In [14]:

    

from __future__ import print_function
import sys
print("Python version is {pv}".format(pv=sys.version))
import numpy as np
print("numpy version is {npv}".format(npv=np.__version__))


    

    




Python version is 2.7.11 |Anaconda 2.3.0 (64-bit)| (default, Dec  6 2015, 18:08:32) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
numpy version is 1.10.4

In [35]:

    

npa = np.arange(20)
print("Our numpy array content is : {my_arr}".format(my_arr=npa))
print("And the mean of this array  is {mean}".format(mean=npa.mean()))
print("the minimum value of this array  is {min} at index {min_pos}]".format(min=npa.min(),min_pos=npa.argmin()))
print("the maximum value of this array  is {max} at index {max_pos}]".format(max=npa.max(),max_pos=npa.argmax()))


    

    




Our numpy array content is : [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19]
And the mean of this array  is 9.5
the minimum value of this array  is 0 at index 0]
the maximum value of this array  is 19 at index 19]

In [17]:

    

np2 = np.arange(20000)


    

In [20]:

    

%timeit [x for x in np2 if x % 2 == 0] # list comprehension


    

    




100 loops, best of 3: 6.65 ms per loop

In [19]:

    

%timeit np2[np2 % 2 == 0]  # numpy boolean selection is really MUCH FASTER


    

    




1000 loops, best of 3: 317 µs per loop

In [23]:

    

npa[npa > 10]


    

    
Out[23]:





array([11, 12, 13, 14, 15, 16, 17, 18, 19])

In [25]:

    

npa[(npa > 10) & (npa < 15)] # numpy booleans can be combined with and & or |


    

    
Out[25]:





array([11, 12, 13, 14])

In [26]:

    

type(npa)


    

    
Out[26]:





numpy.ndarray

In [27]:

    

type(npa[0])


    

    
Out[27]:





numpy.int64

In [31]:

    

np2 = np.array([1.0,2.0])
type(np2[0])


    

    
Out[31]:





numpy.float64

In [32]:

    

np2.dtype


    

    
Out[32]:





dtype('float64')

In [55]:

    

np.random.seed(10)
npr = np.random.random_integers(0,100,2*3)


    

In [56]:

    

npr.shape


    

    
Out[56]:





(6,)

In [57]:

    

npr.reshape(2,3).shape  # let's do it a 2 rows by 3 columns


    

    
Out[57]:





(2, 3)

In [63]:

    

npr.reshape(2,3)


    

    
Out[63]:





array([[  9, 100,  15],
       [ 64,  28,  89]])

In [58]:

    

np3x2 = np.random.random_integers(0,10,(3,2))


    

In [59]:

    

np3x2.shape


    

    
Out[59]:





(3, 2)

In [60]:

    

np3x2


    

    
Out[60]:





array([[ 0,  1],
       [10,  8],
       [ 9,  0]])

In [70]:

    

print("second row is : {arr}".format(arr=np3x2[1,]))
print("Sum of values of second row is : {arr_sum}".format(arr_sum=np3x2[1,].sum()))


    

    




second row is : [10  8]
Sum of values of second row is : 18

In [62]:

    

np3x2[:,1]


    

    
Out[62]:





array([1, 8, 0])

In [72]:

    

np3x2[:,1]


    

    
Out[72]:





array([1, 8, 0])

In [73]:

    

np3x2


    

    
Out[73]:





array([[ 0,  1],
       [10,  8],
       [ 9,  0]])

In [74]:

    

ar


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-74-33cf8b7b9d40> in <module>()
----> 1 ar

NameError: name 'ar' is not defined

In [75]:

    

ar = np.arange(12)
ar


    

    
Out[75]:





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

In [77]:

    

ar2 = np.random.random_integers(12, size=12)
ar2


    

    
Out[77]:





array([ 2,  9,  5,  2,  4,  7,  6,  4, 10,  7, 10,  2])

In [78]:

    

np.concatenate((ar,ar2))


    

    
Out[78]:





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

In [80]:

    

np.vstack((ar,ar2))


    

    
Out[80]:





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

In [ ]: