Basic Numpy

Numerical Python Library



In [2]:

    
import numpy as np



In [4]:

    
l=[1,2,3,4]
l=np.array(l)
l









    Out[4]:





array([1, 2, 3, 4])



In [6]:

    
a=[1,2,3]
b=[3,4,6]
c=np.array([a,b])



In [10]:

    
help(np.shape)









    



Help on function shape in module numpy.core.fromnumeric:

shape(a)
    Return the shape of an array.
    
    Parameters
    ----------
    a : array_like
        Input array.
    
    Returns
    -------
    shape : tuple of ints
        The elements of the shape tuple give the lengths of the
        corresponding array dimensions.
    
    See Also
    --------
    alen
    ndarray.shape : Equivalent array method.
    
    Examples
    --------
    >>> np.shape(np.eye(3))
    (3, 3)
    >>> np.shape([[1, 2]])
    (1, 2)
    >>> np.shape([0])
    (1,)
    >>> np.shape(0)
    ()
    
    >>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
    >>> np.shape(a)
    (2,)
    >>> a.shape
    (2,)



In [11]:

    
c.shape









    Out[11]:





(2, 3)

evenly space size using arrange



In [19]:

    
l=np.arange(2,24,4)
print(l)









    



[ 2  6 10 14 18 22]



In [21]:

    
l.reshape(3,2)









    Out[21]:





array([[ 2,  6],
       [10, 14],
       [18, 22]])



In [22]:

    
l.reshape(2,3)









    Out[22]:





array([[ 2,  6, 10],
       [14, 18, 22]])



In [26]:

    
#linspace(a,b,c)
#linspace gives  a list back in range (a,b) with c intervals
o=np.linspace(0,1,9)
print(o)









    



[ 0.     0.125  0.25   0.375  0.5    0.625  0.75   0.875  1.   ]



In [27]:

    
o.reshape(3,3)









    Out[27]:





array([[ 0.   ,  0.125,  0.25 ],
       [ 0.375,  0.5  ,  0.625],
       [ 0.75 ,  0.875,  1.   ]])



In [30]:

    
print(np.zeros(3))









    



[ 0.  0.  0.]



In [31]:

    
print(np.ones(3))









    



[ 1.  1.  1.]



In [34]:

    
print(np.eye(5))









    



[[ 1.  0.  0.  0.  0.]
 [ 0.  1.  0.  0.  0.]
 [ 0.  0.  1.  0.  0.]
 [ 0.  0.  0.  1.  0.]
 [ 0.  0.  0.  0.  1.]]



In [55]:

    
np.diag(o.reshape(3,3))









    Out[55]:





array([ 0. ,  0.5,  1. ])



In [56]:

    
np.diag([4,5,6])









    Out[56]:





array([[4, 0, 0],
       [0, 5, 0],
       [0, 0, 6]])



In [57]:

    
np.array([1,2,3]*3)









    Out[57]:





array([1, 2, 3, 1, 2, 3, 1, 2, 3])



In [58]:

    
np.repeat([1,2,3],3)









    Out[58]:





array([1, 1, 1, 2, 2, 2, 3, 3, 3])



In [63]:

    
#Uncomment the below line comment the line below it and see the difference
#a=[1,2,4]
a=np.array([1,2,4])
np.vstack([a ,2*a])









    Out[63]:





array([[1, 2, 4],
       [2, 4, 8]])



In [64]:

    
np.hstack([a ,2*a])









    Out[64]:





array([1, 2, 4, 2, 4, 8])

OPerations in numpy



In [91]:

    
x=np.array([1,2,3])
y=np.array([[1,2,2],[1,2,4]])
y.dot(x)









    Out[91]:





array([11, 17])



In [81]:

    
y









    Out[81]:





array([[1, 2, 2],
       [1, 2, 4]])



In [82]:

    
y.T









    Out[82]:





array([[1, 1],
       [2, 2],
       [2, 4]])



In [85]:

    
##Type of array

x.dtype









    Out[85]:





dtype('int64')



In [87]:

    
##Casting array as another type

x.astype('f')









    Out[87]:





array([ 1.,  2.,  3.], dtype=float32)



In [94]:

    
#some math functions
a=np.array([1,3,4,-6,7,4,6,11,2])
a.sum()









    Out[94]:





32



In [95]:

    
a.min()









    Out[95]:





-6



In [96]:

    
a.mean()









    Out[96]:





3.5555555555555554



In [97]:

    
a.std()









    Out[97]:





4.3997755273829622



In [98]:

    
a.argmax()









    Out[98]:





7

Indexiing and slicing



In [101]:

    
b=np.arange(1,18,2)**2



In [104]:

    
b[5]









    Out[104]:





121



In [122]:

    
b[1:4]









    Out[122]:





array([ 9, 25, 49])



In [136]:

    
r=np.arange(0,36)
r.resize(6,6)



In [121]:

    
##some operations
r[r>30]









    Out[121]:





array([31, 32, 33, 34, 35])



In [127]:

    
r[-1,-5:-2]









    Out[127]:





array([31, 32, 33])



In [128]:

    
r[r>30]=30



In [129]:

    
r









    Out[129]:





array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 30, 30, 30, 30, 30]])



In [130]:

    
r2=r[:,:]



In [135]:

    
r2









    Out[135]:





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



In [134]:

    
##Changimg r2 changes r
#Uncommemt thebelow lines to observe it
#r2[:]=0
#r









    Out[134]:





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



In [133]:

    
#s0 to copy use r copy









    Out[133]:





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



In [141]:

    
##so to copy we should use rcopy

r_copy=r.copy()
r_copy









    Out[141]:





array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])



In [143]:

    
##now changing r copy
r_copy[r_copy>10]=-3
r









    Out[143]:





array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])



In [144]:

    
r_copy









    Out[144]:





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



In [152]:

    
r[0,:6]









    Out[152]:





array([0, 1, 2, 3, 4, 5])



In [153]:

    
old = np.array([[1, 1, 1],
                [1, 1, 1]])

new = old
new[0, :2] = 0

print(old)









    



[[0 0 1]
 [1 1 1]]

Iterate over arrays



In [163]:

    
rnd=np.random.randint(1,30,(4,3))



In [164]:

    
rnd









    Out[164]:





array([[28,  4, 13],
       [18,  9, 14],
       [16, 10, 26],
       [12, 28, 25]])



In [167]:

    
for row in range(len(rnd)):
    print(rnd[row])



In [168]:

    
for i , row in enumerate(rnd):
    print(i,"  ",row)









    



0    [28  4 13]
1    [18  9 14]
2    [16 10 26]
3    [12 28 25]



In [169]:

    
for i in zip(rnd):
    print(i)









    



(array([28,  4, 13]),)
(array([18,  9, 14]),)
(array([16, 10, 26]),)
(array([12, 28, 25]),)



In [170]:

    
for i,j in zip(rnd,rnd**2):
    print(i+j)









    



[812  20 182]
[342  90 210]
[272 110 702]
[156 812 650]



In [ ]: