Numpy基础

目录

　　数据结构

　　基础操作

　　矩阵属性

　　矩阵操作

　　矩阵函数

---

数据结构

# -*- coding: utf-8 -*-
import numpy as np
#从文本构建ndarray
a = np.genfromtxt("a.txt", delimiter="	", dtype=int, skip_header=0)
print(type(a))
print(a)
'''
<class 'numpy.ndarray'>
[[1 2 3]
 [4 5 6]]
'''

#直接构造ndarray
a = np.array([[1,2],[3,4]]) #元素类型最好设置成一致
print(type(a))
print(a.dtype)
print(a)
a = np.array([[1,2],[3,4.0]]) 
print(type(a))
print(a.dtype)
print(a)
'''
<class 'numpy.ndarray'>
int32
[[1 2]
 [3 4]]
<class 'numpy.ndarray'>
float64
[[ 1.  2.]
 [ 3.  4.]]
'''

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

#取某个值
print(a[1,2]) #第2行第3列
# 6

#切片
a = np.array([1,2,3,4,5,6])
print(a[:3]) #打印前三个值
#[1 2 3]
a = np.array([[1,2,3],[4,5,6],[7,8,9]])
print(a[:,:2]) #行全取，列只取前两列
'''
[[1 2]
 [4 5]
 [7 8]]
'''

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基础操作 

 

# -*- coding: utf-8 -*-
import numpy as np

# 转bool类型
a = np.array([[1,2],[3,4]]) 
print(a == 2)
'''
[[False  True]
 [False False]]
'''

# 与或运算
a = np.array([[1,2],[3,4]])
b = np.array([[1,2],[0,4]])
print(a&b)
print(a|b)
'''
[[1 2]
 [0 4]]
[[1 2]
 [3 4]]
'''

# 类型转换
a = np.array(["1","2","3","4"])
print(a.dtype)
a = a.astype(float)
print(a.dtype)
print(a)
'''
<U1
float64
[ 1.  2.  3.  4.]
'''

# 获取最值
a = np.array([[1,4],[3,2],[8,6]])
'''
[[1 4]
 [3 2]
 [8 6]]
'''
print(a.min(axis=0)) #取0轴上的最小值
# [1 2]
print(a.min(axis=1)) #取1轴上的最小值
# [1 3 6]
print(a.min()) #取最小值
# 1

# 求和
a = np.array([[1,2],[3,4],[5,6]])
print(a.sum(axis=0))
print(a.sum(axis=1))
print(a.sum())
'''
[ 9 12]
[ 3  7 11]
21
'''

# 求平均数
a = np.array([[1,2],[3,4],[5,6]])
print(a.mean(axis=0))
print(a.mean(axis=1))
print(a.mean())
'''
[ 3.  4.]
[ 1.5  3.5  5.5]
3.5
'''

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矩阵属性 

# -*- coding: utf-8 -*-
import numpy as np

a = np.arange(12)
print(a)
# [ 0  1  2  3  4  5  6  7  8  9 10 11]
a = a.reshape(3,4)
print(a)
'''
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
'''

print(a.shape) #形状
# (3, 4)

print(a.ndim) #维度
# 2

print(a.size) #大小
# 12

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矩阵操作 

 

# -*- coding: utf-8 -*-
import numpy as np

a = np.zeros((2,3)) #0矩阵
print(a)
# [[ 0.  0.  0.]
#  [ 0.  0.  0.]]

a = np.ones((2,3), dtype = np.int32) #1矩阵
print(a)
# [[1 1 1]
#  [1 1 1]]

a = np.arange(100,110,2) #自定义步长矩阵
print(a)
# [100 102 104 106 108]

a = np.random.random((2,3)) #随机矩阵
print(a)
# [[ 0.51411639  0.77741782  0.5720869 ]
#  [ 0.8042447   0.36104249  0.62305819]]

a = np.linspace(1, 2, 5) #在1和2之间 平均取5个点
print(a)
# [ 1.    1.25  1.5   1.75  2.  ]

# 四则运算
a = np.array([2,4,6,8])
b = np.array([1,3,5,10])
print(a-b)
# [ 1  1  1 -2]
print(a-1)
# [1 3 5 7]
print(a**2)
# [ 4 16 36 64]
print(a>5)
# [False False  True  True]

#矩阵乘法
a = np.array([[1,1],
              [0,1]])
b = np.array([[2,0],
              [3,4]])
print(a*b) #对应位置相乘
# [[2 0]
#  [0 4]]
print(a.dot(b)) #矩阵相乘
# [[5 4]
#  [3 4]]

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矩阵函数 

# -*- coding: utf-8 -*-
import numpy as np

a = np.array([[1,2],
              [3,4]])
print(np.exp(a))
# [[  2.71828183   7.3890561 ]
#  [ 20.08553692  54.59815003]]
print(np.sqrt(a))
# [[ 1.          1.41421356]
#  [ 1.73205081  2.        ]]

a = np.random.random((2,3)) * 10
print(a)
# [[ 1.61719344  2.678753    1.26624097]
#  [ 8.54779284  2.81985938  9.78669941]]
print(np.floor(a)) #向下取整
# [[ 1.  2.  1.]
#  [ 8.  2.  9.]]

a = np.array([[1,2],
              [3,4]])
print(a.ravel()) #转化成向量
# [1 2 3 4]

print(a.T) #转置
# [[1 3]
#  [2 4]]

#矩阵拼接
a = np.array([[1,2],
              [3,4]])
b = np.array([[5,6],
              [7,8]])
print(np.vstack((a,b)))
# [[1 2]
#  [3 4]
#  [5 6]
#  [7 8]]
print(np.hstack((a,b)))
# [[1 2 5 6]
#  [3 4 7 8]]


#矩阵切分
a = np.arange(24).reshape(4,6)
print(a)
# [[ 0  1  2  3  4  5]
#  [ 6  7  8  9 10 11]
#  [12 13 14 15 16 17]
#  [18 19 20 21 22 23]]
print(np.hsplit(a,2)) #竖着平均切2份
# [array([[ 0,  1,  2],
#        [ 6,  7,  8],
#        [12, 13, 14],
#        [18, 19, 20]]), 
#  array([[ 3,  4,  5],
#        [ 9, 10, 11],
#        [15, 16, 17],
#        [21, 22, 23]])]
print(np.vsplit(a,2)) #横着平均切2份
# [array([[ 0,  1,  2,  3,  4,  5],
#        [ 6,  7,  8,  9, 10, 11]]), 
#  array([[12, 13, 14, 15, 16, 17],
#        [18, 19, 20, 21, 22, 23]])

print(np.hsplit(a,(2,3))) #竖着在索引2，索引3的位置各切一刀
# [array([[ 0,  1],
#        [ 6,  7],
#        [12, 13],
#        [18, 19]]), 
#  array([[ 2],
#        [ 8],
#        [14],
#        [20]]), 
#  array([[ 3,  4,  5],
#        [ 9, 10, 11],
#        [15, 16, 17],
#        [21, 22, 23]])]

print(np.vsplit(a,(2,3))) #横着在索引2，索引3的位置各切一刀
# [array([[ 0,  1,  2,  3,  4,  5],
#        [ 6,  7,  8,  9, 10, 11]]), 
#  array([[12, 13, 14, 15, 16, 17]]), 
#  array([[18, 19, 20, 21, 22, 23]])]

#复制矩阵
a = np.array([[1,2],
              [3,4]])
b = a #b和a指向同一个内存,改变b的值a也会发生改变
print(id(a)==id(b))
# True
b = a.copy() #b和a的内容一样，但各自有各自的存储单元,改变b的值a不会发生改变
print(id(a)==id(b))
# False


#寻找index值
a = np.arange(20).reshape(4,5)
# [[ 0  1  2  3  4]
#  [ 5  6  7  8  9]
#  [10 11 12 13 14]
#  [15 16 17 18 19]]
print(a.argmax(axis=0))
# [3 3 3 3 3]
print(a.argmax(axis=1))
# [4 4 4 4]


# 扩展矩阵
a = np.array([[1,2],
              [3,4]])
a = np.tile(a,(2,3))
print(a)
# [[1 2 1 2 1 2]
#  [3 4 3 4 3 4]
#  [1 2 1 2 1 2]
#  [3 4 3 4 3 4]]


#排序
a = np.array([[1,4],
              [6,3]])
b = np.sort(a,axis=0) #列排序
print(b)
# [[1 3]
#  [6 4]]
b = np.sort(a,axis=1)#行排序
print(b)
# [[1 4]
#  [3 6]]

a = np.array([1,4,3,2])
print(np.argsort(a)) #打印排序索引
# [0 3 2 1]

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