Scalar



In [47]:

    
x=25.0

Vector



In [48]:

    
pos=[2,4,5]

Matrix



In [49]:

    
a=[[1,2,3],[4,5,6]]



In [50]:

    
a









    Out[50]:





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



In [51]:

    
a[1][1]









    Out[51]:





5

numpy array



In [52]:

    
import numpy as np



In [53]:

    
np.zeros([3,4])









    Out[53]:





array([[ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.]])



In [54]:

    
np.ones([2,3,4],dtype=int)









    Out[54]:





array([[[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]],

       [[1, 1, 1, 1],
        [1, 1, 1, 1],
        [1, 1, 1, 1]]])



In [55]:

    
np.random.rand(3,4)









    Out[55]:





array([[ 0.33066173,  0.2559184 ,  0.92839795,  0.02755838],
       [ 0.69754227,  0.7681915 ,  0.22302007,  0.53866205],
       [ 0.09346035,  0.64775226,  0.05781645,  0.62395045]])



In [56]:

    
a=[[1,2,3],[4,5,6]]
x=np.array(a)



In [57]:

    
x.shape









    Out[57]:





(2, 3)

Ax=b

https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.solve.html

Slicing



In [58]:

    
x=np.random.rand(7)
print(x)









    



[ 0.28237045  0.13296619  0.77677009  0.07883874  0.71175605  0.65052565
  0.30366707]



In [59]:

    
x[0]









    Out[59]:





0.28237044947570589



In [60]:

    
x[2]









    Out[60]:





0.77677008764588318



In [61]:

    
x[6]









    Out[61]:





0.30366707243592983



In [62]:

    
x[-1]









    Out[62]:





0.30366707243592983



In [63]:

    
x[-2]









    Out[63]:





0.65052564782042432



In [64]:

    
x[:2]









    Out[64]:





array([ 0.28237045,  0.13296619])



In [65]:

    
x[-2:]









    Out[65]:





array([ 0.65052565,  0.30366707])



In [66]:

    
x[2:5]









    Out[66]:





array([ 0.77677009,  0.07883874,  0.71175605])



In [67]:

    
x=np.random.rand(4,5)
print(x)









    



[[ 0.33003162  0.62709619  0.23331205  0.49044732  0.91758581]
 [ 0.10654041  0.08715837  0.87734153  0.47795825  0.32568299]
 [ 0.51915732  0.11149859  0.24749932  0.71335898  0.83078321]
 [ 0.6989782   0.78096246  0.45149481  0.3285072   0.71969224]]



In [68]:

    
x[1:,2:4]









    Out[68]:





array([[ 0.87734153,  0.47795825],
       [ 0.24749932,  0.71335898],
       [ 0.45149481,  0.3285072 ]])



In [69]:

    
x[2,1]









    Out[69]:





0.11149859209216317



In [70]:

    
x[:,:2]









    Out[70]:





array([[ 0.33003162,  0.62709619],
       [ 0.10654041,  0.08715837],
       [ 0.51915732,  0.11149859],
       [ 0.6989782 ,  0.78096246]])



In [71]:

    
x[1:3, 1:3]









    Out[71]:





array([[ 0.08715837,  0.87734153],
       [ 0.11149859,  0.24749932]])



In [72]:

    
x=np.random.rand(4,3,2)
print(x)









    



[[[ 0.2099229   0.20793103]
  [ 0.62183639  0.44567042]
  [ 0.99479313  0.42945244]]

 [[ 0.46534569  0.88825702]
  [ 0.49318478  0.75533067]
  [ 0.1275958   0.18675608]]

 [[ 0.78913182  0.44515932]
  [ 0.68122275  0.39810836]
  [ 0.09759379  0.05996145]]

 [[ 0.2448322   0.04435026]
  [ 0.4707253   0.10579906]
  [ 0.40232842  0.3134782 ]]]



In [73]:

    
x.sum(axis=2)









    Out[73]:





array([[ 0.41785393,  1.0675068 ,  1.42424557],
       [ 1.35360271,  1.24851546,  0.31435188],
       [ 1.23429114,  1.07933112,  0.15755524],
       [ 0.28918246,  0.57652436,  0.71580662]])



In [ ]: