Python-randomly select n elements

Reference link  https://blog.csdn.net/weixin_44633882/article/details/103748747 

 

Python randomly select n elements in list or numpy.ndarray

1. Randomly select an element from a list

• random.choice(data)

import random
data = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
random.choice(data) # 随机选取一个元素

2. Randomly select multiple elements from a list

import random
data = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
sample_num = 5
random.sample(data, sample_num) # 结果['a', 'd', 'b', 'f', 'c']，每次运行结果不同。

3. Randomly select multiple elements from data and label

When making a data set, there may be a requirement to use only 50% of the data. Therefore, we randomly sample 30% of the data from the original data set. This is also required, dataand labelis corresponding. Next, talk about my approach. Create an index list, select N indexes in the index list, and extract the data and label data according to these indexes.

import random
data = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
label = [0, 1, 2, 3, 4, 5, 6, 7]
sample_num = int(0.5 * len(data)) # 假设取50%的数据

sample_list = [i for i in range(len(data))] # [0, 1, 2, 3, 4, 5, 6, 7]
sample_list = random.sample(sample_list, sample_num) #随机选取出了 [3, 4, 2, 0]
sample_data = [data[i] for i in sample_list] # ['d', 'e', 'c', 'a']
sample_label = [label[i] for i in label] # [3, 4, 2, 0]

4. Randomly select multiple elements from numpy.ndarray

3. undertake only dataand labelis numpy.ndarraysubject How sample_listto remove it? Those of you who
know numpy.ndarrayslices must all know it. Here I will write briefly.

import numpy as np
data = np.array([[ 0,  1,  2,  3],
                 [ 4,  5,  6,  7],
                 [ 8,  9, 10, 11],
                 [12, 13, 14, 15]]) # shape:(4,4)
label = np.array([1,2,3,4]) # shape:(4,)

sample_num = int(0.5 * len(data)) # 假设取50%的数据
sample_list = [i for i in range(len(data))] # [0, 1, 2, 3]
sample_list = random.sample(sample_list, sample_num) # [1, 2]

data = data[sample_list,:] # array([[ 4,  5,  6,  7], [ 8,  9, 10, 11]])
label = label[sample_list] # array([2, 3])