numpy Task03

最新推荐文章于 2020-11-26 13:08:18 发布

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数组操作

更改形状

numpy.ndarray.shape表示数组的维度，返回一个元组，这个元组的长度就是维度的数目，即 ndim 属性(秩)。

import numpy as np

x = np.array([1,2,9,4,5,6,7,8])
print(x.shape)
x.shape = [2,4]
print(x)

(8,)
[[1 2 9 4]
[5 6 7 8]]

numpy.ndarray.flat 将数组转换为一维的迭代器，可以用for访问数组每一个元素。

import numpy as np
x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
y = x.flat

for i in y:
    print(i,end=',')

11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,

numpy.ndarray.flatten([order=‘C’]) 将数组的副本转换为一维数组，并返回。
【order：‘C’ – 按行，‘F’ – 按列，‘A’ – 原顺序，‘k’ – 元素在内存中的出现顺序。】

import numpy as np
x = np.array([[11, 12, 13, 14, 15],
              [16, 17, 18, 19, 20],
              [21, 22, 23, 24, 25],
              [26, 27, 28, 29, 30],
              [31, 32, 33, 34, 35]])
y = x.flatten()
print(y)

[11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34
35]

numpy.ravel(a, order=‘C’)将数组转换为一维数组，与上面不同的是，此方法返回的是数组的视图
reshape()函数当参数newshape = -1时，表示将数组降为一维

数组转置

numpy.transpose(a,axes=None)
numpy.ndarray.T

import numpy as np
x = np.random.rand(5,5) * 10
x = np.around(x ,2)
print(x)
print("++++++++++++++")
y = x.T
print(y)
print("++++++++++++++")
z = np.transpose(x)
print(z)

[[1.62 1.64 2.18 2.61 3.45]
[3.42 9.84 7.68 4.1 7.24]
[3.95 1.1 0.8 2.89 0.19]
[0.15 0.91 5.34 9.95 8.62]
[7.48 6.67 0.38 2.31 0.33]]
++++++++++++++
[[1.62 3.42 3.95 0.15 7.48]
[1.64 9.84 1.1 0.91 6.67]
[2.18 7.68 0.8 5.34 0.38]
[2.61 4.1 2.89 9.95 2.31]
[3.45 7.24 0.19 8.62 0.33]]
++++++++++++++
[[1.62 3.42 3.95 0.15 7.48]
[1.64 9.84 1.1 0.91 6.67]
[2.18 7.68 0.8 5.34 0.38]
[2.61 4.1 2.89 9.95 2.31]
[3.45 7.24 0.19 8.62 0.33]]

更改维度

当创建了一个数组之后，还可以给它增加一个维度，这在矩阵计算中经常会用到。
【numpy.newaxis = None None】

import numpy as np
x = np.array([1,2,9,4,5,6,7,8])
print(x.shape)
print(x)
print("++++++++++++")
y = x[:,np.newaxis]
print(y.shape)
print(y)

(8,)
[1 2 9 4 5 6 7 8]
++++++++++++
(8, 1)
[[1]
[2]
[9]
[4]
[5]
[6]
[7]
[8]]

numpy.squeeze(a,axis = None)从数组的形状中删除单维度条目，即把shape中为1的维度去掉
【a表示输入是数组；axis用于指定需要删除的维度，但是指定是维度必须为单维度，否则将会报错】

import numpy as np 
x = np.arange(10)
print(x.shape)
x = x[np.newaxis,:]
print(x.shape)
y = np.squeeze(x)
print(y.shape)

(10,)
(1, 10)
(10,)

数组组合

连接沿现有轴的数组序列（原来x,y都是一维的，拼接后的结果也是一维的）。

import numpy as np
x = np.array([1,2,3])
y = np.array([7,8,9])
z = np.concatenate([x,y])
print(z)

[1 2 3 7 8 9]

原来x,y都是二维的，拼接后的结果也是二维的

import numpy as np
x = np.array([1,2,3]).reshape(1,3)
y = np.array([7,8,9]).reshape(1,3)
z = np.concatenate([x,y])
print(z)
print('+++++++++++++++')
z = np.concatenate([x, y], axis=1)
print(z)

[[1 2 3]
[7 8 9]]
+++++++++++++++
[[1 2 3 7 8 9]]

沿着新的轴加入一系列数组（stack）

import numpy as np

x = np.array([[1, 2, 3], [4, 5, 6]])
y = np.array([[7, 8, 9], [10, 11, 12]])
z = np.stack([x, y])
print(z.shape)
print(z)

(2, 2, 3)
[[[ 1 2 3]
[ 4 5 6]]

[[ 7 8 9]
[10 11 12]]]

数组拆分

numpy.split(ary, indices_orsections,sxis=0) 拆分数组

import numpy as np
x = np.array([[11, 12, 13, 14],
              [16, 17, 18, 19],
              [21, 22, 23, 24]])
y = np.split(x,[1,3])
print(y)

[array([[11, 12, 13, 14]]), array([[16, 17, 18, 19],
[21, 22, 23, 24]]), array([], shape=(0, 4), dtype=int32)]

垂直切分是把数组按照高度切分

import numpy as np

x = np.array([[11, 12, 13, 14],
              [16, 17, 18, 19],
              [21, 22, 23, 24]])
y = np.vsplit(x, 3)
print(y)

[array([[11, 12, 13, 14]]), array([[16, 17, 18, 19]]), array([[21, 22, 23, 24]])]

水平切分是把数组按照宽度切分。

import numpy as np

x = np.array([[11, 12, 13, 14],
              [16, 17, 18, 19],
              [21, 22, 23, 24]])
y = np.hsplit(x, 2)
print(y)

[array([[11, 12],
[16, 17],
[21, 22]]), array([[13, 14],
[18, 19],
[23, 24]])]

数组平铺

numpy.tile(A,reps)将原矩阵横向、纵向地复制

import numpy as np

x = np.array([[1, 2], [3, 4]])
print(x)
print("++++++++++")
y = np.tile(x,(1,3))
print(y)

[[1 2]
[3 4]]
++++++++++
[[1 2 1 2 1 2]
[3 4 3 4 3 4]]

numpy.repeat(a, repeats, axis=None)【axis=0,沿着y轴复制，增加了行数；axis=1,沿着x轴复制，增加了列数；repeats,可以为一个数，也可以为一个矩阵；axis=None时就会flatten当前矩阵，实际上变成了一个行向量】

import numpy as np

x = np.array([[1, 2], [3, 4]])
y = np.repeat(x, 2, axis=1)
print(y)

[[1 1 2 2]
[3 3 4 4]]

添加和删除元素

numpy.unique(ar, return_index=False, return_inverse=False,return_counts=False, axis=None)