本文介绍一些numpy的基础运算

Demo.py

#对一维数组进行操作
import numpy as np
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])
print c
c=a+b   # array([10, 21, 32, 43])
print c
c=a*b   # array([  0,  20,  60, 120])
print c
c=b**2  # array([0, 1, 4, 9])
print c
c=10*np.sin(a)  
print c

print b<3 
# array([ True,  True,  True, False], dtype=bool)

#对多维数组进行操作


#关于 sum(), min(), max()的使用
import numpy as np
a=np.random.random((2,4))
#对a的操作是令a中生成一个2行4列的矩阵，且每一元素均是来自从0到1的随机数
#print a
# array([[ 0.94692159,  0.20821798,  0.35339414,  0.2805278 ],
#       [ 0.04836775,  0.04023552,  0.44091941,  0.21665268]])
print np.sum(a)   # 4.4043622002745959
print np.min(a)   # 0.23651223533671784
print np.max(a)   # 0.90438450240606416

#如果你需要对行或者列进行查找运算，就需要在上述代码中为 axis 进行赋值。

print "a =",a
# a = [[ 0.23651224  0.41900661  0.84869417  0.46456022]
# [ 0.60771087  0.9043845   0.36603285  0.55746074]]

print "sum =",np.sum(a,axis=1)#当axis的值为1的时候，将会以行作为查找单元。
# sum = [ 1.96877324  2.43558896]

print "min =",np.min(a,axis=0)#当axis的值为0的时候，将会以列作为查找单元
# min = [ 0.23651224  0.41900661  0.36603285  0.46456022]

print "max =",np.max(a,axis=1)
# max = [ 0.84869417  0.9043845 ]

结果：

[10 19 28 37]
[10 21 32 43]
[  0  20  60 120]
[0 1 4 9]
[-5.44021111  9.12945251 -9.88031624  7.4511316 ]
[ True  True  True False]

[[1 1]
 [0 1]]
[[0 1]
 [2 3]]
[[2 4]
 [2 3]]
[[2 4]
 [2 3]]

4.86697754129
0.392121318071
0.95110386776
a = [[ 0.57126351  0.95110387  0.73275929  0.39212132]
 [ 0.64227461  0.5118515   0.40201937  0.66358408]]
sum = [ 2.64724798  2.21972956]
min = [ 0.57126351  0.5118515   0.40201937  0.39212132]
max = [ 0.95110387  0.66358408]