Load Numpy



In [87]:

    
import numpy as np



In [88]:

    
np.version.full_version









    Out[88]:





'1.13.1'



In [89]:

    
%pwd









    Out[89]:





u'/workshop/matrix/mProto/ml-workshop/src'

Basic Array Operations



In [90]:

    
a = np.array([0,1, 2, 3, 4, 5])
# a = np.array(range(0,6), dtype=np.int32)
a









    Out[90]:





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



In [91]:

    
a.ndim









    Out[91]:





1



In [92]:

    
a.shape









    Out[92]:





(6,)



In [93]:

    
a.dtype









    Out[93]:





dtype('int64')



In [94]:

    
b = a.astype(np.float32)
print a.dtype
print b.dtype









    



int64
float32

Reshape



In [95]:

    
b = a.reshape((3,2))
print b
print b.ndim
print b.shape









    



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

Manipulate Elements



In [96]:

    
b[1][0] = 77
print b









    



[[ 0  1]
 [77  3]
 [ 4  5]]



In [97]:

    
print a









    



[ 0  1 77  3  4  5]



In [98]:

    
c = a.reshape((3, 2)).copy()
print c
c[0][0] = -99
print c









    



[[ 0  1]
 [77  3]
 [ 4  5]]
[[-99   1]
 [ 77   3]
 [  4   5]]



In [99]:

    
print a









    



[ 0  1 77  3  4  5]

Element-wise Mathematics



In [100]:

    
d = a * 2
print d









    



[  0   2 154   6   8  10]



In [101]:

    
e = a ** 2
print e









    



[   0    1 5929    9   16   25]



In [102]:

    
print [1,2,3,4,5] * 2









    



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



In [103]:

    
# print [1,2,3,4,5] ** 2

Indexing & Conditional Indexing



In [104]:

    
b = a[0:2]
b









    Out[104]:





array([0, 1])



In [105]:

    
print a[[2,3,4]]



In [106]:

    
print a > 4









    



[False False  True False False  True]



In [107]:

    
print a[a > 4]



In [108]:

    
idx = np.where(a > 4)
print idx









    



(array([2, 5]),)



In [109]:

    
print a[~(a > 4)]



In [110]:

    
sum(a > 4)









    Out[110]:





2



In [111]:

    
b = a.reshape((3,2))
np.where(b > 4)









    Out[111]:





(array([1, 2]), array([0, 1]))

Descriptive Statistics First-Time



In [112]:

    
from scipy.stats import mode

a = np.array([3, 2, 3, 4, 5, 1, 0, 3, 7, 3])

print np.mean(a)
print np.median(a)
print mode(a)[0], mode(a)[1]
print np.std(a)
print np.var(a)
print np.ptp(a)









    



3.1
3.0
[3] [4]
1.86815416923
3.49
7

For mode function, if there is more than mode value, only the first is returned.



In [113]:

    
m = np.array([0,1, 77, 3, 4, 5, 3, 4])
mode(m)









    Out[113]:





ModeResult(mode=array([3]), count=array([2]))

Performance



In [114]:

    
%timeit sum([x * x for x in xrange(1000)])
%timeit na = np.arange(1000); sum(na * na)
%timeit na = np.arange(1000); na.dot(na)









    



The slowest run took 4.46 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 45 µs per loop
10000 loops, best of 3: 72.1 µs per loop
The slowest run took 7.52 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 2.66 µs per loop