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In [2]:

    
import numpy as np



In [2]:

    
a = np.array([12,312] )



In [3]:

    
a









    Out[3]:





array([ 12, 312])



In [5]:

    
np.arange(100)









    Out[5]:





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, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,
       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,
       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])



In [6]:

    
a = np.array([[12,312],[1,22]] )



In [8]:

    
a.shape









    Out[8]:





(2, 2)



In [9]:

    
a.size









    Out[9]:





4



In [10]:

    
a.dtype









    Out[10]:





dtype('int64')



In [11]:

    
a.strides









    Out[11]:





(16, 8)



In [12]:

    
a[1,0]









    Out[12]:





1



In [13]:

    
a.flags









    Out[13]:





  C_CONTIGUOUS : True
  F_CONTIGUOUS : False
  OWNDATA : True
  WRITEABLE : True
  ALIGNED : True
  UPDATEIFCOPY : False



In [17]:

    
a









    Out[17]:





array([[ 12, 312],
       [  1,  22]])



In [18]:

    
2*a









    Out[18]:





array([[ 24, 624],
       [  2,  44]])



In [19]:

    
a*a









    Out[19]:





array([[  144, 97344],
       [    1,   484]])



In [20]:

    
np.matrix(a)*np.matrix(a)









    Out[20]:





matrix([[  456, 10608],
        [   34,   796]])



In [21]:

    
a.dot(a)









    Out[21]:





array([[  456, 10608],
       [   34,   796]])



In [22]:

    
a = np.arange(1,4)



In [24]:

    
np.outer(a,a)









    Out[24]:





array([[1, 2, 3],
       [2, 4, 6],
       [3, 6, 9]])



In [39]:

    
x = np.linspace( 0.1,0.4,10)



In [40]:

    
x









    Out[40]:





array([ 0.1       ,  0.13333333,  0.16666667,  0.2       ,  0.23333333,
        0.26666667,  0.3       ,  0.33333333,  0.36666667,  0.4       ])



In [27]:

    
np.sin(x)









    Out[27]:





array([ 0.09983342,  0.13293862,  0.16589613,  0.19866933,  0.23122181,
        0.26351739,  0.29552021,  0.3271947 ,  0.35850567,  0.38941834])



In [33]:

    
from math import sin



In [36]:

    
x









    Out[36]:





array([ 0.1       ,  0.13333333,  0.16666667,  0.2       ,  0.23333333,
        0.26666667,  0.3       ,  0.33333333,  0.36666667,  0.4       ])



In [37]:

    
np.sin(x,out=x)









    Out[37]:





array([ 0.09983342,  0.13293862,  0.16589613,  0.19866933,  0.23122181,
        0.26351739,  0.29552021,  0.3271947 ,  0.35850567,  0.38941834])



In [38]:

    
x









    Out[38]:





array([ 0.09983342,  0.13293862,  0.16589613,  0.19866933,  0.23122181,
        0.26351739,  0.29552021,  0.3271947 ,  0.35850567,  0.38941834])



In [42]:

    
list(map(sin,x))









    Out[42]:





[0.09983341664682815,
 0.1329386226223141,
 0.16589613269341505,
 0.19866933079506122,
 0.2312218056342986,
 0.2635173911435351,
 0.2955202066613396,
 0.32719469679615226,
 0.35850567092861724,
 0.3894183423086505]



In [43]:

    
np.sum(np.sin(x))









    Out[43]:





2.462715616230212

Wydajność a cache



In [3]:

    
x = np.zeros((20000,))

y = np.zeros((20000*67,))[::67]

x.shape, y.shape
((20000,), (20000,))

%timeit x.sum()









    



The slowest run took 5.64 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 11.3 µs per loop



In [4]:

    
%timeit y.sum()









    



The slowest run took 50.08 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 24.5 µs per loop



In [3]:

    
import numpy as np



In [4]:

    
a = np.arange(10,200)



In [6]:

    
a[::10]









    Out[6]:





array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110, 120, 130,
       140, 150, 160, 170, 180, 190])



In [7]:

    
x = np.linspace(0,10,15)



In [11]:

    
x[np.sin(x)<0]









    Out[11]:





array([  3.57142857,   4.28571429,   5.        ,   5.71428571,  10.        ])



In [12]:

    
np.sin(x[np.sin(x)<0])









    Out[12]:





array([-0.41672165, -0.91034694, -0.95892427, -0.53870529, -0.54402111])



In [13]:

    
np.nonzero(np.sin(x)<0)









    Out[13]:





(array([ 5,  6,  7,  8, 14]),)



In [14]:

    
x[5]









    Out[14]:





3.5714285714285716



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In [1]:

    
import numpy as np 
x = np.array([[1, 2, 3],
               [4, 5, 6],
               [7, 8, 9]], dtype=np.int8)
y = np.array([[1, 2, 3],
               [4, 5, 6],
               [7, 8, 9]], dtype=np.int8,order='F')



In [2]:

    
str(x.data)









    Out[2]:





'<memory at 0x7fb440087cf0>'



In [3]:

    
str(y.data)









    Out[3]:





'<memory at 0x7fb440087cf0>'



In [20]:

    
x.flags,y.flags









    Out[20]:





(  C_CONTIGUOUS : True
   F_CONTIGUOUS : False
   OWNDATA : True
   WRITEABLE : True
   ALIGNED : True
   UPDATEIFCOPY : False,   C_CONTIGUOUS : False
   F_CONTIGUOUS : True
   OWNDATA : True
   WRITEABLE : True
   ALIGNED : True
   UPDATEIFCOPY : False)



In [2]:

    
x.data.f_contiguous,y.data.f_contiguous









    Out[2]:





(False, True)



In [14]:

    
x.strides,y.strides









    Out[14]:





((3, 1), (1, 3))



In [5]:

    
x.data.hex(),y.data.hex()









    Out[5]:





('010203040506070809', '010203040506070809')



In [17]:

    
d1 = x.data



In [28]:

    
x.data.tobytes()









    Out[28]:





b'\x01\x02\x03\x04\x05\x06\x07\x08\t'



In [31]:

    
y.data.tolist()









    Out[31]:





[[1, 2, 3], [4, 5, 6], [7, 8, 9]]



In [43]:

    
from ctypes import c_int, addressof



In [46]:

    
x.strides,x.T.strides,x.T.copy().strides









    Out[46]:





((3, 1), (1, 3), (3, 1))



In [45]:

    
x.data.hex(),x.transpose().data.hex(),x.transpose().copy().data.hex()









    Out[45]:





('010203040506070809', '010407020508030609', '010407020508030609')



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