[Python知识库]matplotlib二维矩阵散点图及每个点标记

记录NLP作业中利用matplotlib绘制散点图并进行标记

原文：https://blog.csdn.net/wizardforcel/article/details/54782628

M_reduced是要求绘制的散点坐标
words 是单词
word2ind 字典，记录单词与坐标的对应关系

所使用的函数是annotate()

annotate(" 标记的文本 ", xy, xytext)

第一个参数是预标记的文本
第二个参数是预标注的点坐标
第三个参数是预标记文本的坐标

import numpy as np
import matplotlib.pyplot as plt

M_reduced= np.array([[1, 1], [-1, -1], [1, -1], [-1, 1], [0, 0]])
word2ind = {'test1': 0, 'test2': 1, 'test3': 2, 'test4': 3, 'test5': 4}
words = ['test1', 'test2', 'test3', 'test4', 'test5']

fig = plt.figure()
fig_sub1 = fig.add_subplot(111)

for i in range(len(words)):
	fig_sub1.scatter(M_reduced[word2ind[words[i]],0],M_reduced[word2ind[words[i]], 1],color='r',marker='x')
	plt.annotate(words[i], xy = (M_reduced[word2ind[words[i]], 0], M_reduced[word2ind[words[i]], 1]), xytext = (M_reduced[word2ind[words[i]], 0]+0.001, M_reduced[word2ind[words[i]], 1]+0.001)) 


plt.show()

运行结果