DEPS: Clean unused dependencies

ci/deps/actions-310-minimum_versions.yaml

@@ -26,7 +26,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4=4.12.3
-  - blosc=1.21.3
   - bottleneck=1.3.6
   - fastparquet=2024.2.0
   - fsspec=2024.2.0

ci/deps/actions-310.yaml

@@ -24,7 +24,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.12.3
-  - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2024.2.0
   - fsspec>=2024.2.0
@@ -52,7 +51,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0

ci/deps/actions-311-downstream_compat.yaml

@@ -25,7 +25,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.12.3
-  - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2024.2.0
   - fsspec>=2024.2.0
@@ -53,7 +52,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0
@@ -63,14 +62,12 @@ dependencies:
   - cftime
   - dask
   - ipython
-  - geopandas-base
   - seaborn
   - scikit-learn
   - statsmodels
   - coverage
   - pandas-datareader
   - pyyaml
-  - py
   - pip:
     - adbc-driver-postgresql>=0.10.0
     - adbc-driver-sqlite>=0.8.0

ci/deps/actions-311.yaml

@@ -24,7 +24,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.12.3
-  - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2024.2.0
   - fsspec>=2024.2.0
@@ -52,7 +51,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0

ci/deps/actions-312.yaml

@@ -24,7 +24,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.12.3
-  - blosc>=1.21.3
   - bottleneck>=1.3.6
   - fastparquet>=2024.2.0
   - fsspec>=2024.2.0
@@ -52,7 +51,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0

ci/deps/actions-313.yaml

@@ -52,7 +52,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0

doc/source/getting_started/install.rst

@@ -305,7 +305,6 @@ Installable with ``pip install "pandas[hdf5, parquet, feather, spss, excel]"``
 Dependency                                             Minimum Version    pip extra        Notes
 ====================================================== ================== ================ ==========================================================
 `PyTables <https://github.com/PyTables/PyTables>`__    3.8.0              hdf5             HDF5-based reading / writing
-`blosc <https://github.com/Blosc/c-blosc>`__           1.21.3             hdf5             Compression for HDF5; only available on ``conda``
 `zlib <https://github.com/madler/zlib>`__                                 hdf5             Compression for HDF5
 `fastparquet <https://github.com/dask/fastparquet>`__  2024.2.0           -                Parquet reading / writing (pyarrow is default)
 `pyarrow <https://github.com/apache/arrow>`__          10.0.1             parquet, feather Parquet, ORC, and feather reading / writing

environment.yml

@@ -27,7 +27,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=4.12.3
-  - blosc
   - bottleneck>=1.3.6
   - fastparquet>=2024.2.0
   - fsspec>=2024.2.0
@@ -55,7 +54,7 @@ dependencies:
   - scipy>=1.12.0
   - sqlalchemy>=2.0.0
   - tabulate>=0.9.0
-  - xarray>=2024.1.1, <=2024.9.0
+  - xarray>=2024.1.1
   - xlrd>=2.0.1
   - xlsxwriter>=3.2.0
   - zstandard>=0.22.0
@@ -83,8 +82,6 @@ dependencies:
 
   # documentation
   - gitpython  # obtain contributors from git for whatsnew
-  - gitdb
-  - google-auth
   - natsort  # DataFrame.sort_values doctest
   - numpydoc
   - pydata-sphinx-theme=0.16

pandas/compat/_optional.py

@@ -23,7 +23,6 @@
     "adbc-driver-postgresql": "0.10.0",
     "adbc-driver-sqlite": "0.8.0",
     "bs4": "4.12.3",
-    "blosc": "1.21.3",
     "bottleneck": "1.3.6",
     "fastparquet": "2024.2.0",
     "fsspec": "2024.2.0",

pandas/tests/generic/test_to_xarray.py

@@ -6,11 +6,13 @@
     DataFrame,
     MultiIndex,
     Series,
+    StringDtype,
     date_range,
 )
 import pandas._testing as tm
+from pandas.util.version import Version
 
-pytest.importorskip("xarray")
+xarray = pytest.importorskip("xarray")
 
 
 class TestDataFrameToXArray:
@@ -29,13 +31,17 @@ def df(self):
             }
         )
 
-    def test_to_xarray_index_types(self, index_flat, df, using_infer_string):
+    def test_to_xarray_index_types(self, index_flat, df, request):
         index = index_flat
         # MultiIndex is tested in test_to_xarray_with_multiindex
         if len(index) == 0:
             pytest.skip("Test doesn't make sense for empty index")
-
-        from xarray import Dataset
+        elif Version(xarray.__version__) <= Version("2024.9.0"):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="Categorical column not preserved.",
+                )
+            )
 
         df.index = index[:4]
         df.index.name = "foo"
@@ -45,29 +51,22 @@ def test_to_xarray_index_types(self, index_flat, df, using_infer_string):
         assert len(result.coords) == 1
         assert len(result.data_vars) == 8
         tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
-        assert isinstance(result, Dataset)
+        assert isinstance(result, xarray.Dataset)
 
         # idempotency
         # datetimes w/tz are preserved
         # column names are lost
         expected = df.copy()
-        expected["f"] = expected["f"].astype(
-            object if not using_infer_string else "str"
-        )
         expected.columns.name = None
         tm.assert_frame_equal(result.to_dataframe(), expected)
 
     def test_to_xarray_empty(self, df):
-        from xarray import Dataset
-
         df.index.name = "foo"
         result = df[0:0].to_xarray()
         assert result.sizes["foo"] == 0
-        assert isinstance(result, Dataset)
+        assert isinstance(result, xarray.Dataset)
 
     def test_to_xarray_with_multiindex(self, df, using_infer_string):
-        from xarray import Dataset
-
         # MultiIndex
         df.index = MultiIndex.from_product([["a"], range(4)], names=["one", "two"])
         result = df.to_xarray()
@@ -76,7 +75,7 @@ def test_to_xarray_with_multiindex(self, df, using_infer_string):
         assert len(result.coords) == 2
         assert len(result.data_vars) == 8
         tm.assert_almost_equal(list(result.coords.keys()), ["one", "two"])
-        assert isinstance(result, Dataset)
+        assert isinstance(result, xarray.Dataset)
 
         result = result.to_dataframe()
         expected = df.copy()
@@ -88,43 +87,48 @@ def test_to_xarray_with_multiindex(self, df, using_infer_string):
 
 
 class TestSeriesToXArray:
-    def test_to_xarray_index_types(self, index_flat):
+    def test_to_xarray_index_types(self, index_flat, request):
         index = index_flat
+        if (
+            isinstance(index.dtype, StringDtype)
+            and index.dtype.storage == "pyarrow"
+            and Version(xarray.__version__) > Version("2024.9.0")
+        ):
+            request.applymarker(
+                pytest.mark.xfail(
+                    reason="xarray calling reshape of ArrowExtensionArray",
+                    raises=NotImplementedError,
+                )
+            )
         # MultiIndex is tested in test_to_xarray_with_multiindex
 
-        from xarray import DataArray
-
         ser = Series(range(len(index)), index=index, dtype="int64")
         ser.index.name = "foo"
         result = ser.to_xarray()
         repr(result)
         assert len(result) == len(index)
         assert len(result.coords) == 1
         tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
-        assert isinstance(result, DataArray)
+        assert isinstance(result, xarray.DataArray)
 
         # idempotency
         tm.assert_series_equal(result.to_series(), ser)
 
     def test_to_xarray_empty(self):
-        from xarray import DataArray
-
         ser = Series([], dtype=object)
         ser.index.name = "foo"
         result = ser.to_xarray()
         assert len(result) == 0
         assert len(result.coords) == 1
         tm.assert_almost_equal(list(result.coords.keys()), ["foo"])
-        assert isinstance(result, DataArray)
+        assert isinstance(result, xarray.DataArray)
 
     def test_to_xarray_with_multiindex(self):
-        from xarray import DataArray
-
         mi = MultiIndex.from_product([["a", "b"], range(3)], names=["one", "two"])
         ser = Series(range(6), dtype="int64", index=mi)
         result = ser.to_xarray()
         assert len(result) == 2
         tm.assert_almost_equal(list(result.coords.keys()), ["one", "two"])
-        assert isinstance(result, DataArray)
+        assert isinstance(result, xarray.DataArray)
         res = result.to_series()
         tm.assert_series_equal(res, ser)

pandas/tests/test_downstream.py

@@ -103,7 +103,7 @@ def test_xarray_cftimeindex_nearest():
     cftime = pytest.importorskip("cftime")
     xarray = pytest.importorskip("xarray")
 
-    times = xarray.cftime_range("0001", periods=2)
+    times = xarray.date_range("0001", periods=2, use_cftime=True)
     key = cftime.DatetimeGregorian(2000, 1, 1)
     result = times.get_indexer([key], method="nearest")
     expected = 1

pyproject.toml

@@ -68,9 +68,7 @@ gcp = ['gcsfs>=2024.2.0']
 excel = ['odfpy>=1.4.1', 'openpyxl>=3.1.2', 'python-calamine>=0.1.7', 'pyxlsb>=1.0.10', 'xlrd>=2.0.1', 'xlsxwriter>=3.2.0']
 parquet = ['pyarrow>=10.0.1']
 feather = ['pyarrow>=10.0.1']
-hdf5 = [# blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
-        #'blosc>=1.20.1',
-        'tables>=3.8.0']
+hdf5 = ['tables>=3.8.0']
 spss = ['pyreadstat>=1.2.6']
 postgresql = ['SQLAlchemy>=2.0.0', 'psycopg2>=2.9.6', 'adbc-driver-postgresql>=0.10.0']
 mysql = ['SQLAlchemy>=2.0.0', 'pymysql>=1.1.0']
@@ -85,8 +83,6 @@ timezone = ['pytz>=2023.4']
 all = ['adbc-driver-postgresql>=0.10.0',
        'adbc-driver-sqlite>=0.8.0',
        'beautifulsoup4>=4.12.3',
-       # blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
-       #'blosc>=1.21.3',
        'bottleneck>=1.3.6',
        'fastparquet>=2024.2.0',
        'fsspec>=2024.2.0',

requirements-dev.txt

@@ -16,7 +16,6 @@ coverage
 python-dateutil
 numpy<3
 beautifulsoup4>=4.12.3
-blosc
 bottleneck>=1.3.6
 fastparquet>=2024.2.0
 fsspec>=2024.2.0
@@ -44,7 +43,7 @@ s3fs>=2024.2.0
 scipy>=1.12.0
 SQLAlchemy>=2.0.0
 tabulate>=0.9.0
-xarray>=2024.1.1, <=2024.9.0
+xarray>=2024.1.1
 xlrd>=2.0.1
 xlsxwriter>=3.2.0
 zstandard>=0.22.0
@@ -58,8 +57,6 @@ mypy==1.13.0
 tokenize-rt
 pre-commit>=4.2.0
 gitpython
-gitdb
-google-auth
 natsort
 numpydoc
 pydata-sphinx-theme==0.16

scripts/tests/data/deps_expected_random.yaml

@@ -23,7 +23,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4>=5.9.3
-  - blosc
   - bottleneck>=1.3.2
   - fastparquet>=0.6.3
   - fsspec>=2021.07.0

scripts/tests/data/deps_minimum.toml

@@ -63,9 +63,7 @@ gcp = ['gcsfs>=2021.07.0']
 excel = ['odfpy>=1.4.1', 'openpyxl>=3.0.7', 'python-calamine>=0.1.7', 'pyxlsb>=1.0.8', 'xlrd>=2.0.1', 'xlsxwriter>=1.4.3']
 parquet = ['pyarrow>=7.0.0']
 feather = ['pyarrow>=7.0.0']
-hdf5 = [# blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
-        #'blosc>=1.20.1',
-        'tables>=3.6.1']
+hdf5 = ['tables>=3.6.1']
 spss = ['pyreadstat>=1.1.2']
 postgresql = ['SQLAlchemy>=1.4.16', 'psycopg2>=2.8.6']
 mysql = ['SQLAlchemy>=1.4.16', 'pymysql>=1.1.0']
@@ -77,8 +75,6 @@ output_formatting = ['jinja2>=3.0.0', 'tabulate>=0.8.9']
 clipboard = ['PyQt5>=5.15.1', 'qtpy>=2.3.0']
 compression = ['zstandard>=0.15.2']
 all = ['beautifulsoup4>=5.9.3',
-       # blosc only available on conda (https://github.com/Blosc/python-blosc/issues/297)
-       #'blosc>=1.21.0',
        'bottleneck>=1.3.2',
        'fastparquet>=0.6.3',
        'fsspec>=2021.07.0',

scripts/tests/data/deps_unmodified_random.yaml

@@ -23,7 +23,6 @@ dependencies:
 
   # optional dependencies
   - beautifulsoup4
-  - blosc
   - bottleneck>=1.3.2
   - fastparquet>=0.6.3
   - fsspec>=2021.07.0

scripts/validate_min_versions_in_sync.py

@@ -36,7 +36,7 @@
 SETUP_PATH = pathlib.Path("pyproject.toml").resolve()
 YAML_PATH = pathlib.Path("ci/deps")
 ENV_PATH = pathlib.Path("environment.yml")
-EXCLUDE_DEPS = {"tzdata", "blosc", "pyqt", "pyqt5"}
+EXCLUDE_DEPS = {"tzdata", "pyqt", "pyqt5"}
 EXCLUSION_LIST = frozenset(["python=3.8[build=*_pypy]"])
 # pandas package is not available
 # in pre-commit environment