NumPy

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices矩阵), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms离散傅里叶变形, basic linear algebra基本线性代数, basic statistical operations基本统计操作, random simulation随机模拟 and much more.

At the core of the NumPy package, is the ndarray object. This encapsulates n-dimensional arrays of homogeneous同种类 data types, with many operations being performed in compiled code for performance. There are several important differences between NumPy arrays and the standard Python sequences:

• NumPy arrays have a fixed size at creation, unlike Python lists (which can grow dynamically). Changing the

size of an ndarray will create a new array and delete the original.

• The elements in a NumPy array are all required to be of the same data type, and thus will be the same size in

memory. The exception: one can have arrays of (Python, including NumPy) objects, thereby allowing for arrays

of different sized elements.

• NumPy arrays facilitate advanced mathematical and other types of operations on large numbers of data. Typically, such operations are executed more efficiently and with less code than is possible using Python’s built-in

sequences.

• A growing plethora of scientific and mathematical Python-based packages are using NumPy arrays; though

these typically support Python-sequence input, they convert such input to NumPy arrays prior to processing,

and they often output NumPy arrays. In other words, in order to efficiently use much (perhaps even most)

of today’s scientific/mathematical Python-based software, just knowing how to use Python’s built-in sequence

types is insufficient - one also needs to know how to use NumPy arrays.