Numpy使用 Numpy使用

【Numpy使用】layout: post
title: Numpy使用
categories: Python
description: Numpy使用
keywords: Numpy
url: https://lichao890427.github.io/ https://github.com/lichao890427/
Python NumPy使用基本用法 ??NumPy是python的多维数组运算库，NumPy中维度数称为rank，维度称为axe，如[1,2,3]是一维数组。NumPy数组类为ndarray，它的重要属性有：
ndarray.ndim 维度数
ndarray.shape 维度(n,m)
ndarray.size 元素数
ndarray.dtype 元素类型
ndarray.itemsize 元素大小
ndarray.data 原始数据

import numpy as np a = np.arange(15).reshape(3, 5) >>> a array([[ 0,1,2,3,4], [ 5,6,7,8,9], [10, 11, 12, 13, 14]]) >>> a.shape (3, 5) >>> a.ndim 2 >>> a.dtype.name 'int64' >>> a.itemsize 8 >>> a.size 15 >>> type(a) >>> b = np.array([6, 7, 8]) >>> b array([6, 7, 8]) >>> type(b)

创建数组

a = np.array([2,3,4]) b = np.array([(1.5,2,3), (4,5,6)]) >>> np.zeros( (3,4) ) array([[ 0.,0.,0.,0.], [ 0.,0.,0.,0.], [ 0.,0.,0.,0.]]) >>> np.ones( (2,3,4), dtype=np.int16 )# dtype can also be specified array([[[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]], [[ 1, 1, 1, 1], [ 1, 1, 1, 1], [ 1, 1, 1, 1]]], dtype=int16) >>> np.empty( (2,3) )# uninitialized, output may vary array([[3.73603959e-262,6.02658058e-154,6.55490914e-260], [5.30498948e-313,3.14673309e-307,1.00000000e+000]]) >>> np.arange( 10, 30, 5 ) array([10, 15, 20, 25]) >>> np.arange( 0, 2, 0.3 )# it accepts float arguments array([ 0. ,0.3,0.6,0.9,1.2,1.5,1.8]) >>> from numpy import pi >>> np.linspace( 0, 2, 9 )# 9 numbers from 0 to 2 array([ 0.,0.25,0.5 ,0.75,1.,1.25,1.5 ,1.75,2.]) >>> x = np.linspace( 0, 2*pi, 100 )# useful to evaluate function at lots of points >>> f = np.sin(x)

基本操作

a = np.array( [20,30,40,50] ) b = np.arange( 4 ) # array([0, 1, 2, 3]) c = a - b # array([20, 29, 38, 47]) b**2 # 乘方 array([0, 1, 4, 9]) 10*np.sin(a) # array([ 9.12945251, -9.88031624,7.4511316 , -2.62374854]) a<35 # array([ True, True, False, False], dtype=bool)

矩阵乘法使用dot函数完成

>>> A = np.array( [[1,1], ...[0,1]] ) >>> B = np.array( [[2,0], ...[3,4]] ) >>> A*B# elementwise product array([[2, 0], [0, 4]]) >>> A.dot(B)# matrix product array([[5, 4], [3, 4]]) >>> np.dot(A, B)# another matrix product array([[5, 4], [3, 4]])

非数组操作提供在ndarray类中

>>> a = np.random.random((2,3)) >>> a array([[ 0.18626021,0.34556073,0.39676747], [ 0.53881673,0.41919451,0.6852195 ]]) >>> a.sum() 2.5718191614547998 >>> a.min() 0.1862602113776709 >>> a.max() 0.6852195003967595

通用函数如sin cos ex

>>> B = np.arange(3) >>> B array([0, 1, 2]) >>> np.exp(B) array([ 1.,2.71828183,7.3890561 ]) >>> np.sqrt(B) array([ 0.,1.,1.41421356]) >>> C = np.array([2., -1., 4.]) >>> np.add(B, C) array([ 2.,0.,6.])