diff --git a/doc/source/cookbook.rst b/doc/source/cookbook.rst
index 21c8ab4128188..be8457fc14a4f 100644
--- a/doc/source/cookbook.rst
+++ b/doc/source/cookbook.rst
@@ -1228,36 +1228,40 @@ Correlation
 
 The `method` argument within `DataFrame.corr` can accept a callable in addition to the named correlation types.  Here we compute the `distance correlation <https://en.wikipedia.org/wiki/Distance_correlation>`__ matrix for a `DataFrame` object.
 
-.. ipython:: python
-
-    def distcorr(x, y):
-        n = len(x)
-        a = np.zeros(shape=(n, n))
-        b = np.zeros(shape=(n, n))
-
-        for i in range(n):
-            for j in range(i + 1, n):
-                a[i, j] = abs(x[i] - x[j])
-                b[i, j] = abs(y[i] - y[j])
-
-        a += a.T
-        b += b.T
-
-        a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
-        b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
-
-        A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
-        B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
-
-        cov_ab = np.sqrt(np.nansum(A * B)) / n
-        std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
-        std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
-
-        return cov_ab / std_a / std_b
-
-    df = pd.DataFrame(np.random.normal(size=(100, 3)))
+.. code-block:: python
 
-    df.corr(method=distcorr)
+    >>> def distcorr(x, y):
+    ...     n = len(x)
+    ...     a = np.zeros(shape=(n, n))
+    ...     b = np.zeros(shape=(n, n))
+    ...
+    ...     for i in range(n):
+    ...         for j in range(i + 1, n):
+    ...             a[i, j] = abs(x[i] - x[j])
+    ...             b[i, j] = abs(y[i] - y[j])
+    ...
+    ...     a += a.T
+    ...     b += b.T
+    ...
+    ...     a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
+    ...     b_bar = np.vstack([np.nanmean(b, axis=0)] * n)
+    ...
+    ...     A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
+    ...     B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())
+    ...
+    ...     cov_ab = np.sqrt(np.nansum(A * B)) / n
+    ...     std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
+    ...     std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)
+    ...
+    ...     return cov_ab / std_a / std_b
+    ...
+    >>> df = pd.DataFrame(np.random.normal(size=(100, 3)))
+    ...
+    >>> df.corr(method=distcorr)
+              0         1         2
+    0  1.000000  0.171368  0.145302
+    1  0.171368  1.000000  0.189919
+    2  0.145302  0.189919  1.000000
 
 Timedeltas
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