ENH: Add axis argument to Dataframe.corr

doc/source/whatsnew/v1.2.0.rst

@@ -54,7 +54,7 @@ For example:
 Other enhancements
 ^^^^^^^^^^^^^^^^^^
 - :class:`Index` with object dtype supports division and multiplication (:issue:`34160`)
--
+- :meth:`DataFrame.corr` now allows an ``axis`` argument, set to 0 by default (correlation among columns) (:issue:`35002`)
 -
 
 .. _whatsnew_120.api_breaking.python:

pandas/core/frame.py

@@ -20,6 +20,7 @@
     TYPE_CHECKING,
     Any,
     AnyStr,
+    Callable,
     Dict,
     FrozenSet,
     Hashable,
@@ -5787,7 +5788,7 @@ def nsmallest(self, n, columns, keep="first") -> "DataFrame":
                   population    GDP alpha-2
         Tuvalu         11300     38      TV
         Anguilla       11300    311      AI
-        Iceland       337000  17036	     IS
+        Iceland       337000  17036      IS
 
         When using ``keep='last'``, ties are resolved in reverse order:
 
@@ -8116,9 +8117,14 @@ def _series_round(s, decimals):
     # ----------------------------------------------------------------------
     # Statistical methods, etc.
 
-    def corr(self, method="pearson", min_periods=1) -> "DataFrame":
+    def corr(
+        self,
+        method: Union[str, Callable[[np.ndarray, np.ndarray], np.float64]] = "pearson",
+        min_periods: Optional[int] = 1,
+        axis: Union[str, int] = 0,
+    ) -> "DataFrame":
         """
-        Compute pairwise correlation of columns, excluding NA/null values.
+        Compute pairwise correlation of rows or columns, excluding NA/null values.
 
         Parameters
         ----------
@@ -8140,6 +8146,12 @@ def corr(self, method="pearson", min_periods=1) -> "DataFrame":
             to have a valid result. Currently only available for Pearson
             and Spearman correlation.
 
+        axis : {0 or 'index', 1 or 'columns'}, default 0
+            The axis to use. 0 or 'index' to compute column-wise, 1 or 'columns' for
+            row-wise.
+
+            .. versionadded:: 1.2.0
+
         Returns
         -------
         DataFrame
@@ -8162,12 +8174,22 @@ def corr(self, method="pearson", min_periods=1) -> "DataFrame":
               dogs  cats
         dogs   1.0   0.3
         cats   0.3   1.0
+        >>> df.corr(method=histogram_intersection, axis=1)
+            0    1    2    3
+        0  1.0  0.3  0.2  0.3
+        1  0.3  1.0  0.0  0.1
+        2  0.2  0.0  1.0  0.2
+        3  0.3  0.1  0.2  1.0
         """
         numeric_df = self._get_numeric_data()
-        cols = numeric_df.columns
+        axis = numeric_df._get_axis_number(axis)
+        cols = numeric_df._get_agg_axis(axis)
         idx = cols.copy()
         mat = numeric_df.to_numpy(dtype=float, na_value=np.nan, copy=False)
 
+        if axis == 1:
+            mat = mat.transpose()
+
         if method == "pearson":
             correl = libalgos.nancorr(mat, minp=min_periods)
         elif method == "spearman":

pandas/tests/frame/methods/test_cov_corr.py

@@ -175,6 +175,15 @@ def test_corr_int(self):
         df3.cov()
         df3.corr()
 
+    @td.skip_if_no_scipy
+    @pytest.mark.parametrize("meth", ["pearson", "spearman", "kendall"])
+    def test_corr_axes(self, meth):
+        # https://github.com/pandas-dev/pandas/issues/35002
+        df = pd.DataFrame(np.random.normal(size=(10, 4)))
+        expected = df.T.corr(meth, axis=0)
+        result = df.corr(meth, axis=1)
+        tm.assert_frame_equal(result, expected)
+
     @td.skip_if_no_scipy
     @pytest.mark.parametrize(
         "nullable_column", [pd.array([1, 2, 3]), pd.array([1, 2, None])]