diff --git a/conda.recipe/recipe.yaml b/conda.recipe/recipe.yaml
index 21e4a041a..9a03a70fc 100644
--- a/conda.recipe/recipe.yaml
+++ b/conda.recipe/recipe.yaml
@@ -36,21 +36,25 @@ requirements:
     - if: osx
       then:
         - llvm-openmp
-    - numpy
+    - numpy >=1.24
     - pip
-    - scikit-learn >=0.23
+    - scikit-learn >=1.1.0
     - setuptools
     - setuptools-scm
   run:
     - python
     - formulaic >=0.6
     - joblib
+    - narwhals >=2.0.0
     - numexpr
+    - numpy >=1.24
     - packaging
-    - pandas
-    - scikit-learn >=0.23
-    - scipy
-    - tabmat >=4.0.0
+    - pandas >=1.4
+    - pyarrow
+    - scikit-learn >=1.1.0
+    - scipy >=1.8.0
+    - tabmat >=4.1.5
+    - tqdm
 
 tests:
   - python:
diff --git a/pixi.lock b/pixi.lock
index 1dc16e2b3..6ed00ea29 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -7,44 +7,44 @@ environments:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-7_kmp_llvm.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/noarch/attrs-25.4.0-pyh71513ae_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.1-h7ca4310_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.10-h346e085_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.5-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-h7e655bb_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.6-h3cb25bf_6.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.10.7-hc5c8343_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.23.3-ha76f1cc_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.13.3-h3a25ec9_10.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.10.1-hcb69869_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-h7e655bb_3.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.7-h7e655bb_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.35.2-h2ceb62e_4.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.606-hd6e39bc_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyhcf101f3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.9.3-hef928c7_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.13-h2c9d079_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.12.6-hb03c661_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.1-h8b1a151_9.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.7-h28f887f_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.10.7-ha8fc4e3_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.23.3-hdaf4b65_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.13.3-hc63082f_11.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.11.3-h06ab39a_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.4-h8b1a151_4.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.7-h8b1a151_5.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.35.4-h8824e59_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.606-h20b40b1_10.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.16.1-h3a458e0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.13.2-h3a5f585_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.15.0-h2a74896_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.11.0-h3d7a050_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.13.0-hf38f1be_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/backports.zstd-1.2.0-py314h680f03e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.45-default_h4852527_104.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.45-default_hfdba357_104.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.45-default_h4852527_104.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-2.302-mkl.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.11.0-2_hcf00494_mkl.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py310hba01987_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-2.305-mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/blas-devel-3.11.0-5_hcf00494_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py314h3de4e8d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.11.0-h4d9bdce_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-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-2025.11.12-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.0.0-py310he7384ee_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/noarch/click-8.3.1-pyh8f84b5b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_14.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/conda-gcc-specs-14.3.0-he8ccf15_16.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.2-py310ha58568a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.2.3-py314h1807b08_0.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
@@ -55,17 +55,17 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/formulaic-1.2.1-pyhd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.1-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/future-1.0.0-pyhd8ed1ab_2.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-h442bea5_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_14.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc-14.3.0-h0dff253_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-14.3.0-he8b2097_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-14.3.0-h298d278_16.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.2-hd590300_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/git_root-0.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.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/gxx-14.3.0-he448592_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-hc876b51_14.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx-14.3.0-h76987e4_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-14.3.0-h2185e75_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-14.3.0-hee9a62b_16.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2o-py-3.46.0.6-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-12.2.0-h15599e2_0.conda
@@ -75,27 +75,27 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.11-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.7.0-pyhe01879c_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/interface_meta-1.3.0-pyhd8ed1ab_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.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/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-64-2.45-default_hbd61a6d_104.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/libabseil-20250512.1-cxx17_hba17884_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-22.0.0-h773bc41_4_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-22.0.0-h635bf11_4_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-22.0.0-h8c2c5c3_4_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-22.0.0-h635bf11_4_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-22.0.0-h3f74fd7_4_cpu.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-2_h5875eb1_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-22.0.0-hb6ed5f4_6_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-22.0.0-h635bf11_6_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-compute-22.0.0-h8c2c5c3_6_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-22.0.0-h635bf11_6_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-22.0.0-h3f74fd7_6_cpu.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-5_h5875eb1_mkl.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.2.0-hb03c661_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.2.0-hb03c661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-2_hfef963f_mkl.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_hfef963f_mkl.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
       - 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/libcurl-8.17.0-h4e3cde8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.17.0-h4e3cde8_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.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/libev-4.33-hd590300_2.conda
@@ -104,89 +104,87 @@ environments:
       - 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-15.2.0-he0feb66_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_114.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_14.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.2-h6548e54_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_14.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-14.3.0-hf649bbc_116.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.3-h6548e54_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.39.0-hdbdcf42_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.73.1-h3288cfb_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.12.1-default_h7f8ec31_1002.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.12.1-default_hafda6a7_1003.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-2_h5e43f62_mkl.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.11.0-2_hdba1596_mkl.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.1-hb9d3cd8_2.conda
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+  - python_abi 3.14.* *_cp314
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+  - ld64_osx-arm64 956.6 llvm19_1_h6922315_2
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-  - libcblas   3.11.0   2*_mkl
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-  - blas 2.302   openblas
-  - libcblas   3.11.0   2*_openblas
+  - libcblas   3.11.0   5*_openblas
+  - liblapack  3.11.0   5*_openblas
+  - liblapacke 3.11.0   5*_openblas
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   license: BSD-3-Clause
   license_family: BSD
-  size: 18675
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+  build_number: 5
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+  md5: f9decf88743af85c9c9e05556a4c47c0
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-  - liblapack  3.11.0   2*_mkl
-  - libcblas   3.11.0   2*_mkl
+  - liblapack  3.11.0   5*_mkl
+  - libcblas   3.11.0   5*_mkl
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   license: BSD-3-Clause
   license_family: BSD
-  size: 67907
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+  - libblas 3.11.0 5_h4a7cf45_openblas
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-  - liblapacke 3.11.0   2*_openblas
-  - liblapack  3.11.0   2*_openblas
+  - liblapacke 3.11.0   5*_openblas
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-5_hfef963f_mkl.conda
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+  md5: 9b6cb3aa4b7912121c64b97a76ca43d5
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+  - libblas 3.11.0 5_h5875eb1_mkl
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-  - liblapacke 3.11.0   2*_mkl
-  - blas 2.302   mkl
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
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   track_features:
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   license: BSD-3-Clause
   license_family: BSD
-  size: 18557
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-20_linux64_openblas.conda
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@@ -10795,20 +9865,20 @@ packages:
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+  - libblas 3.11.0 5_h51639a9_openblas
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-  - liblapack  3.11.0   2*_openblas
-  - blas 2.302   openblas
-  - liblapacke 3.11.0   2*_openblas
+  - liblapacke 3.11.0   5*_openblas
+  - liblapack  3.11.0   5*_openblas
+  - blas 2.305   openblas
   license: BSD-3-Clause
   license_family: BSD
-  size: 18638
-  timestamp: 1763829127867
+  size: 18548
+  timestamp: 1765819108956
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-20_osxarm64_openblas.conda
   build_number: 20
   sha256: d3a74638f60e034202e373cf2950c69a8d831190d497881d13cbf789434d2489
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   timestamp: 1700568912840
-- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-2_h2a3cdd5_mkl.conda
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-  md5: 5b04388898707d7b8d25c6353818f8f3
+- conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-5_h2a3cdd5_mkl.conda
+  build_number: 5
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+  md5: b3fa8e8b55310ba8ef0060103afb02b5
   depends:
-  - libblas 3.11.0 2_hf2e6a31_mkl
+  - libblas 3.11.0 5_hf2e6a31_mkl
   constrains:
-  - blas 2.302   mkl
-  - liblapacke 3.11.0   2*_mkl
-  - liblapack  3.11.0   2*_mkl
+  - liblapack  3.11.0   5*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - blas 2.305   mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 68271
-  timestamp: 1763829148523
+  size: 68079
+  timestamp: 1765819124349
 - conda: https://conda.anaconda.org/conda-forge/win-64/libcblas-3.11.0-7_hc41557d_netlib.conda
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@@ -10906,9 +9976,9 @@ packages:
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   timestamp: 1749905341688
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.17.0-h4e3cde8_0.conda
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-  md5: 01e149d4a53185622dc2e788281961f2
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.17.0-h4e3cde8_1.conda
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+  md5: 117499f93e892ea1e57fdca16c2e8351
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   - krb5 >=1.21.3,<1.22.0a0
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   - zstd >=1.5.7,<1.6.0a0
   license: curl
   license_family: MIT
-  size: 460366
-  timestamp: 1762333743748
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.17.0-hdece5d2_0.conda
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-  md5: 791003efe92c17ed5949b309c61a5ab1
+  size: 459417
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcurl-8.17.0-hdece5d2_1.conda
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+  md5: 1d6e791c6e264ae139d469ce011aab51
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   license: curl
   license_family: MIT
-  size: 394183
-  timestamp: 1762334288445
-- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.17.0-h43ecb02_0.conda
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-  md5: cfade9be135edb796837e7d4c288c0fb
+  size: 394471
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libcurl-8.17.0-h43ecb02_1.conda
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+  md5: c02248f96a0073904bb085a437143895
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   - libssh2 >=1.11.1,<2.0a0
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   license: curl
   license_family: MIT
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-  timestamp: 1762333969177
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.6-hf598326_0.conda
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-  md5: 3ea79e55a64bff6c3cbd4588c89a527a
+  size: 379189
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-21.1.8-hf598326_0.conda
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+  md5: 780f0251b757564e062187044232c2b7
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   license_family: Apache
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_1.conda
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-  md5: 1399af81db60d441e7c6577307d5cf82
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+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-19.1.7-h6dc3340_2.conda
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+  md5: 9f7810b7c0a731dbc84d46d6005890ef
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+  - libcxx-headers >=19.1.7,<19.1.8.0a0
   license: Apache-2.0 WITH LLVM-exception
   license_family: Apache
-  size: 825628
-  timestamp: 1742451285589
+  size: 23000
+  timestamp: 1764648270121
+- conda: https://conda.anaconda.org/conda-forge/noarch/libcxx-headers-19.1.7-h707e725_2.conda
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+  md5: de91b5ce46dc7968b6e311f9add055a2
+  depends:
+  - __unix
+  constrains:
+  - libcxx-devel 19.1.7
+  license: Apache-2.0 WITH LLVM-exception
+  license_family: Apache
+  size: 830747
+  timestamp: 1764647922410
 - conda: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.25-h17f619e_0.conda
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   timestamp: 1757946133889
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_14.conda
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-  md5: 550dceb769d23bcf0e2f97fd4062d720
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_16.conda
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+  md5: 6d0363467e6ed84f11435eb309f2ff06
   depends:
   - __glibc >=2.17,<3.0.a0
   - _openmp_mutex >=4.5
   constrains:
-  - libgomp 15.2.0 he0feb66_14
-  - libgcc-ng ==15.2.0=*_14
+  - libgcc-ng ==15.2.0=*_16
+  - libgomp 15.2.0 he0feb66_16
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   license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgcc-15.2.0-hcbb3090_14.conda
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+  md5: 8b216bac0de7a9d60f3ddeba2515545c
   depends:
   - _openmp_mutex
   constrains:
-  - libgcc-ng ==15.2.0=*_14
-  - libgomp 15.2.0 14
+  - libgcc-ng ==15.2.0=*_16
+  - libgomp 15.2.0 16
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   license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libgcc-15.2.0-h8ee18e1_14.conda
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   depends:
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   constrains:
-  - libgomp 15.2.0 h8ee18e1_14
+  - libgomp 15.2.0 h8ee18e1_16
+  - libgcc-ng ==15.2.0=*_16
   - msys2-conda-epoch <0.0a0
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+  - libgcc 15.2.0 he0feb66_16
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   license_family: GPL
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.12.0-h6049c3a_1.conda
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-  md5: 791c1603173b9d650ef5efc9e1c27624
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+  timestamp: 1765256804124
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgdal-core-3.12.0-hf05ffb4_4.conda
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   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
-  - libpng >=1.6.50,<1.7.0a0
+  - libpng >=1.6.53,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
-  - libsqlite >=3.51.0,<4.0a0
+  - libsqlite >=3.51.1,<4.0a0
   - libstdcxx >=14
   - libwebp-base >=1.6.0,<2.0a0
   - libxml2
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   - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.47,<10.48.0a0
-  - proj >=9.7.0,<9.8.0a0
+  - proj >=9.7.1,<9.8.0a0
   - xerces-c >=3.3.0,<3.4.0a0
   - zstd >=1.5.7,<1.6.0a0
   constrains:
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   license: MIT
   license_family: MIT
-  size: 12907462
-  timestamp: 1763757161364
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.12.0-hd6dfbec_1.conda
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-  md5: 515721fdddda411e528e9e747e7a351c
+  size: 12888996
+  timestamp: 1765913875126
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgdal-core-3.12.0-h0f2ca60_4.conda
+  sha256: 3abae4119f1d07cc2e73dcd0342465cc7352ec5e84c075c852fcfeedbe51e5cf
+  md5: ad8ac9e787c878b82b9fbf0f14bb232d
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   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
-  - libpng >=1.6.50,<1.7.0a0
+  - libpng >=1.6.53,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
-  - libsqlite >=3.51.0,<4.0a0
+  - libsqlite >=3.51.1,<4.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libxml2
   - libxml2-16 >=2.14.6
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   - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.47,<10.48.0a0
-  - proj >=9.7.0,<9.8.0a0
+  - proj >=9.7.1,<9.8.0a0
   - xerces-c >=3.3.0,<3.4.0a0
   - zstd >=1.5.7,<1.6.0a0
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   license: MIT
   license_family: MIT
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-  timestamp: 1763759561526
-- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.12.0-hd4e8292_1.conda
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-  md5: e80e15cd7c28164c76117825b8f9317b
+  size: 9829726
+  timestamp: 1765913804627
+- conda: https://conda.anaconda.org/conda-forge/win-64/libgdal-core-3.12.0-h4c6072a_4.conda
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+  md5: 99c08cae3b1bacbb7527547186ec081b
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   - libkml >=1.3.0,<1.4.0a0
   - liblzma >=5.8.1,<6.0a0
-  - libpng >=1.6.50,<1.7.0a0
+  - libpng >=1.6.53,<1.7.0a0
   - libspatialite >=5.1.0,<5.2.0a0
-  - libsqlite >=3.51.0,<4.0a0
+  - libsqlite >=3.51.1,<4.0a0
   - libwebp-base >=1.6.0,<2.0a0
   - libxml2
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   - openssl >=3.5.4,<4.0a0
   - pcre2 >=10.47,<10.48.0a0
-  - proj >=9.7.0,<9.8.0a0
+  - proj >=9.7.1,<9.8.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
@@ -11373,42 +10455,42 @@ packages:
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   license: MIT
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-  timestamp: 1763759138538
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_14.conda
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-  md5: fa9d91abc5a9db36fa8dcd1b9a602e61
+  size: 9753087
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_16.conda
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+  md5: 40d9b534410403c821ff64f00d0adc22
   depends:
-  - libgfortran5 15.2.0 h68bc16d_14
+  - libgfortran5 15.2.0 h68bc16d_16
   constrains:
-  - libgfortran-ng ==15.2.0=*_14
+  - libgfortran-ng ==15.2.0=*_16
   license: GPL-3.0-only WITH GCC-exception-3.1
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libgfortran-15.2.0-h07b0088_14.conda
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   depends:
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+  - libgfortran5 15.2.0 hdae7583_16
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+  - libgfortran-ng ==15.2.0=*_16
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+  md5: e5eb2ddedabd0063e442f230755d2062
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+  - libgfortran 15.2.0 h69a702a_16
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-  md5: 3078a2a9a58566a54e579b41b9e88c84
+  size: 27300
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_16.conda
+  sha256: d0e974ebc937c67ae37f07a28edace978e01dc0f44ee02f29ab8a16004b8148b
+  md5: 39183d4e0c05609fd65f130633194e37
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   - libgcc >=15.2.0
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   license_family: GPL
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+  size: 2480559
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+  sha256: 9fb7f4ff219e3fb5decbd0ee90a950f4078c90a86f5d8d61ca608c913062f9b0
+  md5: 265a9d03461da24884ecc8eb58396d57
   depends:
   - libgcc >=15.2.0
   constrains:
   - libgfortran 15.2.0
   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
-  size: 598642
-  timestamp: 1764279461200
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.2-h6548e54_1.conda
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-  md5: f01292fb36b6d00d5c51e5d46b513bcf
+  size: 598291
+  timestamp: 1765258993165
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.86.3-h6548e54_0.conda
+  sha256: 82d6c2ee9f548c84220fb30fb1b231c64a53561d6e485447394f0a0eeeffe0e6
+  md5: 034bea55a4feef51c98e8449938e9cee
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   - libffi >=3.5.2,<3.6.0a0
@@ -11440,30 +10522,30 @@ packages:
   - libzlib >=1.3.1,<2.0a0
   - pcre2 >=10.47,<10.48.0a0
   constrains:
-  - glib 2.86.2 *_1
+  - glib 2.86.3 *_0
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-  timestamp: 1763735724068
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_14.conda
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-  md5: 91349c276f84f590487e4c7f6e90e077
+  size: 3946542
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+  md5: 26c46f90d0e727e95c6c9498a33a09f3
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+  sha256: 9c86aadc1bd9740f2aca291da8052152c32dd1c617d5d4fd0f334214960649bb
+  md5: ab8189163748f95d4cb18ea1952943c3
   depends:
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   constrains:
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   license: GPL-3.0-only WITH GCC-exception-3.1
   license_family: GPL
-  size: 663690
-  timestamp: 1764280815241
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.39.0-hdb79228_0.conda
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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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-  md5: c01021ae525a76fe62720c7346212d74
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libhwloc-2.12.1-default_hafda6a7_1003.conda
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+  md5: 4fe840c6d6b3719b4231ed89d389bb17
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   - libgcc >=14
@@ -11641,11 +10723,11 @@ packages:
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   license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.12.1-default_h64bd3f2_1002.conda
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-  md5: b0cac6e5b06ca5eeb14b4f7cf908619f
+  size: 2449346
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+- conda: https://conda.anaconda.org/conda-forge/win-64/libhwloc-2.12.1-default_h4379cf1_1003.conda
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+  md5: d1699ce4fe195a9f61264a1c29b87035
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   - vc14_runtime >=14.44.35208
   license: BSD-3-Clause
   license_family: BSD
-  size: 2414731
-  timestamp: 1757624335056
+  size: 2412642
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/libhwy-1.3.0-h4c17acf_1.conda
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@@ -11849,36 +10931,36 @@ packages:
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-2_h47877c9_openblas.conda
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-  md5: 9fb20e74a7436dc94dd39d9a9decddc3
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-5_h47877c9_openblas.conda
+  build_number: 5
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   depends:
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+  - libblas 3.11.0 5_h4a7cf45_openblas
   constrains:
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-  - liblapacke 3.11.0   2*_openblas
-  - libcblas   3.11.0   2*_openblas
+  - blas 2.305   openblas
+  - liblapacke 3.11.0   5*_openblas
+  - libcblas   3.11.0   5*_openblas
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-2_h5e43f62_mkl.conda
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-  md5: 4f33d79eda3c82c95a54e8c2981adddb
+  size: 18200
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-5_h5e43f62_mkl.conda
+  build_number: 5
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+  md5: 88155c848e1278b0990692e716c9eab4
   depends:
-  - libblas 3.11.0 2_h5875eb1_mkl
+  - libblas 3.11.0 5_h5875eb1_mkl
   constrains:
-  - liblapacke 3.11.0   2*_mkl
-  - blas 2.302   mkl
-  - libcblas   3.11.0   2*_mkl
+  - liblapacke 3.11.0   5*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
   track_features:
   - blas_mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 18542
-  timestamp: 1763828444709
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 - conda: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-20_linux64_openblas.conda
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   sha256: ad7745b8d0f2ccb9c3ba7aaa7167d62fc9f02e45eb67172ae5f0dfb5a3b1a2cc
@@ -11894,20 +10976,20 @@ packages:
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   timestamp: 1700568424034
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-2_hd9741b5_openblas.conda
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-  md5: f63fdb2504b1fc4527314641fa34efb4
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.11.0-5_hd9741b5_openblas.conda
+  build_number: 5
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+  md5: ca9d752201b7fa1225bca036ee300f2b
   depends:
-  - libblas 3.11.0 2_h51639a9_openblas
+  - libblas 3.11.0 5_h51639a9_openblas
   constrains:
-  - blas 2.302   openblas
-  - libcblas   3.11.0   2*_openblas
-  - liblapacke 3.11.0   2*_openblas
+  - libcblas   3.11.0   5*_openblas
+  - blas 2.305   openblas
+  - liblapacke 3.11.0   5*_openblas
   license: BSD-3-Clause
   license_family: BSD
-  size: 18651
-  timestamp: 1763829141511
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/liblapack-3.9.0-20_osxarm64_openblas.conda
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@@ -11922,20 +11004,20 @@ packages:
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   timestamp: 1700568930669
-- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-2_hf9ab0e9_mkl.conda
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-  md5: 4126ff2758e017e9f9c312a36b961738
+- conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-5_hf9ab0e9_mkl.conda
+  build_number: 5
+  sha256: a2d33f5cc2b8a9042f2af6981c6733ab1a661463823eaa56595a9c58c0ab77e1
+  md5: e62c42a4196dee97d20400612afcb2b1
   depends:
-  - libblas 3.11.0 2_hf2e6a31_mkl
+  - libblas 3.11.0 5_hf2e6a31_mkl
   constrains:
-  - blas 2.302   mkl
-  - liblapacke 3.11.0   2*_mkl
-  - libcblas   3.11.0   2*_mkl
+  - libcblas   3.11.0   5*_mkl
+  - blas 2.305   mkl
+  - liblapacke 3.11.0   5*_mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 80476
-  timestamp: 1763829180808
+  size: 80225
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 - conda: https://conda.anaconda.org/conda-forge/win-64/liblapack-3.11.0-7_h018ca30_netlib.conda
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@@ -11952,36 +11034,36 @@ packages:
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   timestamp: 1763441291332
-- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.11.0-2_hdba1596_mkl.conda
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-  - libcblas 3.11.0 2_hfef963f_mkl
-  - liblapack 3.11.0 2_h5e43f62_mkl
+- conda: https://conda.anaconda.org/conda-forge/linux-64/liblapacke-3.11.0-5_hdba1596_mkl.conda
+  build_number: 5
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+  md5: d7e79a90df7e39c11296053a8d6ffd2b
+  depends:
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+  - libcblas 3.11.0 5_hfef963f_mkl
+  - liblapack 3.11.0 5_h5e43f62_mkl
   constrains:
-  - blas 2.302   mkl
+  - blas 2.305   mkl
   track_features:
   - blas_mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 18595
-  timestamp: 1763828452171
-- conda: https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.11.0-2_h3ae206f_mkl.conda
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-  md5: f04e2779254506a4a4d11913ee077730
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+- conda: https://conda.anaconda.org/conda-forge/win-64/liblapacke-3.11.0-5_h3ae206f_mkl.conda
+  build_number: 5
+  sha256: 72f312334765605cc456faa1a818d2fb6697fddc6fda798eed8408c20df19ad6
+  md5: d2abec9d4ffacb43dbd389d5c2279222
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-  - libcblas 3.11.0 2_h2a3cdd5_mkl
-  - liblapack 3.11.0 2_hf9ab0e9_mkl
+  - libblas 3.11.0 5_hf2e6a31_mkl
+  - libcblas 3.11.0 5_h2a3cdd5_mkl
+  - liblapack 3.11.0 5_hf9ab0e9_mkl
   constrains:
-  - blas 2.302   mkl
+  - blas 2.305   mkl
   license: BSD-3-Clause
   license_family: BSD
-  size: 84621
-  timestamp: 1763829212862
+  size: 84330
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libllvm19-19.1.7-h8e0c9ce_2.conda
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   md5: d1d9b233830f6631800acc1e081a9444
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-21.0.0-h7376487_12_cpu.conda
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-  - openssl >=3.5.4,<4.0a0
-  license: Apache-2.0
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-  size: 1319693
-  timestamp: 1763230290886
-- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-22.0.0-h7376487_4_cpu.conda
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-  md5: 5e9383b1d25179787aff71aaad8208aa
+- conda: https://conda.anaconda.org/conda-forge/linux-64/libparquet-22.0.0-h7376487_6_cpu.conda
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+  md5: 83fd8f55f38ac972947c9eca12dc4657
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+  - libarrow 22.0.0 hb6ed5f4_6_cpu
   - libgcc >=14
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   license_family: MIT
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+  - python_abi 3.10.* *_cp310
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   license: BSD-3-Clause
   license_family: BSD
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   license: BSD-3-Clause
   license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py311h11fd7f3_1.conda
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   - numpy >=1.23,<3
@@ -16351,44 +15031,44 @@ packages:
   - vc >=14.3,<15
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+  - openpyxl >=3.1.0
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   license: BSD-3-Clause
   license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py312hc128f0a_1.conda
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   license: BSD-3-Clause
   license_family: BSD
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-- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py313hc90dcd4_1.conda
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-  md5: 72e76484d7629ec9217e71d9c6281e09
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+  - pyqt5 >=5.15.9
   license: BSD-3-Clause
   license_family: BSD
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-  md5: 2fa3f01d4ac9d04227364da118b5695f
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+- conda: https://conda.anaconda.org/conda-forge/win-64/pandas-2.3.3-py314hd8fd7ce_2.conda
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   - python-dateutil >=2.8.2
   - python-tzdata >=2022.7
   - python_abi 3.14.* *_cp314
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   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
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-  - zstandard >=0.19.0
-  - pandas-gbq >=0.19.0
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   license: BSD-3-Clause
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   - tzdata
   - zstd >=1.5.7,<1.6.0a0
   license: Python-2.0
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-  md5: a4241bce59eecc74d4d2396e108c93b8
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.13.11-hfc2f54d_100_cp313.conda
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   - liblzma >=5.8.1,<6.0a0
   - libmpdec >=4.0.0,<5.0a0
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   - tk >=8.6.13,<8.7.0a0
   - tzdata
   license: Python-2.0
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   python_site_packages_path: lib/python3.13/site-packages
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-  md5: a9ff09231c555da7e30777747318321b
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.14.2-h40d2674_100_cp314.conda
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   - tzdata
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   license: Python-2.0
-  size: 13590581
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-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/python-3.9.23-h7139b31_0_cpython.conda
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-- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.9-h09917c8_101_cp313.conda
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-  md5: 89d992b9d4b9e88ed54346c9c4a24c1c
+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.13.11-h09917c8_100_cp313.conda
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   - libmpdec >=4.0.0,<5.0a0
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   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
   license: Python-2.0
-  size: 16613183
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+  size: 16617922
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+- conda: https://conda.anaconda.org/conda-forge/win-64/python-3.14.2-h4b44e0e_100_cp314.conda
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   - zstd >=1.5.7,<1.6.0a0
   license: Python-2.0
-  size: 16706286
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-- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.9-h4df99d1_101.conda
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+- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.11.14-hd8ed1ab_2.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.6.3-py310h139afa4_0.conda
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+- conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.7.4-py310h139afa4_0.conda
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-  - libgcc >=14
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+  - libgcc >=14
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   license: MIT
   license_family: MIT
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.6.3-py311haee01d2_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.6.3-py313h54dd161_0.conda
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-- conda: https://conda.anaconda.org/conda-forge/linux-64/python-librt-0.6.3-py314h0f05182_0.conda
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+  timestamp: 1764777166076
+- conda: https://conda.anaconda.org/conda-forge/win-64/zstd-1.5.7-h534d264_6.conda
+  sha256: 368d8628424966fd8f9c8018326a9c779e06913dd39e646cf331226acc90e5b2
+  md5: 053b84beec00b71ea8ff7a4f84b55207
   depends:
-  - libzlib >=1.3.1,<2.0a0
-  - ucrt >=10.0.20348.0
   - vc >=14.3,<15
   - vc14_runtime >=14.44.35208
+  - ucrt >=10.0.20348.0
+  - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
-  size: 359229
-  timestamp: 1764431496562
+  size: 388453
+  timestamp: 1764777142545
diff --git a/pixi.toml b/pixi.toml
index 07736334c..3e7cbc8a8 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -47,20 +47,22 @@ cython = "*"
 setuptools-scm = ">=8.1"
 
 [host-dependencies]
-python = ">=3.9"
+python = ">=3.10"
 pip = "*"
 setuptools = ">=61"
 wheel = "*"
 
 [dependencies]
 formulaic = "*"
+narwhals = ">=2.0.0"
 numexpr = "*"
+numpy = ">=1.24"
 packaging = "*"
 pandas = ">=1.4"
 pyarrow = "*"
-scikit-learn = ">=0.23"
-scipy = "*"
-tabmat = ">=4.0.0"
+scikit-learn = ">=1.1.0"
+scipy = ">=1.8.0"
+tabmat = ">=4.1.5"
 tqdm = "*"
 
 [feature.no-tabmat.dependencies]
@@ -77,6 +79,7 @@ attrs = "*"
 click = "*"
 git_root = "*"
 mypy = "*"
+polars = "*"
 psutil = "*"
 pytest = "*"
 pytest-xdist = "*"
@@ -134,15 +137,13 @@ cxx-compiler = "*"
 cython = "!=3.0.4"
 make = "*"
 mako = "*"
-narwhals = ">=1.4.1"
+narwhals = ">=2.0.0"
 pip = "*"
 setuptools-scm = "*"
 xsimd = "<11|>12.1"
 [feature.build-tabmat.target.unix.dependencies]
 jemalloc-local = "*"
 
-[feature.py39.dependencies]
-python = "3.9.*"
 [feature.py310.dependencies]
 python = "3.10.*"
 [feature.py311.dependencies]
@@ -154,17 +155,18 @@ python = "3.13.*"
 
 [feature.oldies.dependencies]
 formulaic = "0.6.*"
+narwhals = "2.0.*"
+numpy = "1.24.*"
 pandas = "1.4.*"
-python = "3.9.*"
-scikit-learn = "0.24.*"
-scipy = "1.7.*"
-tabmat = "4.0.*"
+python = "3.10.*"
+scikit-learn = "==1.1.0"
+scipy = "1.8.*"
+tabmat = "==4.1.5"
 
 [environments]
 default = ["dev", "test"]
 docs = ["docs", "tutorial"]
 benchmark = ["benchmark"]
-py39 = ["py39", "test"]
 py310 = ["py310", "test"]
 py311 = ["py311", "test"]
 py312 = ["py312", "test"]
diff --git a/pyproject.toml b/pyproject.toml
index 11f90e81d..72ed5d315 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,7 @@ select = [
 known-first-party = ["glum", "glum_benchmarks"]
 
 [tool.mypy]
-python_version = '3.9'
+python_version = '3.10'
 exclude = [
   "tests/",
   "src/glum_benchmarks/orig_sklearn_fork/",
diff --git a/setup.py b/setup.py
index 5dbe87c5a..351458279 100644
--- a/setup.py
+++ b/setup.py
@@ -65,7 +65,6 @@
     license="BSD",
     classifiers=[  # Optional
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
         "Programming Language :: Python :: 3.11",
         "Programming Language :: Python :: 3.12",
@@ -78,16 +77,20 @@
             ["glum"] if os.environ.get("CONDA_BUILD") else ["glum", "glum_benchmarks"]
         ),
     ),
-    python_requires=">=3.9",
+    python_requires=">=3.10",
     install_requires=[
+        "formulaic>=0.6",
         "joblib",
+        "narwhals>=2.0.0",
         "numexpr",
-        "numpy",
-        "pandas",
-        "scikit-learn>=0.23",
-        "scipy",
-        "formulaic>=0.6",
-        "tabmat>=4.0.0",
+        "numpy>=1.24",
+        "packaging",
+        "pandas>=1.4",
+        "pyarrow",
+        "scikit-learn>=1.1.0",
+        "scipy>=1.8.0",
+        "tabmat>=4.1.5",
+        "tqdm",
     ],
     entry_points=(
         None
diff --git a/src/glum/_glm.py b/src/glum/_glm.py
index f5ab58b62..fc261bf1c 100644
--- a/src/glum/_glm.py
+++ b/src/glum/_glm.py
@@ -4,16 +4,19 @@
 import typing
 import warnings
 from collections.abc import Iterable, Mapping, Sequence
-from typing import Any, Optional, Union
+from typing import Any, Optional, Union, cast
 
 import formulaic
+import narwhals.stable.v2 as nw
 import numpy as np
 import packaging.version
 import pandas as pd
 import scipy.sparse as sps
 import sklearn as skl
 import tabmat as tm
+from narwhals.typing import IntoDataFrame
 from scipy import linalg, sparse, stats
+from typing_extensions import deprecated
 
 from ._distribution import (
     BinomialDistribution,
@@ -38,7 +41,13 @@
     _least_squares_solver,
     _trust_constr_solver,
 )
-from ._typing import ArrayLike, ShapedArrayLike, VectorLike, WaldTestResult
+from ._typing import (
+    ArrayLike,
+    ShapedArrayLike,
+    ShapedArrayLikeConverted,
+    VectorLike,
+    WaldTestResult,
+)
 from ._utils import (
     add_missing_categories,
     align_df_categories,
@@ -174,6 +183,29 @@ def link_instance(self) -> Link:
         else:
             return get_link(self.link, self.family_instance)
 
+    @property
+    def categorical_levels_(self) -> dict[str, list[str]]:
+        if hasattr(self, "_categorical_levels_"):
+            return self._categorical_levels_
+        if hasattr(self, "feature_dtypes_"):
+            # Compatibility with pickled models
+            return {
+                col: dtype.categories.tolist()
+                for col, dtype in self.feature_dtypes_.items()
+                if isinstance(dtype, pd.CategoricalDtype)
+            }
+        raise AttributeError("No categorical levels stored.")
+
+    @property
+    @deprecated("Use `categorical_levels_` instead.")
+    def feature_dtypes_(self) -> dict[str, Any]:
+        return self._feature_dtypes_
+
+    @feature_dtypes_.setter
+    @deprecated("Use `categorical_levels_` instead.")
+    def feature_dtypes_(self, value: dict[str, Any]) -> None:
+        self._feature_dtypes_ = value
+
     def _get_start_coef(
         self,
         X: Union[tm.MatrixBase, tm.StandardizedMatrix],
@@ -245,9 +277,9 @@ def _get_start_coef(
 
         return coef
 
-    def _convert_from_pandas(
+    def _convert_from_df(
         self,
-        df: pd.DataFrame,
+        df: IntoDataFrame,
         context: Optional[Mapping[str, Any]] = None,
     ) -> tm.MatrixBase:
         """Convert a pandas data frame to a tabmat matrix."""
@@ -256,17 +288,19 @@ def _convert_from_pandas(
 
         cat_missing_method_after_alignment = getattr(self, "cat_missing_method", "fail")
 
-        if hasattr(self, "feature_dtypes_"):
+        df = nw.from_native(df)
+
+        if hasattr(self, "categorical_levels_"):
             df = align_df_categories(
                 df,
-                self.feature_dtypes_,
+                self.categorical_levels_,
                 getattr(self, "has_missing_category_", {}),
                 cat_missing_method_after_alignment,
             )
             if cat_missing_method_after_alignment == "convert":
                 df = add_missing_categories(
                     df=df,
-                    dtypes=self.feature_dtypes_,
+                    categorical_levels=self.categorical_levels_,
                     feature_names=self.feature_names_,
                     cat_missing_name=self.cat_missing_name,
                     categorical_format=self.categorical_format,
@@ -274,7 +308,7 @@ def _convert_from_pandas(
                 # there should be no missing categories after this
                 cat_missing_method_after_alignment = "fail"
 
-        X = tm.from_pandas(
+        X = tm.from_df(
             df,
             drop_first=self.drop_first,
             categorical_format=getattr(  # convention prior to v3
@@ -718,8 +752,8 @@ def linear_predictor(
         elif alpha is not None:
             alpha_index = [self._find_alpha_index(a) for a in alpha]  # type: ignore
 
-        if isinstance(X, pd.DataFrame):
-            X = self._convert_from_pandas(X, context=capture_context(context))
+        if nw.dependencies.is_into_dataframe(X):
+            X = self._convert_from_df(X, context=capture_context(context))
 
         X = check_array_tabmat_compliant(
             X,
@@ -807,8 +841,9 @@ def predict(
         array, shape (n_samples, n_alphas)
             Predicted values times ``sample_weight``.
         """
-        if isinstance(X, pd.DataFrame):
-            X = self._convert_from_pandas(X, context=capture_context(context))
+        if nw.dependencies.is_into_dataframe(X):
+            X = self._convert_from_df(X, context=capture_context(context))
+        X = cast(ShapedArrayLikeConverted, X)
 
         eta = self.linear_predictor(
             X, offset=offset, alpha_index=alpha_index, alpha=alpha, context=context
@@ -1452,8 +1487,8 @@ def covariance_matrix(
                 y = self.y_model_spec_.get_model_matrix(X).toarray().ravel()
                 # This has to go first because X is modified in the next line
 
-            if isinstance(X, pd.DataFrame):
-                X = self._convert_from_pandas(X, context=capture_context(context))
+            if nw.dependencies.is_into_dataframe(X):
+                X = self._convert_from_df(X, context=capture_context(context))
 
             X, y = check_X_y_tabmat_compliant(
                 X,
@@ -1566,8 +1601,8 @@ def covariance_matrix(
     def score(
         self,
         X: ShapedArrayLike,
-        y: ShapedArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
+        y: VectorLike,
+        sample_weight: Optional[VectorLike] = None,
         offset: Optional[ArrayLike] = None,
         *,
         context: Optional[Union[int, Mapping[str, Any]]] = None,
@@ -1724,7 +1759,7 @@ def _should_copy_X(self):
     def _set_up_and_check_fit_args(
         self,
         X: ArrayLike,
-        y: Optional[ArrayLike],
+        y: Optional[VectorLike],
         sample_weight: Optional[VectorLike],
         offset: Optional[VectorLike],
         force_all_finite,
@@ -1747,8 +1782,8 @@ def _set_up_and_check_fit_args(
         copy_X = self._should_copy_X()
         drop_first = getattr(self, "drop_first", False)
 
-        if isinstance(X, pd.DataFrame):
-            if hasattr(self, "formula") and self.formula is not None:
+        if nw.dependencies.is_into_dataframe(X):
+            if getattr(self, "formula", None) is not None:
                 lhs, rhs = parse_formula(
                     self.formula, include_intercept=self.fit_intercept
                 )
@@ -1802,16 +1837,35 @@ def _set_up_and_check_fit_args(
             else:
                 # Maybe TODO: expand categorical penalties with formulas
 
-                self.feature_dtypes_ = X.dtypes.to_dict()
+                if not is_contiguous(X) and self.copy_X is False:
+                    raise ValueError(
+                        "The X matrix is noncontiguous and copy_X = False. "
+                        "To fix this, either set copy_X = None "
+                        "or pass a contiguous matrix."
+                    )
+
+                # Backwards compatibility
+                if isinstance(X, pd.DataFrame):
+                    self.feature_dtypes_ = X.dtypes.to_dict()
+
+                X = cast(nw.DataFrame, nw.from_native(X))  # avoid inferring `Never`
+
+                self._categorical_levels_ = {
+                    col: X[col].cat.get_categories().to_list()
+                    for col, dtype in X.schema.items()
+                    if isinstance(dtype, (nw.Categorical, nw.Enum))
+                }
 
                 self.has_missing_category_ = {
                     col: (getattr(self, "cat_missing_method", "fail") == "convert")
-                    and X[col].isna().any()
-                    for col, dtype in self.feature_dtypes_.items()
-                    if isinstance(dtype, pd.CategoricalDtype)
+                    and X[col].is_null().any()
+                    for col in self.categorical_levels_
                 }
 
-                if any(X.dtypes == "category"):
+                if any(
+                    isinstance(dtype, (nw.Categorical, nw.Enum))
+                    for dtype in X.schema.values()
+                ):
                     P1 = expand_categorical_penalties(
                         self.P1, X, drop_first, self.has_missing_category_
                     )
@@ -1819,7 +1873,7 @@ def _set_up_and_check_fit_args(
                         self.P2, X, drop_first, self.has_missing_category_
                     )
 
-                X = tm.from_pandas(
+                X = tm.from_df(
                     X,
                     drop_first=drop_first,
                     categorical_format=getattr(  # convention prior to v3
@@ -1835,14 +1889,6 @@ def _set_up_and_check_fit_args(
                 "requires y to be passed, but the target y is None."
             )
 
-        if not is_contiguous(X):
-            if self.copy_X is not None and not self.copy_X:
-                raise ValueError(
-                    "The X matrix is noncontiguous and copy_X = False."
-                    "To fix this, either set copy_X = None or pass a contiguous matrix."
-                )
-            X = X.copy()
-
         if (
             not isinstance(X, tm.CategoricalMatrix)
             and hasattr(X, "dtype")
@@ -2146,8 +2192,8 @@ class GeneralizedLinearRegressor(GeneralizedLinearRegressorBase):
         option exists mainly for compatibility with other scikit-learn
         estimators. If ``False``, ``X`` will not be copied and there will be an
         error if you pass an ``X`` in the wrong format, such as providing
-        integer ``X`` and float ``y``. If ``None``, ``X`` will not be copied
-        unless it is in the wrong format.
+        integer ``X`` and float ``y`` (only guaranteed for numpy arrays and pandas data
+        frames). If ``None``, ``X`` will not be copied unless it is in the wrong format.
 
     check_input : bool, optional (default=True)
         Whether to bypass several checks on input: ``y`` values in range of
@@ -2672,8 +2718,8 @@ def fit(
 
     def _compute_information_criteria(
         self,
-        X: ShapedArrayLike,
-        y: ShapedArrayLike,
+        X: ShapedArrayLikeConverted,
+        y: VectorLike,
         sample_weight: Optional[ArrayLike] = None,
         context: Optional[Mapping[str, Any]] = None,
     ):
@@ -2732,7 +2778,7 @@ def _compute_information_criteria(
     def aic(
         self,
         X: ArrayLike,
-        y: ArrayLike,
+        y: VectorLike,
         sample_weight: Optional[ArrayLike] = None,
         *,
         context: Optional[Union[int, Mapping[str, Any]]] = None,
@@ -2769,7 +2815,7 @@ def aic(
     def aicc(
         self,
         X: ArrayLike,
-        y: ArrayLike,
+        y: VectorLike,
         sample_weight: Optional[ArrayLike] = None,
         *,
         context: Optional[Union[int, Mapping[str, Any]]] = None,
@@ -2814,7 +2860,7 @@ def aicc(
     def bic(
         self,
         X: ArrayLike,
-        y: ArrayLike,
+        y: VectorLike,
         sample_weight: Optional[ArrayLike] = None,
         *,
         context: Optional[Union[int, Mapping[str, Any]]] = None,
@@ -2853,7 +2899,7 @@ def _get_info_criteria(
         self,
         crit: str,
         X: ArrayLike,
-        y: ArrayLike,
+        y: VectorLike,
         sample_weight: Optional[ArrayLike] = None,
         context: Optional[Union[int, Mapping[str, Any]]] = None,
     ):
diff --git a/src/glum/_glm_cv.py b/src/glum/_glm_cv.py
index f4e15f8ce..a75482122 100644
--- a/src/glum/_glm_cv.py
+++ b/src/glum/_glm_cv.py
@@ -188,8 +188,8 @@ class GeneralizedLinearRegressorCV(GeneralizedLinearRegressorBase):
         option exists mainly for compatibility with other scikit-learn
         estimators. If ``False``, ``X`` will not be copied and there will be an
         error if you pass an ``X`` in the wrong format, such as providing
-        integer ``X`` and float ``y``. If ``None``, ``X`` will not be copied
-        unless it is in the wrong format.
+        integer ``X`` and float ``y`` (only guaranteed for numpy arrays and pandas data
+        frames). If ``None``, ``X`` will not be copied unless it is in the wrong format.
 
     check_input : bool, optional (default=True)
         Whether to bypass several checks on input: ``y`` values in range of
@@ -348,7 +348,7 @@ def __init__(
         start_params: Optional[np.ndarray] = None,
         selection: str = "cyclic",
         random_state=None,
-        copy_X: bool = True,
+        copy_X: Optional[bool] = None,
         check_input: bool = True,
         verbose=0,
         scale_predictors: bool = False,
diff --git a/src/glum/_typing.py b/src/glum/_typing.py
index 2d21461bc..fcf494419 100644
--- a/src/glum/_typing.py
+++ b/src/glum/_typing.py
@@ -4,6 +4,7 @@
 import pandas as pd
 import scipy.sparse
 import tabmat as tm
+from narwhals.typing import IntoDataFrame
 
 VectorLike = Union[np.ndarray, pd.api.extensions.ExtensionArray, pd.Index, pd.Series]
 
@@ -11,7 +12,7 @@
     list,
     tm.MatrixBase,
     tm.StandardizedMatrix,
-    pd.DataFrame,
+    IntoDataFrame,
     scipy.sparse.spmatrix,
     VectorLike,
 ]
@@ -19,7 +20,14 @@
 ShapedArrayLike = Union[
     tm.MatrixBase,
     tm.StandardizedMatrix,
-    pd.DataFrame,
+    IntoDataFrame,
+    scipy.sparse.spmatrix,
+    VectorLike,
+]
+
+ShapedArrayLikeConverted = Union[
+    tm.MatrixBase,
+    tm.StandardizedMatrix,
     scipy.sparse.spmatrix,
     VectorLike,
 ]
diff --git a/src/glum/_utils.py b/src/glum/_utils.py
index 9bb54aa5f..a449b206c 100644
--- a/src/glum/_utils.py
+++ b/src/glum/_utils.py
@@ -2,6 +2,7 @@
 from collections.abc import Sequence
 from typing import Any, Optional, Union
 
+import narwhals.stable.v2 as nw
 import numpy as np
 import pandas as pd
 import tabmat as tm
@@ -11,50 +12,42 @@
 
 
 def align_df_categories(
-    df, dtypes, has_missing_category, cat_missing_method
-) -> pd.DataFrame:
+    df: nw.DataFrame,
+    categorical_levels: dict[str, list[str]],
+    has_missing_category: dict[str, bool],
+    cat_missing_method: str,
+) -> nw.DataFrame:
     """Align data types for prediction.
 
     This function checks that categorical columns have same categories in the
-    same order as specified in ``dtypes``. If an entry has a category not
-    specified in ``dtypes``, it will be set to ``numpy.nan``.
-
-    Parameters
-    ----------
-    df : pandas.DataFrame
-    dtypes : Dict[str, str | type | pandas.core.dtypes.base.ExtensionDtype]
-    has_missing_category : Dict[str, bool]
-    missing_method : str
+    same order as specified in ``categorical_levels``. If an entry has a category not
+    specified in ``categorical_levels``, it will be set to ``numpy.nan``.
     """
-    if not isinstance(df, pd.DataFrame):
-        raise TypeError(f"Expected `pandas.DataFrame'; got {type(df)}.")
-
-    changed_dtypes = {}
-
-    categorical_dtypes = [
-        column
-        for column, dtype in dtypes.items()
-        if isinstance(dtype, pd.CategoricalDtype) and (column in df)
-    ]
-
-    for column in categorical_dtypes:
-        if not isinstance(df[column].dtype, pd.CategoricalDtype):
-            _logger.info(f"Casting {column} to categorical.")
-            changed_dtypes[column] = df[column].astype(dtypes[column])
-        elif list(df[column].cat.categories) != list(dtypes[column].categories):
+    if not isinstance(df, nw.DataFrame):
+        raise TypeError(f"Expected `narwhals.DataFrame'; got {type(df)}.")
+
+    changed_dtypes: dict[str, nw.Series] = {}
+
+    for column, levels in categorical_levels.items():
+        if column not in df.schema:
+            continue
+        elif not isinstance(df[column].dtype, (nw.Enum)):
+            _logger.info(f"Casting {column} to Enum (categorical).")
+        elif df[column].cat.get_categories().to_list() != levels:
             _logger.info(f"Aligning categories of {column}.")
-            changed_dtypes[column] = df[column].cat.set_categories(
-                dtypes[column].categories
-            )
         else:
             continue
 
+        changed_dtypes[column] = df[column].cast(nw.Enum(levels))
+
         if cat_missing_method == "convert" and not has_missing_category[column]:
-            unseen_categories = set(df[column].unique())
-            unseen_categories = unseen_categories - set(dtypes[column].categories)
+            unseen_categories = set(df[column].unique()) - set(
+                categorical_levels[column]
+            )
         else:
-            unseen_categories = set(df[column].dropna().unique())
-            unseen_categories = unseen_categories - set(dtypes[column].categories)
+            unseen_categories = set(df[column].drop_nulls().unique()) - set(
+                categorical_levels[column]
+            )
 
         if unseen_categories:
             raise ValueError(
@@ -62,51 +55,49 @@ def align_df_categories(
             )
 
     if changed_dtypes:
-        df = df.assign(**changed_dtypes)
+        df = df.with_columns(**changed_dtypes)
 
     return df
 
 
 def add_missing_categories(
-    df,
-    dtypes,
+    df: nw.DataFrame,
+    categorical_levels: dict[str, list[str]],
     feature_names: Sequence[str],
     categorical_format: str,
     cat_missing_name: str,
-) -> pd.DataFrame:
-    if not isinstance(df, pd.DataFrame):
-        raise TypeError(f"Expected `pandas.DataFrame'; got {type(df)}.")
-
-    changed_dtypes = {}
+) -> nw.DataFrame:
+    if not isinstance(df, nw.DataFrame):
+        raise TypeError(f"Expected `narwhals.DataFrame'; got {type(df)}.")
 
-    categorical_dtypes = [
-        column
-        for column, dtype in dtypes.items()
-        if isinstance(dtype, pd.CategoricalDtype) and (column in df)
-    ]
+    changed_dtypes: dict[str, nw.Series] = {}
 
-    for column in categorical_dtypes:
+    for column in categorical_levels:
         if (
             categorical_format.format(name=column, category=cat_missing_name)
             in feature_names
         ):
-            if cat_missing_name in df[column].cat.categories:
+            if cat_missing_name in df[column].cat.get_categories():
                 raise ValueError(
                     f"Missing category {cat_missing_name} already exists in {column}."
                 )
             _logger.info(f"Adding missing category {cat_missing_name} to {column}.")
-            changed_dtypes[column] = df[column].cat.add_categories(cat_missing_name)
-            if df[column].isnull().any():
-                changed_dtypes[column] = changed_dtypes[column].fillna(cat_missing_name)
+            changed_dtypes[column] = df[column].cast(
+                nw.Enum(df[column].cat.get_categories().to_list() + [cat_missing_name])
+            )
+            if df[column].is_null().any():
+                changed_dtypes[column] = changed_dtypes[column].fill_null(
+                    cat_missing_name
+                )
 
     if changed_dtypes:
-        df = df.assign(**changed_dtypes)
+        df = df.with_columns(**changed_dtypes)
 
     return df
 
 
 def expand_categorical_penalties(
-    penalty, X, drop_first, has_missing_category
+    penalty, X: nw.DataFrame, drop_first: bool, has_missing_category: dict[str, bool]
 ) -> Union[np.ndarray, str]:
     """Determine penalty matrices ``P1`` or ``P2`` after expanding categorical columns.
 
@@ -129,9 +120,13 @@ def expand_categorical_penalties(
 
         expanded_penalty = []  # type: ignore
 
-        for element, (column, dt) in zip(penalty, X.dtypes.items()):
-            if isinstance(dt, pd.CategoricalDtype):
-                length = len(dt.categories) + has_missing_category[column] - drop_first
+        for element, (column, dt) in zip(penalty, X.schema.items()):
+            if isinstance(dt, (nw.Enum, nw.Categorical)):
+                length = (
+                    len(X[column].cat.get_categories())
+                    + has_missing_category[column]
+                    - drop_first
+                )
                 expanded_penalty.extend(element for _ in range(length))
             else:
                 expanded_penalty.append(element)
diff --git a/src/glum/_validation.py b/src/glum/_validation.py
index 079ab5516..b9146e838 100644
--- a/src/glum/_validation.py
+++ b/src/glum/_validation.py
@@ -1,9 +1,9 @@
 import copy
 import typing
 
+import narwhals.stable.v2 as nw
 import numpy as np
 import packaging.version
-import pandas as pd
 import sklearn as skl
 import tabmat as tm
 from scipy import sparse
@@ -20,7 +20,7 @@
 def check_array_tabmat_compliant(mat: ArrayLike, drop_first: bool = False, **kwargs):
     to_copy = kwargs.get("copy", False)
 
-    if isinstance(mat, pd.DataFrame):
+    if nw.dependencies.is_into_dataframe(mat):
         raise RuntimeError("DataFrames should have been converted by this point.")
 
     if isinstance(mat, tm.SplitMatrix):
diff --git a/tests/glm/test_glm_regressor.py b/tests/glm/test_glm_regressor.py
index c54aa9730..62fb918dc 100644
--- a/tests/glm/test_glm_regressor.py
+++ b/tests/glm/test_glm_regressor.py
@@ -1214,17 +1214,17 @@ def test_alpha_path(scale_predictors, fit_intercept, P1):
 
 
 def test_passing_noncontiguous_as_X():
-    X = np.random.rand(100, 4)
+    X = np.asfortranarray(np.random.rand(100, 4))
     y = np.random.rand(100)
 
     baseline = GeneralizedLinearRegressor(family="normal", fit_intercept=False).fit(
-        X[:, :2].copy(), y
+        X[2:, :].copy(), y[2:]
     )
     np_view = GeneralizedLinearRegressor(family="normal", fit_intercept=False).fit(
-        X[:, :2], y
+        X[2:, :], y[2:]
     )
     pd_view = GeneralizedLinearRegressor(family="normal", fit_intercept=False).fit(
-        pd.DataFrame(X).iloc[:, :2], y
+        pd.DataFrame(X).iloc[2:, :], y[2:]
     )
     np.testing.assert_almost_equal(baseline.coef_, np_view.coef_)
     np.testing.assert_almost_equal(baseline.coef_, pd_view.coef_)
diff --git a/tests/glm/test_utils.py b/tests/glm/test_utils.py
index 7bc58ee2e..552c2536a 100644
--- a/tests/glm/test_utils.py
+++ b/tests/glm/test_utils.py
@@ -1,3 +1,4 @@
+import narwhals.stable.v2 as nw
 import numpy as np
 import pandas as pd
 import pytest
@@ -7,7 +8,7 @@
 
 @pytest.fixture()
 def df():
-    return pd.DataFrame(
+    _df = pd.DataFrame(
         {
             "x1": np.array([0, 1], dtype="int64"),
             "x2": np.array([0, 1], dtype="bool"),
@@ -19,11 +20,11 @@ def df():
             "x8": pd.Categorical(["a", pd.NA], categories=["b", "a"]),
         }
     )
+    return nw.from_native(_df)
 
 
 def test_align_df_categories_numeric(df):
-    dtypes = {column: np.float64 for column in df}
-    has_missing_category = {column: False for column in df}
+    has_missing_category = {column: False for column in df.columns}
     missing_method = "fail"
 
     expected = pd.DataFrame(
@@ -40,14 +41,15 @@ def test_align_df_categories_numeric(df):
     )
 
     pd.testing.assert_frame_equal(
-        align_df_categories(df, dtypes, has_missing_category, missing_method), expected
+        align_df_categories(df, {}, has_missing_category, missing_method).to_native(),
+        expected,
     )
 
 
 def test_align_df_categories_categorical(df):
     df = df[["x5", "x6", "x7", "x8"]]
-    dtypes = {column: pd.CategoricalDtype(["a", "b"]) for column in df}
-    has_missing_category = {column: False for column in df}
+    categorical_levels = {column: ["a", "b"] for column in df.columns}
+    has_missing_category = {column: False for column in df.columns}
     missing_method = "fail"
 
     expected = pd.DataFrame(
@@ -57,18 +59,19 @@ def test_align_df_categories_categorical(df):
             "x7": pd.Categorical(["a", "b"]),
             "x8": pd.Categorical(["a", pd.NA], categories=["b", "a"]),
         },
-        dtype=pd.CategoricalDtype(["a", "b"]),
+        dtype=pd.CategoricalDtype(["a", "b"], ordered=True),
     )
 
     pd.testing.assert_frame_equal(
-        align_df_categories(df, dtypes, has_missing_category, missing_method),
+        align_df_categories(
+            df, categorical_levels, has_missing_category, missing_method
+        ).to_native(),
         expected,
     )
 
 
 def test_align_df_categories_excess_columns(df):
-    dtypes = {"x1": np.float64}
-    has_missing_category = {column: False for column in df}
+    has_missing_category = {column: False for column in df.columns}
     missing_method = "fail"
 
     expected = pd.DataFrame(
@@ -85,13 +88,13 @@ def test_align_df_categories_excess_columns(df):
     )
 
     pd.testing.assert_frame_equal(
-        align_df_categories(df, dtypes, has_missing_category, missing_method), expected
+        align_df_categories(df, {}, has_missing_category, missing_method).to_native(),
+        expected,
     )
 
 
 def test_align_df_categories_missing_columns(df):
-    dtypes = {"x0": np.float64}
-    has_missing_category = {column: False for column in df}
+    has_missing_category = {column: False for column in df.columns}
     missing_method = "fail"
 
     expected = pd.DataFrame(
@@ -108,15 +111,16 @@ def test_align_df_categories_missing_columns(df):
     )
 
     pd.testing.assert_frame_equal(
-        align_df_categories(df, dtypes, has_missing_category, missing_method), expected
+        align_df_categories(df, {}, has_missing_category, missing_method).to_native(),
+        expected,
     )
 
 
 @pytest.mark.parametrize("has_missings", [False, True])
 def test_align_df_categories_convert(df, has_missings):
     df = df[["x5", "x6", "x7", "x8"]]
-    dtypes = {column: pd.CategoricalDtype(["a", "b"]) for column in df}
-    has_missing_category = {column: has_missings for column in df}
+    categorical_levels = {column: ["a", "b"] for column in df.columns}
+    has_missing_category = {column: has_missings for column in df.columns}
     missing_method = "convert"
 
     expected = pd.DataFrame(
@@ -126,38 +130,38 @@ def test_align_df_categories_convert(df, has_missings):
             "x7": pd.Categorical(["a", "b"]),
             "x8": pd.Categorical(["a", pd.NA], categories=["b", "a"]),
         },
-        dtype=pd.CategoricalDtype(["a", "b"]),
+        dtype=pd.CategoricalDtype(["a", "b"], ordered=True),
     )
 
     if has_missings:
         pd.testing.assert_frame_equal(
             align_df_categories(
                 df[["x5", "x6", "x7", "x8"]],
-                dtypes,
+                categorical_levels,
                 has_missing_category,
                 missing_method,
-            ),
+            ).to_native(),
             expected,
         )
     else:
         with pytest.raises(ValueError, match="contains unseen categories"):
             align_df_categories(
                 df[["x5", "x6", "x7", "x8"]],
-                dtypes,
+                categorical_levels,
                 has_missing_category,
                 missing_method,
             )
 
 
 def test_align_df_categories_raise_on_unseen(df):
-    dtypes = {column: pd.CategoricalDtype(["a", "b"]) for column in df}
-    has_missing_category = {column: False for column in df}
+    categorical_levels = {column: ["a", "b"] for column in df.columns}
+    has_missing_category = {column: False for column in df.columns}
     missing_method = "fail"
 
     with pytest.raises(ValueError, match="contains unseen categories"):
         align_df_categories(
             df,
-            dtypes,
+            categorical_levels,
             has_missing_category,
             missing_method,
         )
@@ -170,7 +174,7 @@ def test_align_df_categories_not_df():
 
 @pytest.fixture()
 def df_na():
-    return pd.DataFrame(
+    df = pd.DataFrame(
         {
             "num": np.array([0, 1], dtype="float64"),
             "cat": pd.Categorical(["a", "b"]),
@@ -178,12 +182,17 @@ def df_na():
             "cat2": pd.Categorical(["a", "b"]),
         }
     )
+    return nw.from_native(df)
 
 
 def test_add_missing_categories(df_na):
     categorical_format = "{name}[{category}]"
     cat_missing_name = "(M)"
-    dtypes = df_na.dtypes
+    categorical_levels = {
+        "cat": ["a", "b", "(M)"],
+        "cat_na": ["a", "(M)"],
+        "cat2": ["a", "b"],
+    }
     feature_names = [
         "num",
         "num[(M)]",
@@ -199,20 +208,26 @@ def test_add_missing_categories(df_na):
     expected = pd.DataFrame(
         {
             "num": np.array([0, 1], dtype="float64"),
-            "cat": pd.Categorical(["a", "b"], categories=["a", "b", "(M)"]),
-            "cat_na": pd.Categorical(["a", "(M)"], categories=["a", "(M)"]),
-            "cat2": pd.Categorical(["a", "b"], categories=["a", "b"]),
+            "cat": pd.Categorical(
+                ["a", "b"], categories=["a", "b", "(M)"], ordered=True
+            ),
+            "cat_na": pd.Categorical(
+                ["a", "(M)"], categories=["a", "(M)"], ordered=True
+            ),
+            "cat2": pd.Categorical(
+                ["a", "b"], categories=["a", "b"]
+            ),  # Note: not ordered as no change was made
         }
     )
 
     pd.testing.assert_frame_equal(
         add_missing_categories(
             df=df_na,
-            dtypes=dtypes,
+            categorical_levels=categorical_levels,
             feature_names=feature_names,
             categorical_format=categorical_format,
             cat_missing_name=cat_missing_name,
-        ),
+        ).to_native(),
         expected,
     )
 
@@ -220,7 +235,11 @@ def test_add_missing_categories(df_na):
 def test_raise_on_existing_missing(df_na):
     categorical_format = "{name}[{category}]"
     cat_missing_name = "(M)"
-    dtypes = df_na.dtypes
+    categorical_levels = {
+        "cat": ["a", "b", "(M)"],
+        "cat_na": ["a", "(M)"],
+        "cat2": ["a", "b"],
+    }
     feature_names = [
         "num",
         "num[(M)]",
@@ -233,14 +252,15 @@ def test_raise_on_existing_missing(df_na):
         "cat2[b]",
     ]
 
-    df = df_na
+    df = df_na.to_native()
     df["cat_na"] = df["cat_na"].cat.add_categories("(M)")
     df.loc[df.cat_na.isna(), "cat_na"] = "(M)"
+    df = nw.from_native(df)
 
     with pytest.raises(ValueError):
         add_missing_categories(
             df=df,
-            dtypes=dtypes,
+            categorical_levels=categorical_levels,
             feature_names=feature_names,
             categorical_format=categorical_format,
             cat_missing_name=cat_missing_name,