Python list is more than a list



In [1]:

    
L = list(range(10))



In [2]:

    
L









    Out[2]:





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



In [3]:

    
type(L)









    Out[3]:





list



In [4]:

    
type(L[0])









    Out[4]:





int



In [5]:

    
L2 = [str(c) for c in L]
type(L2[0])









    Out[5]:





str



In [6]:

    
all(type(e) == int for e in L)









    Out[6]:





True

List can be heterogeneous list



In [7]:

    
L3 = [True, '2', 3.0, 4]
[type(item) for item in L3]









    Out[7]:





[bool, str, float, int]



In [8]:

    
tuple(L3)









    Out[8]:





(True, '2', 3.0, 4)

Creating Arrays from lists



In [9]:

    
import numpy as np
np.array([1, 4, 2, 5, 3])









    Out[9]:





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



In [10]:

    
np.array([3.14, 4, 2, 3])









    Out[10]:





array([3.14, 4.  , 2.  , 3.  ])

Creating Arrays from scratch

Especially for larger arrays, it is more efficient to create arrays from scratch using routines built into Numpy. Here are some examples:



In [11]:

    
# create a random integer array
np.random.randint(0, 10, (3, 3))









    Out[11]:





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



In [12]:

    
# create a 3x3 array of uniform random values
np.random.random((3, 3))









    Out[12]:





array([[0.35426557, 0.04430429, 0.41836319],
       [0.31968574, 0.37538208, 0.33031082],
       [0.64993674, 0.74265398, 0.20838379]])



In [13]:

    
# create 3x3 array of normally distributed data
np.random.normal(0, 1, (3,3))









    Out[13]:





array([[-1.75655372,  0.47198179, -0.92343641],
       [ 1.63128262,  1.08827514, -2.33014078],
       [ 0.51843925,  0.41162106, -1.09581601]])



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