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| -rw-r--r-- | doc/source/user/quickstart.rst | 28 |
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 ------------------- |