ENH Support SDL-based packages and add pyxel. ### Description

This adds SDL-based package support to Pyodide and adds pyxel as a PoC. Most of the troubleshooting is done by @kitao and pyxel devs, a big thanks to them.

This is on top of #3505

1) I linked only js part of SDL libraries to the main module and linked the native parts to the side module. So the size increase of the main module is small enough (~0.1MB increase to the pyodide.asm.js).

Before (in #3505):

```
5145705 pyodide.asm.data
1204075 pyodide.asm.js
8862216 pyodide.asm.wasm
```

After:

```
5145705 pyodide.asm.data
1346524 pyodide.asm.js
8905514 pyodide.asm.wasm
```

2) Emscripten draws everything into `Module.canvas`, but the canvas attribute is not set automatically (which makes sense as we don't output any HTML), so it is the user's job to set it manually.

For now, I am thinking of just mentioning it in the FAQ entry, "you need to set pyodide._module.canvas = ...". But it would be probably nice to have an API to set canvas, as pyodide._module is a private attribute.

### Checklists

- [x] Add a [CHANGELOG](https://github.com/pyodide/pyodide/blob/main/docs/project/changelog.md) entry
- [x] Add / update tests
- [x] Add new / update outdated documentation

### Demo

https://ryanking13.github.io/pyodide-pyxel-test

(mostly borrowed from the pyxel repository: https://github.com/kitao/pyxel/tree/main/wasm)

# xgboost tests are copied from: https://github.com/dmlc/xgboost/tree/master/tests/python
import pathlib

import pytest
from pytest_pyodide import run_in_pyodide

DEMO_PATH = pathlib.Path(__file__).parent / "test_data"


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost"])
def test_compat(selenium):
    import numpy as np
    from xgboost.compat import lazy_isinstance

    a = np.array([1, 2, 3])
    assert lazy_isinstance(a, "numpy", "ndarray")
    assert not lazy_isinstance(a, "numpy", "dataframe")


@pytest.mark.driver_timeout(60)
def test_basic_classification(selenium):
    @run_in_pyodide(packages=["xgboost"])
    def run(selenium, data_train):
        with open("dermatology.data", "wb") as f:
            f.write(data_train)

        import numpy as np
        import xgboost as xgb

        # label need to be 0 to num_class -1
        data = np.loadtxt(
            "./dermatology.data",
            delimiter=",",
            converters={33: lambda x: int(x == "?"), 34: lambda x: int(x) - 1},
        )
        sz = data.shape

        train = data[: int(sz[0] * 0.7), :]
        test = data[int(sz[0] * 0.7) :, :]

        train_X = train[:, :33]
        train_Y = train[:, 34]

        test_X = test[:, :33]
        test_Y = test[:, 34]

        xg_train = xgb.DMatrix(train_X, label=train_Y)
        xg_test = xgb.DMatrix(test_X, label=test_Y)
        # setup parameters for xgboost
        param = {}
        # use softmax multi-class classification
        param["objective"] = "multi:softmax"
        # scale weight of positive examples
        param["eta"] = 0.1  # type: ignore[assignment]
        param["max_depth"] = 6  # type: ignore[assignment]
        param["nthread"] = 4  # type: ignore[assignment]
        param["num_class"] = 6  # type: ignore[assignment]

        watchlist = [(xg_train, "train"), (xg_test, "test")]
        num_round = 5
        bst = xgb.train(param, xg_train, num_round, watchlist)
        # get prediction
        pred = bst.predict(xg_test)
        error_rate = np.sum(pred != test_Y) / test_Y.shape[0]
        assert error_rate < 0.1

        # do the same thing again, but output probabilities
        param["objective"] = "multi:softprob"
        bst = xgb.train(param, xg_train, num_round, watchlist)
        # Note: this convention has been changed since xgboost-unity
        # get prediction, this is in 1D array, need reshape to (ndata, nclass)
        pred_prob = bst.predict(xg_test).reshape(test_Y.shape[0], 6)
        pred_label = np.argmax(pred_prob, axis=1)
        error_rate = np.sum(pred_label != test_Y) / test_Y.shape[0]
        assert error_rate < 0.1

    DATA_TRAIN = (DEMO_PATH / "dermatology.data").read_bytes()
    run(selenium, DATA_TRAIN)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["a", "b", "c"]
    assert dm.feature_types == ["int", "float", "i"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3
    np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))

    # overwrite feature_names and feature_types
    dm = xgb.DMatrix(
        df,
        label=pd.Series([1, 2]),
        feature_names=["x", "y", "z"],
        feature_types=["q", "q", "q"],
    )
    assert dm.feature_names == ["x", "y", "z"]
    assert dm.feature_types == ["q", "q", "q"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3

    # incorrect dtypes
    df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
    with pytest.raises(ValueError):
        xgb.DMatrix(df)

    # numeric columns
    df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["0", "1", "2"]
    assert dm.feature_types == ["int", "float", "i"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3
    np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))

    df = pd.DataFrame([[1, 2.0, 1], [2, 3.0, 1]], columns=[4, 5, 6])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["4", "5", "6"]
    assert dm.feature_types == ["int", "float", "int"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3

    df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
    dummies = pd.get_dummies(df)
    #    B  A_X  A_Y  A_Z
    # 0  1    1    0    0
    # 1  2    0    1    0
    # 2  3    0    0    1
    result, _, _ = xgb.data._transform_pandas_df(dummies, enable_categorical=False)
    exp = np.array([[1.0, 1.0, 0.0, 0.0], [2.0, 0.0, 1.0, 0.0], [3.0, 0.0, 0.0, 1.0]])
    np.testing.assert_array_equal(result, exp)
    dm = xgb.DMatrix(dummies)
    assert dm.feature_names == ["B", "A_X", "A_Y", "A_Z"]
    assert dm.feature_types == ["int", "int", "int", "int"]
    assert dm.num_row() == 3
    assert dm.num_col() == 4

    df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
    dm = xgb.DMatrix(df)
    assert dm.feature_names == ["A=1", "A=2"]
    assert dm.feature_types == ["int", "int"]
    assert dm.num_row() == 3
    assert dm.num_col() == 2

    df_int = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
    dm_int = xgb.DMatrix(df_int)
    df_range = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1))
    dm_range = xgb.DMatrix(df_range)
    assert dm_int.feature_names == ["9", "10"]  # assert not "9 "
    assert dm_int.feature_names == dm_range.feature_names

    # test MultiIndex as columns
    df = pd.DataFrame(
        [(1, 2, 3, 4, 5, 6), (6, 5, 4, 3, 2, 1)],
        columns=pd.MultiIndex.from_tuples(
            (
                ("a", 1),
                ("a", 2),
                ("a", 3),
                ("b", 1),
                ("b", 2),
                ("b", 3),
            )
        ),
    )
    dm = xgb.DMatrix(df)
    assert dm.feature_names == ["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"]
    assert dm.feature_types == ["int", "int", "int", "int", "int", "int"]
    assert dm.num_row() == 2
    assert dm.num_col() == 6

    # test Index as columns
    df = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=pd.Index([1, 2]))
    # print(df.columns, isinstance(df.columns, pd.Index))
    Xy = xgb.DMatrix(df)
    np.testing.assert_equal(np.array(Xy.feature_names), np.array(["1", "2"]))


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_slice(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    rng = np.random.RandomState(1994)
    rows = 100
    X = rng.randint(3, 7, size=rows)
    X = pd.DataFrame({"f0": X})
    y = rng.randn(rows)
    ridxs = [1, 2, 3, 4, 5, 6]
    m = xgb.DMatrix(X, y)
    sliced = m.slice(ridxs)

    assert m.feature_types == sliced.feature_types


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas_categorical(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    rng = np.random.RandomState(1994)
    rows = 100
    X = rng.randint(3, 7, size=rows)
    X = pd.Series(X, dtype="category")
    X = pd.DataFrame({"f0": X})
    y = rng.randn(rows)
    m = xgb.DMatrix(X, y, enable_categorical=True)
    assert m.feature_types[0] == "c"

    X_0 = ["f", "o", "o"]
    X_1 = [4, 3, 2]
    X = pd.DataFrame({"feat_0": X_0, "feat_1": X_1})
    X["feat_0"] = X["feat_0"].astype("category")  # type: ignore[call-overload]
    transformed, _, feature_types = xgb.data._transform_pandas_df(
        X, enable_categorical=True
    )

    assert transformed[:, 0].min() == 0

    # test missing value
    X = pd.DataFrame({"f0": ["a", "b", np.NaN]})
    X["f0"] = X["f0"].astype("category")  # type: ignore[call-overload]
    arr, _, _ = xgb.data._transform_pandas_df(X, enable_categorical=True)
    assert not np.any(arr == -1.0)

    X = X["f0"]  # type: ignore[call-overload]
    y = y[: X.shape[0]]
    with pytest.raises(ValueError, match=r".*enable_categorical.*"):
        xgb.DMatrix(X, y)

    Xy = xgb.DMatrix(X, y, enable_categorical=True)
    assert Xy.num_row() == 3
    assert Xy.num_col() == 1


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_sparse(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    rows = 100
    X = pd.DataFrame(
        {
            "A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
            "B": pd.arrays.SparseArray(np.random.randn(rows)),
            "C": pd.arrays.SparseArray(
                np.random.permutation([True, False] * (rows // 2))
            ),
        }
    )
    y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
    dtrain = xgb.DMatrix(X, y)
    booster = xgb.train({}, dtrain, num_boost_round=4)
    predt_sparse = booster.predict(xgb.DMatrix(X))
    predt_dense = booster.predict(xgb.DMatrix(X.sparse.to_dense()))
    np.testing.assert_allclose(predt_sparse, predt_dense)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas_label(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    # label must be a single column
    df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
    with pytest.raises(ValueError):
        xgb.data._transform_pandas_df(df, False, None, None, "label", "float")

    # label must be supported dtype
    df = pd.DataFrame({"A": np.array(["a", "b", "c"], dtype=object)})
    with pytest.raises(ValueError):
        xgb.data._transform_pandas_df(df, False, None, None, "label", "float")

    df = pd.DataFrame({"A": np.array([1, 2, 3], dtype=int)})
    result, _, _ = xgb.data._transform_pandas_df(
        df, False, None, None, "label", "float"
    )
    np.testing.assert_array_equal(result, np.array([[1.0], [2.0], [3.0]], dtype=float))
    dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
    assert dm.num_row() == 3
    assert dm.num_col() == 2


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_weight(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    kRows = 32
    kCols = 8

    X = np.random.randn(kRows, kCols)
    y = np.random.randn(kRows)
    w = np.random.uniform(size=kRows).astype(np.float32)
    w_pd = pd.DataFrame(w)
    data = xgb.DMatrix(X, y, w_pd)

    assert data.num_row() == kRows
    assert data.num_col() == kCols

    np.testing.assert_array_equal(data.get_weight(), w)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "numpy", "scipy"])
def test_scipy_sparse(selenium):
    import numpy as np
    import scipy
    import xgboost as xgb

    n_rows = 100
    n_cols = 10
    X = scipy.sparse.random(n_rows, n_cols, format="csr")
    y = np.random.randn(n_rows)
    dtrain = xgb.DMatrix(X, y)
    booster = xgb.train({}, dtrain, num_boost_round=1)
    copied_predt = booster.predict(xgb.DMatrix(X))
    predt = booster.inplace_predict(X)
    np.testing.assert_allclose(copied_predt, predt)

# xgboost tests are copied from: https://github.com/dmlc/xgboost/tree/master/tests/python
import pathlib

import pytest
from pytest_pyodide import run_in_pyodide

DEMO_PATH = pathlib.Path(__file__).parent / "test_data"


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost"])
def test_compat(selenium):
    import numpy as np
    from xgboost.compat import lazy_isinstance

    a = np.array([1, 2, 3])
    assert lazy_isinstance(a, "numpy", "ndarray")
    assert not lazy_isinstance(a, "numpy", "dataframe")


@pytest.mark.driver_timeout(60)
def test_basic_classification(selenium):
    @run_in_pyodide(packages=["xgboost"])
    def run(selenium, data_train):
        with open("dermatology.data", "wb") as f:
            f.write(data_train)

        import numpy as np
        import xgboost as xgb

        # label need to be 0 to num_class -1
        data = np.loadtxt(
            "./dermatology.data",
            delimiter=",",
            converters={33: lambda x: int(x == "?"), 34: lambda x: int(x) - 1},
        )
        sz = data.shape

        train = data[: int(sz[0] * 0.7), :]
        test = data[int(sz[0] * 0.7) :, :]

        train_X = train[:, :33]
        train_Y = train[:, 34]

        test_X = test[:, :33]
        test_Y = test[:, 34]

        xg_train = xgb.DMatrix(train_X, label=train_Y)
        xg_test = xgb.DMatrix(test_X, label=test_Y)
        # setup parameters for xgboost
        param = {}
        # use softmax multi-class classification
        param["objective"] = "multi:softmax"
        # scale weight of positive examples
        param["eta"] = 0.1  # type: ignore[assignment]
        param["max_depth"] = 6  # type: ignore[assignment]
        param["nthread"] = 4  # type: ignore[assignment]
        param["num_class"] = 6  # type: ignore[assignment]

        watchlist = [(xg_train, "train"), (xg_test, "test")]
        num_round = 5
        bst = xgb.train(param, xg_train, num_round, watchlist)
        # get prediction
        pred = bst.predict(xg_test)
        error_rate = np.sum(pred != test_Y) / test_Y.shape[0]
        assert error_rate < 0.1

        # do the same thing again, but output probabilities
        param["objective"] = "multi:softprob"
        bst = xgb.train(param, xg_train, num_round, watchlist)
        # Note: this convention has been changed since xgboost-unity
        # get prediction, this is in 1D array, need reshape to (ndata, nclass)
        pred_prob = bst.predict(xg_test).reshape(test_Y.shape[0], 6)
        pred_label = np.argmax(pred_prob, axis=1)
        error_rate = np.sum(pred_label != test_Y) / test_Y.shape[0]
        assert error_rate < 0.1

    DATA_TRAIN = (DEMO_PATH / "dermatology.data").read_bytes()
    run(selenium, DATA_TRAIN)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]], columns=["a", "b", "c"])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["a", "b", "c"]
    assert dm.feature_types == ["int", "float", "i"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3
    np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))

    # overwrite feature_names and feature_types
    dm = xgb.DMatrix(
        df,
        label=pd.Series([1, 2]),
        feature_names=["x", "y", "z"],
        feature_types=["q", "q", "q"],
    )
    assert dm.feature_names == ["x", "y", "z"]
    assert dm.feature_types == ["q", "q", "q"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3

    # incorrect dtypes
    df = pd.DataFrame([[1, 2.0, "x"], [2, 3.0, "y"]], columns=["a", "b", "c"])
    with pytest.raises(ValueError):
        xgb.DMatrix(df)

    # numeric columns
    df = pd.DataFrame([[1, 2.0, True], [2, 3.0, False]])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["0", "1", "2"]
    assert dm.feature_types == ["int", "float", "i"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3
    np.testing.assert_array_equal(dm.get_label(), np.array([1, 2]))

    df = pd.DataFrame([[1, 2.0, 1], [2, 3.0, 1]], columns=[4, 5, 6])
    dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
    assert dm.feature_names == ["4", "5", "6"]
    assert dm.feature_types == ["int", "float", "int"]
    assert dm.num_row() == 2
    assert dm.num_col() == 3

    df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
    dummies = pd.get_dummies(df)
    #    B  A_X  A_Y  A_Z
    # 0  1    1    0    0
    # 1  2    0    1    0
    # 2  3    0    0    1
    result, _, _ = xgb.data._transform_pandas_df(dummies, enable_categorical=False)
    exp = np.array([[1.0, 1.0, 0.0, 0.0], [2.0, 0.0, 1.0, 0.0], [3.0, 0.0, 0.0, 1.0]])
    np.testing.assert_array_equal(result, exp)
    dm = xgb.DMatrix(dummies)
    assert dm.feature_names == ["B", "A_X", "A_Y", "A_Z"]
    assert dm.feature_types == ["int", "int", "int", "int"]
    assert dm.num_row() == 3
    assert dm.num_col() == 4

    df = pd.DataFrame({"A=1": [1, 2, 3], "A=2": [4, 5, 6]})
    dm = xgb.DMatrix(df)
    assert dm.feature_names == ["A=1", "A=2"]
    assert dm.feature_types == ["int", "int"]
    assert dm.num_row() == 3
    assert dm.num_col() == 2

    df_int = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=[9, 10])
    dm_int = xgb.DMatrix(df_int)
    df_range = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=range(9, 11, 1))
    dm_range = xgb.DMatrix(df_range)
    assert dm_int.feature_names == ["9", "10"]  # assert not "9 "
    assert dm_int.feature_names == dm_range.feature_names

    # test MultiIndex as columns
    df = pd.DataFrame(
        [(1, 2, 3, 4, 5, 6), (6, 5, 4, 3, 2, 1)],
        columns=pd.MultiIndex.from_tuples(
            (
                ("a", 1),
                ("a", 2),
                ("a", 3),
                ("b", 1),
                ("b", 2),
                ("b", 3),
            )
        ),
    )
    dm = xgb.DMatrix(df)
    assert dm.feature_names == ["a 1", "a 2", "a 3", "b 1", "b 2", "b 3"]
    assert dm.feature_types == ["int", "int", "int", "int", "int", "int"]
    assert dm.num_row() == 2
    assert dm.num_col() == 6

    # test Index as columns
    df = pd.DataFrame([[1, 1.1], [2, 2.2]], columns=pd.Index([1, 2]))
    # print(df.columns, isinstance(df.columns, pd.Index))
    Xy = xgb.DMatrix(df)
    np.testing.assert_equal(np.array(Xy.feature_names), np.array(["1", "2"]))


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_slice(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    rng = np.random.RandomState(1994)
    rows = 100
    X = rng.randint(3, 7, size=rows)
    X = pd.DataFrame({"f0": X})
    y = rng.randn(rows)
    ridxs = [1, 2, 3, 4, 5, 6]
    m = xgb.DMatrix(X, y)
    sliced = m.slice(ridxs)

    assert m.feature_types == sliced.feature_types


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas_categorical(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    rng = np.random.RandomState(1994)
    rows = 100
    X = rng.randint(3, 7, size=rows)
    X = pd.Series(X, dtype="category")
    X = pd.DataFrame({"f0": X})
    y = rng.randn(rows)
    m = xgb.DMatrix(X, y, enable_categorical=True)
    assert m.feature_types[0] == "c"

    X_0 = ["f", "o", "o"]
    X_1 = [4, 3, 2]
    X = pd.DataFrame({"feat_0": X_0, "feat_1": X_1})
    X["feat_0"] = X["feat_0"].astype("category")  # type: ignore[call-overload]
    transformed, _, feature_types = xgb.data._transform_pandas_df(
        X, enable_categorical=True
    )

    assert transformed[:, 0].min() == 0

    # test missing value
    X = pd.DataFrame({"f0": ["a", "b", np.NaN]})
    X["f0"] = X["f0"].astype("category")  # type: ignore[call-overload]
    arr, _, _ = xgb.data._transform_pandas_df(X, enable_categorical=True)
    assert not np.any(arr == -1.0)

    X = X["f0"]  # type: ignore[call-overload]
    y = y[: X.shape[0]]
    with pytest.raises(ValueError, match=r".*enable_categorical.*"):
        xgb.DMatrix(X, y)

    Xy = xgb.DMatrix(X, y, enable_categorical=True)
    assert Xy.num_row() == 3
    assert Xy.num_col() == 1


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_sparse(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    rows = 100
    X = pd.DataFrame(
        {
            "A": pd.arrays.SparseArray(np.random.randint(0, 10, size=rows)),
            "B": pd.arrays.SparseArray(np.random.randn(rows)),
            "C": pd.arrays.SparseArray(
                np.random.permutation([True, False] * (rows // 2))
            ),
        }
    )
    y = pd.Series(pd.arrays.SparseArray(np.random.randn(rows)))
    dtrain = xgb.DMatrix(X, y)
    booster = xgb.train({}, dtrain, num_boost_round=4)
    predt_sparse = booster.predict(xgb.DMatrix(X))
    predt_dense = booster.predict(xgb.DMatrix(X.sparse.to_dense()))
    np.testing.assert_allclose(predt_sparse, predt_dense)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas", "pytest"])
def test_pandas_label(selenium):
    import numpy as np
    import pandas as pd
    import pytest
    import xgboost as xgb

    # label must be a single column
    df = pd.DataFrame({"A": ["X", "Y", "Z"], "B": [1, 2, 3]})
    with pytest.raises(ValueError):
        xgb.data._transform_pandas_df(df, False, None, None, "label", "float")

    # label must be supported dtype
    df = pd.DataFrame({"A": np.array(["a", "b", "c"], dtype=object)})
    with pytest.raises(ValueError):
        xgb.data._transform_pandas_df(df, False, None, None, "label", "float")

    df = pd.DataFrame({"A": np.array([1, 2, 3], dtype=int)})
    result, _, _ = xgb.data._transform_pandas_df(
        df, False, None, None, "label", "float"
    )
    np.testing.assert_array_equal(result, np.array([[1.0], [2.0], [3.0]], dtype=float))
    dm = xgb.DMatrix(np.random.randn(3, 2), label=df)
    assert dm.num_row() == 3
    assert dm.num_col() == 2


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "pandas"])
def test_pandas_weight(selenium):
    import numpy as np
    import pandas as pd
    import xgboost as xgb

    kRows = 32
    kCols = 8

    X = np.random.randn(kRows, kCols)
    y = np.random.randn(kRows)
    w = np.random.uniform(size=kRows).astype(np.float32)
    w_pd = pd.DataFrame(w)
    data = xgb.DMatrix(X, y, w_pd)

    assert data.num_row() == kRows
    assert data.num_col() == kCols

    np.testing.assert_array_equal(data.get_weight(), w)


@pytest.mark.driver_timeout(60)
@run_in_pyodide(packages=["xgboost", "numpy", "scipy"])
def test_scipy_sparse(selenium):
    import numpy as np
    import scipy
    import xgboost as xgb

    n_rows = 100
    n_cols = 10
    X = scipy.sparse.random(n_rows, n_cols, format="csr")
    y = np.random.randn(n_rows)
    dtrain = xgb.DMatrix(X, y)
    booster = xgb.train({}, dtrain, num_boost_round=1)
    copied_predt = booster.predict(xgb.DMatrix(X))
    predt = booster.inplace_predict(X)
    np.testing.assert_allclose(copied_predt, predt)
