In [18]:

    

import numpy as np
a = np.arange(15).reshape(3, 5)
a


    

    
Out[18]:





array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

In [19]:

    

a.shape


    

    
Out[19]:





(3, 5)

In [20]:

    

#the number of axes (dimensions) of the array
a.ndim


    

    
Out[20]:





2

In [4]:

    

a.dtype.name


    

    
Out[4]:





'int32'

In [21]:

    

#the total number of elements of the array
a.size


    

    
Out[21]:





15

In [24]:

    

np.zeros ((3,4))


    

    
Out[24]:





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

In [28]:

    

np.ones( (2,3,4), dtype=np.int32 )


    

    
Out[28]:





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 [29]:

    

#To create sequences of numbers
np.arange( 10, 30, 5 )


    

    
Out[29]:





array([10, 15, 20, 25])

In [10]:

    

np.arange( 0, 2, 0.3 )


    

    
Out[10]:





array([ 0. ,  0.3,  0.6,  0.9,  1.2,  1.5,  1.8])

In [14]:

    

np.arange(12).reshape(4,3)


    

    
Out[14]:





array([[ 0,  1,  2],
       [ 3,  4,  5],
       [ 6,  7,  8],
       [ 9, 10, 11]])

In [16]:

    

np.random.random((2,3))


    

    
Out[16]:





array([[ 0.40130659,  0.45452825,  0.79776512],
       [ 0.63220592,  0.74591134,  0.64130737]])

In [30]:

    

from numpy import pi
np.linspace( 0, 2*pi, 100 )


    

    
Out[30]:





array([ 0.        ,  0.06346652,  0.12693304,  0.19039955,  0.25386607,
        0.31733259,  0.38079911,  0.44426563,  0.50773215,  0.57119866,
        0.63466518,  0.6981317 ,  0.76159822,  0.82506474,  0.88853126,
        0.95199777,  1.01546429,  1.07893081,  1.14239733,  1.20586385,
        1.26933037,  1.33279688,  1.3962634 ,  1.45972992,  1.52319644,
        1.58666296,  1.65012947,  1.71359599,  1.77706251,  1.84052903,
        1.90399555,  1.96746207,  2.03092858,  2.0943951 ,  2.15786162,
        2.22132814,  2.28479466,  2.34826118,  2.41172769,  2.47519421,
        2.53866073,  2.60212725,  2.66559377,  2.72906028,  2.7925268 ,
        2.85599332,  2.91945984,  2.98292636,  3.04639288,  3.10985939,
        3.17332591,  3.23679243,  3.30025895,  3.36372547,  3.42719199,
        3.4906585 ,  3.55412502,  3.61759154,  3.68105806,  3.74452458,
        3.8079911 ,  3.87145761,  3.93492413,  3.99839065,  4.06185717,
        4.12532369,  4.1887902 ,  4.25225672,  4.31572324,  4.37918976,
        4.44265628,  4.5061228 ,  4.56958931,  4.63305583,  4.69652235,
        4.75998887,  4.82345539,  4.88692191,  4.95038842,  5.01385494,
        5.07732146,  5.14078798,  5.2042545 ,  5.26772102,  5.33118753,
        5.39465405,  5.45812057,  5.52158709,  5.58505361,  5.64852012,
        5.71198664,  5.77545316,  5.83891968,  5.9023862 ,  5.96585272,
        6.02931923,  6.09278575,  6.15625227,  6.21971879,  6.28318531])

In [13]:

    

np.sin(np.linspace( 0, 2*pi, 100 ))


    

    
Out[13]:





array([  0.00000000e+00,   6.34239197e-02,   1.26592454e-01,
         1.89251244e-01,   2.51147987e-01,   3.12033446e-01,
         3.71662456e-01,   4.29794912e-01,   4.86196736e-01,
         5.40640817e-01,   5.92907929e-01,   6.42787610e-01,
         6.90079011e-01,   7.34591709e-01,   7.76146464e-01,
         8.14575952e-01,   8.49725430e-01,   8.81453363e-01,
         9.09631995e-01,   9.34147860e-01,   9.54902241e-01,
         9.71811568e-01,   9.84807753e-01,   9.93838464e-01,
         9.98867339e-01,   9.99874128e-01,   9.96854776e-01,
         9.89821442e-01,   9.78802446e-01,   9.63842159e-01,
         9.45000819e-01,   9.22354294e-01,   8.95993774e-01,
         8.66025404e-01,   8.32569855e-01,   7.95761841e-01,
         7.55749574e-01,   7.12694171e-01,   6.66769001e-01,
         6.18158986e-01,   5.67059864e-01,   5.13677392e-01,
         4.58226522e-01,   4.00930535e-01,   3.42020143e-01,
         2.81732557e-01,   2.20310533e-01,   1.58001396e-01,
         9.50560433e-02,   3.17279335e-02,  -3.17279335e-02,
        -9.50560433e-02,  -1.58001396e-01,  -2.20310533e-01,
        -2.81732557e-01,  -3.42020143e-01,  -4.00930535e-01,
        -4.58226522e-01,  -5.13677392e-01,  -5.67059864e-01,
        -6.18158986e-01,  -6.66769001e-01,  -7.12694171e-01,
        -7.55749574e-01,  -7.95761841e-01,  -8.32569855e-01,
        -8.66025404e-01,  -8.95993774e-01,  -9.22354294e-01,
        -9.45000819e-01,  -9.63842159e-01,  -9.78802446e-01,
        -9.89821442e-01,  -9.96854776e-01,  -9.99874128e-01,
        -9.98867339e-01,  -9.93838464e-01,  -9.84807753e-01,
        -9.71811568e-01,  -9.54902241e-01,  -9.34147860e-01,
        -9.09631995e-01,  -8.81453363e-01,  -8.49725430e-01,
        -8.14575952e-01,  -7.76146464e-01,  -7.34591709e-01,
        -6.90079011e-01,  -6.42787610e-01,  -5.92907929e-01,
        -5.40640817e-01,  -4.86196736e-01,  -4.29794912e-01,
        -3.71662456e-01,  -3.12033446e-01,  -2.51147987e-01,
        -1.89251244e-01,  -1.26592454e-01,  -6.34239197e-02,
        -2.44929360e-16])

In [35]:

    

#the product operator * operates elementwise in NumPy arrays
a = np.array( [20,30,40,50] )
b = np.arange( 4 )
#print a 
#print b
#b
c = a-b
#print c
b**2
#print b**2
print a<35


    

    




[ True  True False False]

In [39]:

    

#The matrix product can be performed using the dot function or method
A = np.array( [[1,1],
               [0,1]] )
B = np.array( [[2,0],
               [3,4]] )
print A
print B
#print A*B
print A.dot(B)
print np.dot(A, B)


    

    




[[1 1]
 [0 1]]
[[2 0]
 [3 4]]
[[5 4]
 [3 4]]
[[5 4]
 [3 4]]

In [ ]: