numpy库的基础用法

#起别名避免重名

import numpy as np

# #打印版本号

# print(np.version.version) #1.16.2

# #声明一个numpy数组，一层list

# nlist = np.array([1,2,3])

# print(nlist) #[1 2 3]

# #ndim方法用来查看数组的属性--维度

# print(nlist.ndim) #1

# #使用shape属性来打印多维数组的形状，返回一个tuple，个数，/行数，列数

# print(nlist.shape) #(3,)

# #声明一个二维数组，二层list

# nlist_2 = np.array([[1,2,3],[4,5,6]])

# print(nlist_2)

# #[[1 2 3]

# #[4 5 6]]

# print(nlist_2.ndim) #2

# print(nlist_2.shape) #(2, 3)

# #声明一个三维数组，三层list

# nlist3 = np.array([[[1,2,3],[4,5,6],[7,8,9]]])

# print(nlist3)

# # [[[1 2 3]

# # [4 5 6]

# # [7 8 9]]]

# print(nlist3.ndim) #3

# #使用shape属性来打印多维数组的形状，返回一个tuple，个数，/行数，列数

# print(nlist3.shape) #(1, 3, 3)

# #使用size()方法来打印多维数组的元素个数

# print(np.size(nlist)) #3

# print(np.size(nlist_2)) #6

# #打印numpy多维度数组的数据类型

# print(type([1,2,3])) #<class 'list'>

# print(type(nlist)) #<class 'numpy.ndarray'>

# #使用python内置dtype属性来打印多维度数组内部元素的数据类型

# print(type(123)) #<class 'int'>

# print(nlist.dtype) #int32

# #itemsize属性，来打印多维数组中的数据类型大小，字节

# print(nlist.itemsize) #4

# print(nlist_2.itemsize) #4

# print(nlist3.itemsize) #4

# #data属性，用来打印数据缓冲区--buffer---/也就是内存地址/

# print(nlist.data) #<memory at 0x000001AF3F0BEA08>

# print(nlist_2.data) #<memory at 0x000001FB22BF5CF0>

# print(nlist3.data) #<memory at 0x000001FB1A730D68>

# #使用reshape()方法，根据形状反向生成多维数组

# nlist_3 = np.array(range(24)).reshape((3,2,4)) #3个二维数组，2每组2行，4列数

# print(nlist_3)

# #使用浮点--元素类型

# nlist_float = np.array([1.0, 2.0, 3.0])

# print(nlist_float.dtype) #float64

# #使用字符串-元素类型

# nlist_str = np.array(['1','2','3'])

# print(nlist_str.dtype) #<U1

# print(range(20))

# print(type(range(20)))

# nlist_4 = np.array([[[[1,2,3,4,5],[6,7,8,9,10],[11,12,13,14,15],[16,17,18,19,20]]]])

# print(nlist_4)

# print(nlist_4.ndim)

# print(nlist_4.shape)

# print(nlist_4.itemsize)

# print(nlist_4.dtype)

# print(np.size(nlist_4))

# nlist_4 = np.array(range(20)).reshape((1,1,4,5))

# print(nlist_4.ndim)

# nlist_2_true = np.array([[True,True,True],[True,True,True],[True,True,True]])

# print(nlist_2_true)

# i = []

# nlist2_true = [ i.append(True) for x in range(20) ]

# print(i)

# nlist_2_true = np.array(range(20)).reshape((1,3,3))

# print(nlist_2_true)

#导入科学计算库

import numpy as np

import pandas as pd

import random

# #声明一个size为20的四维数组

# nlist_4 = np.array(range(20)).reshape((2,5,1,2))

# print(nlist_4)

# print(nlist_4.ndim)

# print(nlist_4.shape)

# #声明一个三行三列的数组

# nlist_33 = np.array([[1,2,3],[1,2,3],[5,7,8]])

# print(nlist_33)

# print(nlist_33.shape)

# print(nlist_33.ndim)

# print(nlist_33.itemsize) #元素字节

# print(nlist_33.size) #长度

# print(np.size(nlist_33))

# print(np.shape(nlist_33))

# #使用ones()自动生成元素为1的多维数组

# nlist_ones = np.ones((4,4))

# print(nlist_ones)

# print(nlist_ones.dtype) #元素float64

# #使用zeros()来生成元素为0的多维数组

# nlist_zeros = np.zeros((4,4))

# print(nlist_zeros)

# #使用empty（）方法来生成随机多维数组，使用第二个参数指定元素类型

# nlist_empty = np.empty([2,2],dtype=np.int)

# print(nlist_empty)

# print(nlist_empty.dtype) #int32

# # numpy把普通list转换为数组

# x = [1,2,3]

# print(type(x))

# nlist = np.asarray(x)

# print(type(nlist))

# print(nlist)

# y = [(1,2,3),(4,5)]

# nlist_y = np.asarray(y)

# print(nlist_y.ndim) #1

# #frombuffer 通过字符串（buffer内存地址）字节切片来生成多维数组

# #b强转byte字节

# my_str = b'Hello World'

# nlist_str = np.frombuffer(my_str,dtype='S1')

# print(nlist_str)

# x = np.array([[1,2],[3,4]])

# print(x)

# #指定axis属性，可以指定当前多维数组的维度

# sum0 = np.sum(x,axis = 0,keepdims=True) #axis = 0/行级/

# print(sum0)

# sum1 = np.sum(x,axis=1,keepdims=True) #axis = 1/列级/

# print(sum1)

# #多维数组赋值

# x = np.array([1,2])

# x[1] = 3

# y = x.copy()

# y[0] = 3

# print(x)

# d = [{'gender': 0}, {'gender': 1}, {'gender': 0}]

# #维度级的运算

# a = np.array([[1,2],[3,4],[5,6]])

# b = np.array([[11,22],[33,44],[55,66]])

# #vstack()方法---维度一样--- 栈式连纵

# suma = np.vstack((a,b))

# print(suma)

# #hstack()方法---维度一样--- 横向连纵

# sumb = np.hstack((a,b))

# print(sumb)

# #多维数组调用

# nl = np.array([[1,2],[3,4],[5,6]])

# print(nl[[2]])

# print(nl[0][0])

# print(nl[1][1])

# nl[1,1] = 444

# print(nl)

# #删除方法 delete

# #删除nlist第二行

# print(np.delete(nl,1,axis=0))

# print(np.delete(nl,0,axis=1))

# a=np.arange(0, 20, 5)

# print(a)

# print(a.dtype)

# b=np.arange(0, 3.0, 0.4)

# print(b)

# print(b.dtype)

# a=np.arange(1,5).reshape((2,2))

# b=np.arange(3,7).reshape((2,2))

# print(a)

# print(b)

# 1创建一个长度为10的一维全为0的多维数组，然后让第5个元素等于1

# ll = np.zeros((10,))

# print(ll)

# print(ll.ndim)

# print(ll.size)

# ll[4] = 1

# print(ll)

#2创建一个每一行都是从0到4的5*5矩阵

# # l_2 = np.array([[0,1,2,3,4],[0,1,2,3,4],[0,1,2,3,4],[0,1,2,3,4],[0,1,2,3,4]])

# l_2 = np.array([ range(5)]*5).reshape(5,5)

# print(l_2)

# print(l_2.ndim) #2维

# print(l_2.shape)

# # 3假如给定一个3*3的二维数组，如何交换其中两行的元素？

# vv = np.random.randint(0,100,size=(3,3))

# print(vv)

# print('/ / / / / / / / / / / / / / / / / ')

# print(vv[[1,0,2]])

# print(vv[[2,0,1]])

# print(vv[[0,2,1]])

# # 4原数组为一维数组，内容为从 0 到 100，抽取出所有偶数。

numpy库的基础用法

猜你喜欢