用 Python 中的 matplotlib 绘图时，如何根据样本点的 label 设置不同的颜色？

# 生成样本点
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=500,
                           n_features=10,
                           n_classes=5,
                           n_informative=4,                          
                           random_state=0)

# 默认绘图
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 8))
plt.scatter(X[:, 0], X[:, 1], marker='o')

默认绘图结果如下：

1. 指定参数c

# 1. 指定参数c
plt.figure(figsize=(8, 8))
plt.scatter(X[:, 0], X[:, 1], marker='o', c='g')

绘图结果如下：


（图片来源： https://blog.csdn.net/qiu931110/article/details/68130199 ）

参数 c 可以等于： ['c', 'b', 'g', 'r', 'm', 'y', 'k', 'w']

b ——blue
c ——cyan
g ——green
k ——black
m ——magenta
r ——red
w ——white
y ——yellow

2. c=list，设置cmap

# 2. c=y，设置cmap
plt.figure(figsize=(8, 8))
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='coolwarm')

plt.figure(figsize=(12, 8))
plt.subplot(231)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='plasma')
plt.subplot(232)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='Oranges')
plt.subplot(233)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='summer')
plt.subplot(234)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='Spectral')
plt.subplot(235)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='Set1')
plt.subplot(236)
plt.scatter(X[:, 0], X[:, 1], marker='o', c=y, cmap='rainbow')
plt.show()

colormap，也称color bar，matplotlib模块中内嵌了一大批常用的colormaps。请参阅官方文档 color example code: colormaps_reference.py

import numpy as np
import matplotlib.pyplot as plt
# Have colormaps separated into categories:
# http://matplotlib.org/examples/color/colormaps_reference.html
cmaps = [('Perceptually Uniform Sequential', [
            'viridis', 'plasma', 'inferno', 'magma']),
         ('Sequential', [
            'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
            'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
            'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
         ('Sequential (2)', [
            'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
            'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
            'hot', 'afmhot', 'gist_heat', 'copper']),
         ('Diverging', [
            'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
            'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
         ('Qualitative', [
            'Pastel1', 'Pastel2', 'Paired', 'Accent',
            'Dark2', 'Set1', 'Set2', 'Set3',
            'tab10', 'tab20', 'tab20b', 'tab20c']),
         ('Miscellaneous', [
            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
            'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
def plot_color_gradients(cmap_category, cmap_list, nrows):
    fig, axes = plt.subplots(nrows=nrows)
    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
    for ax, name in zip(axes, cmap_list):
        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
        pos = list(ax.get_position().bounds)
        x_text = pos[0] - 0.01
        y_text = pos[1] + pos[3]/2.
        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
    # Turn off *all* ticks & spines, not just the ones with colormaps.
    for ax in axes:
        ax.set_axis_off()
for cmap_category, cmap_list in cmaps:
    plot_color_gradients(cmap_category, cmap_list, nrows)
plt.show()

‘Perceptually Uniform Sequential’：

‘Sequential’：

‘Sequential2’：

Diverging：

Qualitative：

Miscellaneous：

非常感谢： 【python图像处理】彩色映射

此外更多参考阅读：

• 官方文档 color example code: colormaps_reference.py
• 【python图像处理】彩色映射（续篇）
• PYthon——plt.scatter各参数详解
• 原 matplotlib散点scatter学习2，参数测试(marker1)