1. ndarray對象
ndarray是numpy中的一個N維數組對象，可以進行矢量算術運算，它是一個通用的同構數據多維容器，即其中的所有元素必須是相同類型的。
可以使用array函數創建數組，每個數組都有一個shape（一個表示各維度大小的元組）和一個dtype（一個用於說明數組數據類型的對象）。
使用zeros和ones函數可以分別創建數據全0或全1的數組。
numpy.ones(shape, dtype=None,order='C')：其中shape表示返回數組的形狀；dtype表示數組數據的類型，默認為float64；order可以取'C'或'F'，表示是否在內存中用C或者Fortran形式以連續順序（row- or column-wise）存放多維數據。
2. matrix對象
numpy庫提供了matrix類，使用matrix類創建的是matrix對象。matrix對象是繼承ndarray而來，因此它們和ndarray有相同的屬性和方法。但是它們之間有六個重要的區別，使用時一定要注意：
1) Matrix objects can be created using a string notation to allow Matlab-style syntax where spaces separate columns and semicolons (‘;’) separate rows.
2) Matrix objects are always two-dimensional. This has far-reaching implications, in that m.ravel() is still two-dimensional (with a 1 in the first dimension) and item selection returns two-dimensional objects so that sequence behavior is fundamentally different than arrays.
3) Matrix objects over-ride multiplication to be matrix-multiplication. Make sure you understand this for functions that you may want to receive matrices. Especially in light of the fact that asanyarray(m) returns a matrix when m is a matrix.
4) Matrix objects over-ride power to be matrix raised to a power. The same warning about using power inside a function that uses asanyarray(...) to get an array object holds for this fact.
5) The default __array_priority__ of matrix objects is 10.0, and therefore mixed operations with ndarrays always produce matrices.
6) Matrices have special attributes which make calculations easier. These are
使用numpy.matrix可以創建一個矩陣對象，numpy.mat是它的縮寫。它可以根據其他matrixs，字符串，或者其他可以轉化為ndarray的數據創建新的矩陣對象。
