TST/CLN: test_cov_corr

pandas/tests/frame/methods/test_cov_corr.py

@@ -29,17 +29,16 @@ def test_cov(self, float_frame, float_string_frame):
         frame = float_frame.copy()
         frame["A"][:5] = np.nan
         frame["B"][5:10] = np.nan
-        result = float_frame.cov(min_periods=len(float_frame) - 8)
-        expected = float_frame.cov()
+        result = frame.cov(min_periods=len(frame) - 8)
+        expected = frame.cov()
         expected.loc["A", "B"] = np.nan
         expected.loc["B", "A"] = np.nan
+        tm.assert_frame_equal(result, expected)
 
         # regular
-        float_frame["A"][:5] = np.nan
-        float_frame["B"][:10] = np.nan
-        cov = float_frame.cov()
-
-        tm.assert_almost_equal(cov["A"]["C"], float_frame["A"].cov(float_frame["C"]))
+        result = frame.cov()
+        expected = frame["A"].cov(frame["C"])
+        tm.assert_almost_equal(result["A"]["C"], expected)
 
         # exclude non-numeric types
         result = float_string_frame.cov()
@@ -101,10 +100,7 @@ def test_corr_scipy_method(self, float_frame, method):
     # ---------------------------------------------------------------------
 
     @td.skip_if_no_scipy
-    def test_corr_non_numeric(self, float_frame, float_string_frame):
-        float_frame["A"][:5] = np.nan
-        float_frame["B"][5:10] = np.nan
-
+    def test_corr_non_numeric(self, float_string_frame):
         # exclude non-numeric types
         result = float_string_frame.corr()
         expected = float_string_frame.loc[:, ["A", "B", "C", "D"]].corr()
@@ -143,27 +139,27 @@ def test_corr_constant(self, meth):
         assert isna(rs.values).all()
 
     @td.skip_if_no_scipy
-    def test_corr_int_and_boolean(self):
+    @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
         df = DataFrame({"a": [True, False], "b": [1, 0]})
 
         expected = DataFrame(np.ones((2, 2)), index=["a", "b"], columns=["a", "b"])
-        for meth in ["pearson", "kendall", "spearman"]:
 
-            with warnings.catch_warnings(record=True):
-                warnings.simplefilter("ignore", RuntimeWarning)
-                result = df.corr(meth)
-            tm.assert_frame_equal(result, expected)
+        with warnings.catch_warnings(record=True):
+            warnings.simplefilter("ignore", RuntimeWarning)
+            result = df.corr(meth)
+        tm.assert_frame_equal(result, expected)
 
-    def test_corr_cov_independent_index_column(self):
+    @pytest.mark.parametrize("method", ["cov", "corr"])
+    def test_corr_cov_independent_index_column(self, method):
         # GH#14617
         df = DataFrame(np.random.randn(4 * 10).reshape(10, 4), columns=list("abcd"))
-        for method in ["cov", "corr"]:
-            result = getattr(df, method)()
-            assert result.index is not result.columns
-            assert result.index.equals(result.columns)
+        result = getattr(df, method)()
+        assert result.index is not result.columns
+        assert result.index.equals(result.columns)
 
     def test_corr_invalid_method(self):
         # GH#22298
@@ -174,10 +170,10 @@ def test_corr_invalid_method(self):
 
     def test_corr_int(self):
         # dtypes other than float64 GH#1761
-        df3 = DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})
+        df = DataFrame({"a": [1, 2, 3, 4], "b": [1, 2, 3, 4]})
 
-        df3.cov()
-        df3.corr()
+        df.cov()
+        df.corr()
 
     @td.skip_if_no_scipy
     @pytest.mark.parametrize(