[PYTHON] Continuously color with matplotlib scatter plot

A note on how to continuously color matplotlib scatter plots.

As a point

Convert continuous real numbers to RGB continuous values using cmap
Use the facecolors and ʻedgecolors arguments provided by plt.scatter () ` There are two.

(20/01/24 10:34 postscript) I corrected the code example below by letting me know the easier way in the comments. Thank you @kochory!

Code example

We visualized the low-dimensional embedding of the data set Boston housing for the regression problem and the objective variable (property price in this example) with a scatter plot.

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_boston
from sklearn.manifold import TSNE

X, y = load_boston(return_X_y=True)
tsne = TSNE(n_components=2, random_state=0)
embX = tsne.fit_transform(X)

# 20/01/24 10:34 Corrected based on the comments received
plt.scatter(embX[:, 0], embX[:, 1], c=y, cmap='coolwarm')

#The following is the code before the addition
#Point 1:Convert y to an array of RGBA
#cm = plt.get_cmap('coolwarm')   #Get matplotlib colormap
#normalized_y = (y -y.min()) / (y.max() - y.min())   #Objective variable y to 0~Normalize to 1
#color_levels = (normalized_y * 256).astype(int)     #y further 0~Convert to 255
#colors = [cm(cl) for cl in color_levels]            # cm(cl)Returns an RGBA tuple
#Point 2:Specify the argument of facecolors
#plt.scatter(embX[:, 0], embX[:, 1], s=10, facecolors=colors)