import numpy as np#创建数组（给array函数传递Python序列对象）
a = np.array([1,2,3,4,5])
b = np.array((1,2,3,4,5,6))
c = np.array([ [1,2,3,4,5], [6,7,8,9,10] ])#数组的大小用shape属性获得
print(type(a), a.shape, a, '\n')
print(type(b), b.shape, b,'\n')
print(type(c),c.shape, c,'\n')#改变数组的shape属性，改变自身元素排列
c.shape = 2, 5
print(c.shape, c)c.shape = 10, -1
print(c.shape, c)#通过reshape改变数组排序，赋值给新数组，但是共享同一块内存
d = b.reshape((2,3))
print(d.shape, d)
b[1]=100
print(b,d)

输出:

<class 'numpy.ndarray'> (5,) [1 2 3 4 5]

<class 'numpy.ndarray'> (6,) [1 2 3 4 5 6]

<class 'numpy.ndarray'> (2, 5) [[ 1 2 3 4 5]
[ 6 7 8 9 10]]

(2, 5) [[ 1 2 3 4 5]
[ 6 7 8 9 10]]
(10, 1) [[ 1]
[ 2]
[ 3]
[ 4]
[ 5]
[ 6]
[ 7]
[ 8]
[ 9]
[10]]
(2, 3) [[1 2 3]
[4 5 6]]
[ 1 100 3 4 5 6] [[ 1 100 3]
[ 4 5 6]]

import numpy as np#创建数组（通过numpy函数）
a = np.arange(0, 1, 0.1) #不包括终值
b = np.linspace(0, 1, 10) #包括终值，等差10个数
c = np.logspace(0, 2, 10) #从1到100，等比10个数

s = "abcdef"
d = np.fromstring(s, dtype=np.int8)
e = np.fromstring(s, dtype=np.int16)
print(a,'\n',b,'\n',c,'\n',d,'\n',e,'\n')

输出：

[ 0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]
[ 0. 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556
0.66666667 0.77777778 0.88888889 1. ]
[ 1. 1.66810054 2.7825594 4.64158883 7.74263683
12.91549665 21.5443469 35.93813664 59.94842503 100. ]
[ 97 98 99 100 101 102]
[25185 25699 26213]

import numpy as np#创建10个元素的一维数组
def func(i):return i%4+1print ( np.fromfunction(func,(10,)) )

输出：

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

import numpy as npdef func(i,j):return (i + 1) * (j + 1)print(np.fromfunction(func, (9,9)))

输出：

[[ 1. 2. 3. 4. 5. 6. 7. 8. 9.]
[ 2. 4. 6. 8. 10. 12. 14. 16. 18.]
[ 3. 6. 9. 12. 15. 18. 21. 24. 27.]
[ 4. 8. 12. 16. 20. 24. 28. 32. 36.]
[ 5. 10. 15. 20. 25. 30. 35. 40. 45.]
[ 6. 12. 18. 24. 30. 36. 42. 48. 54.]
[ 7. 14. 21. 28. 35. 42. 49. 56. 63.]
[ 8. 16. 24. 32. 40. 48. 56. 64. 72.]
[ 9. 18. 27. 36. 45. 54. 63. 72. 81.]]

# -*- coding: utf-8 -*-
import numpy as npmatrix = np.array([[1,2,3], [4,5,6]]) #矩阵
print("dim; ",matrix.ndim)
print("shape: ",matrix.shape)
print("size: ",matrix.size)list1 = np.array([1,2,3,4],dtype=np.int32)
print("list1 dtype: ",list1.dtype)list2 = np.array([1,2,3,4])
print("list2 dtype: ",list2.dtype)list3 = np.array([1,2,3,4],dtype=np.float)
print("list3 dtype: ",list3.dtype)list4 = np.array([1,2,3,4],dtype=np.float32)
print("list4 dtype: ",list4.dtype)list5 = np.ones((3,4),dtype=np.int)
print("list5: ",list5)list6 = np.empty((3,4))
print("list6: ",list6)list7 = np.arange(5,15).reshape((2,5))
print("list7: ",list7)list8 = np.linspace(1,11,10)
print("list8: ",list8)

输出;

dim; 2
shape: (2, 3)
size: 6
list1 dtype: int32
list2 dtype: int32
list3 dtype: float64
list4 dtype: float32
list5: [[1 1 1 1]
[1 1 1 1]
[1 1 1 1]]
list6: [[ 6.95332630e-310 1.69118108e-306 2.04722549e-306 1.29061142e-306]
[ 2.22522597e-306 1.33511969e-306 1.29061753e-306 1.11261027e-306]
[ 9.34609790e-307 1.11260619e-306 1.42410974e-306 8.34449381e-308]]
list7: [[ 5 6 7 8 9]
[10 11 12 13 14]]
list8: [ 1. 2.11111111 3.22222222 4.33333333 5.44444444
6.55555556 7.66666667 8.77777778 9.88888889 11. ]

# -*- coding: utf-8 -*-
import numpy as npa = np.arange(5)
b = np.array([1,2,3,4,5])print("a: ",a)
print("b: ",b)
addc = a + b
print("add： ", addc)minusc = a -b
print("minus: ",minusc)timec = a * b
print("times: ",timec)squc = a**2
print("square: ",squc)sinc = 10 * np.sin(a)
print("sin: ",sinc)print("compare: ",a<3)matrix1 = np.array([[1,2,3,4],[5,6,7,8]])
matrix2 = np.arange(8).reshape((4,2))
print("matrix *: ",np.dot(matrix1,matrix2))
print("matrix *",matrix1.dot(matrix2))suiji = np.random.random((2,4))
print("suiji: ",suiji)
print("max: ",np.max(suiji))
print("min: ",np.min(suiji))
print("sum: ",np.sum(suiji))
print("col: ",np.min(suiji,axis=0))
print("row: ",np.max(suiji,axis=1))

a: [0 1 2 3 4]
b: [1 2 3 4 5]
add： [1 3 5 7 9]
minus: [-1 -1 -1 -1 -1]
times: [ 0 2 6 12 20]
square: [ 0 1 4 9 16]
sin: [ 0. 8.41470985 9.09297427 1.41120008 -7.56802495]
compare: [ True True True False False]
matrix *: [[ 40 50]
[ 88 114]]
matrix * [[ 40 50]
[ 88 114]]
suiji: [[ 0.79302826 0.02704441 0.19401082 0.02216562]
[ 0.66149996 0.77353779 0.66565688 0.53205038]]
max: 0.793028259974
min: 0.0221656169264
sum: 3.66899411306
col: [ 0.66149996 0.02704441 0.19401082 0.02216562]
row: [ 0.79302826 0.77353779]