Numpy

Using NumPy

After install NumPy, import it as a library:

import numpy as np

Creating NumPy Arrays

From a Python List

my_list = [1,2,3]
np.array(my_list)

my_matrix = [[1,2,3],[4,5,6],[7,8,9]]
np.array(my_matrix)

Built-in Methods

np.zeros((5,5)) : Generate arrays of zeros
np.ones((3,3)) : Generate arrays of ones
np.eye(4) : Creates an identity matrix

arange
Return evenly spaced values within a given interval.

np.arange(0,10)
>> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
np.arange(0,11,2)
>> array([ 0,  2,  4,  6,  8, 10])

linspace
Return evenly spaced numbers over a specified interval.
np.linspace(0,10,5)
>> array([ 0. , 2.5, 5. , 7.5, 10. ])
Note: arange 和 linspace不同：arange是給每個數的間隔，linspace是給有多少個數
random

rand
Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).
np.random.rand(2,2)
>> array([[0.64065034, 0.16643695],
[0.5944612 , 0.96361622]])

randn
Return a sample (or samples) from the “standard normal” distribution over.

np.random.randn(2,2)
>> array([[-0.10332894,  0.12954978],
       [ 0.535127  , -2.51244816]])

randint
Return random integers from low (inclusive) to high (exclusive).

np.random.randint(1,100,10)
>> array([13, 64, 27, 63, 46, 68, 92, 10, 58, 24])

reshape
Returns an array containing the same data with a new shape.

arr = np.arange(25)
arr.reshape(5,5)
>> 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]])

Attributes

shape
Shape is an attribute that arrays have.

arr = np.arange(4)
arr.shape
>> (4,) #vector
arr.reshape(1,4).shape
>> (1, 4)

dtype
Use dtype to grab the data type of the object in the array.
arr.dtype
>> dtype('int64')

NumPy Indexing and Selection

The simplest way to pick one or some elements of an array looks very similar to python lists:

arr.dtype
>> dtype('int64')