a1=[1,2,3,4]
a1+1
TypeError Traceback (most recent call last)
<ipython-input-11-a66fed7b98b2> in <module>()
1 a1=[1,2,3,4]
----> 2 a1+1
TypeError: can only concatenate list (not "int") to list
import numpy as np
a=np.array([1,2,3,4])
a+1
a+[1,2,3,4]
a+[1]
a*2
b=np.array([[1,2],[3,4]])
b+2
a+[1,2]
array([2, 3, 4, 5])
array([2, 4, 6, 8])
array([2, 3, 4, 5])
array([2, 4, 6, 8])
array([[3, 4],
[5, 6]])
ValueError Traceback (most recent call last)
<ipython-input-15-e36b81d20138> in <module>()
1 import numpy as np
2 a=np.array([1,2,3,4])
----> 3 a+[1,2]
ValueError: operands could not be broadcast together with shapes (4,) (2,)
2**a
a**2
array([ 2, 4, 8, 16])
array([ 1, 4, 9, 16])
'''
ones(shape[,dtype,order])
根据形状和类型返回一个新的元素全部为1的数组
shape:int或者int元组 定义返回数组的形状，形如：（2，3）或者2
dtype:数据类型，可选 返回数组的数据类型，例如：numpy：int8，默认numpy：float64
'''
b=np.ones(4)+1
b
a-b
np.ones(4,dtype=np.int)
np.ones((2,3))
a*b
array([ 2., 2., 2., 2.])
array([-1., 0., 1., 2.])
array([1, 1, 1, 1])
array([[ 1., 1., 1.],
[ 1., 1., 1.]])
array([ 2., 4., 6., 8.])
j=np.arange(5)
j
2**(j+1)-j
array([0, 1, 2, 3, 4])
array([ 2, 3, 6, 13, 28])
a=np.arange(10000)
'''
%取模运算 格式化输出
'''
%timeit a+1
np.arange(1,5)
#第一个值为起始点，第二个值为终止点，第三个值为步长
np.arange(1,10,2)
The slowest run took 5.33 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 8.08 µs per loop
array([1, 2, 3, 4])
array([1, 3, 5, 7, 9])
l=range(10000)
%timeit[i+1 for i in l]
1000 loops, best of 3: 360 µs per loop
range(5)
range(1,5)
range(1,10,2)
[0, 1, 2, 3, 4]
[1, 2, 3, 4]
[1, 3, 5, 7, 9]
c=np.ones((3,3))
c*c#不是矩阵乘法
array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]])
c.dot(c)
array([[ 3., 3., 3.],
[ 3., 3., 3.],
[ 3., 3., 3.]])
a=np.array([1,2,3,4])
b=np.array([4,2,2,4])
a==b
a>b
a=np.array([1,1,0,0],dtype=bool)
b=np.array([1,0,1,0],dtype=bool)
np.logical_or(a,b)
np.logical_and(a,b)
a=np.arange(10)
np.sin(a)
np.log(a)
np.log10(a)
np.exp(a)
array([False, True, False, True], dtype=bool)
array([False, False, True, False], dtype=bool)
array([ True, True, True, False], dtype=bool)
array([ True, False, False, False], dtype=bool)
array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,
-0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849])
array([ -inf, 0. , 0.69314718, 1.09861229, 1.38629436,
1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458])
array([ -inf, 0. , 0.30103 , 0.47712125, 0.60205999,
0.69897 , 0.77815125, 0.84509804, 0.90308999, 0.95424251])
array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,
2.00855369e+01, 5.45981500e+01, 1.48413159e+02,
4.03428793e+02, 1.09663316e+03, 2.98095799e+03,
8.10308393e+03])
a=np.arange(4)
print a
a.reshape(4,1)+np.array([2,3,5])
[0 1 2 3]
Out[81]:
array([[2, 3, 5],
[3, 4, 6],
[4, 5, 7],
[5, 6, 8]])
a=np.triu(np.ones((3,3)),-1)
a
a=np.triu(np.ones((3,3)),0)
a
a=np.triu(np.ones((3,3)),1)
a
a.T
array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 0., 1., 1.]])
array([[ 1., 1., 1.],
[ 0., 1., 1.],
[ 0., 0., 1.]])
array([[ 0., 1., 1.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
array([[ 0., 0., 0.],
[ 1., 0., 0.],
[ 1., 1., 0.]])
a=np.array([1,2,3,4])
b=np.array([2,3,4,5])
c=np.array([1,2,3,4])
#数组间的比较
np.array_equal(a,b)
np.array_equal(a,c)
False
True
Linear algebra The sub-module numpy.linalg implements basic linear algebra, such as solving linear systems, singular value decomposition, etc. However, it is not guaranteed to be compiled using efficient routines, and thus we recommend the use of scipy.linalg, as detailed in section scipy_linalg
a=np.array([1,2,3,4])
np.sum(a)
a.sum()
a.sum(axis=0)
a.sum(axis=1)
a[:,0].sum(),a[:,1].sum()
a[0,:].sum(),a[1,:].sum()
10
10
array([3, 3])
array([2, 4])
(3, 3)
(2, 4)
x=np.random.rand(2,2,2)
x
print x.sum(axis=2)
x.sum(axis=2)[0,1]
x[0,1,:].sum()
array([[[ 0.18897886, 0.3597148 ],
[ 0.45634364, 0.97641483]],
[[ 0.3529697 , 0.83570539],
[ 0.0629451 , 0.96301844]]])
[[ 1.70568465 1.36214561]
[ 0.50200916 0.98907998]]
1.3621456131271461
1.3621456131271461
x=np.array([1,2,3,1])
x.mean()
np.median(x)
y=np.array([[1,2,3],[5,6,1]])
np.median(y,axis=-1)#最后一个axis
x.std()
x.min()
x.max()
x.argmin()#the index of min
x.argmax()#the index of max
1.75
1.5
array([ 2., 5.])
0.82915619758884995
1
3
0
2
np.all([True,True,True,False])#是否都为True
np.any([True,False,True])
False
True
可以使用在数组比较
a=np.zeros((100,100))
np.any(a!=0)
np.all(a==a)
a=np.array([1,2,3,4])
b=np.array([2,3,4,5])
np.all(a>b)
False
True
False