MAINT: >>> # style cleanups requested

* reviewer requested that the cases where
I switched from free-floating comments to
`>>> # comments` be reverted to free-floating
in docstrings

3 files changed, 21 insertions, 13 deletions

diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py
index 60cb62bb5..80fa1466e 100644
--- a/numpy/core/_add_newdocs.py
+++ b/numpy/core/_add_newdocs.py
@@ -317,8 +317,8 @@ add_newdoc('numpy.core', 'nditer',
 
     Examples
     --------
-    >>> #Here is how we might write an ``iter_add`` function, using the
-    >>> #Python iterator protocol::
+    Here is how we might write an ``iter_add`` function, using the
+    Python iterator protocol::
 
     >>> def iter_add_py(x, y, out=None):
     ...     addop = np.add
@@ -329,7 +329,7 @@ add_newdoc('numpy.core', 'nditer',
     ...             addop(a, b, out=c)
     ...     return it.operands[2]
 
-    >>> # Here is the same function, but following the C-style pattern::
+    Here is the same function, but following the C-style pattern::
 
     >>> def iter_add(x, y, out=None):
     ...    addop = np.add
@@ -341,7 +341,7 @@ add_newdoc('numpy.core', 'nditer',
     ...            it.iternext()
     ...        return it.operands[2]
 
-    >>> # Here is an example outer product function::
+    Here is an example outer product function::
 
     >>> def outer_it(x, y, out=None):
     ...     mulop = np.multiply
@@ -361,7 +361,7 @@ add_newdoc('numpy.core', 'nditer',
     array([[1, 2, 3],
            [2, 4, 6]])
 
-    >>> # Here is an example function which operates like a "lambda" ufunc::
+    Here is an example function which operates like a "lambda" ufunc::
 
     >>> def luf(lamdaexpr, *args, **kwargs):
     ...    '''luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)'''
diff --git a/numpy/core/einsumfunc.py b/numpy/core/einsumfunc.py
index d5fdca785..83b7d8287 100644
--- a/numpy/core/einsumfunc.py
+++ b/numpy/core/einsumfunc.py
@@ -1324,16 +1324,24 @@ def einsum(*operands, **kwargs):
     particularly significant with larger arrays:
 
     >>> a = np.ones(64).reshape(2,4,8)
-    >>> # Basic `einsum`: ~1520ms  (benchmarked on 3.1GHz Intel i5.)
+
+    Basic `einsum`: ~1520ms  (benchmarked on 3.1GHz Intel i5.)
+
     >>> for iteration in range(500):
     ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a)
-    >>> # Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
+
+    Sub-optimal `einsum` (due to repeated path calculation time): ~330ms
+
     >>> for iteration in range(500):
     ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')
-    >>> # Greedy `einsum` (faster optimal path approximation): ~160ms
+
+    Greedy `einsum` (faster optimal path approximation): ~160ms
+
     >>> for iteration in range(500):
     ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='greedy')
-    >>> # Optimal `einsum` (best usage pattern in some use cases): ~110ms
+
+    Optimal `einsum` (best usage pattern in some use cases): ~110ms
+
     >>> path = np.einsum_path('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize='optimal')[0]
     >>> for iteration in range(500):
     ...     _ = np.einsum('ijk,ilm,njm,nlk,abc->',a,a,a,a,a, optimize=path)
diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py
index 474556b30..240eac6ce 100644
--- a/numpy/core/fromnumeric.py
+++ b/numpy/core/fromnumeric.py
@@ -1645,7 +1645,7 @@ def ravel(a, order='C'):
 
     Examples
     --------
-    >>> # It is equivalent to ``reshape(-1, order=order)``.
+    It is equivalent to ``reshape(-1, order=order)``.
 
     >>> x = np.array([[1, 2, 3], [4, 5, 6]])
     >>> np.ravel(x)
@@ -1657,15 +1657,15 @@ def ravel(a, order='C'):
     >>> np.ravel(x, order='F')
     array([1, 4, 2, 5, 3, 6])
 
-    >>> # When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
+    When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
 
     >>> np.ravel(x.T)
     array([1, 4, 2, 5, 3, 6])
     >>> np.ravel(x.T, order='A')
     array([1, 2, 3, 4, 5, 6])
 
-    >>> # When ``order`` is 'K', it will preserve orderings that are neither 'C'
-    >>> # nor 'F', but won't reverse axes:
+    When ``order`` is 'K', it will preserve orderings that are neither 'C'
+    nor 'F', but won't reverse axes:
 
     >>> a = np.arange(3)[::-1]; a
     array([2, 1, 0])