#encoding=utf-8
import numpy as np

lst=[[1,2,3],[6,7,8]]
print (type(lst))
np_lst=np.array(lst)
print (type(np_lst))
np_lst=np.array(lst,dtype=np.float)
print np_lst
print (np_lst.shape)#几行几列
print (np_lst.ndim)#维度
print (np_lst.dtype)#类型
print (np_lst.itemsize)#所占字节
print (np_lst.size)#大小

print (np.zeros([2,4]))#定义数组
print (np.ones([3,2]))
print (np.random.rand())#随机数
print (np.random.rand(2,4))
print (np.random.randint(1,10,3))#生成1-10随机的三个整数
print (np.random.choice([1,2,3,5,10,100,666]))#选择指定数字
print (np.random.beta(1,10,100))#beta分布

print (np.arange(1,11).reshape([2,-1]))#生成1-10的等差数列并显示为2行5列

list=np.array([[[1,2,3],
               [4,5,6]],
              [[7,8,9],
               [10,11,12]]
              ])
print (list.sum(axis=0))#axis指定一个维度求和 axis越大越深入 =0对最外层求和 1+7 2+8 3+9 4+10 5+11 6+12
print (list.sum(axis=1))#内层求和 1+4 2+5 3+6 7+10 8+11 9+12
print (list.sum(axis=2))#再深入一层 1+2+3 4+5+6 7+8+9 10+11+12
print (list.max(axis=1))#求最大值
print (list.min(axis=2))#求最小值

lst1=np.array([1,2,3])
lst2=np.array([10,11,12])
print (np.concatenate((lst1,lst2),axis=0))#追加到一起
print (np.vstack((lst1,lst2)))#上下追加到一起 两行
print (np.hstack((lst1,lst2)))#合在一起
print (np.split(lst1,3))#分成3组

output:

<type 'list'>
<type 'numpy.ndarray'>
[[ 1.  2.  3.]
 [ 6.  7.  8.]]
(2, 3)
2
float64
8
6
[[ 0.  0.  0.  0.]
 [ 0.  0.  0.  0.]]
[[ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]]
0.0790300286594
[[ 0.40570725  0.40072322  0.75697423  0.45873189]
 [ 0.84684053  0.80063956  0.02231413  0.57768334]]
[4 5 8]
10
[  1.98939407e-02   1.61285413e-02   1.18716706e-02   8.10961771e-02
   1.63533368e-01   1.44668421e-01   5.11693434e-02   6.59941834e-02
   2.23286959e-02   2.15972016e-01   8.63734013e-02   8.06557908e-02
   1.22880147e-01   1.09686347e-02   6.54980124e-02   1.98280109e-03
   4.08960959e-05   4.99724974e-02   4.46814021e-02   1.38706779e-01
   1.24582838e-01   5.32977093e-02   1.01838024e-01   2.46115301e-01
   6.15260218e-03   1.08537025e-01   1.69927681e-02   1.03185373e-01
   2.02785750e-02   4.68360049e-02   2.35353598e-02   1.53166093e-01
   7.08847154e-02   1.43549143e-02   9.55657510e-02   7.62737256e-02
   5.94670869e-03   5.71465328e-03   3.98867568e-02   6.37016822e-02
   9.69537649e-02   3.52414465e-02   5.60588196e-03   1.45640846e-01
   3.06005428e-02   7.66577878e-02   1.98901419e-02   1.04611224e-01
   1.45486287e-02   4.63747783e-03   1.00559794e-01   4.14864166e-02
   6.97617286e-02   1.21598675e-02   5.18675502e-02   2.67329995e-01
   2.71693357e-01   7.61304550e-02   7.19672528e-02   6.30126936e-03
   3.79026503e-02   2.35909430e-01   5.86051618e-02   2.11428839e-01
   1.35133707e-01   6.62907241e-02   1.38296699e-01   2.88886522e-01
   2.40987292e-02   1.76062934e-02   1.90230382e-01   2.09219429e-01
   3.40778112e-02   4.66014797e-02   5.47428038e-03   2.20511410e-02
   3.19067152e-02   2.47324231e-02   3.66452719e-02   2.97249683e-02
   2.26196050e-01   1.59556704e-02   5.66573658e-04   1.44624707e-01
   8.91173413e-02   4.21186597e-02   3.77105859e-03   2.68561111e-01
   1.88337068e-02   1.12529504e-01   1.14325549e-01   6.22131659e-04
   4.74532828e-01   1.57684391e-01   2.60435883e-02   1.53169399e-03
   2.44662204e-02   2.86626061e-02   1.47198619e-01   4.32705246e-02]
[[ 1  2  3  4  5]
 [ 6  7  8  9 10]]
[[ 8 10 12]
 [14 16 18]]
[[ 5  7  9]
 [17 19 21]]
[[ 6 15]
 [24 33]]
[[ 4  5  6]
 [10 11 12]]
[[ 1  4]
 [ 7 10]]
[ 1  2  3 10 11 12]
[[ 1  2  3]
 [10 11 12]]
[ 1  2  3 10 11 12]
[array([1]), array([2]), array([3])]