Python NumPy Package

In the NumPy package we can use arrays to represent vectors, matrices, and higher-order data structures

First import the NumPy package:

From NumPy import*

There are several ways to initialize an NumPy array, such as

1.python list or meta-ancestor

2. Using the Arrange,linspace function

3. Reading data from a file

Example: List generation NumPy array:

V=array ([1,2,3,4])

M=array ([[1,2],[3,4]])

The V and M objects are the Ndarray types provided by the NumPy module

The difference between the v,m is that they have different dimensions.

They can get their dimensions through Ndarray.shape.

Ndarray.size get their number of elements

Example: M.shape m.size

You can also use shape (m) and size (m)

So why not use list?

Define element types that can be displayed by using the Dtype keyword when creating an array

Using array generation functions

X=arange (0,10,1) 0 to 10 pitch 1

Linspace (0,10,25) 0 to 101 of 25 total data

X,y=mgrid[0:5,0:5]

To generate a random array:

From NumPy import random

Random.rand (5,5) generates 5*5 matrix with random values from 0 to 1

RANDOM.RANDN (5,5) random value

Zeros ((3,3)) 3*3 0 matrix

Ones ((3,3)) 3*3 is a matrix of 1

File I/O creates an array:

CSV is a commonly used data format file type, using the Numpy.genfromtxt function to read

Use Numpy.savetxt to store an array in a CSV file

M=random.rand (3,3)

Savetxt ("Random-maxtrix.csv", M)

Nnmpy Native File type:

Save and read with Numpy.save and Numpy.load

Save ("Numpy.npy", M)

Load ("Numpy.npy")

Manipulating arrays:

Use square brackets:

M[1]

m[1,1]

If it is an n-dimensional array, omitting an index value during retrieval returns a whole row

M[1]

Use: can achieve the same effect.

M[1,:] One row m[:,1] a column

You can also use index values to assign values

A[1:3] will return 1th, Element 2nd---------Index This block is actually the same as the list.

A negative index is calculated from the end of the array. M[-1] The number of the first to count

The index tiles are the same in a two-dimensional array.

NumPy function

where function can convert an index mask to an indexed position

diag function extracts array diagonal lines

The take function is similar to the Advanced index usage

Choose Select multiple arrays to form a new array

When we subtraction between matrices, the default behavior is multiply-by-item

Matrix and matrix using dot function, multiplication of vectors

To map an array object to a matrix type

Python NumPy Package