Let's import the numpy module.

In [1]: 
import numpy as np
In [3]: 
n = 10  # CHANGE ME
a1 = list(range(n))
a2 = np.arange(n)

if n <= 10:
    print(a1)
    print(a2)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
[0 1 2 3 4 5 6 7 8 9]
In [4]: 
%timeit [i**2 for i in a1]

100000 loops, best of 3: 2.36 µs per loop
In [5]: 
%timeit a2**2

1000000 loops, best of 3: 1.06 µs per loop

Numpy Arrays: much less flexible, but:

  • much faster
  • less memory

Ways to create a numpy array:

  • Casting from a list
In [6]: 
np.array([1,2,3])
Out[6]:
array([1, 2, 3])
  • linspace
In [7]: 
np.linspace(-1, 1, 10)
Out[7]:
array([-1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,
        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ])
  • zeros
In [8]: 
np.zeros((10,10), np.float64)
Out[8]:
array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])

Operations on arrays propagate to all elements:

In [9]: 
a = np.array([1.2, 3, 4])
b = np.array([0.5, 0, 1])

Addition, multiplication, power, .. are all elementwise:

In [10]: 
a+b
Out[10]:
array([ 1.7,  3. ,  5. ])
In [11]: 
a*b
Out[11]:
array([ 0.6,  0. ,  4. ])
In [12]: 
a**b
Out[12]:
array([ 1.09544512,  1.        ,  4.        ])

Matrix multiplication is np.dot(A, B) for two 2D arrays.

Numpy arrays have two (most) important attributes:

In [13]: 
a = np.random.rand(5, 4, 3)
a.shape
Out[13]:
(5, 4, 3)
In [14]: 
a.dtype
Out[14]:
dtype('float64')

Other dtypes include np.complex64, np.int32, ...