In [1]:

    

import numpy as np


    

In [2]:

    

my_list = np.array(range(10))
print(type(my_list))
my_list


    

    




<class 'numpy.ndarray'>






    
Out[2]:





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

In [3]:

    

np.arange(10)


    

    
Out[3]:





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

In [4]:

    

np.linspace(0, 10, 20)


    

    
Out[4]:





array([  0.        ,   0.52631579,   1.05263158,   1.57894737,
         2.10526316,   2.63157895,   3.15789474,   3.68421053,
         4.21052632,   4.73684211,   5.26315789,   5.78947368,
         6.31578947,   6.84210526,   7.36842105,   7.89473684,
         8.42105263,   8.94736842,   9.47368421,  10.        ])

In [5]:

    

my_list[0]


    

    
Out[5]:





0

In [6]:

    

my_list[0:2]


    

    
Out[6]:





array([0, 1])

In [9]:

    

my_list * 2


    

    
Out[9]:





array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])

In [10]:

    

my_list[ my_list % 2 == 0 ] * 2


    

    
Out[10]:





array([ 0,  4,  8, 12, 16])

In [12]:

    

my_list[ (my_list < 3) | (my_list > 6) ]


    

    
Out[12]:





array([0, 1, 2, 7, 8, 9])

In [14]:

    

my_list_one = np.arange(0, 10)
my_list_two = np.arange(10, 20)

print(my_list_one)
print(my_list_two)
my_list_one + my_list_two


    

    




[0 1 2 3 4 5 6 7 8 9]
[10 11 12 13 14 15 16 17 18 19]






    
Out[14]:





array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28])

In [16]:

    

my_list.shape


    

    
Out[16]:





(10,)

In [17]:

    

my_list.reshape(2, 5)


    

    
Out[17]:





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

In [18]:

    

my_list.reshape(5, 2)


    

    
Out[18]:





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

In [19]:

    

### Stats


    

In [20]:

    

my_list.mean()


    

    
Out[20]:





4.5

In [21]:

    

np.mean(my_list)


    

    
Out[21]:





4.5

In [22]:

    

my_list.sum()


    

    
Out[22]:





45

In [23]:

    

np.mean([0, 1, 2, 3, np.nan])


    

    
Out[23]:





nan

In [25]:

    

np.nanmean([1, 2, 3, np.nan])


    

    
Out[25]:





2.0

In [26]:

    

np.nanmax([1, 2, 3, np.nan])


    

    
Out[26]:





3.0

In [27]:

    

np.nanmin([1, 2, 3, np.nan])


    

    
Out[27]:





1.0

In [28]:

    

[np.random.rand() for x in range(10)]


    

    
Out[28]:





[0.11417637488232857,
 0.16953968214996096,
 0.8060580229610691,
 0.49552351558559204,
 0.7666045886200948,
 0.13917592678058777,
 0.8437397145144154,
 0.7794950766849901,
 0.4841293664074877,
 0.2149788938404672]

In [31]:

    

np.mean([np.random.rand() for x in range(100000)])


    

    
Out[31]:





0.50042409821794409

In [34]:

    

[np.random.rand() for x in range(10)]


    

    
Out[34]:





[0.5042523582139986,
 0.30120690235513814,
 0.12109979557652817,
 0.8149755582447495,
 0.3475941996751155,
 0.3598559123066033,
 0.7743055994007709,
 0.11831590155119198,
 0.14645077644303428,
 0.2015445773962391]

In [36]:

    

np.random.seed(2017)
[np.random.rand() for x in range(10)]


    

    
Out[36]:





[0.020960225406117416,
 0.7670701646824878,
 0.44791979980060215,
 0.12054161556730447,
 0.9307729610869862,
 0.6495504104423961,
 0.14067106021701203,
 0.23159433754757908,
 0.22647529063859462,
 0.2600549453493213]

In [37]:

    

np.random.rand(5, 2)


    

    
Out[37]:





array([[ 0.11287225,  0.63190484],
       [ 0.38730813,  0.31624562],
       [ 0.63089059,  0.29465108],
       [ 0.94486453,  0.15162571],
       [ 0.07616901,  0.70390961]])

In [38]:

    

np.math.log(10)
np.log(10)


    

    
Out[38]:





2.3025850929940459

In [39]:

    

np.math.log2(10)


    

    
Out[39]:





3.321928094887362

In [40]:

    

np.exp( np.log(10) )


    

    
Out[40]:





10.000000000000002

In [ ]: