Skipif no scipy (#18794)

Author: WillAyd

pandas/tests/dtypes/test_common.py

@@ -9,6 +9,7 @@
 
 import pandas.core.dtypes.common as com
 import pandas.util.testing as tm
+import pandas.util._test_decorators as td
 
 
 class TestPandasDtype(object):
@@ -132,21 +133,22 @@ def test_is_object():
     assert not com.is_object_dtype([1, 2, 3])
 
 
-def test_is_sparse():
+@pytest.mark.parametrize("check_scipy", [
+    False, pytest.param(True, marks=td.skip_if_no_scipy)
+])
+def test_is_sparse(check_scipy):
     assert com.is_sparse(pd.SparseArray([1, 2, 3]))
     assert com.is_sparse(pd.SparseSeries([1, 2, 3]))
 
     assert not com.is_sparse(np.array([1, 2, 3]))
 
-    # This test will only skip if the previous assertions
-    # pass AND scipy is not installed.
-    sparse = pytest.importorskip("scipy.sparse")
-    assert not com.is_sparse(sparse.bsr_matrix([1, 2, 3]))
+    if check_scipy:
+        import scipy.sparse
+        assert not com.is_sparse(scipy.sparse.bsr_matrix([1, 2, 3]))
 
 
+@td.skip_if_no_scipy
 def test_is_scipy_sparse():
-    tm._skip_if_no_scipy()
-
     from scipy.sparse import bsr_matrix
     assert com.is_scipy_sparse(bsr_matrix([1, 2, 3]))
 
@@ -501,7 +503,10 @@ def test_is_bool_dtype():
     assert com.is_bool_dtype(pd.Index([True, False]))
 
 
-def test_is_extension_type():
+@pytest.mark.parametrize("check_scipy", [
+    False, pytest.param(True, marks=td.skip_if_no_scipy)
+])
+def test_is_extension_type(check_scipy):
     assert not com.is_extension_type([1, 2, 3])
     assert not com.is_extension_type(np.array([1, 2, 3]))
     assert not com.is_extension_type(pd.DatetimeIndex([1, 2, 3]))
@@ -517,10 +522,9 @@ def test_is_extension_type():
     s = pd.Series([], dtype=dtype)
     assert com.is_extension_type(s)
 
-    # This test will only skip if the previous assertions
-    # pass AND scipy is not installed.
-    sparse = pytest.importorskip("scipy.sparse")
-    assert not com.is_extension_type(sparse.bsr_matrix([1, 2, 3]))
+    if check_scipy:
+        import scipy.sparse
+        assert not com.is_extension_type(scipy.sparse.bsr_matrix([1, 2, 3]))
 
 
 def test_is_complex_dtype():

pandas/tests/dtypes/test_inference.py

@@ -37,6 +37,7 @@
     _ensure_int32,
     _ensure_categorical)
 from pandas.util import testing as tm
+import pandas.util._test_decorators as td
 
 
 @pytest.fixture(params=[True, False], ids=str)
@@ -1190,8 +1191,8 @@ def test_nan_to_nat_conversions():
         assert (s[8].value == np.datetime64('NaT').astype(np.int64))
 
 
+@td.skip_if_no_scipy
 def test_is_scipy_sparse(spmatrix):  # noqa: F811
-    tm._skip_if_no_scipy()
     assert is_scipy_sparse(spmatrix([[0, 1]]))
     assert not is_scipy_sparse(np.array([1]))
 

pandas/tests/frame/test_analytics.py

@@ -22,6 +22,7 @@
 import pandas.io.formats.printing as printing
 
 import pandas.util.testing as tm
+import pandas.util._test_decorators as td
 from pandas.tests.frame.common import TestData
 
 
@@ -30,22 +31,22 @@ class TestDataFrameAnalytics(TestData):
     # ---------------------------------------------------------------------=
     # Correlation and covariance
 
+    @td.skip_if_no_scipy
     def test_corr_pearson(self):
-        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('pearson')
 
+    @td.skip_if_no_scipy
     def test_corr_kendall(self):
-        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
         self._check_method('kendall')
 
+    @td.skip_if_no_scipy
     def test_corr_spearman(self):
-        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
@@ -62,8 +63,8 @@ def _check_method(self, method='pearson', check_minp=False):
             expected.loc['A', 'B'] = expected.loc['B', 'A'] = nan
             tm.assert_frame_equal(result, expected)
 
+    @td.skip_if_no_scipy
     def test_corr_non_numeric(self):
-        tm._skip_if_no_scipy()
         self.frame['A'][:5] = nan
         self.frame['B'][5:10] = nan
 
@@ -72,9 +73,8 @@ def test_corr_non_numeric(self):
         expected = self.mixed_frame.loc[:, ['A', 'B', 'C', 'D']].corr()
         tm.assert_frame_equal(result, expected)
 
+    @td.skip_if_no_scipy
     def test_corr_nooverlap(self):
-        tm._skip_if_no_scipy()
-
         # nothing in common
         for meth in ['pearson', 'kendall', 'spearman']:
             df = DataFrame({'A': [1, 1.5, 1, np.nan, np.nan, np.nan],
@@ -88,9 +88,8 @@ def test_corr_nooverlap(self):
             assert rs.loc['B', 'B'] == 1
             assert isna(rs.loc['C', 'C'])
 
+    @td.skip_if_no_scipy
     def test_corr_constant(self):
-        tm._skip_if_no_scipy()
-
         # constant --> all NA
 
         for meth in ['pearson', 'spearman']:
@@ -106,9 +105,8 @@ def test_corr_int(self):
         df3.cov()
         df3.corr()
 
+    @td.skip_if_no_scipy
     def test_corr_int_and_boolean(self):
-        tm._skip_if_no_scipy()
-
         # when dtypes of pandas series are different
         # then ndarray will have dtype=object,
         # so it need to be properly handled
@@ -719,8 +717,8 @@ def test_sem(self):
             result = nanops.nansem(arr, axis=0)
             assert not (result < 0).any()
 
+    @td.skip_if_no_scipy
     def test_skew(self):
-        tm._skip_if_no_scipy()
         from scipy.stats import skew
 
         def alt(x):
@@ -730,9 +728,8 @@ def alt(x):
 
         self._check_stat_op('skew', alt)
 
+    @td.skip_if_no_scipy
     def test_kurt(self):
-        tm._skip_if_no_scipy()
-
         from scipy.stats import kurtosis
 
         def alt(x):

pandas/tests/frame/test_missing.py

@@ -16,6 +16,7 @@
 from pandas.util.testing import assert_series_equal, assert_frame_equal
 
 import pandas.util.testing as tm
+import pandas.util._test_decorators as td
 from pandas.tests.frame.common import TestData, _check_mixed_float
 
 
@@ -646,9 +647,8 @@ def test_interp_nan_idx(self):
         with pytest.raises(NotImplementedError):
             df.interpolate(method='values')
 
+    @td.skip_if_no_scipy
     def test_interp_various(self):
-        tm._skip_if_no_scipy()
-
         df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                         'C': [1, 2, 3, 5, 8, 13, 21]})
         df = df.set_index('C')
@@ -695,8 +695,8 @@ def test_interp_various(self):
         expected.A.loc[13] = 5
         assert_frame_equal(result, expected, check_dtype=False)
 
+    @td.skip_if_no_scipy
     def test_interp_alt_scipy(self):
-        tm._skip_if_no_scipy()
         df = DataFrame({'A': [1, 2, np.nan, 4, 5, np.nan, 7],
                         'C': [1, 2, 3, 5, 8, 13, 21]})
         result = df.interpolate(method='barycentric')
@@ -739,8 +739,6 @@ def test_interp_rowwise(self):
         expected[4] = expected[4].astype(np.float64)
         assert_frame_equal(result, expected)
 
-        # scipy route
-        tm._skip_if_no_scipy()
         result = df.interpolate(axis=1, method='values')
         assert_frame_equal(result, expected)
 
@@ -753,17 +751,20 @@ def test_rowwise_alt(self):
                         1: [1, 2, 3, 4, 3, 2, 1, 0, -1]})
         df.interpolate(axis=0)
 
-    def test_interp_leading_nans(self):
+    @pytest.mark.parametrize("check_scipy", [
+        False, pytest.param(True, marks=td.skip_if_no_scipy)
+    ])
+    def test_interp_leading_nans(self, check_scipy):
         df = DataFrame({"A": [np.nan, np.nan, .5, .25, 0],
                         "B": [np.nan, -3, -3.5, np.nan, -4]})
         result = df.interpolate()
         expected = df.copy()
         expected['B'].loc[3] = -3.75
         assert_frame_equal(result, expected)
 
-        tm._skip_if_no_scipy()
-        result = df.interpolate(method='polynomial', order=1)
-        assert_frame_equal(result, expected)
+        if check_scipy:
+            result = df.interpolate(method='polynomial', order=1)
+            assert_frame_equal(result, expected)
 
     def test_interp_raise_on_only_mixed(self):
         df = DataFrame({'A': [1, 2, np.nan, 4],

pandas/tests/plotting/test_datetimelike.py

@@ -643,10 +643,10 @@ def test_secondary_y_ts(self):
         assert ax.get_yaxis().get_visible()
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_secondary_kde(self):
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
 
         ser = Series(np.random.randn(10))

pandas/tests/plotting/test_deprecated.py

@@ -24,9 +24,8 @@
 class TestDeprecatedNameSpace(TestPlotBase):
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_scatter_plot_legacy(self):
-        tm._skip_if_no_scipy()
-
         df = pd.DataFrame(randn(100, 2))
 
         with tm.assert_produces_warning(FutureWarning):

pandas/tests/plotting/test_frame.py

@@ -1398,8 +1398,8 @@ def test_boxplot_subplots_return_type(self):
                 check_ax_title=False)
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_df(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -1422,8 +1422,8 @@ def test_kde_df(self):
         self._check_ax_scales(axes, yaxis='log')
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_missing_vals(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -1949,8 +1949,8 @@ def test_hist_colors(self):
         tm.close()
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_colors(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -1974,8 +1974,8 @@ def test_kde_colors(self):
         self._check_colors(ax.get_lines(), linecolors=rgba_colors)
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_colors_and_styles_subplots(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")

pandas/tests/plotting/test_misc.py

@@ -52,8 +52,8 @@ def test_bootstrap_plot(self):
 @td.skip_if_no_mpl
 class TestDataFramePlots(TestPlotBase):
 
+    @td.skip_if_no_scipy
     def test_scatter_matrix_axis(self):
-        tm._skip_if_no_scipy()
         scatter_matrix = plotting.scatter_matrix
 
         with tm.RNGContext(42):

pandas/tests/plotting/test_series.py

@@ -589,6 +589,7 @@ def test_plot_fails_with_dupe_color_and_style(self):
             x.plot(style='k--', color='k', ax=ax)
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_hist_kde(self):
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -602,7 +603,6 @@ def test_hist_kde(self):
         ylabels = ax.get_yticklabels()
         self._check_text_labels(ylabels, [''] * len(ylabels))
 
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         _check_plot_works(self.ts.plot.kde)
         _check_plot_works(self.ts.plot.density)
@@ -615,8 +615,8 @@ def test_hist_kde(self):
         self._check_text_labels(ylabels, [''] * len(ylabels))
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_kwargs(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -633,8 +633,8 @@ def test_kde_kwargs(self):
         self._check_text_labels(ax.yaxis.get_label(), 'Density')
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_kde_missing_vals(self):
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -665,6 +665,7 @@ def test_hist_kwargs(self):
             tm.close()
 
     @pytest.mark.slow
+    @td.skip_if_no_scipy
     def test_hist_kde_color(self):
         if not self.mpl_ge_1_5_0:
             pytest.skip("mpl is not supported")
@@ -675,7 +676,6 @@ def test_hist_kde_color(self):
         assert len(ax.patches) == 10
         self._check_colors(ax.patches, facecolors=['b'] * 10)
 
-        tm._skip_if_no_scipy()
         _skip_if_no_scipy_gaussian_kde()
         _, ax = self.plt.subplots()
         ax = self.ts.plot.kde(logy=True, color='r', ax=ax)