Pin pandas for pylibcudf testing

ci/test_cudf_polars_polars_tests.sh

@@ -48,6 +48,9 @@ sed -i '/PydanticDeprecatedSince212/a \    warnings.simplefilter("ignore", Depre
 # Remove upper bound on aiosqlite once we support polars >1.36.1
 sed -i 's/^aiosqlite/aiosqlite>=0.21.0,<0.22.0/' polars/py-polars/requirements-dev.txt
 
+# Remove upper bound on pandas once we support 3.0.0+
+sed -i 's/^pandas/pandas>=2.0,<2.4.0/' polars/py-polars/requirements-dev.txt
+
 # Pyparsing release 3.3.0 deprecates the enablePackrat method, which is used by the
 # version of pyiceberg that polars is currently pinned to. We can remove this skip
 # when we move to a newer version of polars using a pyiceberg where this issue is fixed

conda/environments/all_cuda-129_arch-aarch64.yaml

@@ -64,7 +64,6 @@ dependencies:
 - nvtx>=0.2.1
 - openpyxl
 - packaging
-- pandas
 - pandas>=2.0,<2.4.0
 - pandoc
 - polars>=1.30,<1.36

conda/environments/all_cuda-129_arch-x86_64.yaml

@@ -64,7 +64,6 @@ dependencies:
 - nvtx>=0.2.1
 - openpyxl
 - packaging
-- pandas
 - pandas>=2.0,<2.4.0
 - pandoc
 - polars>=1.30,<1.36

conda/environments/all_cuda-131_arch-aarch64.yaml

@@ -64,7 +64,6 @@ dependencies:
 - nvtx>=0.2.1
 - openpyxl
 - packaging
-- pandas
 - pandas>=2.0,<2.4.0
 - pandoc
 - polars>=1.30,<1.36

conda/environments/all_cuda-131_arch-x86_64.yaml

@@ -64,7 +64,6 @@ dependencies:
 - nvtx>=0.2.1
 - openpyxl
 - packaging
-- pandas
 - pandas>=2.0,<2.4.0
 - pandoc
 - polars>=1.30,<1.36

cpp/tests/rolling/rolling_test.cpp

@@ -1,5 +1,5 @@
 /*
- * SPDX-FileCopyrightText: Copyright (c) 2019-2025, NVIDIA CORPORATION.
+ * SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
  * SPDX-License-Identifier: Apache-2.0
  */
 
@@ -815,7 +815,7 @@ TYPED_TEST_SUITE(RollingTest, cudf::test::FixedWidthTypesWithoutFixedPoint);
 // simple example from Pandas docs
 TYPED_TEST(RollingTest, SimpleStatic)
 {
-  // https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
+  // https://pandas.pydata.org/pandas-docs/version/2.3.3/reference/api/pandas.DataFrame.rolling.html
   auto const col_data              = cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 0, 4});
   const std::vector<bool> col_mask = {1, 1, 1, 0, 1};
 
@@ -962,7 +962,7 @@ TYPED_TEST(RollingTest, NegativeWindowSizes)
 // simple example from Pandas docs:
 TYPED_TEST(RollingTest, SimpleDynamic)
 {
-  // https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html
+  // https://pandas.pydata.org/pandas-docs/version/2.3.3/reference/api/pandas.DataFrame.rolling.html
   auto const col_data              = cudf::test::make_type_param_vector<TypeParam>({0, 1, 2, 0, 4});
   const std::vector<bool> col_mask = {1, 1, 1, 0, 1};
 

dependencies.yaml

@@ -694,7 +694,7 @@ dependencies:
       - output_types: [conda, requirements, pyproject]
         packages:
           - fsspec>=0.6.0
-          - pandas>=2.0,<2.4.0
+          - &pandas pandas>=2.0,<2.4.0
   run_pylibcudf:
     common:
       - output_types: [conda, requirements, pyproject]
@@ -878,7 +878,7 @@ dependencies:
           - nanoarrow
           - hypothesis>=6.131.7
           - *numba
-          - pandas
+          - *pandas
       - output_types: conda
         packages:
           - python-xxhash

docs/cudf/source/conf.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2018-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2018-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
 # cudf documentation build configuration file, created by
@@ -326,7 +326,7 @@ def clean_all_xml_files(path):
     "nanoarrow": ("https://arrow.apache.org/nanoarrow/latest", None),
     "numpy": ("https://numpy.org/doc/stable", None),
     "pandas": (
-        "https://pandas.pydata.org/pandas-docs/stable/",
+        "https://pandas.pydata.org/pandas-docs/version/2.3.3/",
         None,
     ),
     "polars": ("https://docs.pola.rs/api/python/stable/", None),

docs/cudf/source/cudf_pandas/faq.md

@@ -195,7 +195,7 @@ This means that automatic conversion between GPU and CPU types and automatic fal
 There are a few known limitations that you should be aware of:
 
 - Because fallback involves copying data from GPU to CPU and back,
-  [value mutability](https://pandas.pydata.org/pandas-docs/stable/getting_started/overview.html#mutability-and-copying-of-data)
+  [value mutability](https://pandas.pydata.org/pandas-docs/version/2.3.3/getting_started/overview.html#mutability-and-copying-of-data)
   of Pandas objects is not always guaranteed. You should follow the
   pandas recommendation to favor immutable operations.
 - For performance reasons, joins and join-based operations are not

docs/cudf/source/user_guide/groupby.md

@@ -9,7 +9,7 @@ myst:
 # GroupBy
 
 cuDF supports a small (but important) subset of Pandas' [groupby
-API](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html).
+API](https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/groupby.html).
 
 ## Summary of supported operations
 
@@ -28,7 +28,7 @@ API](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html).
    equivalent Pandas function. See the section on
    [apply](#groupby-apply) for more details.
 9. `GroupBy.pipe` similar to
-   [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#piping-function-calls).
+   [Pandas](https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/groupby.html#piping-function-calls).
 
 ## Grouping
 
@@ -271,4 +271,4 @@ a
   4     4     5     9
 ```
 
-[describe]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#flexible-apply
+[describe]: https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/groupby.html#flexible-apply

python/cudf/cudf/core/column/datetime.py

@@ -383,7 +383,7 @@ def _round_dt(
         ],
         freq: str,
     ) -> ColumnBase:
-        # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timedelta.resolution_string.html
+        # https://pandas.pydata.org/pandas-docs/version/2.3.3/reference/api/pandas.Timedelta.resolution_string.html
         old_to_new_freq_map = {
             "H": "h",
             "N": "ns",

python/cudf/cudf/core/column_accessor.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2021-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2021-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 from __future__ import annotations
@@ -61,7 +61,7 @@ def from_zip(cls, data: Iterator):
     def __getitem__(self, key):
         """Recursively apply dict.__getitem__ for nested elements."""
         # As described in the pandas docs
-        # https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced-indexing-with-hierarchical-index
+        # https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/advanced.html#advanced-indexing-with-hierarchical-index
         # accessing nested elements of a multiindex must be done using a tuple.
         # Lists and other sequences are treated as accessing multiple elements
         # at the top level of the index.

python/cudf/cudf/tests/reshape/test_pivot.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 import pandas as pd
@@ -43,7 +43,7 @@ def test_pivot_simple(index, column, data):
 
 def test_pivot_multi_values():
     # from Pandas docs:
-    # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pivot.html
+    # https://pandas.pydata.org/pandas-docs/version/2.3.3/reference/api/pandas.DataFrame.pivot.html
     pdf = pd.DataFrame(
         {
             "foo": ["one", "one", "one", "two", "two", "two"],

python/cudf/cudf/tests/series/methods/test_replace.py

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 import re
@@ -189,7 +189,7 @@ def test_series_fillna_numerical(
         psr = psr.copy(deep=True)
     # TODO: These tests should use Pandas' nullable int type
     # when we support a recent enough version of Pandas
-    # https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
+    # https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/integer_na.html
     if np.dtype(numeric_types_as_str).kind != "f" and psr.dtype.kind == "i":
         psr = psr.astype(
             cudf.utils.dtypes.np_dtypes_to_pandas_dtypes[

python/cudf/cudf/utils/ioutils.py

@@ -932,7 +932,7 @@
 ----------
 path_or_buf : string, buffer or path object
     Path to the file to open, or an open `HDFStore
-    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+    <https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/io.html#hdf5-pytables>`_.
     object.
     Supports any object implementing the ``__fspath__`` protocol.
     This includes :class:`pathlib.Path` and py._path.local.LocalPath
@@ -943,7 +943,7 @@
 mode : {'r', 'r+', 'a'}, optional
     Mode to use when opening the file. Ignored if path_or_buf is a
     `Pandas HDFS
-    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+    <https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/io.html#hdf5-pytables>`_.
     Default is 'r'.
 where : list, optional
     A list of Term (or convertible) objects.
@@ -987,7 +987,7 @@
 please use append mode and a different a key.
 
 For more information see the `user guide
-<https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables>`_.
+<https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/io.html#hdf5-pytables>`_.
 
 Parameters
 ----------
@@ -1017,7 +1017,7 @@
     List of columns to create as indexed data columns for on-disk
     queries, or True to use all columns. By default only the axes
     of the object are indexed. See `Query via Data Columns
-    <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-hdf5-query-data-columns>`_.
+    <https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/io.html#io-hdf5-query-data-columns>`_.
     Applicable only to format='table'.
 complevel : {0-9}, optional
     Specifies a compression level for data.

python/cudf/cudf/utils/queryutils.py

@@ -52,7 +52,7 @@ def visit_Name(self, node):
 def query_parser(text):
     """The query expression parser.
 
-    See https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.query.html
+    See https://pandas.pydata.org/pandas-docs/version/2.3.3/generated/pandas.DataFrame.query.html
 
     * names with '@' prefix are global reference.
     * other names must be column names of the dataframe.

python/pylibcudf/pylibcudf/expressions.pyx

@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2024-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2024-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 import ast
 import functools
@@ -331,7 +331,7 @@ _python_cudf_operator_map = {
 # corresponding libcudf C++ AST operators.
 _python_cudf_function_map = {
     # TODO: Operators listed on
-    # https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#expression-evaluation-via-eval  # noqa: E501
+    # https://pandas.pydata.org/pandas-docs/version/2.3.3/user_guide/enhancingperf.html#expression-evaluation-via-eval  # noqa: E501
     # that we don't support yet:
     # expm1, log1p, arctan2 and log10.
     "isnull": ASTOperator.IS_NULL,

python/pylibcudf/pyproject.toml

@@ -46,7 +46,7 @@ test = [
     "nanoarrow",
     "numba-cuda[cu13]>=0.22.2,<0.23.0",
     "numba>=0.60.0,<0.62.0",
-    "pandas",
+    "pandas>=2.0,<2.4.0",
     "pyarrow>=15.0.0,!=17.0.0; platform_machine=='aarch64'",
     "pyarrow>=15.0.0; platform_machine=='x86_64'",
     "pytest",