Numpy学习

爱被打了一巴掌 2022-05-31 03:44 217阅读 0赞

> 本文首发于我的博客：[gongyanli.com][]  
> 参考视频：[莫烦的numpy视频/][numpy]  
> 我的简书：[https://www.jianshu.com/p/bbdf304547a4][https_www.jianshu.com_p_bbdf304547a4]

前言：和pandas一样，numpy曾经也一起看得，还是忘记了。还是推荐那个视频，短小上手快。我写的东西有时候我会进行更新，所以有些写好的内容，后面也可能会加新内容，所以大家不要见怪啊。因为一般写东西numpy在前pandas在后，但是我好像是反着的，所以这个前言是我后来加的。因为强迫症的我，一篇文章有了前言，就觉得每篇都该有。

## 一、安装 ##

windows：pip install numpy(anaconda下)
    mac：pip install numpy
    linux:pip install numpy

## 二、导入 ##

import numpy as np

## 三、Numpy属性 ##

ndim:维度
    shape:行数和列数
    size：元素个数

## 四、Numpy–创建array ##

array：创建数组
    dtype：指定数据类型
    zeros：创建数据全为0
    ones：创建数据全为1
    empty：创建数据接近0
    arrange：按指定范围创建数据
    linspace：创建线段
    
    a = np.array([2,23,4])  # list 1d
    print(a)
    # [2 23 4]
    
    a = np.array([2,23,4],dtype=np.int)  # np.int32/float/float32
    print(a.dtype)
    # int 64
    
    a = np.array([[2,23,4],[2,32,4]])  # 2d 矩阵 2行3列
    print(a)
    """
    [[ 2 23  4]
     [ 2 32  4]]
    """
    
    a = np.zeros((3,4)) # 数据全为0，3行4列
    """
    array([[ 0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.]])
    """
    
    
    a = np.ones((3,4),dtype = np.int)   # 数据为1，3行4列
    """
    array([[1, 1, 1, 1],
           [1, 1, 1, 1],
           [1, 1, 1, 1]])
    """
    
    a = np.empty((3,4)) # 数据为empty，3行4列
    """
    array([[  0.00000000e+000,   4.94065646e-324,   9.88131292e-324,
              1.48219694e-323],
           [  1.97626258e-323,   2.47032823e-323,   2.96439388e-323,
              3.45845952e-323],
           [  3.95252517e-323,   4.44659081e-323,   4.94065646e-323,
              5.43472210e-323]])
    """
    
    a = np.arange(10,20,2) # 10-19 的数据，2步长
    """
    array([10, 12, 14, 16, 18])
    """
    
    a = np.arange(12).reshape((3,4))    # 3行4列，0到11
    """
    array([[ 0,  1,  2,  3],
           [ 4,  5,  6,  7],
           [ 8,  9, 10, 11]])
    """
    
    a = np.linspace(1,10,20)    # 开始端1，结束端10，且分割成20个数据，生成线段
    """
    array([  1.        ,   1.47368421,   1.94736842,   2.42105263,
             2.89473684,   3.36842105,   3.84210526,   4.31578947,
             4.78947368,   5.26315789,   5.73684211,   6.21052632,
             6.68421053,   7.15789474,   7.63157895,   8.10526316,
             8.57894737,   9.05263158,   9.52631579,  10.        ])
    """
    
    a = np.linspace(1,10,20).reshape((5,4)) # 更改shape
    """
    array([[  1.        ,   1.47368421,   1.94736842,   2.42105263],
           [  2.89473684,   3.36842105,   3.84210526,   4.31578947],
           [  4.78947368,   5.26315789,   5.73684211,   6.21052632],
           [  6.68421053,   7.15789474,   7.63157895,   8.10526316],
           [  8.57894737,   9.05263158,   9.52631579,  10.        ]])
    """

## 五、Numpy–基础运算 ##

a=np.array([10,20,30,40])   # array([10, 20, 30, 40])
    b=np.arange(4)              # array([0, 1, 2, 3])
    
    c=a-b  # array([10, 19, 28, 37])
    c=a+b   # array([10, 21, 32, 43])
    c=a*b   # array([  0,  20,  60, 120])
    c=b**2  # array([0, 1, 4, 9])  # 二次方
    c=10*np.sin(a)  
    # array([-5.44021111,  9.12945251, -9.88031624,  7.4511316 ])
    print(b<3)  
    # array([ True,  True,  True, False], dtype=bool)
    
    a=np.array([[1,1],[0,1]])
    b=np.arange(4).reshape((2,2))
    
    print(a)
    # array([[1, 1],
    #       [0, 1]])
    
    print(b)
    # array([[0, 1],
    #       [2, 3]])
    
    c_dot = np.dot(a,b)
    # array([[2, 4],
    #       [2, 3]])
    
    c_dot_2 = a.dot(b)
    # array([[2, 4],
    #       [2, 3]])
    
    a=np.random.random((2,4))
    print(a)
    # array([[ 0.94692159,  0.20821798,  0.35339414,  0.2805278 ],
    #       [ 0.04836775,  0.04023552,  0.44091941,  0.21665268]])
    
    np.sum(a)   # 4.4043622002745959
    np.min(a)   # 0.23651223533671784
    np.max(a)   # 0.90438450240606416
    
    print("sum =",np.sum(a,axis=1))
    # sum = [ 1.96877324  2.43558896]
    
    print("min =",np.min(a,axis=0))
    # min = [ 0.23651224  0.41900661  0.36603285  0.46456022]
    
    print("max =",np.max(a,axis=1))
    # max = [ 0.84869417  0.9043845 ]
    
    
    
    A = np.arange(2,14).reshape((3,4)) 
    
    # array([[ 2, 3, 4, 5]
    #        [ 6, 7, 8, 9]
    #        [10,11,12,13]])
    
    print(np.argmin(A))    # 0
    print(np.argmax(A))    # 11
    print(np.mean(A))        # 7.5
    print(np.average(A))     # 7.5
    print(A.mean())          # 7.5
    print(A.median())       # 7.5
    print(np.cumsum(A)) 
    
    # [2 5 9 14 20 27 35 44 54 65 77 90]
    print(np.diff(A))    
    
    # [[1 1 1]
    #  [1 1 1]
    #  [1 1 1]]
    print(np.nonzero(A))    
    
    # (array([0,0,0,0,1,1,1,1,2,2,2,2]),array([0,1,2,3,0,1,2,3,0,1,2,3]))
    
    
    A = np.arange(14,2, -1).reshape((3,4)) 
    
    # array([[14, 13, 12, 11],
    #       [10,  9,  8,  7],
    #       [ 6,  5,  4,  3]])
    
    print(np.sort(A))    
    
    # array([[11,12,13,14]
    #        [ 7, 8, 9,10]
    #        [ 3, 4, 5, 6]])
    
    print(np.transpose(A))    
    print(A.T)
    
    # array([[14,10, 6]
    #        [13, 9, 5]
    #        [12, 8, 4]
    #        [11, 7, 3]])
    # array([[14,10, 6]
    #        [13, 9, 5]
    #        [12, 8, 4]
    #        [11, 7, 3]])
    
    print(A)
    # array([[14,13,12,11]
    #        [10, 9, 8, 7]
    #        [ 6, 5, 4, 3]])
    
    print(np.clip(A,5,9))    
    # array([[ 9, 9, 9, 9]
    #        [ 9, 9, 8, 7]
    #        [ 6, 5, 5, 5]])

## 六、Numpy–索引 ##

A = np.arange(3,15)
    
    # array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
    
    print(A[3])    # 6
    
    A = np.arange(3,15).reshape((3,4))
    """
    array([[ 3,  4,  5,  6]
           [ 7,  8,  9, 10]
           [11, 12, 13, 14]])
    """
    
    print(A[2])         
    # [11 12 13 14]
    
    print(A[1][1])      # 8
    print(A[1, 1])      # 8
    print(A[1, 1:3])    # [8 9]
    for row in A:
        print(row)
    """    
    [ 3,  4,  5, 6]
    [ 7,  8,  9, 10]
    [11, 12, 13, 14]
    """
    for column in A.T:
        print(column)
    """  
    [ 3,  7,  11]
    [ 4,  8,  12]
    [ 5,  9,  13]
    [ 6, 10,  14]
    """
    
    A = np.arange(3,15).reshape((3,4))
    
    print(A.flatten())   
    # array([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
    
    for item in A.flat:
        print(item)
    
    # 3
    # 4
    ……
    # 14

## 七、Numpy–array合并 ##

np.vstack()

A = np.array([1,1,1])
    B = np.array([2,2,2])
    
    print(np.vstack((A,B)))    # vertical stack
    """
    [[1,1,1]
     [2,2,2]]
    """
    C = np.vstack((A,B))      
    print(A.shape,C.shape)
    
    # (3,) (2,3)

np.hstack()

D = np.hstack((A,B))       # horizontal stack
    
    print(D)
    # [1,1,1,2,2,2]
    
    print(A.shape,D.shape)
    # (3,) (6,)

np.newaxis()  
print(A\[np.newaxis,:\])  
\# \[\[1 1 1\]\]

print(A[np.newaxis,:].shape)
    # (1,3)
    
    print(A[:,np.newaxis])
    """
    [[1]
    [1]
    [1]]
    """
    
    print(A[:,np.newaxis].shape)
    # (3,1)
    
    
    A = np.array([1,1,1])[:,np.newaxis]
    B = np.array([2,2,2])[:,np.newaxis]
    
    C = np.vstack((A,B))   # vertical stack
    D = np.hstack((A,B))   # horizontal stack
    
    print(D)
    """
    [[1 2]
    [1 2]
    [1 2]]
    """
    
    print(A.shape,D.shape)
    # (3,1) (3,2)

np.concatenate()

C = np.concatenate((A,B,B,A),axis=0)
    
    print(C)
    """
    array([[1],
           [1],
           [1],
           [2],
           [2],
           [2],
           [2],
           [2],
           [2],
           [1],
           [1],
           [1]])
    """
    
    D = np.concatenate((A,B,B,A),axis=1)
    
    print(D)
    """
    array([[1, 2, 2, 1],
           [1, 2, 2, 1],
           [1, 2, 2, 1]])
    """

## 八、Numpy–array分割 ##

A = np.arange(12).reshape((3, 4))
    print(A)
    """
    array([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
    """
    
    print(np.split(A, 2, axis=1)) #从左往右分割，纵向分割
    """
    [array([[0, 1],
            [4, 5],
            [8, 9]]), array([[ 2,  3],
            [ 6,  7],
            [10, 11]])]
    """
    
    print(np.split(A, 3, axis=0)) #从上往下分割，横向分割
    
    # [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
    
    print(np.array_split(A, 3, axis=1)) #不等量分割
    """
    [array([[0, 1],
            [4, 5],
            [8, 9]]), array([[ 2],
            [ 6],
            [10]]), array([[ 3],
            [ 7],
            [11]])]
    """
    
    print(np.vsplit(A, 3)) #等于 print(np.split(A, 3, axis=0))
    
    # [array([[0, 1, 2, 3]]), array([[4, 5, 6, 7]]), array([[ 8,  9, 10, 11]])]
    
    
    print(np.hsplit(A, 2)) #等于 print(np.split(A, 2, axis=1))
    """
    [array([[0, 1],
           [4, 5],
           [8, 9]]), array([[ 2,  3],
            [ 6,  7],
            [10, 11]])]

八、Numpy–copy and deep copy

a = np.arange(4)
    # array([0, 1, 2, 3])
    
    b = a
    c = a
    d = b
    # a、b、c、d中只要任何一个数组的元素发生变化，与之相等的元素会随之变化
    
    b=a.copy()
    # 当a发生变化时，b不会随之发生变化

[gongyanli.com]: http://gongyanli.com/Numpy%E5%AD%A6%E4%B9%A0/
[numpy]: https://morvanzhou.github.io/tutorials/data-manipulation/np-pd/
[https_www.jianshu.com_p_bbdf304547a4]: https://www.jianshu.com/p/bbdf304547a4