-h4852527_5.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-h8261f1e_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.41.1-he9a06e4_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h8f9b012_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-h4852527_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.1-h8261f1e_0.conda
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libvulkan-loader-1.4.328.1-h5279c79_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.6.0-hd42ef1d_0.conda
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   - aws-c-common >=0.12.4,<0.12.5.0a0
+  - aws-c-io >=0.22.0,<0.22.1.0a0
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   license: Apache-2.0
   license_family: APACHE
   purls: []
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   license: Apache-2.0
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+  - aws-c-io >=0.22.0,<0.22.1.0a0
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   license: Apache-2.0
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+  - s2n >=1.5.26,<1.5.27.0a0
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   license: Apache-2.0
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+  - aws-c-common >=0.12.4,<0.12.5.0a0
   license: Apache-2.0
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+  - aws-c-io >=0.22.0,<0.22.1.0a0
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   license: Apache-2.0
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   - aws-c-http >=0.10.4,<0.10.5.0a0
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   license: Apache-2.0
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+  - aws-c-common >=0.12.4,<0.12.5.0a0
+  - aws-c-io >=0.22.0,<0.22.1.0a0
   license: Apache-2.0
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   license: Apache-2.0
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   license: Apache-2.0
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-  - python_abi 3.13.* *_cp313
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+  - python_abi 3.9.* *_cp39
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+  name: contourpy
+  version: 1.3.4.dev1
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+  - numpy>=1.25
+  - furo ; extra == 'docs'
+  - sphinx>=7.2 ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
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+  - selenium ; extra == 'bokeh'
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+  - types-pillow ; extra == 'mypy'
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+  - pillow ; extra == 'test'
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+  - pytest-cov ; extra == 'test-no-images'
+  - pytest-rerunfailures<16 ; extra == 'test-no-images'
+  - pytest-xdist ; extra == 'test-no-images'
+  - wurlitzer ; extra == 'test-no-images'
+  requires_python: '>=3.11'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/contourpy/1.3.4.dev1/contourpy-1.3.4.dev1-cp313-cp313-macosx_11_0_arm64.whl
+  name: contourpy
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+  - sphinx>=7.2 ; extra == 'docs'
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+  - wurlitzer ; extra == 'test-no-images'
+  requires_python: '>=3.11'
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+  name: contourpy
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+  - wurlitzer ; extra == 'test-no-images'
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+  name: contourpy
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+  - furo ; extra == 'docs'
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+  - selenium ; extra == 'bokeh'
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+  - mypy==1.17.0 ; extra == 'mypy'
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+  - wurlitzer ; extra == 'test-no-images'
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py311hdf67eae_2.conda
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   size: 296706
   timestamp: 1756544800085
+- conda: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.3-py314h9891dd4_2.conda
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   size: 260411
   timestamp: 1756545032560
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.3.3-py314h6b18a25_2.conda
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+  - python_abi 3.14.* *_cp314
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+- conda: https://conda.anaconda.org/conda-forge/win-64/contourpy-1.3.3-py314h909e829_2.conda
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+  md5: 696a6638cc1059b4da6b8b16dc81988e
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   - gmp >=6.3.0,<7.0a0
   - mpc >=1.3.1,<2.0a0
   - mpfr >=4.2.1,<5.0a0
-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
   license: LGPL-3.0-or-later
   license_family: LGPL
   purls:
   - pkg:pypi/gmpy2?source=hash-mapping
-  size: 161039
-  timestamp: 1756740107158
+  size: 162864
+  timestamp: 1756739927182
 - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.14-hecca717_2.conda
   sha256: 25ba37da5c39697a77fce2c9a15e48cf0a84f1464ad2aafbe53d8357a9f6cc8c
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.43-h021d004_4.conda
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-  md5: a891e341072432fafb853b3762957cbf
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   - gdk-pixbuf >=2.42.12,<3.0a0
   - glib-tools
-  - harfbuzz >=10.4.0
+  - harfbuzz >=11.0.0,<12.0a0
   - hicolor-icon-theme
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   - libcups >=2.3.3,<3.0a0
   - libexpat >=2.6.4,<3.0a0
   - libgcc >=13
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-  - libxkbcommon >=1.8.0,<2.0a0
+  - libxkbcommon >=1.8.1,<2.0a0
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-  - pango >=1.56.1,<2.0a0
+  - pango >=1.56.3,<2.0a0
   - wayland >=1.23.1,<2.0a0
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   - xorg-libxcomposite >=0.4.6,<1.0a0
   - xorg-libxcursor >=1.2.3,<2.0a0
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@@ -16158,11 +16864,11 @@ packages:
   license: LGPL-2.0-or-later
   license_family: LGPL
   purls: []
-  size: 5563940
-  timestamp: 1741694746664
-- conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.43-h0c6a113_5.conda
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-  md5: 67d00e9cfe751cfe581726c5eff7c184
+  size: 5585389
+  timestamp: 1743405684985
+- conda: https://conda.anaconda.org/conda-forge/linux-64/gtk3-3.24.43-h993cebd_6.conda
+  sha256: 004688fbb2c479b200a6d85ef38c3129fcd4ce13537b7ee2371d962b372761c1
+  md5: f9f33c65b20e6a61f21714785e3613ec
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   - fonts-conda-ecosystem
-  - fribidi >=1.0.10,<2.0a0
-  - gdk-pixbuf >=2.42.12,<3.0a0
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+  - gdk-pixbuf >=2.44.4,<3.0a0
   - glib-tools
-  - harfbuzz >=11.0.0,<12.0a0
+  - harfbuzz >=11.5.1
   - hicolor-icon-theme
   - libcups >=2.3.3,<2.4.0a0
   - libcups >=2.3.3,<3.0a0
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-  - liblzma >=5.6.4,<6.0a0
-  - libxkbcommon >=1.8.1,<2.0a0
+  - libexpat >=2.7.1,<3.0a0
+  - libgcc >=14
+  - libglib >=2.86.0,<3.0a0
+  - liblzma >=5.8.1,<6.0a0
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   - libzlib >=1.3.1,<2.0a0
-  - pango >=1.56.3,<2.0a0
-  - wayland >=1.23.1,<2.0a0
+  - pango >=1.56.4,<2.0a0
+  - wayland >=1.24.0,<2.0a0
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   - xorg-libxcomposite >=0.4.6,<1.0a0
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   - xorg-libxext >=1.3.6,<2.0a0
-  - xorg-libxfixes >=6.0.1,<7.0a0
+  - xorg-libxfixes >=6.0.2,<7.0a0
   - xorg-libxi >=1.8.2,<2.0a0
   - xorg-libxinerama >=1.1.5,<1.2.0a0
   - xorg-libxrandr >=1.5.4,<2.0a0
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   license: LGPL-2.0-or-later
   license_family: LGPL
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-  size: 5585389
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/gtk3-3.24.43-h70b172e_5.conda
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-  md5: 38eeb48f9466e5763567d1be1b7ff444
+  size: 5587108
+  timestamp: 1761327349586
+- conda: https://conda.anaconda.org/conda-forge/osx-64/gtk3-3.24.43-h5e629aa_6.conda
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+  md5: dbd0346e44fcbda7fe4f6eaf42597ef9
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-  - fribidi >=1.0.10,<2.0a0
-  - gdk-pixbuf >=2.42.12,<3.0a0
+  - fribidi >=1.0.16,<2.0a0
+  - gdk-pixbuf >=2.44.4,<3.0a0
   - glib-tools
-  - harfbuzz >=11.0.0,<12.0a0
+  - harfbuzz >=11.5.1
   - hicolor-icon-theme
-  - libexpat >=2.6.4,<3.0a0
-  - libglib >=2.84.0,<3.0a0
-  - libintl >=0.23.1,<1.0a0
-  - liblzma >=5.6.4,<6.0a0
+  - libexpat >=2.7.1,<3.0a0
+  - libglib >=2.86.0,<3.0a0
+  - libintl >=0.25.1,<1.0a0
+  - liblzma >=5.8.1,<6.0a0
   - libzlib >=1.3.1,<2.0a0
-  - pango >=1.56.3,<2.0a0
+  - pango >=1.56.4,<2.0a0
   license: LGPL-2.0-or-later
   license_family: LGPL
   purls: []
-  size: 4916900
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gtk3-3.24.43-h07173f4_5.conda
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-  md5: 8353369d4c2ecc5afd888405d3226fd9
+  size: 4922163
+  timestamp: 1761327865236
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/gtk3-3.24.43-h5febe37_6.conda
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+  md5: a99f96906158ebae5e3c0904bcd45145
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-  - fribidi >=1.0.10,<2.0a0
-  - gdk-pixbuf >=2.42.12,<3.0a0
+  - fribidi >=1.0.16,<2.0a0
+  - gdk-pixbuf >=2.44.4,<3.0a0
   - glib-tools
-  - harfbuzz >=11.0.0,<12.0a0
+  - harfbuzz >=11.5.1
   - hicolor-icon-theme
-  - libexpat >=2.6.4,<3.0a0
-  - libglib >=2.84.0,<3.0a0
-  - libintl >=0.23.1,<1.0a0
-  - liblzma >=5.6.4,<6.0a0
+  - libexpat >=2.7.1,<3.0a0
+  - libglib >=2.86.0,<3.0a0
+  - libintl >=0.25.1,<1.0a0
+  - liblzma >=5.8.1,<6.0a0
   - libzlib >=1.3.1,<2.0a0
-  - pango >=1.56.3,<2.0a0
+  - pango >=1.56.4,<2.0a0
   license: LGPL-2.0-or-later
   license_family: LGPL
   purls: []
-  size: 4792338
-  timestamp: 1743406461562
+  size: 4768791
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/gts-0.7.6-h977cf35_4.conda
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@@ -16322,17 +17028,17 @@ packages:
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   size: 95967
   timestamp: 1756364871835
-- conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.5.0-h15599e2_0.conda
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-  md5: 47599428437d622bfee24fbd06a2d0b4
+- conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.5.1-h15599e2_0.conda
+  sha256: 3bf149eab76768ed10f95eba015ca996cd6be7dc666996a004c4a8340a57cd60
+  md5: b90a6ec73cc7d630981f78d4c7ca8fed
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   - graphite2 >=1.3.14,<2.0a0
   - icu >=75.1,<76.0a0
   - libexpat >=2.7.1,<3.0a0
-  - libfreetype >=2.14.0
-  - libfreetype6 >=2.14.0
+  - libfreetype >=2.14.1
+  - libfreetype6 >=2.14.1
   - libgcc >=14
   - libglib >=2.86.0,<3.0a0
   - libstdcxx >=14
@@ -16340,8 +17046,8 @@ packages:
   license: MIT
   license_family: MIT
   purls: []
-  size: 2048134
-  timestamp: 1757867460348
+  size: 2427482
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.1.0-h15599e2_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-11.5.0-hc5d3ef4_0.conda
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-  - libcxx >=19
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-  - libzlib >=1.3.1,<2.0a0
-  license: MIT
-  license_family: MIT
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-  timestamp: 1757867604495
-- conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-11.5.1-hc5d3ef4_0.conda
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-  md5: 9363d59f8ba44515263514f12ac2c1aa
+- conda: https://conda.anaconda.org/conda-forge/osx-64/harfbuzz-12.1.0-hc5d3ef4_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-11.5.0-haf38c7b_0.conda
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-  license: MIT
-  license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-11.5.1-haf38c7b_0.conda
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-  md5: effd4cec7d221d77cd937a83132f5b9e
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/harfbuzz-12.1.0-haf38c7b_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/win-64/harfbuzz-11.5.0-h5f2951f_0.conda
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-  license: MIT
-  license_family: MIT
-  purls: []
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 - conda: https://conda.anaconda.org/conda-forge/win-64/harfbuzz-12.1.0-h5f2951f_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/hf-xet-1.1.10-py310h77a0e7f_1.conda
+- conda: https://conda.anaconda.org/conda-forge/linux-64/hf-xet-1.2.0-py310hb823017_0.conda
   noarch: python
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+  - __glibc >=2.17,<3.0.a0
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   - cpython >=3.10
-  - openssl >=3.5.2,<4.0a0
+  - openssl >=3.5.4,<4.0a0
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   license: Apache-2.0
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   purls:
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/hf-xet-1.1.10-py310hdcd56a2_1.conda
+  size: 2703061
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/hf-xet-1.2.0-py310h6ce4931_0.conda
   noarch: python
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   - cpython >=3.10
-  - openssl >=3.5.2,<4.0a0
+  - openssl >=3.5.4,<4.0a0
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   license: Apache-2.0
   license_family: APACHE
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-- conda: https://conda.anaconda.org/conda-forge/win-64/hf-xet-1.1.10-py310h63875d3_1.conda
+  - pkg:pypi/hf-xet?source=hash-mapping
+  size: 2493380
+  timestamp: 1761341556504
+- conda: https://conda.anaconda.org/conda-forge/win-64/hf-xet-1.2.0-py310hfb9af98_0.conda
   noarch: python
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   - ucrt >=10.0.20348.0
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   - libcrc32c >=1.1.2,<1.2.0a0
-  - libcurl >=8.14.1,<9.0a0
+  - libcurl >=8.16.0,<9.0a0
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   - libgoogle-cloud-storage >=2.39.0,<2.40.0a0
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   - lz4-c >=1.10.0,<1.11.0a0
-  - orc >=2.2.0,<2.2.1.0a0
+  - orc >=2.2.1,<2.2.2.0a0
   - snappy >=1.2.2,<1.3.0a0
   - ucrt >=10.0.20348.0
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-  - arrow-cpp <0.0a0
-  - apache-arrow-proc =*=cpu
   - parquet-cpp <0.0a0
+  - apache-arrow-proc =*=cpu
+  - arrow-cpp <0.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 3961264
-  timestamp: 1757792271412
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-15.0.2-h7599340_55_cpu.conda
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   sha256: 9842fe6ba600f21332a9c2d0f671a3b06ba07792d4d5d10139f7ccfdddb04cf8
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-21.0.0-h635bf11_3_cpu.conda
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-  md5: 12fe67afbd946adae49856b275478d0f
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-21.0.0-h635bf11_9_cpu.conda
+  build_number: 9
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-  - libarrow-compute 21.0.0 h8c2c5c3_3_cpu
+  - libarrow 21.0.0 hf201b43_9_cpu
+  - libarrow-compute 21.0.0 h8c2c5c3_9_cpu
   - libgcc >=14
   - libstdcxx >=14
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 659383
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+  size: 581055
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-  md5: d432e30aa5dfbe9f6bd46d55f41c37ef
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-acero-21.0.0-h2db2d7d_9_cpu.conda
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-  - libarrow 21.0.0 h45d9b8c_3_cpu
-  - libarrow-compute 21.0.0 h7751554_3_cpu
+  - libarrow 21.0.0 hb6a727e_9_cpu
+  - libarrow-compute 21.0.0 h7751554_9_cpu
   - libcxx >=19
   - libopentelemetry-cpp >=1.21.0,<1.22.0a0
   - libprotobuf >=6.31.1,<6.31.2.0a0
   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 552885
-  timestamp: 1757792950873
+  size: 552453
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-  md5: ce2388289d7651561f9d7b3925fab93f
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-acero-21.0.0-hc317990_9_cpu.conda
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-  - libarrow-compute 21.0.0 h75845d1_3_cpu
+  - libarrow 21.0.0 hce8344c_9_cpu
+  - libarrow-compute 21.0.0 h75845d1_9_cpu
   - libcxx >=19
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   license: Apache-2.0
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   purls: []
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-acero-21.0.0-h7d8d6a5_9_cpu.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-21.0.0-h8c2c5c3_3_cpu.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-21.0.0-h8c2c5c3_9_cpu.conda
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+  - libarrow 21.0.0 hf201b43_9_cpu
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-  md5: 04bc68b09550b89bb1439b9da4ea5473
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+  timestamp: 1761150392062
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-compute-21.0.0-h7751554_9_cpu.conda
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   - libabseil >=20250512.1,<20250513.0a0
-  - libarrow 21.0.0 h45d9b8c_3_cpu
+  - libarrow 21.0.0 hb6a727e_9_cpu
   - libcxx >=19
   - libopentelemetry-cpp >=1.21.0,<1.22.0a0
   - libprotobuf >=6.31.1,<6.31.2.0a0
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-compute-21.0.0-h75845d1_3_cpu.conda
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-  md5: 5fb9a57355e70f50f86f42c2f5d67159
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libarrow-compute-21.0.0-h75845d1_9_cpu.conda
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+  - libarrow 21.0.0 hce8344c_9_cpu
   - libcxx >=19
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   - libprotobuf >=6.31.1,<6.31.2.0a0
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-compute-21.0.0-h2db994a_3_cpu.conda
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libarrow-compute-21.0.0-h2db994a_9_cpu.conda
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   license: Apache-2.0
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-  md5: 630dfffcaf67b800607164d4b5b08bf7
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-  - libarrow-compute 21.0.0 h8c2c5c3_3_cpu
+  - libarrow 21.0.0 hf201b43_9_cpu
+  - libarrow-acero 21.0.0 h635bf11_9_cpu
+  - libarrow-compute 21.0.0 h8c2c5c3_9_cpu
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   license: Apache-2.0
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-  md5: 551f864deab361df1c9524982867f9ce
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libarrow-dataset-21.0.0-h2db2d7d_9_cpu.conda
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+  - libarrow 21.0.0 hb6a727e_9_cpu
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   - libcxx >=19
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+  - libparquet 21.0.0 habb56ca_9_cpu
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   purls: []
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   license: Apache-2.0
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+  - blas 2.137   openblas
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-  - libcblas   3.9.0   35*_mkl
-  - liblapack  3.9.0   35*_mkl
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   license: BSD-3-Clause
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-  - blas 2.136   openblas
-  - liblapack  3.9.0   36*_openblas
-  - mkl <2025
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-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
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-  - liblapack  3.9.0   35*_openblas
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   - mkl <2025
-  - liblapack  3.9.0   36*_openblas
-  - liblapacke 3.9.0   36*_openblas
-  - blas 2.136   openblas
-  - libcblas   3.9.0   36*_openblas
+  - blas 2.137   openblas
+  - liblapacke 3.9.0   37*_openblas
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   license_family: BSD
   purls: []
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-  - libcblas   3.9.0   36*_openblas
-  - liblapack  3.9.0   36*_openblas
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+  - liblapack  3.9.0   37*_openblas
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   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-  - blas 2.135   openblas
-  - liblapack  3.9.0   35*_openblas
+  - blas 2.137   openblas
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-  - liblapacke 3.9.0   35*_mkl
-  - liblapack  3.9.0   35*_mkl
+  - liblapacke 3.9.0   37*_mkl
+  - blas 2.137   mkl
+  - liblapack  3.9.0   37*_mkl
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   license: BSD-3-Clause
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-  - liblapack  3.9.0   36*_openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
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-  md5: 9e7889a68e05f95bb9089400b334f594
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libgrpc-1.73.1-h451496d_1.conda
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+  md5: d6ea2acfae86b523b54938c6bc30e378
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   - libabseil * cxx17*
   - libabseil >=20250512.1,<20250513.0a0
-  - libcxx >=18
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-  - libre2-11 >=2024.7.2
+  - libre2-11 >=2025.8.12
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.1,<4.0a0
+  - openssl >=3.5.4,<4.0a0
   - re2
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   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 6209150
-  timestamp: 1751713120189
+  size: 5468625
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgrpc-1.67.1-h0a426d6_2.conda
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-  md5: 32fbcf10c4d9982e1cfec578a333def1
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   - libabseil * cxx17*
   - libabseil >=20250512.1,<20250513.0a0
-  - libcxx >=18
+  - libcxx >=19
   - libprotobuf >=6.31.1,<6.31.2.0a0
-  - libre2-11 >=2024.7.2
+  - libre2-11 >=2025.8.12
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.1,<4.0a0
+  - openssl >=3.5.4,<4.0a0
   - re2
   constrains:
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   license: Apache-2.0
   license_family: APACHE
   purls: []
-  size: 4618885
-  timestamp: 1751705260982
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-  md5: 9adc6511fdf045fbd7096ecd1fc534dd
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgrpc-1.73.1-h317e13b_1.conda
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   - libabseil * cxx17*
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   - libprotobuf >=6.31.1,<6.31.2.0a0
-  - libre2-11 >=2024.7.2
+  - libre2-11 >=2025.8.12
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.1,<4.0a0
+  - openssl >=3.5.4,<4.0a0
   - re2
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
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-  md5: d821210ab60be56dd27b5525ed18366d
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.12.1-default_h7f8ec31_1002.conda
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   - libgcc >=14
   - libstdcxx >=14
-  - libxml2 >=2.13.8,<2.14.0a0
+  - libxml2
+  - libxml2-16 >=2.14.6
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-  license: BSD-3-Clause
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-35_h47877c9_openblas.conda
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-  md5: aa0b36b71d44f74686f13b9bfabec891
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-37_h47877c9_openblas.conda
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-  - libblas 3.9.0 35_h4a7cf45_openblas
+  - libblas 3.9.0 37_h4a7cf45_openblas
   constrains:
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-  - blas 2.135   openblas
-  - libcblas   3.9.0   35*_openblas
+  - blas 2.137   openblas
+  - liblapacke 3.9.0   37*_openblas
+  - libcblas   3.9.0   37*_openblas
   license: BSD-3-Clause
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   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-35_h5e43f62_mkl.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-37_h5e43f62_mkl.conda
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   depends:
-  - libblas 3.9.0 35_h5875eb1_mkl
+  - libblas 3.9.0 37_h5875eb1_mkl
   constrains:
-  - blas 2.135   mkl
-  - liblapacke 3.9.0   35*_mkl
-  - libcblas   3.9.0   35*_mkl
+  - liblapacke 3.9.0   37*_mkl
+  - blas 2.137   mkl
+  - libcblas   3.9.0   37*_mkl
   track_features:
   - blas_mkl
   license: BSD-3-Clause
   license_family: BSD
   purls: []
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-36_h47877c9_openblas.conda
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-  md5: 55daaac7ecf8ebd169cdbe34dc79549e
-  depends:
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-  constrains:
-  - liblapacke 3.9.0   36*_openblas
-  - libcblas   3.9.0   36*_openblas
-  - blas 2.136   openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 17409
-  timestamp: 1758396509549
+  size: 17510
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-20_osx64_openblas.conda
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@@ -21169,36 +21732,21 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-35_h859234e_openblas.conda
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-  md5: 340a8f781528d59abac136c0fa9f6a4c
+- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-37_h859234e_openblas.conda
+  build_number: 37
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+  md5: abf96060ac52487961009e1fafec3e96
   depends:
-  - libblas 3.9.0 35_he492b99_openblas
+  - libblas 3.9.0 37_he492b99_openblas
   constrains:
-  - liblapacke 3.9.0   35*_openblas
-  - blas 2.135   openblas
-  - libcblas   3.9.0   35*_openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/liblapack-3.9.0-36_h859234e_openblas.conda
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-  md5: 5614cabd1b003b5287183b3e0f149c7e
-  depends:
-  - libblas 3.9.0 36_he492b99_openblas
-  constrains:
-  - liblapacke 3.9.0   36*_openblas
-  - blas 2.136   openblas
-  - libcblas   3.9.0   36*_openblas
+  - libcblas   3.9.0   37*_openblas
+  - liblapacke 3.9.0   37*_openblas
+  - blas 2.137   openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
   size: 17704
-  timestamp: 1758397422264
+  timestamp: 1760213576216
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-20_osxarm64_openblas.conda
   build_number: 20
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@@ -21214,36 +21762,21 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-35_hd9741b5_openblas.conda
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-  md5: 270789394f52de7ae5e5328dfe7e26c1
-  depends:
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-  constrains:
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-  - libcblas   3.9.0   35*_openblas
-  - blas 2.135   openblas
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
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-  timestamp: 1757447527491
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-36_hd9741b5_openblas.conda
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-  md5: e0b918b8232902da02c2c5b4eb81f4d5
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-37_hd9741b5_openblas.conda
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+  md5: 53335fc42466f597d0bc6d66a9ed4468
   depends:
-  - libblas 3.9.0 36_h51639a9_openblas
+  - libblas 3.9.0 37_h51639a9_openblas
   constrains:
-  - libcblas   3.9.0   36*_openblas
-  - liblapacke 3.9.0   36*_openblas
-  - blas 2.136   openblas
+  - liblapacke 3.9.0   37*_openblas
+  - blas 2.137   openblas
+  - libcblas   3.9.0   37*_openblas
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 17728
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 - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.9.0-35_hf9ab0e9_mkl.conda
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@@ -21331,9 +21864,9 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm21-21.1.2-hf7376ad_0.conda
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-  md5: 85ccb5afca5e1fa67364ea19154e8148
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libllvm21-21.1.4-hf7376ad_0.conda
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+  md5: da21f286c4466912cc579911068034b6
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
@@ -21345,8 +21878,8 @@ packages:
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
   purls: []
-  size: 44347174
-  timestamp: 1758823362425
+  size: 44344723
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda
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-- conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.30-openmp_h83c2472_2.conda
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-  md5: 89edf77977f520c4245567460d065821
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libopenblas-0.3.30-openmp_h6006d49_2.conda
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+  md5: 9b66105b30ae81dbdd37111e9a5784f1
   depends:
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   license_family: BSD
   purls: []
-  size: 6262457
-  timestamp: 1755473612572
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.25-openmp_h6c19121_0.conda
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@@ -21616,9 +22149,9 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.30-openmp_h60d53f8_2.conda
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-  md5: d004259fd8d3d2798b16299d6ad6c9e9
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libopenblas-0.3.30-openmp_ha158390_2.conda
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+  md5: e9f522513b5bbc6381f124f46e78fe36
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   license: BSD-3-Clause
   license_family: BSD
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-  size: 4284696
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libopengl-1.7.0-ha4b6fd6_2.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-21.0.0-h790f06f_3_cpu.conda
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-  md5: 0568ba99a1f6c0ef7a04ca23dc78905a
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-21.0.0-h7376487_9_cpu.conda
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+  md5: 20ecc22fe0593b2e7eae6a034f807604
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-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
-  - sleef >=3.8,<4.0a0
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+  - python >=3.11,<3.12.0a0 *_cpython
+  - python_abi 3.11.* *_cp311
+  - sleef >=3.9.0,<4.0a0
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   - openblas * openmp_*
-  - pytorch 2.7.1 cpu_generic_*_4
   - pytorch-gpu <0.0a0
+  - libopenblas * openmp_*
+  - pytorch-cpu 2.8.0
+  - pytorch 2.8.0 cpu_generic_*_1
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   license_family: BSD
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-  md5: 6b010f0d63ba53eb202192cbdbc787b6
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-  - python >=3.11,<3.12.0a0 *_cpython
-  - python_abi 3.11.* *_cp311
-  - sleef >=3.8,<4.0a0
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+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  - sleef >=3.9.0,<4.0a0
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-  - pytorch 2.7.1 cpu_generic_*_4
+  - pytorch-cpu 2.8.0
   - openblas * openmp_*
+  - pytorch 2.8.0 cpu_generic_*_1
+  - libopenblas * openmp_*
   - pytorch-gpu <0.0a0
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   - mkl >=2024.2.2,<2025.0a0
-  - sleef >=3.8,<4.0a0
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   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   constrains:
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   - pytorch-gpu <0.0a0
-  - pytorch 2.7.1 cpu_mkl_*_104
-  - pytorch-cpu 2.7.1
+  - pytorch-cpu 2.8.0
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   license_family: BSD
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   constrains:
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+  - libvulkan-headers 1.4.328.1.*
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   purls: []
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-  - libxml2
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   - python_abi 3.11.* *_cp311
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+  md5: a9b6ebf475194b0e5ad43168e9b936a7
   depends:
   - libprotobuf >=6.31.1,<6.31.2.0a0
   - libzlib >=1.3.1,<2.0a0
@@ -26062,8 +26634,8 @@ packages:
   license: Apache-2.0
   license_family: Apache
   purls: []
-  size: 1155619
-  timestamp: 1754216976525
+  size: 1064397
+  timestamp: 1759424869069
 - conda: https://conda.anaconda.org/conda-forge/noarch/overrides-7.7.0-pyhd8ed1ab_1.conda
   sha256: 1840bd90d25d4930d60f57b4f38d4e0ae3f5b8db2819638709c36098c6ba770c
   md5: e51f1e4089cad105b6cac64bd8166587
@@ -26088,6 +26660,354 @@ packages:
   - pkg:pypi/packaging?source=hash-mapping
   size: 62477
   timestamp: 1745345660407
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pandas/3.0.0.dev0+2573.g9234ed5733/pandas-3.0.0.dev0+2573.g9234ed5733-cp313-cp313-macosx_10_13_x86_64.whl
+  name: pandas
+  version: 3.0.0.dev0+2573.g9234ed5733
+  requires_dist:
+  - numpy>=1.26.0
+  - python-dateutil>=2.8.2
+  - tzdata>=2023.3
+  - hypothesis>=6.116.0 ; extra == 'test'
+  - pytest>=8.3.4 ; extra == 'test'
+  - pytest-xdist>=3.6.1 ; extra == 'test'
+  - pyarrow>=13.0.0 ; extra == 'pyarrow'
+  - bottleneck>=1.4.2 ; extra == 'performance'
+  - numba>=0.60.0 ; extra == 'performance'
+  - numexpr>=2.10.2 ; extra == 'performance'
+  - scipy>=1.14.1 ; extra == 'computation'
+  - xarray>=2024.10.0 ; extra == 'computation'
+  - fsspec>=2024.10.0 ; extra == 'fss'
+  - s3fs>=2024.10.0 ; extra == 'aws'
+  - gcsfs>=2024.10.0 ; extra == 'gcp'
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+  - openpyxl>=3.1.5 ; extra == 'excel'
+  - python-calamine>=0.3.0 ; extra == 'excel'
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+  - xlsxwriter>=3.2.0 ; extra == 'excel'
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+  - pyarrow>=13.0.0 ; extra == 'feather'
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+  - lxml>=5.3.0 ; extra == 'xml'
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+  - pyqt5>=5.15.9 ; extra == 'all'
+  - pyreadstat>=1.2.8 ; extra == 'all'
+  - pytest>=8.3.4 ; extra == 'all'
+  - pytest-xdist>=3.6.1 ; extra == 'all'
+  - python-calamine>=0.3.0 ; extra == 'all'
+  - pytz>=2024.2 ; extra == 'all'
+  - pyxlsb>=1.0.10 ; extra == 'all'
+  - qtpy>=2.4.2 ; extra == 'all'
+  - scipy>=1.14.1 ; extra == 'all'
+  - s3fs>=2024.10.0 ; extra == 'all'
+  - sqlalchemy>=2.0.36 ; extra == 'all'
+  - tables>=3.10.1 ; extra == 'all'
+  - tabulate>=0.9.0 ; extra == 'all'
+  - xarray>=2024.10.0 ; extra == 'all'
+  - xlrd>=2.0.1 ; extra == 'all'
+  - xlsxwriter>=3.2.0 ; extra == 'all'
+  - zstandard>=0.23.0 ; extra == 'all'
+  requires_python: '>=3.11'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pandas/3.0.0.dev0+2573.g9234ed5733/pandas-3.0.0.dev0+2573.g9234ed5733-cp313-cp313-macosx_11_0_arm64.whl
+  name: pandas
+  version: 3.0.0.dev0+2573.g9234ed5733
+  requires_dist:
+  - numpy>=1.26.0
+  - python-dateutil>=2.8.2
+  - tzdata>=2023.3
+  - hypothesis>=6.116.0 ; extra == 'test'
+  - pytest>=8.3.4 ; extra == 'test'
+  - pytest-xdist>=3.6.1 ; extra == 'test'
+  - pyarrow>=13.0.0 ; extra == 'pyarrow'
+  - bottleneck>=1.4.2 ; extra == 'performance'
+  - numba>=0.60.0 ; extra == 'performance'
+  - numexpr>=2.10.2 ; extra == 'performance'
+  - scipy>=1.14.1 ; extra == 'computation'
+  - xarray>=2024.10.0 ; extra == 'computation'
+  - fsspec>=2024.10.0 ; extra == 'fss'
+  - s3fs>=2024.10.0 ; extra == 'aws'
+  - gcsfs>=2024.10.0 ; extra == 'gcp'
+  - odfpy>=1.4.1 ; extra == 'excel'
+  - openpyxl>=3.1.5 ; extra == 'excel'
+  - python-calamine>=0.3.0 ; extra == 'excel'
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+  - xlsxwriter>=3.2.0 ; extra == 'excel'
+  - pyarrow>=13.0.0 ; extra == 'parquet'
+  - pyarrow>=13.0.0 ; extra == 'feather'
+  - pyiceberg>=0.8.1 ; extra == 'iceberg'
+  - tables>=3.10.1 ; extra == 'hdf5'
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+  - sqlalchemy>=2.0.36 ; extra == 'mysql'
+  - pymysql>=1.1.1 ; extra == 'mysql'
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+  - lxml>=5.3.0 ; extra == 'html'
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+  - adbc-driver-sqlite>=1.2.0 ; extra == 'all'
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+  - fsspec>=2024.10.0 ; extra == 'all'
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+  - hypothesis>=6.116.0 ; extra == 'all'
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+  - odfpy>=1.4.1 ; extra == 'all'
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+  - pyiceberg>=0.8.1 ; extra == 'all'
+  - pymysql>=1.1.1 ; extra == 'all'
+  - pyqt5>=5.15.9 ; extra == 'all'
+  - pyreadstat>=1.2.8 ; extra == 'all'
+  - pytest>=8.3.4 ; extra == 'all'
+  - pytest-xdist>=3.6.1 ; extra == 'all'
+  - python-calamine>=0.3.0 ; extra == 'all'
+  - pytz>=2024.2 ; extra == 'all'
+  - pyxlsb>=1.0.10 ; extra == 'all'
+  - qtpy>=2.4.2 ; extra == 'all'
+  - scipy>=1.14.1 ; extra == 'all'
+  - s3fs>=2024.10.0 ; extra == 'all'
+  - sqlalchemy>=2.0.36 ; extra == 'all'
+  - tables>=3.10.1 ; extra == 'all'
+  - tabulate>=0.9.0 ; extra == 'all'
+  - xarray>=2024.10.0 ; extra == 'all'
+  - xlrd>=2.0.1 ; extra == 'all'
+  - xlsxwriter>=3.2.0 ; extra == 'all'
+  - zstandard>=0.23.0 ; extra == 'all'
+  requires_python: '>=3.11'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pandas/3.0.0.dev0+2573.g9234ed5733/pandas-3.0.0.dev0+2573.g9234ed5733-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
+  name: pandas
+  version: 3.0.0.dev0+2573.g9234ed5733
+  requires_dist:
+  - numpy>=1.26.0
+  - python-dateutil>=2.8.2
+  - tzdata>=2023.3
+  - hypothesis>=6.116.0 ; extra == 'test'
+  - pytest>=8.3.4 ; extra == 'test'
+  - pytest-xdist>=3.6.1 ; extra == 'test'
+  - pyarrow>=13.0.0 ; extra == 'pyarrow'
+  - bottleneck>=1.4.2 ; extra == 'performance'
+  - numba>=0.60.0 ; extra == 'performance'
+  - numexpr>=2.10.2 ; extra == 'performance'
+  - scipy>=1.14.1 ; extra == 'computation'
+  - xarray>=2024.10.0 ; extra == 'computation'
+  - fsspec>=2024.10.0 ; extra == 'fss'
+  - s3fs>=2024.10.0 ; extra == 'aws'
+  - gcsfs>=2024.10.0 ; extra == 'gcp'
+  - odfpy>=1.4.1 ; extra == 'excel'
+  - openpyxl>=3.1.5 ; extra == 'excel'
+  - python-calamine>=0.3.0 ; extra == 'excel'
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+  - xlrd>=2.0.1 ; extra == 'excel'
+  - xlsxwriter>=3.2.0 ; extra == 'excel'
+  - pyarrow>=13.0.0 ; extra == 'parquet'
+  - pyarrow>=13.0.0 ; extra == 'feather'
+  - pyiceberg>=0.8.1 ; extra == 'iceberg'
+  - tables>=3.10.1 ; extra == 'hdf5'
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+  - psycopg2>=2.9.10 ; extra == 'postgresql'
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+  - fsspec>=2024.10.0 ; extra == 'all'
+  - gcsfs>=2024.10.0 ; extra == 'all'
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+  - hypothesis>=6.116.0 ; extra == 'all'
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+  - matplotlib>=3.9.3 ; extra == 'all'
+  - numba>=0.60.0 ; extra == 'all'
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+  - pyarrow>=13.0.0 ; extra == 'all'
+  - pyiceberg>=0.8.1 ; extra == 'all'
+  - pymysql>=1.1.1 ; extra == 'all'
+  - pyqt5>=5.15.9 ; extra == 'all'
+  - pyreadstat>=1.2.8 ; extra == 'all'
+  - pytest>=8.3.4 ; extra == 'all'
+  - pytest-xdist>=3.6.1 ; extra == 'all'
+  - python-calamine>=0.3.0 ; extra == 'all'
+  - pytz>=2024.2 ; extra == 'all'
+  - pyxlsb>=1.0.10 ; extra == 'all'
+  - qtpy>=2.4.2 ; extra == 'all'
+  - scipy>=1.14.1 ; extra == 'all'
+  - s3fs>=2024.10.0 ; extra == 'all'
+  - sqlalchemy>=2.0.36 ; extra == 'all'
+  - tables>=3.10.1 ; extra == 'all'
+  - tabulate>=0.9.0 ; extra == 'all'
+  - xarray>=2024.10.0 ; extra == 'all'
+  - xlrd>=2.0.1 ; extra == 'all'
+  - xlsxwriter>=3.2.0 ; extra == 'all'
+  - zstandard>=0.23.0 ; extra == 'all'
+  requires_python: '>=3.11'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pandas/3.0.0.dev0+2573.g9234ed5733/pandas-3.0.0.dev0+2573.g9234ed5733-cp313-cp313-win_amd64.whl
+  name: pandas
+  version: 3.0.0.dev0+2573.g9234ed5733
+  requires_dist:
+  - numpy>=1.26.0
+  - python-dateutil>=2.8.2
+  - tzdata>=2023.3
+  - hypothesis>=6.116.0 ; extra == 'test'
+  - pytest>=8.3.4 ; extra == 'test'
+  - pytest-xdist>=3.6.1 ; extra == 'test'
+  - pyarrow>=13.0.0 ; extra == 'pyarrow'
+  - bottleneck>=1.4.2 ; extra == 'performance'
+  - numba>=0.60.0 ; extra == 'performance'
+  - numexpr>=2.10.2 ; extra == 'performance'
+  - scipy>=1.14.1 ; extra == 'computation'
+  - xarray>=2024.10.0 ; extra == 'computation'
+  - fsspec>=2024.10.0 ; extra == 'fss'
+  - s3fs>=2024.10.0 ; extra == 'aws'
+  - gcsfs>=2024.10.0 ; extra == 'gcp'
+  - odfpy>=1.4.1 ; extra == 'excel'
+  - openpyxl>=3.1.5 ; extra == 'excel'
+  - python-calamine>=0.3.0 ; extra == 'excel'
+  - pyxlsb>=1.0.10 ; extra == 'excel'
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+  - xlsxwriter>=3.2.0 ; extra == 'excel'
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+  - pyarrow>=13.0.0 ; extra == 'feather'
+  - pyiceberg>=0.8.1 ; extra == 'iceberg'
+  - tables>=3.10.1 ; extra == 'hdf5'
+  - pyreadstat>=1.2.8 ; extra == 'spss'
+  - sqlalchemy>=2.0.36 ; extra == 'postgresql'
+  - psycopg2>=2.9.10 ; extra == 'postgresql'
+  - adbc-driver-postgresql>=1.2.0 ; extra == 'postgresql'
+  - sqlalchemy>=2.0.36 ; extra == 'mysql'
+  - pymysql>=1.1.1 ; extra == 'mysql'
+  - sqlalchemy>=2.0.36 ; extra == 'sql-other'
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+  - tabulate>=0.9.0 ; extra == 'output-formatting'
+  - pyqt5>=5.15.9 ; extra == 'clipboard'
+  - qtpy>=2.4.2 ; extra == 'clipboard'
+  - zstandard>=0.23.0 ; extra == 'compression'
+  - pytz>=2024.2 ; extra == 'timezone'
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+  - gcsfs>=2024.10.0 ; extra == 'all'
+  - html5lib>=1.1 ; extra == 'all'
+  - hypothesis>=6.116.0 ; extra == 'all'
+  - jinja2>=3.1.5 ; extra == 'all'
+  - lxml>=5.3.0 ; extra == 'all'
+  - matplotlib>=3.9.3 ; extra == 'all'
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+  - openpyxl>=3.1.5 ; extra == 'all'
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+  - pyarrow>=13.0.0 ; extra == 'all'
+  - pyiceberg>=0.8.1 ; extra == 'all'
+  - pymysql>=1.1.1 ; extra == 'all'
+  - pyqt5>=5.15.9 ; extra == 'all'
+  - pyreadstat>=1.2.8 ; extra == 'all'
+  - pytest>=8.3.4 ; extra == 'all'
+  - pytest-xdist>=3.6.1 ; extra == 'all'
+  - python-calamine>=0.3.0 ; extra == 'all'
+  - pytz>=2024.2 ; extra == 'all'
+  - pyxlsb>=1.0.10 ; extra == 'all'
+  - qtpy>=2.4.2 ; extra == 'all'
+  - scipy>=1.14.1 ; extra == 'all'
+  - s3fs>=2024.10.0 ; extra == 'all'
+  - sqlalchemy>=2.0.36 ; extra == 'all'
+  - tables>=3.10.1 ; extra == 'all'
+  - tabulate>=0.9.0 ; extra == 'all'
+  - xarray>=2024.10.0 ; extra == 'all'
+  - xlrd>=2.0.1 ; extra == 'all'
+  - xlsxwriter>=3.2.0 ; extra == 'all'
+  - zstandard>=0.23.0 ; extra == 'all'
+  requires_python: '>=3.11'
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-1.5.3-py39h2ad29b5_1.conda
   sha256: ab03a569f9910c27de04ab10b8f9e5cd3481df5920ca88617aea4a761a5cf1e9
   md5: 0d89bced73199385857310d3a648757d
@@ -26102,165 +27022,113 @@ packages:
   license: BSD-3-Clause
   license_family: BSD
   purls:
-  - pkg:pypi/pandas?source=hash-mapping
-  size: 11984938
-  timestamp: 1680108441522
-- conda: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.3.2-py311hed34c8f_0.conda
-  sha256: ac5372b55c12644ba4bab81270bb294fb70197f86c9b3ede57dfe367ecc6f198
-  md5: f98711aba4ad00ea3c286dcea5f57c1f
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-  - python >=3.11,<3.12.0a0
-  - python-dateutil >=2.8.2
-  - python-tzdata >=2022.7
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   license_family: BSD
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.3.3-py311hdb8e4fa_1.conda
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   constrains:
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-  md5: 1528d744a31b20442ca7c1f365a28cc2
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/pandas-2.3.3-py314ha3d490a_1.conda
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+  - pytz >=2020.1
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+  - python_abi 3.11.* *_cp311
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+  - python_abi 3.12.* *_cp312
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+  - html5lib >=1.1
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   license_family: BSD
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+  - pkg:pypi/pandas?source=compressed-mapping
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@@ -26984,6 +27801,58 @@ packages:
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   size: 13956530
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+- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py314hd8fd7ce_1.conda
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+  - pytz >=2020.1
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+  license: BSD-3-Clause
+  license_family: BSD
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 - conda: https://conda.anaconda.org/conda-forge/noarch/pandocfilters-1.5.0-pyhd8ed1ab_0.tar.bz2
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+  md5: 8678577a52161cc4e1c93fcc18e8a646
   depends:
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-  - python >=3.9
+  - python >=3.10
+  - python
   license: BSD-2-Clause AND PSF-2.0
-  license_family: BSD
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-  size: 186594
-  timestamp: 1733792482894
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+  timestamp: 1760998269054
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.46-h1321c63_0.conda
   sha256: 5c7380c8fd3ad5fc0f8039069a45586aa452cf165264bc5a437ad80397b32934
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   timestamp: 1733327448200
-- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py311h3df08e7_2.conda
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-  md5: 54537a0323ab81bb3ec8f7d52a956556
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pillow/12.1.0.dev0/pillow-12.1.0.dev0-cp313-cp313-macosx_10_13_x86_64.whl
+  name: pillow
+  version: 12.1.0.dev0
+  requires_dist:
+  - furo ; extra == 'docs'
+  - olefile ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
+  - sphinx-autobuild ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-inline-tabs ; extra == 'docs'
+  - sphinxext-opengraph ; extra == 'docs'
+  - olefile ; extra == 'fpx'
+  - olefile ; extra == 'mic'
+  - arro3-compute ; extra == 'test-arrow'
+  - arro3-core ; extra == 'test-arrow'
+  - nanoarrow ; extra == 'test-arrow'
+  - pyarrow ; extra == 'test-arrow'
+  - check-manifest ; extra == 'tests'
+  - coverage>=7.4.2 ; extra == 'tests'
+  - defusedxml ; extra == 'tests'
+  - markdown2 ; extra == 'tests'
+  - olefile ; extra == 'tests'
+  - packaging ; extra == 'tests'
+  - pyroma>=5 ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-timeout ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  - trove-classifiers>=2024.10.12 ; extra == 'tests'
+  - defusedxml ; extra == 'xmp'
+  requires_python: '>=3.10'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pillow/12.1.0.dev0/pillow-12.1.0.dev0-cp313-cp313-macosx_11_0_arm64.whl
+  name: pillow
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+  - olefile ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
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+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-inline-tabs ; extra == 'docs'
+  - sphinxext-opengraph ; extra == 'docs'
+  - olefile ; extra == 'fpx'
+  - olefile ; extra == 'mic'
+  - arro3-compute ; extra == 'test-arrow'
+  - arro3-core ; extra == 'test-arrow'
+  - nanoarrow ; extra == 'test-arrow'
+  - pyarrow ; extra == 'test-arrow'
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+  - markdown2 ; extra == 'tests'
+  - olefile ; extra == 'tests'
+  - packaging ; extra == 'tests'
+  - pyroma>=5 ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-timeout ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  - trove-classifiers>=2024.10.12 ; extra == 'tests'
+  - defusedxml ; extra == 'xmp'
+  requires_python: '>=3.10'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pillow/12.1.0.dev0/pillow-12.1.0.dev0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+  name: pillow
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+  requires_dist:
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+  - olefile ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
+  - sphinx-autobuild ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-inline-tabs ; extra == 'docs'
+  - sphinxext-opengraph ; extra == 'docs'
+  - olefile ; extra == 'fpx'
+  - olefile ; extra == 'mic'
+  - arro3-compute ; extra == 'test-arrow'
+  - arro3-core ; extra == 'test-arrow'
+  - nanoarrow ; extra == 'test-arrow'
+  - pyarrow ; extra == 'test-arrow'
+  - check-manifest ; extra == 'tests'
+  - coverage>=7.4.2 ; extra == 'tests'
+  - defusedxml ; extra == 'tests'
+  - markdown2 ; extra == 'tests'
+  - olefile ; extra == 'tests'
+  - packaging ; extra == 'tests'
+  - pyroma>=5 ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-timeout ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  - trove-classifiers>=2024.10.12 ; extra == 'tests'
+  - defusedxml ; extra == 'xmp'
+  requires_python: '>=3.10'
+- pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/pillow/12.1.0.dev0/pillow-12.1.0.dev0-cp313-cp313-win_amd64.whl
+  name: pillow
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+  - olefile ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
+  - sphinx-autobuild ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-inline-tabs ; extra == 'docs'
+  - sphinxext-opengraph ; extra == 'docs'
+  - olefile ; extra == 'fpx'
+  - olefile ; extra == 'mic'
+  - arro3-compute ; extra == 'test-arrow'
+  - arro3-core ; extra == 'test-arrow'
+  - nanoarrow ; extra == 'test-arrow'
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+  - markdown2 ; extra == 'tests'
+  - olefile ; extra == 'tests'
+  - packaging ; extra == 'tests'
+  - pyroma>=5 ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-timeout ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  - trove-classifiers>=2024.10.12 ; extra == 'tests'
+  - defusedxml ; extra == 'xmp'
+  requires_python: '>=3.10'
+- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py311h98278a2_3.conda
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+  - python
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   license: HPND
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   - libwebp-base >=1.6.0,<2.0a0
-  - libxcb >=1.17.0,<2.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
   - libzlib >=1.3.1,<2.0a0
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-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
   - tk >=8.6.13,<8.7.0a0
+  - libxcb >=1.17.0,<2.0a0
+  - libtiff >=4.7.0,<4.8.0a0
+  - libfreetype >=2.14.1
+  - libfreetype6 >=2.14.1
+  - python_abi 3.12.* *_cp312
+  - openjpeg >=2.5.3,<3.0a0
   license: HPND
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-  - pkg:pypi/pillow?source=compressed-mapping
-  size: 975317
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+  - pkg:pypi/pillow?source=hash-mapping
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py313ha492abd_3.conda
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+  - pkg:pypi/pillow?source=hash-mapping
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.3.0-py314hdf18abd_3.conda
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   license: HPND
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+- conda: https://conda.anaconda.org/conda-forge/osx-64/pillow-11.3.0-py311ha88f94d_3.conda
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-  - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
+  - libwebp-base >=1.6.0,<2.0a0
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-  license: HPND
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   license: HPND
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-  size: 906882
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   license: HPND
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-  - pkg:pypi/pillow?source=compressed-mapping
+  - pkg:pypi/pillow?source=hash-mapping
   size: 963129
   timestamp: 1758208741247
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+  - libjpeg-turbo >=3.1.0,<4.0a0
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+  - libtiff >=4.7.0,<4.8.0a0
+  - libxcb >=1.17.0,<2.0a0
+  - tk >=8.6.13,<8.7.0a0
+  - openjpeg >=2.5.3,<3.0a0
+  license: HPND
+  purls:
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+  size: 992074
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+  - ucrt >=10.0.20348.0
+  - python_abi 3.11.* *_cp311
+  - libxcb >=1.17.0,<2.0a0
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   - lcms2 >=2.17,<3.0a0
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   - openjpeg >=2.5.3,<3.0a0
-  - python >=3.11,<3.12.0a0
-  - python_abi 3.11.* *_cp311
+  - libjpeg-turbo >=3.1.0,<4.0a0
+  - libfreetype >=2.14.1
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-  - libtiff >=4.7.0,<4.8.0a0
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+  - python_abi 3.12.* *_cp312
   - libxcb >=1.17.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
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-  - python >=3.13,<3.14.0a0
-  - python_abi 3.13.* *_cp313
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-  - ucrt >=10.0.20348.0
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   license: HPND
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-  size: 898496
-  timestamp: 1758013111396
+  size: 931680
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   size: 944083
   timestamp: 1758208682964
+- conda: https://conda.anaconda.org/conda-forge/win-64/pillow-11.3.0-py314hb9a687b_3.conda
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+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libtiff >=4.7.0,<4.8.0a0
+  - openjpeg >=2.5.3,<3.0a0
+  - libfreetype >=2.14.1
+  - libfreetype6 >=2.14.1
+  - libjpeg-turbo >=3.1.0,<4.0a0
+  - libwebp-base >=1.6.0,<2.0a0
+  - libxcb >=1.17.0,<2.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - tk >=8.6.13,<8.7.0a0
+  - lcms2 >=2.17,<3.0a0
+  - python_abi 3.14.* *_cp314
+  license: HPND
+  purls:
+  - pkg:pypi/pillow?source=hash-mapping
+  size: 972043
+  timestamp: 1758208665046
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pixman-0.46.4-h54a6638_1.conda
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-  md5: cc9d9a3929503785403dbfad9f707145
+- conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.0-pyhcf101f3_0.conda
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+  md5: 5c7a868f8241e64e1cf5fdf4962f23e2
   depends:
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   - python
   license: MIT
   license_family: MIT
   purls:
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-  size: 23653
-  timestamp: 1756227402815
+  - pkg:pypi/platformdirs?source=hash-mapping
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+  version: 6.3.1
+  sha256: 8b4420d1dcf2b040f5983eed433f95732ed24930e496d36eb70d211923532e64
+  requires_dist:
+  - narwhals>=1.15.1
+  - packaging
+  - numpy ; extra == 'express'
+  - kaleido>=1.0.0 ; extra == 'kaleido'
+  - pytest ; extra == 'dev-core'
+  - requests ; extra == 'dev-core'
+  - ruff==0.11.12 ; extra == 'dev-core'
+  - plotly[dev-core] ; extra == 'dev-build'
+  - build ; extra == 'dev-build'
+  - jupyter ; extra == 'dev-build'
+  - plotly[dev-build] ; extra == 'dev-optional'
+  - plotly[kaleido] ; extra == 'dev-optional'
+  - anywidget ; extra == 'dev-optional'
+  - colorcet ; extra == 'dev-optional'
+  - fiona<=1.9.6 ; python_full_version < '3.9' and extra == 'dev-optional'
+  - geopandas ; extra == 'dev-optional'
+  - inflect ; extra == 'dev-optional'
+  - numpy ; extra == 'dev-optional'
+  - orjson ; extra == 'dev-optional'
+  - pandas ; extra == 'dev-optional'
+  - pdfrw ; extra == 'dev-optional'
+  - pillow ; extra == 'dev-optional'
+  - plotly-geo ; extra == 'dev-optional'
+  - polars[timezone] ; extra == 'dev-optional'
+  - pyarrow ; extra == 'dev-optional'
+  - pyshp ; extra == 'dev-optional'
+  - pytz ; extra == 'dev-optional'
+  - scikit-image ; extra == 'dev-optional'
+  - scipy ; extra == 'dev-optional'
+  - shapely ; extra == 'dev-optional'
+  - statsmodels ; extra == 'dev-optional'
+  - vaex ; python_full_version < '3.10' and extra == 'dev-optional'
+  - xarray ; extra == 'dev-optional'
+  - plotly[dev-optional] ; extra == 'dev'
+  requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/plotly-6.3.0-pyhd8ed1ab_0.conda
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@@ -27748,6 +28756,21 @@ packages:
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   size: 4921172
   timestamp: 1755067356284
+- conda: https://conda.anaconda.org/conda-forge/noarch/plotly-6.3.1-pyhd8ed1ab_0.conda
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+  depends:
+  - narwhals >=1.15.1
+  - packaging
+  - python >=3.10
+  constrains:
+  - ipywidgets >=7.6
+  license: MIT
+  license_family: MIT
+  purls:
+  - pkg:pypi/plotly?source=hash-mapping
+  size: 5227137
+  timestamp: 1759457672006
 - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
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@@ -27770,6 +28793,43 @@ packages:
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   size: 49052
   timestamp: 1733239818090
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+  name: polars
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+  sha256: 40d2f357b4d9e447ad28bd2c9923e4318791a7c18eb68f31f1fbf11180f41391
+  requires_dist:
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+  - polars-runtime-64==1.34.0 ; extra == 'rt64'
+  - polars-runtime-compat==1.34.0 ; extra == 'rtcompat'
+  - polars-cloud>=0.0.1a1 ; extra == 'polars-cloud'
+  - numpy>=1.16.0 ; extra == 'numpy'
+  - pandas ; extra == 'pandas'
+  - polars[pyarrow] ; extra == 'pandas'
+  - pyarrow>=7.0.0 ; extra == 'pyarrow'
+  - pydantic ; extra == 'pydantic'
+  - fastexcel>=0.9 ; extra == 'calamine'
+  - openpyxl>=3.0.0 ; extra == 'openpyxl'
+  - xlsx2csv>=0.8.0 ; extra == 'xlsx2csv'
+  - xlsxwriter ; extra == 'xlsxwriter'
+  - polars[calamine,openpyxl,xlsx2csv,xlsxwriter] ; extra == 'excel'
+  - adbc-driver-manager[dbapi] ; extra == 'adbc'
+  - adbc-driver-sqlite[dbapi] ; extra == 'adbc'
+  - connectorx>=0.3.2 ; extra == 'connectorx'
+  - sqlalchemy ; extra == 'sqlalchemy'
+  - polars[pandas] ; extra == 'sqlalchemy'
+  - polars[adbc,connectorx,sqlalchemy] ; extra == 'database'
+  - fsspec ; extra == 'fsspec'
+  - deltalake>=1.0.0 ; extra == 'deltalake'
+  - pyiceberg>=0.7.1 ; extra == 'iceberg'
+  - gevent ; extra == 'async'
+  - cloudpickle ; extra == 'cloudpickle'
+  - matplotlib ; extra == 'graph'
+  - altair>=5.4.0 ; extra == 'plot'
+  - great-tables>=0.8.0 ; extra == 'style'
+  - tzdata ; sys_platform == 'win32' and extra == 'timezone'
+  - cudf-polars-cu12 ; extra == 'gpu'
+  - polars[async,cloudpickle,database,deltalake,excel,fsspec,graph,iceberg,numpy,pandas,plot,pyarrow,pydantic,style,timezone] ; extra == 'all'
+  requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-0.20.15-py39h87fa3cb_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/polars-1.33.1-default_h11bb3fb_1.conda
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-  md5: 48ab135e789bb03f2a98968fa4cec410
+- conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.34.0-pyh6a1acc5_0.conda
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+  md5: d398dbcb3312bbebc2b2f3dbb98b4262
   depends:
-  - polars-default ==1.33.1 py310h8278905_1
+  - polars-runtime-32 ==1.34.0
+  - python >=3.10
+  - python
+  constrains:
+  - numpy >=1.16.0
+  - pyarrow >=7.0.0
+  - fastexcel >=0.9
+  - openpyxl >=3.0.0
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+  - connectorx >=0.3.2
+  - deltalake >=1.0.0
+  - pyiceberg >=0.7.1
+  - altair >=5.4.0
+  - great_tables >=0.8.0
+  - polars-runtime-32 ==1.34.0
+  - polars-runtime-64 ==1.34.0
+  - polars-runtime-compat ==1.34.0
   license: MIT
   license_family: MIT
-  purls: []
-  size: 5954
-  timestamp: 1758063432580
+  purls:
+  - pkg:pypi/polars?source=hash-mapping
+  size: 506495
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 - conda: https://conda.anaconda.org/conda-forge/osx-64/polars-0.20.15-py39hcc1e243_0.conda
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-  license: MIT
-  license_family: MIT
-  purls: []
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-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
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+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
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   license: MIT
   license_family: MIT
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-  size: 382439
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@@ -29229,6 +30455,14 @@ packages:
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   size: 123256
   timestamp: 1747560884456
+- pypi: https://files.pythonhosted.org/packages/10/5e/1aa9a93198c6b64513c9d7752de7422c06402de6600a8767da1524f9570b/pyparsing-3.2.5-py3-none-any.whl
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+  sha256: e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e
+  requires_dist:
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+  - jinja2 ; extra == 'diagrams'
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 - conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.3-pyhe01879c_2.conda
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   license_family: MIT
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+  - pkg:pypi/pyparsing?source=hash-mapping
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   timestamp: 1753873557076
-- conda: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.4-pyhcf101f3_0.conda
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+  - pkg:pypi/pyparsing?source=hash-mapping
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-  - qt6-main 6.9.2.*
-  - qt6-main >=6.9.2,<6.10.0a0
+  - qt6-main 6.9.3.*
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+  - qt6-main 6.9.3.*
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   - libopengl >=1.7.0,<2.0a0
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-  - qt6-main >=6.9.2,<6.10.0a0
+  - qt6-main 6.9.3.*
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+  - python >=3.14.0rc3,<3.15.0a0
+  - python_abi 3.14.* *_cp314
   - qt6-main 6.9.3.*
   - qt6-main >=6.9.3,<6.10.0a0
   license: LGPL-3.0-only
+  license_family: LGPL
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+  - libxml2
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   - python_abi 3.12.* *_cp312
-  - qt6-main 6.9.2.*
-  - qt6-main >=6.9.2,<6.10.0a0
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   - python_abi 3.13.* *_cp313
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   - ucrt >=10.0.20348.0
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   license: LGPL-3.0-only
+  license_family: LGPL
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   license_family: MIT
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 - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-cov-6.2.1-pyhd8ed1ab_0.conda
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 - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.12-hd63d673_1_cpython.conda
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   - zstd >=1.5.7,<1.6.0a0
   constrains:
-  - qt 6.9.2
+  - qt 5.15.15
+  license: LGPL-3.0-only
+  license_family: LGPL
+  purls: []
+  size: 52624585
+  timestamp: 1760351016368
+- conda: https://conda.anaconda.org/conda-forge/win-64/qt-main-5.15.15-hb098fff_6.conda
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+  - gstreamer >=1.24.11,<1.25.0a0
+  - icu >=75.1,<76.0a0
+  - krb5 >=1.21.3,<1.22.0a0
+  - libclang13 >=21.1.3
+  - libglib >=2.86.0,<3.0a0
+  - libjpeg-turbo >=3.1.0,<4.0a0
+  - libpng >=1.6.50,<1.7.0a0
+  - libsqlite >=3.50.4,<4.0a0
+  - libzlib >=1.3.1,<2.0a0
+  - openssl >=3.5.4,<4.0a0
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - zstd >=1.5.7,<1.6.0a0
+  constrains:
+  - qt 5.15.15
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 52405921
-  timestamp: 1758011263853
-- conda: https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.3-h5c1c036_0.conda
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-  md5: cc0bffcaf6410aba9c6581dfdc18012d
+  size: 59506014
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/qt6-main-6.9.3-h5c1c036_1.conda
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+  md5: 762af6d08fdfa7a45346b1466740bacd
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   - alsa-lib >=1.2.14,<1.3.0a0
@@ -30963,11 +32317,11 @@ packages:
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   - fontconfig >=2.15.0,<3.0a0
   - fonts-conda-ecosystem
-  - harfbuzz >=12.0.0
+  - harfbuzz >=12.1.0
   - icu >=75.1,<76.0a0
   - krb5 >=1.21.3,<1.22.0a0
-  - libclang-cpp21.1 >=21.1.2,<21.2.0a0
-  - libclang13 >=21.1.2
+  - libclang-cpp21.1 >=21.1.4,<21.2.0a0
+  - libclang13 >=21.1.4
   - libcups >=2.3.3,<2.4.0a0
   - libdrm >=2.4.125,<2.5.0a0
   - libegl >=1.7.0,<2.0a0
@@ -30977,20 +32331,20 @@ packages:
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   - libglib >=2.86.0,<3.0a0
   - libjpeg-turbo >=3.1.0,<4.0a0
-  - libllvm21 >=21.1.2,<21.2.0a0
+  - libllvm21 >=21.1.4,<21.2.0a0
   - libpng >=1.6.50,<1.7.0a0
   - libpq >=18.0,<19.0a0
   - libsqlite >=3.50.4,<4.0a0
   - libstdcxx >=14
   - libtiff >=4.7.1,<4.8.0a0
-  - libvulkan-loader >=1.4.313.0,<2.0a0
+  - libvulkan-loader >=1.4.328.1,<2.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libxcb >=1.17.0,<2.0a0
-  - libxkbcommon >=1.11.0,<2.0a0
+  - libxkbcommon >=1.12.2,<2.0a0
   - libxml2
   - libxml2-16 >=2.14.6
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.3,<4.0a0
+  - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.46,<10.47.0a0
   - wayland >=1.24.0,<2.0a0
   - xcb-util >=0.4.1,<0.5.0a0
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   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 54537863
-  timestamp: 1759251856141
-- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.2-ha0de62e_2.conda
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-  - krb5 >=1.21.3,<1.22.0a0
-  - libclang13 >=21.1.1
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-  - libjpeg-turbo >=3.1.0,<4.0a0
-  - libpng >=1.6.50,<1.7.0a0
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-  - libtiff >=4.7.0,<4.8.0a0
-  - libwebp-base >=1.6.0,<2.0a0
-  - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.2,<4.0a0
-  - pcre2 >=10.46,<10.47.0a0
-  - ucrt >=10.0.20348.0
-  - vc >=14.3,<15
-  - vc14_runtime >=14.44.35208
-  - zstd >=1.5.7,<1.6.0a0
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-  - qt 6.9.2
-  license: LGPL-3.0-only
-  license_family: LGPL
-  purls: []
-  size: 82001367
-  timestamp: 1758025652766
-- conda: https://conda.anaconda.org/conda-forge/win-64/qt6-main-6.9.3-ha0de62e_0.conda
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-  md5: ba19851ccdcee8d0a074cb3738b08f60
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+  md5: ca2bfad3a24794a0f7cf413b03906ade
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   - double-conversion >=3.3.1,<3.4.0a0
-  - harfbuzz >=12.0.0
+  - harfbuzz >=12.1.0
   - icu >=75.1,<76.0a0
   - krb5 >=1.21.3,<1.22.0a0
-  - libclang13 >=21.1.2
+  - libclang13 >=21.1.4
   - libglib >=2.86.0,<3.0a0
   - libjpeg-turbo >=3.1.0,<4.0a0
   - libpng >=1.6.50,<1.7.0a0
   - libsqlite >=3.50.4,<4.0a0
   - libtiff >=4.7.1,<4.8.0a0
-  - libvulkan-loader >=1.4.313.0,<2.0a0
+  - libvulkan-loader >=1.4.328.1,<2.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libzlib >=1.3.1,<2.0a0
-  - openssl >=3.5.3,<4.0a0
+  - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.46,<10.47.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
@@ -31074,8 +32399,8 @@ packages:
   license: LGPL-3.0-only
   license_family: LGPL
   purls: []
-  size: 96316397
-  timestamp: 1759254007679
+  size: 95659243
+  timestamp: 1761312853504
 - conda: https://conda.anaconda.org/conda-forge/linux-64/rdma-core-59.0-hecca717_0.conda
   sha256: ce0e671ce8b6151b135eb6b5aea9caed4d4032a416f73a04a3fb29048fd772c3
   md5: d95e4c5679876a9d3f2211263f75dc9c
@@ -31202,12 +32527,12 @@ packages:
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   size: 252359
   timestamp: 1740379663071
-- conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.36.2-pyh29332c3_0.conda
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-  md5: 9140f1c09dd5489549c6a33931b943c7
+- conda: https://conda.anaconda.org/conda-forge/noarch/referencing-0.37.0-pyhcf101f3_0.conda
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+  md5: 870293df500ca7e18bedefa5838a22ab
   depends:
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-  - python >=3.9
+  - python >=3.10
   - rpds-py >=0.7.0
   - typing_extensions >=4.4.0
   - python
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   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/regex-2025.9.1-py311h49ec1c0_0.conda
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+  size: 51788
+  timestamp: 1760379115194
+- conda: https://conda.anaconda.org/conda-forge/linux-64/regex-2025.10.23-py311h49ec1c0_0.conda
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+  md5: c3089a374203059e7795b074afadbb76
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   - __glibc >=2.17,<3.0.a0
   - libgcc >=14
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  license: Python-2.0
+  license: Apache-2.0 AND CNRI-Python
   license_family: PSF
   purls:
   - pkg:pypi/regex?source=hash-mapping
-  size: 414767
-  timestamp: 1757802920782
-- conda: https://conda.anaconda.org/conda-forge/linux-64/regex-2025.9.1-py313h07c4f96_0.conda
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-  md5: 4eddb33945cddd823829257965f5ad51
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+  md5: c2ef385c6d137ad66e616039f3712120
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   - libgcc >=14
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  license: Python-2.0
+  license: Apache-2.0 AND CNRI-Python
   license_family: PSF
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+  - pkg:pypi/regex?source=compressed-mapping
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/regex-2025.10.23-py311h9408147_0.conda
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   - python >=3.11,<3.12.0a0 *_cpython
   - python_abi 3.11.* *_cp311
-  license: Python-2.0
+  license: Apache-2.0 AND CNRI-Python
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-  - python >=3.12,<3.13.0a0 *_cpython
-  - python_abi 3.12.* *_cp312
-  license: Python-2.0
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: Apache-2.0 AND CNRI-Python
   license_family: PSF
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   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
-  license: Python-2.0
+  license: Apache-2.0 AND CNRI-Python
   license_family: PSF
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+- conda: https://conda.anaconda.org/conda-forge/win-64/regex-2025.10.23-py313h5ea7bf4_0.conda
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   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
-  license: Python-2.0
+  license: Apache-2.0 AND CNRI-Python
   license_family: PSF
   purls:
   - pkg:pypi/regex?source=hash-mapping
-  size: 368215
-  timestamp: 1757803317440
+  size: 369681
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 - conda: https://conda.anaconda.org/conda-forge/noarch/requests-2.25.0-pyhd3deb0d_0.tar.bz2
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   timestamp: 1740240332327
-- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.27.1-py311h902ca64_1.conda
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-  md5: 622c389c080689ba1575a0750eb0209d
+- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.28.0-py311h902ca64_1.conda
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+  md5: 6f0b18ac51ff0b43ea247431d1e23c87
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@@ -31395,15 +32720,15 @@ packages:
   license_family: MIT
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-  timestamp: 1756737832651
-- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.27.1-py313h843e2db_1.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/rpds-py-0.28.0-py313h843e2db_1.conda
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+  md5: b4e5c52948b191bfb04da5788217afdb
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-  - __glibc >=2.17,<3.0.a0
   - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
   - python_abi 3.13.* *_cp313
   constrains:
   - __glibc >=2.17
@@ -31411,15 +32736,15 @@ packages:
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.27.1-py311h1c3fc1a_1.conda
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+  size: 380486
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.28.0-py311h71babbd_1.conda
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-  - python 3.11.* *_cpython
   - __osx >=11.0
+  - python 3.11.* *_cpython
   - python_abi 3.11.* *_cp311
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/rpds-py-0.27.1-py312h6f58b40_1.conda
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+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 127864
+  timestamp: 1757930108791
 - conda: https://conda.anaconda.org/conda-forge/osx-64/zstandard-0.25.0-py311h62e9434_0.conda
   sha256: be241ea3ca603d68654beeab4c991c225c9361378a107f72c2433ddfdff88132
   md5: 5425495af6b0b010230320d618022f20
@@ -35050,6 +37021,23 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 396477
   timestamp: 1757930170468
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstandard-0.25.0-py314h163e31d_0.conda
+  sha256: 5b707d7b80d9b410fce776a439273213745ffc3fa4553ec31f264bbaf63a6ec6
+  md5: c824d8cd887ce1d7af8963ca4087a764
+  depends:
+  - python
+  - cffi >=1.11
+  - zstd >=1.5.7,<1.5.8.0a0
+  - __osx >=11.0
+  - python 3.14.* *_cp314
+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 125883
+  timestamp: 1757930173407
 - conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py311hf893f09_0.conda
   sha256: 3b66d3cb738a9993e8432d1a03402d207128166c4ef0c928e712958e51aff325
   md5: d26077d20b4bba54f4c718ed1313805f
@@ -35113,6 +37101,27 @@ packages:
   - pkg:pypi/zstandard?source=hash-mapping
   size: 380849
   timestamp: 1757930139118
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstandard-0.25.0-py314h4667ab5_0.conda
+  sha256: 285890e987cb14b2ac27f74a4ae844eb80e73654ab3321f085e13538c6ac7d23
+  md5: 765406da1e9f31231a56ea4d2e191d7c
+  depends:
+  - python
+  - cffi >=1.11
+  - zstd >=1.5.7,<1.5.8.0a0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - vc >=14.3,<15
+  - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - zstd >=1.5.7,<1.6.0a0
+  - python_abi 3.14.* *_cp314
+  license: BSD-3-Clause
+  license_family: BSD
+  purls:
+  - pkg:pypi/zstandard?source=hash-mapping
+  size: 285971
+  timestamp: 1757930137171
 - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
   sha256: a4166e3d8ff4e35932510aaff7aa90772f84b4d07e9f6f83c614cba7ceefe0eb
   md5: 6432cb5d4ac0046c3ac0a8a0f95842f9
diff --git a/pyproject.toml b/pyproject.toml
index b8b6b07fc..0d90692c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,11 +8,11 @@ dynamic = ["version"]
 description = "Machine learning with dataframes"
 readme = "README.rst"
 authors = [
-    {name = "Riccardo Cappuzzo"},
-    {name = "Jerome Dockes"},
-    {name = "Guillaume Lemaitre"},
-    {name = "Vincent Maladiere"},
-    {name = "Gael Varoquaux"},
+  { name = "Riccardo Cappuzzo" },
+  { name = "Jerome Dockes" },
+  { name = "Guillaume Lemaitre" },
+  { name = "Vincent Maladiere" },
+  { name = "Gael Varoquaux" },
 ]
 license = "BSD-3-Clause"
 license-files = ["LICENSE.txt"]
@@ -31,14 +31,14 @@ classifiers = [
 ]
 requires-python = ">=3.9"
 dependencies = [
-    "numpy>=1.23.5",
-    "pandas>=1.5.3",
-    "scikit-learn>=1.2.1",
-    "scipy>=1.9.3",
-    "jinja2>=3.1.2",
-    "matplotlib>=3.4.3",
-    "requests>=2.25.0",
-    "pydot",
+  "numpy>=1.23.5",
+  "pandas>=1.5.3",
+  "scikit-learn>=1.2.1",
+  "scipy>=1.9.3",
+  "jinja2>=3.1.2",
+  "matplotlib>=3.4.3",
+  "requests>=2.25.0",
+  "pydot",
 ]
 
 [tool.setuptools.dynamic]
@@ -46,41 +46,39 @@ version = { file = "skrub/VERSION.txt" }
 
 [project.optional-dependencies]
 dev = [
-    "ipykernel",
-    "ipython",
-    "jupyterlab",
-
-    # doc
-    "jupyterlite-sphinx",
-    "jupyterlite-pyodide-kernel",
-    "numpydoc",
-    "pydata-sphinx-theme",
-    "seaborn",
-    "sphinx",
-    "sphinx-copybutton",
-    "sphinx-gallery",
-    "sphinxext-opengraph",
-    "sphinx-autosummary-accessors",
-    "statsmodels",
-
-    # lint
-    "black==23.3.0",
-    "ruff==0.4.8",
-    "pre-commit",
-
-    # test
-    "pytest",
-    "pytest-cov",
-    "pytest-xdist",
-
-    # optional
-    "pyarrow",
-    "polars",
-    "plotly",
-]
-transformers = [
-    "sentence-transformers",
+  "ipykernel",
+  "ipython",
+  "jupyterlab",
+
+  # doc
+  "jupyterlite-sphinx",
+  "jupyterlite-pyodide-kernel",
+  "numpydoc",
+  "pydata-sphinx-theme",
+  "seaborn",
+  "sphinx",
+  "sphinx-copybutton",
+  "sphinx-gallery",
+  "sphinxext-opengraph",
+  "sphinx-autosummary-accessors",
+  "statsmodels",
+
+  # lint
+  "black==23.3.0",
+  "ruff==0.4.8",
+  "pre-commit",
+
+  # test
+  "pytest",
+  "pytest-cov",
+  "pytest-xdist",
+
+  # optional
+  "pyarrow",
+  "polars",
+  "plotly",
 ]
+transformers = ["sentence-transformers"]
 
 [project.urls]
 Homepage = "https://skrub-data.org/"
@@ -90,7 +88,7 @@ Issues = "https://github.com/skrub-data/skrub/issues"
 [tool.setuptools]
 packages = ["skrub"]
 
-[tool.pixi.project]
+[tool.pixi.workspace]
 channels = ["conda-forge", "pytorch"]
 platforms = ["linux-64", "osx-arm64", "osx-64", "win-64"]
 
@@ -169,6 +167,31 @@ pyarrow = "==15.0.2"
 polars = "==0.20.15"
 plotly = "*"
 
+[tool.pixi.feature.nightly-dependencies.pypi-options]
+# Get the nightly of as many dependencies as possible
+extra-index-urls = [
+  # scikit-learn, numpy, pandas...
+  "https://pypi.anaconda.org/scientific-python-nightly-wheels/simple",
+  # pyarrow
+  "https://pypi.fury.io/arrow-nightlies",
+]
+
+[tool.pixi.feature.nightly-dependencies.pypi-dependencies]
+# Have to repeat dependencies here because otherwise pixi pulls from conda channels
+# rather than PyPI indexes
+skrub = { path = ".", editable = true }
+numpy = "*"
+pandas = "*"
+scikit-learn = "*"
+scipy = "*"
+jinja2 = "*"
+matplotlib = "*"
+pydot = "*"
+requests = "*"
+pyarrow = "*"
+polars = "*"
+plotly = "*"
+
 [tool.pixi.feature.polars-without-pyarrow.dependencies]
 polars = "*"
 plotly = "*"
@@ -211,7 +234,12 @@ doc = ["optional", "doc", "transformers"]
 test = ["optional", "test"]
 dev = ["dev", "optional", "doc", "lint", "test", "transformers"]
 ci-py309-min-deps = ["py309", "min-dependencies", "test"]
-ci-py309-min-optional-deps = ["py309", "min-dependencies", "min-optional-dependencies", "test"]
+ci-py309-min-optional-deps = [
+  "py309",
+  "min-dependencies",
+  "min-optional-dependencies",
+  "test",
+]
 ci-py311-transformers = ["py311", "transformers", "test"]
 ci-py312-latest-deps = ["py312", "test"]
 ci-py312-latest-optional-deps = ["py312", "optional", "test"]
@@ -219,6 +247,9 @@ ci-py313-latest-deps = ["py313", "test"]
 ci-py313-polars-without-pyarrow = ["py313", "test", "polars-without-pyarrow"]
 ci-py313-latest-optional-deps = ["py313", "optional", "test"]
 
+# no-default-feature is necessary for pixi to download from nightly PyPI indexes rather than conda channels
+ci-nightly-deps = { no-default-feature = true, features = [ "py313", "nightly-dependencies", "test" ] }
+
 [tool.black]
 line-length = 88
 target_version = ['py310', 'py311']
@@ -241,60 +272,60 @@ exclude = '''
 # max line length for black
 line-length = 88
 target-version = "py38"
-exclude=[
-    ".git",
-    "__pycache__",
-    "dist",
-    "doc/_build",
-    "doc/auto_examples",
-    "build",
+exclude = [
+  ".git",
+  "__pycache__",
+  "dist",
+  "doc/_build",
+  "doc/auto_examples",
+  "build",
 ]
 
 [tool.ruff.lint]
 # all rules can be found here: https://beta.ruff.rs/docs/rules/
 select = ["E", "F", "W", "I"]
-ignore=[
-    # space before : (needed for how black formats slicing)
-    "E203",
-    # do not assign a lambda expression, use a def
-    "E731",
-    # do not use variables named 'l', 'O', or 'I'
-    "E741",
+ignore = [
+  # space before : (needed for how black formats slicing)
+  "E203",
+  # do not assign a lambda expression, use a def
+  "E731",
+  # do not use variables named 'l', 'O', or 'I'
+  "E741",
 ]
 
 [tool.ruff.lint.per-file-ignores]
 # It's fine not to put the import at the top of the file in the examples
 # folder.
-"examples/*"=["E402"]
-"doc/conf.py"=["E402"]
+"examples/*" = ["E402"]
+"doc/conf.py" = ["E402"]
 
 [tool.pytest.ini_options]
 filterwarnings = [
-    # Turn deprecation warnings into errors
-    "error::FutureWarning",
-    "error::DeprecationWarning",
-
-    # Ignore warning from np.in1d since the future behavior is already the desired
-    # behavior. TODO remove when numpy min version >= 1.25.
-    'ignore:elementwise\ comparison\ failed:FutureWarning',
-    # TODO remove when pandas min version >= 2.2.0
-    'ignore:.*utcfromtimestamp\(\) is deprecated.*:DeprecationWarning',
-    # TODO remove when joblib min version >= 1.4.0
-    'ignore:.*ast\.Num is deprecated.*:DeprecationWarning',
-    # TODO remove when joblib min version >= 1.4.0
-    'ignore:.*ast\.NameConstant is deprecated.*:DeprecationWarning',
-    # TODO remove when joblib min version >= 1.4.0
-    'ignore:.*Attribute n is deprecated.*:DeprecationWarning',
-    # FIXME we need to decided what to do with pyarrow that is required by pandas >= 3.0
-    'ignore:(?s).*Pyarrow will become a required dependency of pandas.*:DeprecationWarning',
-    # accessing .values on a pandas dataframe raises this warning after numpy 1.25;
-    # should be addressed in pandas
-    'ignore:np.find_common_type is deprecated.*:DeprecationWarning',
-    'ignore:.*distutils Version classes are deprecated.*',
-    'ignore:.*`resume_download` is deprecated.*',
-    'ignore:.*`clean_up_tokenization_spaces` was not set.*',
-    'ignore:The current process just got forked.*',
-    'ignore:.*`clean_up_tokenization_spaces` was not set.*',
+  # Turn deprecation warnings into errors
+  "error::FutureWarning",
+  "error::DeprecationWarning",
+
+  # Ignore warning from np.in1d since the future behavior is already the desired
+  # behavior. TODO remove when numpy min version >= 1.25.
+  'ignore:elementwise\ comparison\ failed:FutureWarning',
+  # TODO remove when pandas min version >= 2.2.0
+  'ignore:.*utcfromtimestamp\(\) is deprecated.*:DeprecationWarning',
+  # TODO remove when joblib min version >= 1.4.0
+  'ignore:.*ast\.Num is deprecated.*:DeprecationWarning',
+  # TODO remove when joblib min version >= 1.4.0
+  'ignore:.*ast\.NameConstant is deprecated.*:DeprecationWarning',
+  # TODO remove when joblib min version >= 1.4.0
+  'ignore:.*Attribute n is deprecated.*:DeprecationWarning',
+  # FIXME we need to decided what to do with pyarrow that is required by pandas >= 3.0
+  'ignore:(?s).*Pyarrow will become a required dependency of pandas.*:DeprecationWarning',
+  # accessing .values on a pandas dataframe raises this warning after numpy 1.25;
+  # should be addressed in pandas
+  'ignore:np.find_common_type is deprecated.*:DeprecationWarning',
+  'ignore:.*distutils Version classes are deprecated.*',
+  'ignore:.*`resume_download` is deprecated.*',
+  'ignore:.*`clean_up_tokenization_spaces` was not set.*',
+  'ignore:The current process just got forked.*',
+  'ignore:.*`clean_up_tokenization_spaces` was not set.*',
 ]
 addopts = "--doctest-modules"
 doctest_optionflags = "NORMALIZE_WHITESPACE ELLIPSIS"
diff --git a/skrub/_apply_to_cols.py b/skrub/_apply_to_cols.py
index 3c5cb9ed4..d65c64595 100644
--- a/skrub/_apply_to_cols.py
+++ b/skrub/_apply_to_cols.py
@@ -343,8 +343,8 @@ class ApplyToCols(TransformerMixin, BaseEstimator):
          birthday    city
     0  29/01/2024  London
     >>> df.dtypes
-    birthday    object
-    city        object
+    birthday    ...
+    city        ...
     dtype: object
     >>> ToDatetime().fit_transform(df["birthday"])
     0   2024-01-29
@@ -382,7 +382,7 @@ class ApplyToCols(TransformerMixin, BaseEstimator):
 
     >>> transformed.dtypes
     birthday    datetime64[...]
-    city                object
+    city                ...
     dtype: object
     >>> to_datetime.transformers_
     {'birthday': ToDatetime()}
diff --git a/skrub/_clean_categories.py b/skrub/_clean_categories.py
index 108a2688f..c5c58efea 100644
--- a/skrub/_clean_categories.py
+++ b/skrub/_clean_categories.py
@@ -77,14 +77,14 @@ class CleanCategories(SingleColumnTransformer):
     1      two
     2    three
     Name: c, dtype: category
-    Categories (3, object): ['one', 'three', 'two']
+    Categories (3, ...): ['one', 'three', 'two']
     >>> cleaner = CleanCategories()
     >>> cleaner.fit_transform(s)
     0      one
     1      two
     2    three
     Name: c, dtype: category
-    Categories (3, object): ['one', 'three', 'two']
+    Categories (3, ...): ['one', 'three', 'two']
     >>> cleaner.fit_transform(s) is s
     True
 
@@ -121,7 +121,7 @@ class CleanCategories(SingleColumnTransformer):
     0    1
     1    2
     Name: c, dtype: category
-    Categories (2, object): ['1', '2']
+    Categories (2, ...): ['1', '2']
 
     We can see above that the output categories are strings.
 
@@ -167,7 +167,7 @@ class CleanCategories(SingleColumnTransformer):
     1    b
     2    c
     Name: c, dtype: category
-    Categories (3, object): ['a', 'b', 'c']
+    Categories (3, ...): ['a', 'b', 'c']
 
     The categories themselves are not checked and may vary depending on the
     input; handling unseen categories is the responsibility of encoders such as
@@ -178,7 +178,7 @@ class CleanCategories(SingleColumnTransformer):
     1      y
     2    NaN
     Name: c, dtype: category
-    Categories (2, object): ['x', 'y']
+    Categories (2, ...): ['x', 'y']
 
     The category dtype transformations do not apply to polars; in polars all
     Categorical columns have Strings as the type of their categories.
diff --git a/skrub/_data_ops/_parallel_coord.py b/skrub/_data_ops/_parallel_coord.py
index 2c562df2a..2d66b01dd 100644
--- a/skrub/_data_ops/_parallel_coord.py
+++ b/skrub/_data_ops/_parallel_coord.py
@@ -91,7 +91,7 @@ def _prepare_obj_column(col):
     is_null = col.isna().values
     encoder = OrdinalEncoder()
     encoded_not_null = encoder.fit_transform(
-        col.dropna().astype(str).values[:, None]
+        col.dropna().astype(str).to_frame()
     ).ravel()
     encoded = np.full(col.shape, -1.0)
     encoded[~is_null] = encoded_not_null
diff --git a/skrub/_dataframe/_common.py b/skrub/_dataframe/_common.py
index 479ac738c..3d3a347f9 100644
--- a/skrub/_dataframe/_common.py
+++ b/skrub/_dataframe/_common.py
@@ -803,8 +803,19 @@ def is_string(col):
 
 @is_string.specialize("pandas", argument_type="Column")
 def _is_string_pandas(col):
+    # Pandas 3.0 introduces str dtypes, which are not parsed as "object"
+    if (
+        parse_version(pd.__version__).base_version
+        >= parse_version("3.0.0").base_version
+    ):
+        # In pandas, a categorical column *is* a string dtype, but *is not* an
+        # object dtype
+        if isinstance(col.dtype, pd.CategoricalDtype):  # pragma: no cover
+            return False  # pragma: no cover
+        return pandas.api.types.is_string_dtype(col[~col.isna()])  # pragma: no cover
     if col.dtype == pd.StringDtype():
         return True
+    # In pandas, a categorical column *is* a string dtype, but *is not* an object dtype
     if not pd.api.types.is_object_dtype(col):
         return False
     if parse_version(pd.__version__) < parse_version("2.0.0"):
diff --git a/skrub/_dataframe/tests/test_common.py b/skrub/_dataframe/tests/test_common.py
index 12e589c56..b7e107380 100644
--- a/skrub/_dataframe/tests/test_common.py
+++ b/skrub/_dataframe/tests/test_common.py
@@ -13,6 +13,7 @@
 import pandas as pd
 import pytest
 from numpy.testing import assert_array_equal
+from packaging.version import parse
 from pandas.testing import assert_frame_equal as pd_assert_frame_equal
 
 import skrub
@@ -92,7 +93,17 @@ def test_is_column(df_module):
 
 def test_to_list(df_module):
     col = ns.col(df_module.example_dataframe, "str-col")
-    assert ns.to_list(col) == ["one", None, "three", "four"]
+    if ns.is_pandas(col) and parse(pd.__version__).major >= parse("3.0.0").major:
+        # In pandas 3.0, nulls in string dtypes have type np.nan, but nullable dtypes
+        # become None
+        # To avoid adding even more conditions I'm checking all elements one by one
+        #
+        to_list = ns.to_list(col)
+        assert to_list[0] == "one"
+        assert pd.isna(to_list[1])
+        assert to_list[2:] == ["three", "four"]
+    else:
+        assert ns.to_list(col) == ["one", None, "three", "four"]
 
 
 def test_to_numpy(df_module, example_data_dict):
@@ -537,6 +548,16 @@ def test_is_string(df_module):
         assert not ns.is_string(ns.col(df, col))
 
 
+def test_sentinel_is_string_pandas_3(df_module):
+    if df_module.name != "pandas":
+        return
+    pd_version = parse(pd.__version__)
+    if pd_version.major < parse("3.0.0").major:
+        return
+    if not pd_version.is_prerelease:
+        pytest.fail("This test should fail when pandas 3.x is released.")
+
+
 def test_to_string(df_module):
     s = ns.to_string(df_module.make_column("_", list(range(5))))
     assert ns.is_string(s)
@@ -759,7 +780,11 @@ def test_value_counts(df_module):
     counts = ns.sort(counts, by="value")
     expected = df_module.make_dataframe({"value": ["a", "b"], "count": [3, 1]})
     expected = ns.sort(expected, by="value")
-    df_module.assert_frame_equal(counts, expected)
+    if ns.is_pandas(col) and parse(pd.__version__).major >= parse("3.0.0").major:
+        # Added to avoid a failing type check since we don't care about dtype here
+        assert (ns.to_numpy(expected) == ns.to_numpy(counts)).all()
+    else:
+        df_module.assert_frame_equal(counts, expected)
 
 
 @pytest.mark.parametrize("q", [0.0, 0.3, 1.0])
diff --git a/skrub/_datetime_encoder.py b/skrub/_datetime_encoder.py
index 829c42704..65c860d12 100644
--- a/skrub/_datetime_encoder.py
+++ b/skrub/_datetime_encoder.py
@@ -216,7 +216,7 @@ class DatetimeEncoder(SingleColumnTransformer):
     0       Monday = 1
     1              NaN
     2    Wednesday = 3
-    Name: login, dtype: object
+    Name: login, dtype: ...
     >>> login.dt.day_of_week
     0    0.0
     1    NaN
@@ -253,7 +253,7 @@ class DatetimeEncoder(SingleColumnTransformer):
     >>> s
     0    2024-04-14
     1    2024-05-15
-    Name: birthday, dtype: object
+    Name: birthday, dtype: ...
     >>> DatetimeEncoder().fit_transform(s)
     Traceback (most recent call last):
         ...
diff --git a/skrub/_deduplicate.py b/skrub/_deduplicate.py
index 17ee6685f..208dc1e42 100644
--- a/skrub/_deduplicate.py
+++ b/skrub/_deduplicate.py
@@ -218,7 +218,7 @@ def deduplicate(
     white    white
     white    white
     white    white
-    dtype: object
+    dtype: ...
 
     The translation table above is actually a series, giving the deduplicated values,
     and indexed by the original values.
diff --git a/skrub/_table_vectorizer.py b/skrub/_table_vectorizer.py
index 5dc8ef33e..3f48d5b38 100644
--- a/skrub/_table_vectorizer.py
+++ b/skrub/_table_vectorizer.py
@@ -256,10 +256,10 @@ class Cleaner(TransformerMixin, BaseEstimator):
     1    two  23/02/2024   N/A  2.0
     2    two  12/03/2024  12.2  2.5
     3  three  13/03/2024   N/A  3.0
-    >>> df.dtypes
-    A    object
-    B    object
-    C    object
+    >>> df.dtypes # doctest: +SKIP
+    A       ...
+    B       ...
+    C       ...
     D   float64
     dtype: object
 
@@ -274,10 +274,10 @@ class Cleaner(TransformerMixin, BaseEstimator):
     2    two 2024-03-12  12.2  2.5
     3  three 2024-03-13   NaN  3.0
 
-    >>> cleaner.fit_transform(df).dtypes
-    A            object
+    >>> cleaner.fit_transform(df).dtypes  # doctest: +SKIP
+    A               ...
     B    datetime64[ns]
-    C            object
+    C               ...
     D           float64
     dtype: object
 
@@ -577,9 +577,9 @@ class TableVectorizer(TransformerMixin, BaseEstimator):
     2    two  12/03/2024  12.2
     3  three  13/03/2024   N/A
     >>> df.dtypes
-    A    object
-    B    object
-    C    object
+    A         ...
+    B         ...
+    C         ...
     dtype: object
 
     >>> vectorizer = TableVectorizer()
@@ -609,7 +609,7 @@ class TableVectorizer(TransformerMixin, BaseEstimator):
     OneHotEncoder(drop='if_binary', dtype='float32', handle_unknown='ignore',
                   sparse_output=False)
     >>> vectorizer.transformers_['A'].categories_
-    [array(['one', 'three', 'two'], dtype=object)]
+    [array(['one', 'three', 'two'], dtype=...)]
 
     We can see the columns grouped by the kind of encoder that was applied
     to them:
@@ -658,9 +658,9 @@ class TableVectorizer(TransformerMixin, BaseEstimator):
     >>> vectorizer.transformers_['A'] is not vectorizer.transformers_['B']
     True
     >>> vectorizer.transformers_['A'].categories_
-    [array(['one', 'two'], dtype=object)]
+    [array(['one', 'two'], dtype=...)]
     >>> vectorizer.transformers_['B'].categories_
-    [array(['four', 'three'], dtype=object)]
+    [array(['four', 'three'], dtype=...)]
 
     **Overriding the transformer for specific columns**
 
diff --git a/skrub/_tabular_pipeline.py b/skrub/_tabular_pipeline.py
index 2c50518ac..49ec7e9cc 100644
--- a/skrub/_tabular_pipeline.py
+++ b/skrub/_tabular_pipeline.py
@@ -203,7 +203,7 @@ def tabular_pipeline(estimator, *, n_jobs=None):
     >>> y = [0, 1, 0, 1]
     >>> X
        last_visit   medication insulin_prescriptions  fasting_glucose
-    0  2020-01-02         None                   N/A               35
+    0  2020-01-02          ...                   N/A               35
     1  2021-04-01    metformin                    13              140
     2  2024-12-05  paracetamol                     0               44
     3  2023-08-10   gliclazide                    17              137
diff --git a/skrub/_to_categorical.py b/skrub/_to_categorical.py
index 99fe262a7..da5de7a83 100644
--- a/skrub/_to_categorical.py
+++ b/skrub/_to_categorical.py
@@ -50,18 +50,18 @@ class ToCategorical(SingleColumnTransformer):
     A string column is converted to a categorical column.
 
     >>> s = pd.Series(['one', 'two', None], name='c')
-    >>> s
+    >>> s # doctest: +SKIP
     0     one
     1     two
-    2    None
-    Name: c, dtype: object
+    2     ...
+    Name: c, dtype: ...
     >>> to_cat = ToCategorical()
-    >>> to_cat.fit_transform(s)
+    >>> to_cat.fit_transform(s) # doctest: +SKIP
     0    one
     1    two
-    2    NaN
-    Name: c, dtype: category
-    Categories (2, object): ['one', 'two']
+    2    ...
+    Name: c, dtype: ...
+    Categories (2, ...): ['one', 'two']
 
     The dtypes (the list of categories) of the outputs of ``transform`` may
     vary. This transformer only ensures the dtype is Categorical to mark the
@@ -73,7 +73,7 @@ class ToCategorical(SingleColumnTransformer):
     0    four
     1    five
     Name: c, dtype: category
-    Categories (2, object): ['five', 'four']
+    Categories (2, ...): ['five', 'four']
 
     Columns that already have a Categorical dtype are passed through:
 
@@ -112,8 +112,7 @@ class ToCategorical(SingleColumnTransformer):
     2     <NA>
     Name: c, dtype: category
     Categories (2, string): [cat A, cat B]
-    >>> _.cat.categories.dtype
-    string[python]
+    >>> _.cat.categories.dtype # doctest: +SKIP
 
     Polars string columns are converted to the ``Categorical`` dtype (not ``Enum``). As
     for pandas, categories may vary across calls to ``transform``.
diff --git a/skrub/datasets/tests/test_utils.py b/skrub/datasets/tests/test_utils.py
index 6e5d929b9..7573e5a09 100644
--- a/skrub/datasets/tests/test_utils.py
+++ b/skrub/datasets/tests/test_utils.py
@@ -2,10 +2,16 @@
 import sys
 import tempfile
 from pathlib import Path
+from unittest.mock import MagicMock, patch
 
 import pytest
 
-from skrub.datasets._utils import DATA_HOME_ENVAR_NAME, get_data_dir, get_data_home
+from skrub.datasets._utils import (
+    DATA_HOME_ENVAR_NAME,
+    _extract_archive,
+    get_data_dir,
+    get_data_home,
+)
 
 
 @pytest.mark.parametrize("data_home_type", ["string", "path"])
@@ -58,3 +64,46 @@ def test_get_data_home_with_envar(monkeypatch, tmp_path):
 
         assert get_data_home() == dirpath
         assert dirpath.exists()
+
+
+def test_extract_archive_exception_unlink_called():
+    dataset_dir = MagicMock()
+    dataset_dir.name = "test_dataset"
+    archive_path = MagicMock(spec=Path)
+    archive_path.exists.return_value = True
+
+    mock_temp_dir = str(Path("mock_temp_dir"))
+    # Patch both tempfile.mkdtemp and shutil.unpack_archive
+    # tempfile.mkdtemp and shutil.unpack_archive are used in _extract_archive
+    # We simulate unpack_archive raising an exception to test that unlink is called
+    with (
+        patch("tempfile.mkdtemp", return_value=mock_temp_dir),
+        patch("shutil.unpack_archive") as mock_unpack,
+    ):
+        # Simulate unpack failing
+        mock_unpack.side_effect = ValueError("Test error")
+        with pytest.raises(ValueError):
+            _extract_archive(dataset_dir, archive_path)
+        archive_path.unlink.assert_called_once()
+
+
+def test_extract_archive_unlink_raises():
+    dataset_dir = MagicMock()
+    dataset_dir.name = "test_dataset"
+    archive_path = MagicMock(spec=Path)
+    archive_path.exists.return_value = True
+    # Setup unlink to fail
+    archive_path.unlink.side_effect = OSError("unlink failed")
+
+    mock_temp_dir = str(Path("mock_temp_dir"))
+    # Patch both tempfile.mkdtemp and shutil.unpack_archive
+    # tempfile.mkdtemp and shutil.unpack_archive are used in _extract_archive
+    with (
+        patch("tempfile.mkdtemp", return_value=mock_temp_dir),
+        patch("shutil.unpack_archive") as mock_unpack,
+    ):
+        mock_unpack.side_effect = ValueError("Test error")
+        # The original error should still be raised
+        with pytest.raises(ValueError):
+            _extract_archive(dataset_dir, archive_path)
+        archive_path.unlink.assert_called_once()
diff --git a/skrub/selectors/_selectors.py b/skrub/selectors/_selectors.py
index b87b68e35..6fabd02b3 100644
--- a/skrub/selectors/_selectors.py
+++ b/skrub/selectors/_selectors.py
@@ -175,7 +175,7 @@ def numeric():
     i8          int8
     bool_       bool
     Bool_    boolean
-    str_      object
+    str_     ...
     dtype: object
 
     >>> s.select(df, s.numeric())
@@ -226,7 +226,7 @@ def integer():
     i8          int8
     bool_       bool
     Bool_    boolean
-    str_      object
+    str_      ...
     dtype: object
 
     >>> s.select(df, s.integer())
@@ -277,7 +277,7 @@ def float():
     i8          int8
     bool_       bool
     Bool_    boolean
-    str_      object
+    str_      ...
     dtype: object
 
     >>> s.select(df, s.float())
@@ -314,7 +314,7 @@ def any_date():
     >>> df.dtypes
     dt           datetime64[...]
     tzdt    datetime64[..., UTC]
-    str_                 object
+    str_                 ...
     dtype: object
 
     >>> s.select(df, s.any_date())
@@ -346,7 +346,7 @@ def categorical():
     1      B        B
 
     >>> df.dtypes
-    string        object
+    string        ...
     category    category
     dtype: object
 
@@ -384,9 +384,9 @@ def string():
     1  B  10  B  B
 
     >>> df.dtypes
-    os            object
+    os            ...
     o             object
-    s     string...
+    s     ...
     c           category
     dtype: object
 
@@ -509,6 +509,6 @@ def has_nulls():
           b     c
     0   0.0     a
     1   NaN     b
-    2  20.0  None
+    2  20.0  ...
     """
     return Filter(sbd.has_nulls, name="has_nulls")
diff --git a/skrub/selectors/tests/test_selectors.py b/skrub/selectors/tests/test_selectors.py
index d89b0ea76..07cf0e3a5 100644
--- a/skrub/selectors/tests/test_selectors.py
+++ b/skrub/selectors/tests/test_selectors.py
@@ -3,6 +3,7 @@
 import types
 
 import numpy as np
+import pandas as pd
 import pytest
 
 from skrub import _dataframe as sbd
@@ -78,6 +79,13 @@ def test_dtype_selectors(df_module):
         assert s.any_date().expand(df) == ["datetime-col"]
 
 
+def test_dtype_pandas_object():
+    # Testing for behavior with object and string columns
+    df = pd.DataFrame({"string-object": ["foo", "bar"], "object-object": ["baz", 42]})
+
+    assert s.string().expand(df) == ["string-object"]
+
+
 def test_cardinality_below(df_module, monkeypatch):
     df = df_module.example_dataframe
     assert s.cardinality_below(3).expand(df) == ["bool-col", "bool-not-null-col"]
diff --git a/skrub/tests/test_clean_categories.py b/skrub/tests/test_clean_categories.py
index 666ec44fe..325a11f3b 100644
--- a/skrub/tests/test_clean_categories.py
+++ b/skrub/tests/test_clean_categories.py
@@ -49,6 +49,14 @@ def test_clean_categories(df_module):
     for vals in ["x", "y", None], [1.1, 2.2, None]:
         assert sbd.is_categorical(cleaner.transform(df_module.make_column("c", vals)))
 
+    if sbd.is_pandas(s):
+        # Added to avoid a failing type check since we don't care about dtype here
+        assert (
+            sbd.to_numpy(s) == sbd.to_numpy(CleanCategories().fit_transform(s))
+        ).all()
+    else:
+        df_module.assert_column_equal(s, CleanCategories().fit_transform(s))
+
 
 def test_error_with_string_categories():
     # Make codecov happy
diff --git a/skrub/tests/test_on_subframe.py b/skrub/tests/test_on_subframe.py
index 04b1eaff6..3977b6337 100644
--- a/skrub/tests/test_on_subframe.py
+++ b/skrub/tests/test_on_subframe.py
@@ -3,6 +3,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from packaging.version import parse
 from pandas.testing import assert_index_equal
 from sklearn.base import BaseEstimator
 from sklearn.preprocessing import FunctionTransformer
@@ -70,7 +71,20 @@ def test_empty_output(df_module, use_fit_transform):
         out = transformer.fit_transform(df)
     else:
         out = transformer.fit(df).transform(df)
-    df_module.assert_frame_equal(out, s.select(df, ()))
+    # Selecting no columns to have an empty dataframe
+    selected = s.select(df, ())
+
+    # I need to add a special case for pandas 3.0 here because the type of the
+    # empty dataframe with pandas 3.0 is different from that of out, but here
+    # we don't care about that dtype.
+    if sbd.is_pandas(df) and parse(pd.__version__).major >= parse("3.0.0").major:
+        out = sbd.to_numpy(out)
+        selected = sbd.to_numpy(selected)
+        # With pandas 3.0, selected has type "string" rather than "empty"
+        assert out.shape == selected.shape
+        assert (out == selected).all()
+    else:
+        df_module.assert_frame_equal(out, selected)
 
 
 def _to_XXX(names):
diff --git a/skrub/tests/test_table_vectorizer.py b/skrub/tests/test_table_vectorizer.py
index d4daed451..f802a1b8e 100644
--- a/skrub/tests/test_table_vectorizer.py
+++ b/skrub/tests/test_table_vectorizer.py
@@ -1016,4 +1016,9 @@ def test_date_format(df_module):
     )
     cleaner = Cleaner(datetime_format="%d %B %Y")
     transformed = cleaner.fit_transform(X)
-    df_module.assert_column_equal(transformed["date"], expected["date"])
+
+    # This is needed because the transformed version of the date column is formatted
+    # by pandas using the format, and has a different resolution than the expected one.
+    transformed_to_list = sbd.to_list(transformed["date"])
+    expected_to_list = sbd.to_list(expected["date"])
+    assert transformed_to_list == expected_to_list
diff --git a/skrub/tests/test_to_str.py b/skrub/tests/test_to_str.py
index 1f20d3531..a6684c98c 100644
--- a/skrub/tests/test_to_str.py
+++ b/skrub/tests/test_to_str.py
@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from packaging.version import parse
 
 from skrub import _dataframe as sbd
 from skrub._apply_to_cols import RejectColumn
@@ -14,7 +15,10 @@ def test_to_str(df_module):
     out = ToStr().fit_transform(df_module.make_column("", values))
     assert sbd.is_string(out)
     if df_module.name == "pandas":
-        assert out.dtype == "O"
+        if sbd.is_pandas(out) and parse(pd.__version__).major >= parse("3.0.0").major:
+            assert out.dtype == pd.StringDtype(na_value=np.nan)
+        else:
+            assert out.dtype == "O"
         assert pd.NA not in out
     expected = df_module.make_column(
         "", [None if v is None else str(v) for v in values]
@@ -42,13 +46,21 @@ def test_rejected_columns(df_module):
 
 def test_pandas_string():
     s = pd.Series(["a", "b"], dtype="string")
-    assert ToStr().fit_transform(s).dtype == "O"
+
+    if sbd.is_pandas(s) and parse(pd.__version__).major >= parse("3.0.0").major:
+        assert ToStr().fit_transform(s).dtype == pd.StringDtype(na_value=np.nan)
+    else:
+        assert ToStr().fit_transform(s).dtype == "O"
 
 
 def test_pandas_na():
     s = pd.Series(["a", pd.NA], dtype="str")
-    assert s[1] is pd.NA
+    if sbd.is_pandas(s) and parse(pd.__version__).major >= parse("3.0.0").major:
+        assert s[1] is np.nan
+    else:
+        assert s[1] is pd.NA
     out = ToStr().fit_transform(s)
+    # This check is not needed with pandas 3.0
     assert out[1] is not pd.NA
     assert np.isnan(out[1])