Introduction to arrays using numpy

Arrays are used to store multiple values in one single variable.
An array is a kind of list.
All the elements in an array are the exact same type

Let's use the numpy python library to handle arrays

>>> import numpy as np

data type int64

>>> ti = np.array([1, 2, 3, 4])
>>> ti
array([1, 2, 3, 4])
>>> ti.dtype
dtype('int64')
>>> 

data type float64

>>> tf = np.array([1.5, 2.5, 3.5, 4.5])
>>> tf.dtype
dtype('float64')

access to some elements

>>> t = np.array ([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> t
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
>>> t[:6]
array([0, 1, 2, 3, 4, 5])
>>> t
array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19])

multi dimensions array

>>> tf2d = np.array([[1.5, 2, 3], [4, 5, 6]])
>>> tf2d
array([[1.5, 2. , 3. ],
       [4. , 5. , 6. ]])
>>> tf2d.dtype
dtype('float64')
>>> tf2d.shape
(2, 3)
>>> tf2d.ndim
2
>>> tf2d.size
6

random number (float) generation

>>> np.random.rand(10)
array([0.67966246, 0.26205002, 0.02549579, 0.11316062, 0.87369288,
       0.16210068, 0.51009515, 0.92700258, 0.6370769 , 0.06820358])

>>> np.random.rand(3,2)
array([[0.78813667, 0.92470323],
       [0.63210563, 0.97820931],
       [0.44739855, 0.03799558]])