DOC: Simplify terminology

For some reason this file insisted on using multiple redundant terms
for array dimensionality, and in particular liked the word "rank",
which is confusing and very rarely used in my experience. This commit
drops the word rank and removes a number of parenthetical "X (which is
to say Y)" phrasings.

1 files changed, 12 insertions, 16 deletions

diff --git a/doc/source/user/quickstart.rst b/doc/source/user/quickstart.rst
index 87b0de2af..4a10faae8 100644
--- a/doc/source/user/quickstart.rst
+++ b/doc/source/user/quickstart.rst
@@ -25,14 +25,12 @@ The Basics
 
 NumPy's main object is the homogeneous multidimensional array. It is a
 table of elements (usually numbers), all of the same type, indexed by a
-tuple of positive integers. In NumPy dimensions are called *axes*. The
-number of axes is *rank*.
+tuple of positive integers. In NumPy dimensions are called *axes*.
 
-For example, the coordinates of a point in 3D space ``[1, 2, 1]`` is
-an array of rank 1, because it has one axis. That axis has 3 elements
-in it, so we say it has a length of 3. In the example pictured
-below, the array has rank 2 (it is 2-dimensional). The first dimension
-(axis) has a length of 2, the second dimension has a length of 3.
+For example, the coordinates of a point in 3D space ``[1, 2, 1]`` has
+one axis. That axis has 3 elements in it, so we say it has a length
+of 3. In the example pictured below, the array has 2 axes. The first
+axis has a length of 2, the second axis has a length of 3.
 
 ::
 
@@ -46,14 +44,12 @@ arrays and offers less functionality. The more important attributes of
 an ``ndarray`` object are:
 
 ndarray.ndim
-    the number of axes (dimensions) of the array. In the Python world,
-    the number of dimensions is referred to as *rank*.
+    the number of axes (dimensions) of the array.
 ndarray.shape
     the dimensions of the array. This is a tuple of integers indicating
     the size of the array in each dimension. For a matrix with *n* rows
     and *m* columns, ``shape`` will be ``(n,m)``. The length of the
-    ``shape`` tuple is therefore the rank, or number of dimensions,
-    ``ndim``.
+    ``shape`` tuple is therefore the number of axes, ``ndim``.
 ndarray.size
     the total number of elements of the array. This is equal to the
     product of the elements of ``shape``.
@@ -537,8 +533,8 @@ remaining axes. NumPy also allows you to write this using dots as
 ``b[i,...]``.
 
 The **dots** (``...``) represent as many colons as needed to produce a
-complete indexing tuple. For example, if ``x`` is a rank 5 array (i.e.,
-it has 5 axes), then
+complete indexing tuple. For example, if ``x`` is an array with 5
+axes, then
 
 -  ``x[1,2,...]`` is equivalent to ``x[1,2,:,:,:]``,
 -  ``x[...,3]`` to ``x[:,:,:,:,3]`` and
@@ -1245,9 +1241,9 @@ selecting the slices we want::
 
 Note that the length of the 1D boolean array must coincide with the
 length of the dimension (or axis) you want to slice. In the previous
-example, ``b1`` is a 1-rank array with length 3 (the number of *rows* in
-``a``), and ``b2`` (of length 4) is suitable to index the 2nd rank
-(columns) of ``a``.
+example, ``b1`` has length 3 (the number of *rows* in ``a``), and
+``b2`` (of length 4) is suitable to index the 2nd axis (columns) of
+``a``.
 
 The ix_() function
 -------------------