在 python 中绘制巨大散点 plot 的问题。 有一个更好的方法吗？

Question

I built a huge ndarray object which is 2059263, 2 dimensions (one column for x-axis and another for y-axis). I tried to plot a scatter plot using different color of points. I created another same size list of arrays, which contain the information of the origin ndarray. So I tried to plot the scatter plot based on the category of information list combined with ndarray axis information using matplotlib.pyplot. I tried to use if and elif condition loop, but the loop is infinite... Is there another way to deal with a huge axis array to plot the scatter plot...? Or did I make a mistake in coding? Is there a faster way to do this? Probably the problem is that the origin axis array and information of each points are separated... attached my code below:

import re
import matplotlib.pyplot as plt

cdict = {0 : 'b', 1 : 'c', 2 : 'g', 3 : 'm', 4 : 'r', 5 : 'y',
         6 : 'brown', 7 : 'gold', 8 : 'lightseagreen', 9 : 'indigo', 10 : 'maroon',
         11 : 'cyan', 12 : 'olive', 13 : 'deeppink', 14 : 'sienna', 15 : 'crimson',
         16 : 'peru', 17 : 'lime', 18 : 'navy', 19 : 'orange'}

count = 1
for i in range(len(mapping)):
    if count != int(atr_list[i][1]):
        print("wrong sequence")
        print(count)
        print(atr_list[i])
        break
    else:
        attri = re.search('^\d{3}[0-9]', atr_list[i][0])

        if int(attri.group()) < 2001:
            colo = 0
        elif int(attri.group()) > 2000 and int(attri.group()) < 2006:
            colo = 1
        elif int(attri.group()) > 2005 and int(attri.group()) < 2011:
            colo = 2
        elif int(attri.group()) > 2010 and int(attri.group()) < 2016:
            colo = 3
        elif int(attri.group()) > 2015:
            colo = 4
        plt.scatter(mapping[i, 0], mapping[i, 1], c=cdict[colo])
        count += 1

plt.xlim(mapping[:, 0].min(), mapping[:, 0].max()) #
plt.ylim(mapping[:, 1].min(), mapping[:, 1].max()) #
plt.xlabel('t-SNE_x') #
plt.ylabel('t-SNE_y') #

plt.show() #

Answer 1

I figured out that that was my coding mistake. I should have wrote plt.scatter() function not in the loop but outside of the loop. Since I plot the scatter plot million time, it was seemed like an infinite loop. I was stupid.