Merge pull request #11560 from roman-khomenko/roman-khomenko/fix-kendall-for-num-and-bool

BUG: Fix bug for kendall corr when in DF num and bool

Author: jreback

doc/source/whatsnew/v0.17.1.txt

@@ -123,7 +123,7 @@ Bug Fixes
 
 - Bug in ``DataFrame.to_dict()`` produces a ``np.datetime64`` object instead of ``Timestamp`` when only datetime is present in data (:issue:`11327`)
 
-
+- Bug in ``DataFrame.corr()`` raises exception when computes Kendall correlation for DataFrames with boolean and not boolean columns (:issue:`11560`)
 
 - Bug in the link-time error caused by C ``inline`` functions on FreeBSD 10+ (with ``clang``) (:issue:`10510`)
 - Bug in ``DataFrame.to_csv`` in passing through arguments for formatting ``MultiIndexes``, including ``date_format`` (:issue:`7791`)

pandas/core/frame.py

@@ -4411,16 +4411,21 @@ def corr(self, method='pearson', min_periods=1):
         else:
             if min_periods is None:
                 min_periods = 1
-            mat = mat.T
+            mat = com._ensure_float64(mat).T
             corrf = nanops.get_corr_func(method)
             K = len(cols)
             correl = np.empty((K, K), dtype=float)
             mask = np.isfinite(mat)
             for i, ac in enumerate(mat):
                 for j, bc in enumerate(mat):
+                    if i > j:
+                        continue
+
                     valid = mask[i] & mask[j]
                     if valid.sum() < min_periods:
                         c = NA
+                    elif i == j:
+                        c = 1.
                     elif not valid.all():
                         c = corrf(ac[valid], bc[valid])
                     else:

pandas/tests/test_frame.py

@@ -8009,12 +8009,14 @@ def test_corr_nooverlap(self):
         # nothing in common
         for meth in ['pearson', 'kendall', 'spearman']:
             df = DataFrame({'A': [1, 1.5, 1, np.nan, np.nan, np.nan],
-                            'B': [np.nan, np.nan, np.nan, 1, 1.5, 1]})
+                            'B': [np.nan, np.nan, np.nan, 1, 1.5, 1],
+                            'C': [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]})
             rs = df.corr(meth)
             self.assertTrue(isnull(rs.ix['A', 'B']))
             self.assertTrue(isnull(rs.ix['B', 'A']))
             self.assertEqual(rs.ix['A', 'A'], 1)
             self.assertEqual(rs.ix['B', 'B'], 1)
+            self.assertTrue(isnull(rs.ix['C', 'C']))
 
     def test_corr_constant(self):
         tm._skip_if_no_scipy()
@@ -8035,6 +8037,18 @@ def test_corr_int(self):
         df3.cov()
         df3.corr()
 
+    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
+        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']:
+            assert_frame_equal(df.corr(meth), expected)
+
     def test_cov(self):
         # min_periods no NAs (corner case)
         expected = self.frame.cov()