Adds triangular option to DataFrame.corr, closes #22840

doc/source/whatsnew/v0.24.0.txt

@@ -21,6 +21,7 @@ New features
 the user to override the engine's default behavior to include or omit the
 dataframe's indexes from the resulting Parquet file. (:issue:`20768`)
 - :meth:`DataFrame.corr` and :meth:`Series.corr` now accept a callable for generic calculation methods of correlation, e.g. histogram intersection (:issue:`22684`)
+- :meth:`DataFrame.corr` now accepts an argument indicating whether or not a triangular correlation matrix should be returned (:issue:`22840`)
 
 
 .. _whatsnew_0240.enhancements.extension_array_operators:

pandas/core/frame.py

@@ -6667,7 +6667,7 @@ def _series_round(s, decimals):
     # ----------------------------------------------------------------------
     # Statistical methods, etc.
 
-    def corr(self, method='pearson', min_periods=1):
+    def corr(self, method='pearson', min_periods=1, tri=None):
         """
         Compute pairwise correlation of columns, excluding NA/null values
 
@@ -6686,6 +6686,14 @@ def corr(self, method='pearson', min_periods=1):
             to have a valid result. Currently only available for pearson
             and spearman correlation
 
+        tri : {'upper', 'lower'} or None, default : None
+            Whether or not to return the upper / lower triangular
+            correlation matrix.  (This is useful for example when
+            one wishes to easily identify highly collinear columns
+            in a DataFrame.)
+
+            .. versionadded:: 0.24.0
+
         Returns
         -------
         y : DataFrame
@@ -6701,6 +6709,13 @@ def corr(self, method='pearson', min_periods=1):
               dogs cats
         dogs   1.0  0.3
         cats   0.3  1.0
+        >>> df = pd.DataFrame(np.random.normal(size=(10, 2)))
+        >>> df[2] = df[0]
+        >>> df.corr(tri='lower').where(lambda x: x == 1)
+             0    1    2
+        0  NaN  NaN  NaN
+        1  NaN  NaN  NaN
+        2  1.0  NaN  NaN
         """
         numeric_df = self._get_numeric_data()
         cols = numeric_df.columns
@@ -6741,7 +6756,23 @@ def corr(self, method='pearson', min_periods=1):
                              "'spearman', or 'kendall', '{method}' "
                              "was supplied".format(method=method))
 
-        return self._constructor(correl, index=idx, columns=cols)
+        corr_mat = self._constructor(correl, index=idx, columns=cols)
+
+        if tri is not None:
+            mask = np.tril(np.ones_like(correl,
+                                        dtype=np.bool),
+                           k=-1)
+
+            if tri == 'lower':
+                return corr_mat.where(mask)
+            elif tri == 'upper':
+                return corr_mat.where(mask.T)
+            else:
+                raise ValueError("tri must be either 'lower', "
+                                 "or 'upper', '{tri_method}' "
+                                 "was supplied".format(tri_method=tri))
+        else:
+            return corr_mat
 
     def cov(self, min_periods=None):
         """

pandas/tests/frame/test_analytics.py

@@ -138,6 +138,26 @@ def test_corr_invalid_method(self):
         with tm.assert_raises_regex(ValueError, msg):
             df.corr(method="____")
 
+    def test_corr_tril(self):
+        # GH PR #22840
+        df = pd.DataFrame(np.array([np.arange(100)] * 5).T)
+        result = df.corr(tri='lower')
+        mask = np.tril(np.ones_like(result,
+                                    dtype=np.bool),
+                       k=-1)
+        expected = pd.DataFrame(np.ones_like(result)).where(mask)
+        tm.assert_frame_equal(result, expected)
+
+    def test_corr_triu(self):
+        # GH PR #22840
+        df = pd.DataFrame(np.array([np.arange(100)] * 5).T)
+        result = df.corr(tri='upper')
+        mask = np.triu(np.ones_like(result,
+                                    dtype=np.bool),
+                       k=1)
+        expected = pd.DataFrame(np.ones_like(result)).where(mask)
+        tm.assert_frame_equal(result, expected)
+
     def test_cov(self):
         # min_periods no NAs (corner case)
         expected = self.frame.cov()