numpy里面的array是多维的。每个纬度叫做axes，axes的数量叫做rank
eg：
[1, 2, 1]
is an array of rank 1，因为只有一个axis，axis的长度为3.
[[1.,0.,0.],
[0.,1.,2.]]
这个array 的rank为2，（2-dimensional）
NumPy 的array的类名叫做 ndarray，numpy.array 和标准python的类名 array.array 是不一样的，array.array 是一维的， 同时会提供比较少的方法。那么ndarray的一些重要的attibutes如下：
ndarray.ndimthe number of axes (dimensions) of the array. In the Python world, the number of dimensions is referred to as rank.
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.
ndarray.size
the total number of elements of the array. This is equal to the product of the elements ofshape.
ndarray.dtype
an object describing the type of the elements in the array. One can create or specify dtype’s using standard Python types. Additionally NumPy provides types of its own. numpy.int32, numpy.int16, and numpy.float64 are some examples.
ndarray.itemsize
the size in bytes of each element of the array. For example, an array of elements of typefloat64hasitemsize8 (=64/8), while one of typecomplex32hasitemsize4 (=32/8). It is equivalent tondarray.dtype.itemsize.
ndarray.data
the buffer containing the actual elements of the array. Normally, we won’t need to use this attribute because we will access the elements in an array using indexing facilities.
