TST: Use importorskip over skip_if_no_scipy (#54387)

* TST: Use importorskip over skip_if_no_scipy

* another importorskip

Author: mroeschke

pandas/conftest.py

@@ -1868,7 +1868,7 @@ def spmatrix(request):
     """
     Yields scipy sparse matrix classes.
     """
-    from scipy import sparse
+    sparse = pytest.importorskip("scipy.sparse")
 
     return getattr(sparse, request.param + "_matrix")
 

pandas/tests/arrays/sparse/test_accessor.py

@@ -3,8 +3,6 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 from pandas import SparseDtype
 import pandas._testing as tm
@@ -46,7 +44,6 @@ def test_from_coo(self):
         expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]")
         tm.assert_series_equal(result, expected)
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize(
         "sort_labels, expected_rows, expected_cols, expected_values_pos",
         [
@@ -67,7 +64,7 @@ def test_from_coo(self):
     def test_to_coo(
         self, sort_labels, expected_rows, expected_cols, expected_values_pos
     ):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
         values = SparseArray([0, np.nan, 1, 0, None, 3], fill_value=0)
         index = pd.MultiIndex.from_tuples(
@@ -89,7 +86,7 @@ def test_to_coo(
         A, rows, cols = ss.sparse.to_coo(
             row_levels=(0, 1), column_levels=(2, 3), sort_labels=sort_labels
         )
-        assert isinstance(A, scipy.sparse.coo_matrix)
+        assert isinstance(A, sp_sparse.coo_matrix)
         tm.assert_numpy_array_equal(A.toarray(), expected_A)
         assert rows == expected_rows
         assert cols == expected_cols
@@ -109,25 +106,23 @@ def test_accessor_raises(self):
     @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
     @pytest.mark.parametrize("labels", [None, list(string.ascii_letters[:10])])
     @pytest.mark.parametrize("dtype", ["float64", "int64"])
-    @td.skip_if_no_scipy
     def test_from_spmatrix(self, format, labels, dtype):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
         sp_dtype = SparseDtype(dtype, np.array(0, dtype=dtype).item())
 
-        mat = scipy.sparse.eye(10, format=format, dtype=dtype)
+        mat = sp_sparse.eye(10, format=format, dtype=dtype)
         result = pd.DataFrame.sparse.from_spmatrix(mat, index=labels, columns=labels)
         expected = pd.DataFrame(
             np.eye(10, dtype=dtype), index=labels, columns=labels
         ).astype(sp_dtype)
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize("format", ["csc", "csr", "coo"])
-    @td.skip_if_no_scipy
     def test_from_spmatrix_including_explicit_zero(self, format):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        mat = scipy.sparse.random(10, 2, density=0.5, format=format)
+        mat = sp_sparse.random(10, 2, density=0.5, format=format)
         mat.data[0] = 0
         result = pd.DataFrame.sparse.from_spmatrix(mat)
         dtype = SparseDtype("float64", 0.0)
@@ -138,34 +133,32 @@ def test_from_spmatrix_including_explicit_zero(self, format):
         "columns",
         [["a", "b"], pd.MultiIndex.from_product([["A"], ["a", "b"]]), ["a", "a"]],
     )
-    @td.skip_if_no_scipy
     def test_from_spmatrix_columns(self, columns):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
         dtype = SparseDtype("float64", 0.0)
 
-        mat = scipy.sparse.random(10, 2, density=0.5)
+        mat = sp_sparse.random(10, 2, density=0.5)
         result = pd.DataFrame.sparse.from_spmatrix(mat, columns=columns)
         expected = pd.DataFrame(mat.toarray(), columns=columns).astype(dtype)
         tm.assert_frame_equal(result, expected)
 
     @pytest.mark.parametrize(
         "colnames", [("A", "B"), (1, 2), (1, pd.NA), (0.1, 0.2), ("x", "x"), (0, 0)]
     )
-    @td.skip_if_no_scipy
     def test_to_coo(self, colnames):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
         df = pd.DataFrame(
             {colnames[0]: [0, 1, 0], colnames[1]: [1, 0, 0]}, dtype="Sparse[int64, 0]"
         )
         result = df.sparse.to_coo()
-        expected = scipy.sparse.coo_matrix(np.asarray(df))
+        expected = sp_sparse.coo_matrix(np.asarray(df))
         assert (result != expected).nnz == 0
 
     @pytest.mark.parametrize("fill_value", [1, np.nan])
-    @td.skip_if_no_scipy
     def test_to_coo_nonzero_fill_val_raises(self, fill_value):
+        pytest.importorskip("scipy")
         df = pd.DataFrame(
             {
                 "A": SparseArray(
@@ -179,10 +172,9 @@ def test_to_coo_nonzero_fill_val_raises(self, fill_value):
         with pytest.raises(ValueError, match="fill value must be 0"):
             df.sparse.to_coo()
 
-    @td.skip_if_no_scipy
     def test_to_coo_midx_categorical(self):
         # GH#50996
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
         midx = pd.MultiIndex.from_arrays(
             [
@@ -193,7 +185,7 @@ def test_to_coo_midx_categorical(self):
 
         ser = pd.Series(1, index=midx, dtype="Sparse[int]")
         result = ser.sparse.to_coo(row_levels=["x"], column_levels=["y"])[0]
-        expected = scipy.sparse.coo_matrix(
+        expected = sp_sparse.coo_matrix(
             (np.array([1, 1]), (np.array([0, 1]), np.array([0, 1]))), shape=(2, 2)
         )
         assert (result != expected).nnz == 0
@@ -226,11 +218,10 @@ def test_density(self):
 
     @pytest.mark.parametrize("dtype", ["int64", "float64"])
     @pytest.mark.parametrize("dense_index", [True, False])
-    @td.skip_if_no_scipy
     def test_series_from_coo(self, dtype, dense_index):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        A = scipy.sparse.eye(3, format="coo", dtype=dtype)
+        A = sp_sparse.eye(3, format="coo", dtype=dtype)
         result = pd.Series.sparse.from_coo(A, dense_index=dense_index)
 
         index = pd.MultiIndex.from_tuples(
@@ -246,12 +237,11 @@ def test_series_from_coo(self, dtype, dense_index):
 
         tm.assert_series_equal(result, expected)
 
-    @td.skip_if_no_scipy
     def test_series_from_coo_incorrect_format_raises(self):
         # gh-26554
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        m = scipy.sparse.csr_matrix(np.array([[0, 1], [0, 0]]))
+        m = sp_sparse.csr_matrix(np.array([[0, 1], [0, 0]]))
         with pytest.raises(
             TypeError, match="Expected coo_matrix. Got csr_matrix instead."
         ):

pandas/tests/arrays/sparse/test_constructors.py

@@ -2,7 +2,6 @@
 import pytest
 
 from pandas._libs.sparse import IntIndex
-import pandas.util._test_decorators as td
 
 import pandas as pd
 from pandas import (
@@ -188,35 +187,32 @@ def test_constructor_inferred_fill_value(self, data, fill_value):
 
     @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
     @pytest.mark.parametrize("size", [0, 10])
-    @td.skip_if_no_scipy
     def test_from_spmatrix(self, size, format):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        mat = scipy.sparse.random(size, 1, density=0.5, format=format)
+        mat = sp_sparse.random(size, 1, density=0.5, format=format)
         result = SparseArray.from_spmatrix(mat)
 
         result = np.asarray(result)
         expected = mat.toarray().ravel()
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize("format", ["coo", "csc", "csr"])
-    @td.skip_if_no_scipy
     def test_from_spmatrix_including_explicit_zero(self, format):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        mat = scipy.sparse.random(10, 1, density=0.5, format=format)
+        mat = sp_sparse.random(10, 1, density=0.5, format=format)
         mat.data[0] = 0
         result = SparseArray.from_spmatrix(mat)
 
         result = np.asarray(result)
         expected = mat.toarray().ravel()
         tm.assert_numpy_array_equal(result, expected)
 
-    @td.skip_if_no_scipy
     def test_from_spmatrix_raises(self):
-        import scipy.sparse
+        sp_sparse = pytest.importorskip("scipy.sparse")
 
-        mat = scipy.sparse.eye(5, 4, format="csc")
+        mat = sp_sparse.eye(5, 4, format="csc")
 
         with pytest.raises(ValueError, match="not '4'"):
             SparseArray.from_spmatrix(mat)

pandas/tests/dtypes/test_common.py

@@ -211,11 +211,10 @@ def test_is_sparse(check_scipy):
             assert not com.is_sparse(scipy.sparse.bsr_matrix([1, 2, 3]))
 
 
-@td.skip_if_no_scipy
 def test_is_scipy_sparse():
-    from scipy.sparse import bsr_matrix
+    sp_sparse = pytest.importorskip("scipy.sparse")
 
-    assert com.is_scipy_sparse(bsr_matrix([1, 2, 3]))
+    assert com.is_scipy_sparse(sp_sparse.bsr_matrix([1, 2, 3]))
 
     assert not com.is_scipy_sparse(SparseArray([1, 2, 3]))
 

pandas/tests/dtypes/test_inference.py

@@ -33,7 +33,6 @@
     missing as libmissing,
     ops as libops,
 )
-import pandas.util._test_decorators as td
 
 from pandas.core.dtypes import inference
 from pandas.core.dtypes.common import (
@@ -1970,9 +1969,9 @@ def test_nan_to_nat_conversions():
     assert s[8] is pd.NaT
 
 
-@td.skip_if_no_scipy
 @pytest.mark.filterwarnings("ignore::PendingDeprecationWarning")
 def test_is_scipy_sparse(spmatrix):
+    pytest.importorskip("scipy")
     assert is_scipy_sparse(spmatrix([[0, 1]]))
     assert not is_scipy_sparse(np.array([1]))
 

pandas/tests/frame/methods/test_cov_corr.py

@@ -104,8 +104,8 @@ class TestDataFrameCorr:
     # DataFrame.corr(), as opposed to DataFrame.corrwith
 
     @pytest.mark.parametrize("method", ["pearson", "kendall", "spearman"])
-    @td.skip_if_no_scipy
     def test_corr_scipy_method(self, float_frame, method):
+        pytest.importorskip("scipy")
         float_frame.loc[float_frame.index[:5], "A"] = np.nan
         float_frame.loc[float_frame.index[5:10], "B"] = np.nan
         float_frame.loc[float_frame.index[:10], "A"] = float_frame["A"][10:20]
@@ -123,10 +123,10 @@ def test_corr_non_numeric(self, float_string_frame):
         expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr()
         tm.assert_frame_equal(result, expected)
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"])
     def test_corr_nooverlap(self, meth):
         # nothing in common
+        pytest.importorskip("scipy")
         df = DataFrame(
             {
                 "A": [1, 1.5, 1, np.nan, np.nan, np.nan],
@@ -153,12 +153,12 @@ def test_corr_constant(self, meth):
         rs = df.corr(meth)
         assert isna(rs.values).all()
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"])
     def test_corr_int_and_boolean(self, meth):
         # when dtypes of pandas series are different
         # then ndarray will have dtype=object,
         # so it need to be properly handled
+        pytest.importorskip("scipy")
         df = DataFrame({"a": [True, False], "b": [1, 0]})
 
         expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"])
@@ -193,7 +193,6 @@ def test_corr_int(self):
         df.cov()
         df.corr()
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize(
         "nullable_column", [pd.array([1, 2, 3]), pd.array([1, 2, None])]
     )
@@ -204,6 +203,7 @@ def test_corr_int(self):
     @pytest.mark.parametrize("method", ["pearson", "spearman", "kendall"])
     def test_corr_nullable_integer(self, nullable_column, other_column, method):
         # https://github.com/pandas-dev/pandas/issues/33803
+        pytest.importorskip("scipy")
         data = DataFrame({"a": nullable_column, "b": other_column})
         result = data.corr(method=method)
         expected = DataFrame(np.ones((2, 2)), columns=["a", "b"], index=["a", "b"])
@@ -249,23 +249,23 @@ def test_calc_corr_small_numbers(self):
         expected = DataFrame({"A": [1.0, 1.0], "B": [1.0, 1.0]}, index=["A", "B"])
         tm.assert_frame_equal(result, expected)
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize("method", ["pearson", "spearman", "kendall"])
     def test_corr_min_periods_greater_than_length(self, method):
+        pytest.importorskip("scipy")
         df = DataFrame({"A": [1, 2], "B": [1, 2]})
         result = df.corr(method=method, min_periods=3)
         expected = DataFrame(
             {"A": [np.nan, np.nan], "B": [np.nan, np.nan]}, index=["A", "B"]
         )
         tm.assert_frame_equal(result, expected)
 
-    @td.skip_if_no_scipy
     @pytest.mark.parametrize("meth", ["pearson", "kendall", "spearman"])
     @pytest.mark.parametrize("numeric_only", [True, False])
     def test_corr_numeric_only(self, meth, numeric_only):
         # when dtypes of pandas series are different
         # then ndarray will have dtype=object,
         # so it need to be properly handled
+        pytest.importorskip("scipy")
         df = DataFrame({"a": [1, 0], "b": [1, 0], "c": ["x", "y"]})
         expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"])
         if numeric_only:
@@ -422,25 +422,25 @@ def test_corr_numerical_instabilities(self):
         expected = DataFrame({0: [1.0, -1.0], 1: [-1.0, 1.0]})
         tm.assert_frame_equal(result - 1, expected - 1, atol=1e-17)
 
-    @td.skip_if_no_scipy
     def test_corrwith_spearman(self):
         # GH#21925
+        pytest.importorskip("scipy")
         df = DataFrame(np.random.default_rng(2).random(size=(100, 3)))
         result = df.corrwith(df**2, method="spearman")
         expected = Series(np.ones(len(result)))
         tm.assert_series_equal(result, expected)
 
-    @td.skip_if_no_scipy
     def test_corrwith_kendall(self):
         # GH#21925
+        pytest.importorskip("scipy")
         df = DataFrame(np.random.default_rng(2).random(size=(100, 3)))
         result = df.corrwith(df**2, method="kendall")
         expected = Series(np.ones(len(result)))
         tm.assert_series_equal(result, expected)
 
-    @td.skip_if_no_scipy
     def test_corrwith_spearman_with_tied_data(self):
         # GH#48826
+        pytest.importorskip("scipy")
         df1 = DataFrame(
             {
                 "A": [1, np.nan, 7, 8],

pandas/tests/frame/methods/test_interpolate.py

@@ -202,8 +202,8 @@ def test_interp_nan_idx(self):
         with pytest.raises(NotImplementedError, match=msg):
             df.interpolate(method="values")
 
-    @td.skip_if_no_scipy
     def test_interp_various(self):
+        pytest.importorskip("scipy")
         df = DataFrame(
             {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
         )
@@ -241,8 +241,8 @@ def test_interp_various(self):
         expected.loc[13, "A"] = 5
         tm.assert_frame_equal(result, expected, check_dtype=False)
 
-    @td.skip_if_no_scipy
     def test_interp_alt_scipy(self):
+        pytest.importorskip("scipy")
         df = DataFrame(
             {"A": [1, 2, np.nan, 4, 5, np.nan, 7], "C": [1, 2, 3, 5, 8, 13, 21]}
         )