diff --git a/conda.recipe/recipe.yaml b/conda.recipe/recipe.yaml
index 21e4a041a..35b65c1ab 100644
--- a/conda.recipe/recipe.yaml
+++ b/conda.recipe/recipe.yaml
@@ -45,6 +45,7 @@ requirements:
     - python
     - formulaic >=0.6
     - joblib
+    - narwhals >=2.0.0
     - numexpr
     - packaging
     - pandas
diff --git a/pixi.lock b/pixi.lock
index ec956722e..89c702114 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -653,6 +653,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.35.2-py310hffdcd12_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.1.3-py314h0f05182_0.conda
@@ -842,6 +844,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-runtime-32-1.35.2-py310h34bb384_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.1.3-py314h9d33bd4_0.conda
@@ -989,6 +993,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/polars-runtime-32-1.35.2-py310hca7251b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.1.3-py314hc5dbbe4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda
@@ -2262,6 +2268,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.5.0-pyhcf101f3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.35.2-py310hffdcd12_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/psutil-7.1.3-py314h0f05182_0.conda
@@ -2454,6 +2462,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-runtime-32-1.35.2-py310h34bb384_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/psutil-7.1.3-py314h9d33bd4_0.conda
@@ -2607,6 +2617,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/polars-runtime-32-1.35.2-py310hca7251b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.52-pyha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.1.3-py314hc5dbbe4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda
@@ -2800,6 +2812,7 @@ environments:
       linux-64:
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       - 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/_python_abi3_support-1.0-hd8ed1ab_2.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-he9688bd_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.9.5-h346e085_1.conda
@@ -2829,6 +2842,7 @@ environments:
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       - 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-hb991d5c_7.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.9-py313hd8ed1ab_101.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-3.1.6-py313hc80a56d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
@@ -2923,6 +2937,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.35.2-py310hffdcd12_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-22.0.0-py313h78bf25f_0.conda
@@ -2932,6 +2948,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/python-3.13.9-hc97d973_101_cp313.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.9-h4df99d1_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.13-8_cp313.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda
@@ -2961,6 +2978,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_2.conda
       osx-arm64:
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.9.1-hd6d1d5d_4.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.9.5-hca30140_1.conda
@@ -2997,6 +3015,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compiler-rt-19.1.7-h855ad52_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-19.1.7-he32a8d3_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.9-py313hd8ed1ab_101.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.11.0-h88570a1_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
@@ -3082,6 +3101,8 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh145f28c_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-runtime-32-1.35.2-py310h34bb384_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/prometheus-cpp-1.3.0-h0967b3e_0.conda
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@@ -3091,6 +3112,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.9-h4df99d1_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda
@@ -3121,6 +3143,7 @@ environments:
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       win-64:
       - conda: https://conda.anaconda.org/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda
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@@ -3141,6 +3164,7 @@ environments:
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.9-py313hd8ed1ab_101.conda
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@@ -3204,6 +3228,8 @@ environments:
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
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@@ -3212,6 +3238,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.9-h4df99d1_101.conda
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@@ -3250,6 +3277,7 @@ environments:
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       - 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/_python_abi3_support-1.0-hd8ed1ab_2.conda
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@@ -3281,6 +3309,7 @@ environments:
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@@ -3360,6 +3389,7 @@ environments:
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+      - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.0.1-pyhe01879c_0.conda
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@@ -3373,6 +3403,8 @@ environments:
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+      - conda: https://conda.anaconda.org/conda-forge/linux-64/polars-1.33.1-default_h755bcc6_0.conda
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       - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
@@ -5806,6 +5923,8 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pip-25.2-pyh8b19718_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/polars-1.33.1-default_hd0420bf_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/polars-default-1.33.1-py39he906d20_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/psutil-7.0.0-py39ha55e580_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-21.0.0-py39hcbf5309_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/pyarrow-core-21.0.0-py39hca79ef2_0_cpu.conda
@@ -5814,6 +5933,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.8.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/python-3.9.23-h8c5b53a_0_cpython.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.9.23-hd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.2-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.9-8_cp39.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/pytz-2025.2-pyhd8ed1ab_0.conda
@@ -7689,6 +7809,36 @@ packages:
   license_family: BSD
   size: 225780
   timestamp: 1756544971020
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.10.19-py310hd8ed1ab_2.conda
+  noarch: generic
+  sha256: a6e5a75a2a79d7bb2519930b8e897ee062983d58af97d82050698e3df4b3b100
+  md5: 5aad50a3b1adaffbc9713a2a8dd87bfc
+  depends:
+  - python >=3.10,<3.11.0a0
+  - python_abi * *_cp310
+  license: Python-2.0
+  size: 50196
+  timestamp: 1761172103204
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.11.14-py311hd8ed1ab_2.conda
+  noarch: generic
+  sha256: c871fe68dcc6b79b322e4fcf9f2b131162094c32a68d48c87aa8582995948a01
+  md5: 43ed151bed1a0eb7181d305fed7cf051
+  depends:
+  - python >=3.11,<3.12.0a0
+  - python_abi * *_cp311
+  license: Python-2.0
+  size: 47257
+  timestamp: 1761172995774
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.12.12-py312hd8ed1ab_1.conda
+  noarch: generic
+  sha256: b88c76a6d6b45378552ccfd9e88b2a073161fe83fd1294c8fa103ffd32f7934a
+  md5: 99d689ccc1a360639eec979fd7805be9
+  depends:
+  - python >=3.12,<3.13.0a0
+  - python_abi * *_cp312
+  license: Python-2.0
+  size: 45767
+  timestamp: 1761175217281
 - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.9-py313hd8ed1ab_101.conda
   noarch: generic
   sha256: 31da683e8a15e2062adfb29c9fb23d4253550a0b3c9be1cd45530f88796b4644
@@ -7709,6 +7859,16 @@ packages:
   license: Python-2.0
   size: 49003
   timestamp: 1761175499490
+- conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.9.23-py39hd8ed1ab_0.conda
+  noarch: generic
+  sha256: c7e0c7cf0467e80dc00107950e7295a794262d4fe3109cf6e07a90b627ea5b59
+  md5: 59965790c6aef69b417ccb9938512ec9
+  depends:
+  - python >=3.9,<3.10.0a0
+  - python_abi * *_cp39
+  license: Python-2.0
+  size: 49247
+  timestamp: 1749059639291
 - conda: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.11.0-hfcd1e18_0.conda
   sha256: 3fcc97ae3e89c150401a50a4de58794ffc67b1ed0e1851468fcc376980201e25
   md5: 5da8c935dca9186673987f79cef0b2a5
@@ -14224,6 +14384,16 @@ packages:
   license_family: MIT
   size: 11766
   timestamp: 1745776666688
+- conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.0.1-pyhe01879c_0.conda
+  sha256: 167ed2f6100909830863531faa2dce250eedee78f2d64c4e5506dc3f3ae3c354
+  md5: 5f0dea40791cecf0f82882b9eea7f7c1
+  depends:
+  - python >=3.9
+  - python
+  license: MIT
+  license_family: MIT
+  size: 240527
+  timestamp: 1753814733349
 - conda: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.1.2-pyhe01879c_0.conda
   sha256: 54c58f45029b79a1fec25dc6f6179879afa4dddb73e5c38c85e574f66bb1d930
   md5: 90d3b6c75c144e8c461b846410d7c0bf
@@ -16740,6 +16910,198 @@ packages:
   license_family: MIT
   size: 24246
   timestamp: 1747339794916
+- conda: https://conda.anaconda.org/conda-forge/linux-64/polars-1.33.1-default_h755bcc6_0.conda
+  sha256: 1e087571dd53b179e42b7d47acd6031921c9b9de4c341a408fc8bb23096d28c3
+  md5: 1884a1a6acc457c8e4b59b0f6450e140
+  depends:
+  - polars-default ==1.33.1 py39hf521cc8_0
+  license: MIT
+  license_family: MIT
+  size: 5729
+  timestamp: 1757413932841
+- conda: https://conda.anaconda.org/conda-forge/noarch/polars-1.35.2-pyh6a1acc5_0.conda
+  sha256: 21619fb41876fb3ad602de2f1a493891fd02bd82f0659d0ccb4d8ddd3271eca7
+  md5: 24e8f78d79881b3c035f89f4b83c565c
+  depends:
+  - polars-runtime-32 ==1.35.2
+  - python >=3.10
+  - python
+  constrains:
+  - numpy >=1.16.0
+  - pyarrow >=7.0.0
+  - fastexcel >=0.9
+  - openpyxl >=3.0.0
+  - xlsx2csv >=0.8.0
+  - connectorx >=0.3.2
+  - deltalake >=1.0.0
+  - pyiceberg >=0.7.1
+  - altair >=5.4.0
+  - great_tables >=0.8.0
+  - polars-runtime-32 ==1.35.2
+  - polars-runtime-64 ==1.35.2
+  - polars-runtime-compat ==1.35.2
+  license: MIT
+  license_family: MIT
+  size: 513921
+  timestamp: 1763738199817
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-1.33.1-default_h107b989_0.conda
+  sha256: 396fe6dd7d25ecd05ca1e290465c6aab30388adbb27f6b80c776f44548b9a627
+  md5: 7bb9d21a632ff0e2240c475438591db7
+  depends:
+  - polars-default ==1.33.1 py39h31c57e4_0
+  license: MIT
+  license_family: MIT
+  size: 5754
+  timestamp: 1757413441375
+- conda: https://conda.anaconda.org/conda-forge/win-64/polars-1.33.1-default_hd0420bf_0.conda
+  sha256: 8b2229f96725090d4ef1c23b0082562b048cb2a9c2abfa0c3b8e5cbcf2cf95e5
+  md5: 3cb9d0e5f40305440675a5f094c3b721
+  depends:
+  - polars-default ==1.33.1 py39he906d20_0
+  license: MIT
+  license_family: MIT
+  size: 5753
+  timestamp: 1757413424803
+- conda: https://conda.anaconda.org/conda-forge/linux-64/polars-default-1.33.1-py39hf521cc8_0.conda
+  noarch: python
+  sha256: 789e3a969a1b437c1df80265088ddc656dbb6f69443c0a9724a4b587faf0da42
+  md5: 900f486d119d5c83d14c812068a3ecad
+  depends:
+  - python
+  - libgcc >=14
+  - __glibc >=2.17,<3.0.a0
+  - libstdcxx >=14
+  - libgcc >=14
+  - _python_abi3_support 1.*
+  - cpython >=3.9
+  constrains:
+  - numpy >=1.16.0
+  - pyarrow >=7.0.0
+  - fastexcel >=0.9
+  - openpyxl >=3.0.0
+  - xlsx2csv >=0.8.0
+  - connectorx >=0.3.2
+  - deltalake >=1.0.0
+  - pyiceberg >=0.7.1
+  - altair >=5.4.0
+  - great_tables >=0.8.0
+  - polars-lts-cpu <0.0.0a0
+  - polars-u64-idx <0.0.0a0
+  - __glibc >=2.17
+  license: MIT
+  license_family: MIT
+  size: 32170742
+  timestamp: 1757413932841
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-default-1.33.1-py39h31c57e4_0.conda
+  noarch: python
+  sha256: 65a0122d2ed894a9f94dcd96bd9de740ceffbf1b34341e8d6e1b1be0aa207a3a
+  md5: 0bac077a11de4673eeb6aa609ab7bd69
+  depends:
+  - python
+  - __osx >=11.0
+  - libcxx >=19
+  - _python_abi3_support 1.*
+  - cpython >=3.9
+  constrains:
+  - numpy >=1.16.0
+  - pyarrow >=7.0.0
+  - fastexcel >=0.9
+  - openpyxl >=3.0.0
+  - xlsx2csv >=0.8.0
+  - connectorx >=0.3.2
+  - deltalake >=1.0.0
+  - pyiceberg >=0.7.1
+  - altair >=5.4.0
+  - great_tables >=0.8.0
+  - polars-lts-cpu <0.0.0a0
+  - polars-u64-idx <0.0.0a0
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  size: 29418459
+  timestamp: 1757413441375
+- conda: https://conda.anaconda.org/conda-forge/win-64/polars-default-1.33.1-py39he906d20_0.conda
+  noarch: python
+  sha256: f30d7e72d445014d63ec907900331b20ddab87c729996dd2c39be1ae2a53854d
+  md5: 5ae3ef1e9e0afb6c5a86100d05890970
+  depends:
+  - python
+  - 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
+  - _python_abi3_support 1.*
+  - cpython >=3.9
+  constrains:
+  - numpy >=1.16.0
+  - pyarrow >=7.0.0
+  - fastexcel >=0.9
+  - openpyxl >=3.0.0
+  - xlsx2csv >=0.8.0
+  - connectorx >=0.3.2
+  - deltalake >=1.0.0
+  - pyiceberg >=0.7.1
+  - altair >=5.4.0
+  - great_tables >=0.8.0
+  - polars-lts-cpu <0.0.0a0
+  - polars-u64-idx <0.0.0a0
+  license: MIT
+  license_family: MIT
+  size: 35019348
+  timestamp: 1757413424802
+- conda: https://conda.anaconda.org/conda-forge/linux-64/polars-runtime-32-1.35.2-py310hffdcd12_0.conda
+  noarch: python
+  sha256: a2294d8079173544b519e1564ce4897dc176b92dbba9508c75c42e8cba6fe680
+  md5: 2b90c3aaf73a5b6028b068cf3c76e0b7
+  depends:
+  - python
+  - __glibc >=2.17,<3.0.a0
+  - libstdcxx >=14
+  - libgcc >=14
+  - _python_abi3_support 1.*
+  - cpython >=3.10
+  constrains:
+  - __glibc >=2.17
+  license: MIT
+  license_family: MIT
+  size: 33271651
+  timestamp: 1763738199812
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/polars-runtime-32-1.35.2-py310h34bb384_0.conda
+  noarch: python
+  sha256: 3f1b00a0ee4e56edfe272d60d51b3c243abeb1e0f872288182ef82d60f7c0d7b
+  md5: a5f4a3ca01f292a6b21aa42bb1c5a9aa
+  depends:
+  - python
+  - __osx >=11.0
+  - libcxx >=19
+  - _python_abi3_support 1.*
+  - cpython >=3.10
+  constrains:
+  - __osx >=11.0
+  license: MIT
+  license_family: MIT
+  size: 30029154
+  timestamp: 1763738030442
+- conda: https://conda.anaconda.org/conda-forge/win-64/polars-runtime-32-1.35.2-py310hca7251b_0.conda
+  noarch: python
+  sha256: 03fcd84f828086be2cf658fb6250dfa665da241900f61326cae4c5d8c49642b7
+  md5: 56f24763ee87604f694db3e0c74ae38c
+  depends:
+  - python
+  - 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
+  - _python_abi3_support 1.*
+  - cpython >=3.10
+  license: MIT
+  license_family: MIT
+  size: 36375197
+  timestamp: 1763738028556
 - conda: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.3.0-pyha770c72_0.conda
   sha256: 66b6d429ab2201abaa7282af06b17f7631dcaafbc5aff112922b48544514b80a
   md5: bc6c44af2a9e6067dd7e949ef10cdfba
@@ -18459,6 +18821,33 @@ packages:
   license_family: BSD
   size: 244628
   timestamp: 1755304154927
+- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.10.19-hd8ed1ab_2.conda
+  sha256: 460ea488d7b545eb434cb58a6022df14bd0ba0a9cbe2d1b5816ba9fc268615c3
+  md5: 6f3b7a672f47e87de27a9941b5876f60
+  depends:
+  - cpython 3.10.19.*
+  - python_abi * *_cp310
+  license: Python-2.0
+  size: 50183
+  timestamp: 1761172142297
+- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.11.14-hd8ed1ab_2.conda
+  sha256: 9261923f0dc8a3c8c517c29d8f3b7eea80f2577b094198239895bd7a88b534c5
+  md5: a4effc7e6eb335d0e1080a5554590425
+  depends:
+  - cpython 3.11.14.*
+  - python_abi * *_cp311
+  license: Python-2.0
+  size: 47569
+  timestamp: 1761172935811
+- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.12.12-hd8ed1ab_1.conda
+  sha256: 59f17182813f8b23709b7d4cfda82c33b72dd007cb729efa0033c609fbd92122
+  md5: c20172b4c59fbe288fa50cdc1b693d73
+  depends:
+  - cpython 3.12.12.*
+  - python_abi * *_cp312
+  license: Python-2.0
+  size: 45888
+  timestamp: 1761175248278
 - conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.13.9-h4df99d1_101.conda
   sha256: 7535b9cb2414e34c73ed4a97a90bcadcc76b9d47d0bb8ef5002c592d85fe022d
   md5: f41e3c1125e292e6bfcea8392a3de3d8
@@ -18477,6 +18866,15 @@ packages:
   license: Python-2.0
   size: 48968
   timestamp: 1761175555295
+- conda: https://conda.anaconda.org/conda-forge/noarch/python-gil-3.9.23-hd8ed1ab_0.conda
+  sha256: 60faea237d55d9ff27f7a77dc720af552f2a79690d7a8718089c730255334d40
+  md5: 7f35c7c4b3484c74844bb8ede73a02bc
+  depends:
+  - cpython 3.9.23.*
+  - python_abi * *_cp39
+  license: Python-2.0
+  size: 49213
+  timestamp: 1749059665408
 - conda: https://conda.anaconda.org/conda-forge/noarch/python-json-logger-2.0.7-pyhd8ed1ab_0.conda
   sha256: 4790787fe1f4e8da616edca4acf6a4f8ed4e7c6967aa31b920208fc8f95efcca
   md5: a61bf9ec79426938ff785eb69dbb1960
diff --git a/pixi.toml b/pixi.toml
index 07736334c..f0cc70bce 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -54,6 +54,7 @@ wheel = "*"
 
 [dependencies]
 formulaic = "*"
+narwhals = ">=2.0.0"
 numexpr = "*"
 packaging = "*"
 pandas = ">=1.4"
@@ -77,6 +78,7 @@ attrs = "*"
 click = "*"
 git_root = "*"
 mypy = "*"
+polars = "*"
 psutil = "*"
 pytest = "*"
 pytest-xdist = "*"
@@ -134,7 +136,7 @@ cxx-compiler = "*"
 cython = "!=3.0.4"
 make = "*"
 mako = "*"
-narwhals = ">=1.4.1"
+narwhals = ">=2.0.0"
 pip = "*"
 setuptools-scm = "*"
 xsimd = "<11|>12.1"
@@ -154,6 +156,7 @@ python = "3.13.*"
 
 [feature.oldies.dependencies]
 formulaic = "0.6.*"
+narwhals = "2.0.*"
 pandas = "1.4.*"
 python = "3.9.*"
 scikit-learn = "0.24.*"
diff --git a/src/glum/_glm.py b/src/glum/_glm.py
index f5ab58b62..c15c2d029 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,
@@ -38,12 +41,17 @@
     _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,
     expand_categorical_penalties,
-    is_contiguous,
     safe_toarray,
     standardize,
     standardize_warm_start,
@@ -174,6 +182,29 @@ 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 +276,9 @@ 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 +287,19 @@ 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 +307,7 @@ 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 +751,8 @@ 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 +840,9 @@ 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 +1486,8 @@ 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 +1600,8 @@ 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 +1758,7 @@ 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 +1781,8 @@ 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 +1836,28 @@ def _set_up_and_check_fit_args(
             else:
                 # Maybe TODO: expand categorical penalties with formulas
 
-                self.feature_dtypes_ = X.dtypes.to_dict()
+                # 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 +1865,7 @@ 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 +1881,6 @@ 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")
@@ -2672,8 +2710,8 @@ 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 +2770,7 @@ 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 +2807,7 @@ 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 +2852,7 @@ 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 +2891,7 @@ 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/_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..ffc56fbaa 100644
--- a/src/glum/_utils.py
+++ b/src/glum/_utils.py
@@ -2,8 +2,8 @@
 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
 from scipy import sparse
 
@@ -11,50 +11,42 @@
 
 
 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 +54,49 @@ 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 +119,13 @@ 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)
@@ -142,16 +136,6 @@ def expand_categorical_penalties(
         return penalty
 
 
-def is_contiguous(X) -> bool:
-    if isinstance(X, np.ndarray):
-        return X.flags["C_CONTIGUOUS"] or X.flags["F_CONTIGUOUS"]
-    elif isinstance(X, pd.DataFrame):
-        return is_contiguous(X.values)
-    else:
-        # If not a numpy array or pandas data frame, we assume it is contiguous.
-        return True
-
-
 def safe_toarray(X) -> np.ndarray:
     """Return a numpy array."""
     if sparse.issparse(X):
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_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,