DOC : move shape to front to match rest of docs

1 files changed, 4 insertions, 4 deletions

diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py
index 7bab66313..135053e43 100644
--- a/numpy/lib/function_base.py
+++ b/numpy/lib/function_base.py
@@ -263,7 +263,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None):
     normed : bool, optional
         If False, returns the number of samples in each bin. If True,
         returns the bin density ``bin_count / sample_count / bin_volume``.
-    weights : array_like (N,), optional
+    weights : (N,) array_like, optional
         An array of values `w_i` weighing each sample `(x_i, y_i, z_i, ...)`.
         Weights are normalized to 1 if normed is True. If normed is False,
         the values of the returned histogram are equal to the sum of the
@@ -885,9 +885,9 @@ def copy(a, order='K'):
 def gradient(f, *varargs, **kwargs):
     """
     Return the gradient of an N-dimensional array.
-    
+
     The gradient is computed using second order accurate central differences
-    in the interior and either first differences or second order accurate 
+    in the interior and either first differences or second order accurate
     one-sides (forward or backwards) differences at the boundaries. The
     returned gradient hence has the same shape as the input array.
 
@@ -901,7 +901,7 @@ def gradient(f, *varargs, **kwargs):
     edge_order : {1, 2}, optional
         Gradient is calculated using N\ :sup:`th` order accurate differences
         at the boundaries. Default: 1.
-      
+
         .. versionadded:: 1.9.1
 
     Returns