这篇博客将介绍如何使用Python，PCA对iris数据集降维2，3并进行2D,3D散点图绘制（包括有图例&无图例，有标题Label&无标题Label）。 着重介绍怎么一次性添加多类型的图例到图表，通过显式获取scatter。

# 方法1

scatter = ax.scatter(x_reduced[:, 0], x_reduced[:, 1], x_reduced[:, 2], c=iris.target)

# 方法2

scatter = plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=iris.target, marker="d")

# 添加图例名称到图标

plt.legend(handles=scatter.legend_elements()[0],

labels=sp_names,

title="species", loc="upper right")

1. 效果图

对鸢尾花进行PCA降维成2维后进行绘制，Seaborn效果图如下：

对鸢尾花进行PCA降维成2维后进行绘制，Seaborn添加标题及散点拟合线 效果图如下：

对鸢尾花进行PCA降维成3维后进行绘制，Matplotlib3D效果图如下：

对鸢尾花进行PCA降维成3维简单绘制效果图如下：

对鸢尾花进行PCA降维成3维后进行绘制，添加中文标题，xyz轴描述及图例，不同类型用不同的样式mark 效果图如下：

2. 源码

# 使用PCA对鸢尾花特征进行降维2维/3维并绘制 2D,3D散点图（有图例&无图例，有标题Lable&无标题Lable）

# python iris_pca.py

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import seaborn as sns

from mpl_toolkits.mplot3d import Axes3D

from sklearn import datasets

from sklearn.decomposition import PCA

# 支持中文

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

iris = datasets.load_iris()

R = np.array(iris.data)

R_cov = np.cov(R, rowvar=False)

iris_covmat = pd.DataFrame(data=R_cov, columns=iris.feature_names)

iris_covmat.index = iris.feature_names

eig_values, eig_vectors = np.linalg.eig(R_cov)

def plot_2D_Seaborn():

featureVector = eig_vectors[:, :2]

featureVector_t = np.transpose(featureVector)

# R is the original iris dataset

R_t = np.transpose(R)

newDataset_t = np.matmul(featureVector_t, R_t)

newDataset = np.transpose(newDataset_t)

# 可视化 绘制2D图

# 鸢尾花数据创建dataframe

df = pd.DataFrame(data=newDataset, columns=['PC1', 'PC2'])

y = pd.Series(iris.target)

y = y.replace(0, 'setosa')

y = y.replace(1, 'versicolor')

y = y.replace(2, 'virginica')

df['Target'] = y

# 绘制2维数据，fit_reg是否拟合线

sns.lmplot(x='PC1', y='PC2', data=df, hue='Target', fit_reg=False, legend=True)

# sns.lmplot(x='PC1', y='PC2', data=df, hue='Target', fit_reg=True, legend=True)

plt.title("Seaborn鸢尾花降维2维图") # 会被截取不全

plt.show()

# PCA降2维

# 无图例 & 有图例

def plot_2D_PCA_Legend():

# 进行PCA降维

x_reduced = PCA(n_components=2).fit_transform(iris.data)

y = pd.Series(iris.target)

y = y.replace(0, 'setosa')

y = y.replace(1, 'versicolor')

y = y.replace(2, 'virginica')

fig = plt.figure()

ax = fig.add_subplot()

# 2D散点图无图例

ax.scatter(x_reduced[:, 0], x_reduced[:, 1], c=iris.target, marker="d")

plt.show()

print(np.unique(np.array(y)).tolist())

print(np.unique(iris.target).tolist())

# ax.legend(np.unique(iris.target).tolist())

# ax.legend(np.unique(np.array(y)).tolist(),loc='upper right')

# 2D散点图有标题，label，图例

plt.figure(figsize=(8, 6))

sp_names = np.unique(np.array(y)).tolist()

scatter = plt.scatter(x_reduced[:, 0], x_reduced[:, 1], c=iris.target, marker="d")

plt.title('鸢尾花降维2维图')

plt.xlabel("PC1", size=18)

plt.ylabel("PC2", size=18)

# 添加图例名称到图标

plt.legend(handles=scatter.legend_elements()[0],

labels=sp_names,

title="species", loc="upper right")

plt.show()

# PCA降3维

# 无图例 & 有图例

def plot_3D_PCA_Legend():

# 进行PCA降维

x_reduced = PCA(n_components=3).fit_transform(iris.data)

y = pd.Series(iris.target)

y = y.replace(0, 'setosa')

y = y.replace(1, 'versicolor')

y = y.replace(2, 'virginica')

fig = plt.figure()

ax = Axes3D(fig)

# 3D散点图无图例

ax.scatter(x_reduced[:, 0], x_reduced[:, 1], x_reduced[:, 2], c=iris.target)

plt.show()

sp_names = np.unique(np.array(y)).tolist()

fig = plt.figure()

ax = Axes3D(fig)

# 3D散点图有标题，label，图例

scatter = ax.scatter(x_reduced[:, 0], x_reduced[:, 1], x_reduced[:, 2], c=iris.target)

ax.set_title('鸢尾花降维3维图')

ax.set_xlabel("PC1", size=18)

ax.set_ylabel("PC2", size=18)

ax.set_zlabel("PC3", size=18)

# 添加图例名称到图标

ax.legend(handles=scatter.legend_elements()[0],

labels=sp_names,

title="species", loc="upper right")

plt.show()

# 不同的PCA降维

def plot_3D():

featureVector = eig_vectors[:, :3]

featureVector_t = np.transpose(featureVector)

# R is the original iris dataset

R_t = np.transpose(R)

newDataset_t = np.matmul(featureVector_t, R_t)

newDataset = np.transpose(newDataset_t)

# 构建DataFrame

df = pd.DataFrame(data=newDataset, columns=['PC1', 'PC2', 'PC3'])

y = pd.Series(iris.target)

y = y.replace(0, 'setosa')

y = y.replace(1, 'versicolor')

y = y.replace(2, 'virginica')

df['Target'] = y

# print(df.head(5))

# 根据其中一列分组

df = df.groupby("Target")

# print(df.groups) # key

# print(df.get_group('setosa')) # 某个value

# matplot支持的点样式及点颜色

marks = ['.', '*', 's', ',', 'o', 'v', '^', '<', '>', '1', '2', '3', '4', '8', 'p', 'P', 'h', 'H', '+', 'x', 'X',

'D', 'd', '|', '_']

colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']

fig = plt.figure()

ax = fig.add_subplot(projection='3d')

ax.set_title('鸢尾花降维3维图')

for i, key in enumerate(df.groups.keys()):

val = df.get_group(key)

ax.scatter(val["PC1"], val["PC2"], val["PC3"], c=colors[i % len(colors)], marker=marks[i % len(marks)],

label=key)

ax.set_xlabel('PC1')

ax.set_ylabel('PC2')

ax.set_zlabel('PC3')

ax.legend(loc='upper right')

plt.show()

plot_2D_Seaborn()

plot_2D_PCA_Legend()

plot_3D_PCA_Legend()

plot_3D()

参考

https://datavizpyr.com/add-legend-to-scatterplot-colored-by-a-variable-with-matplotlib-in-python/https://blog.csdn.net/weixin_39531761/article/details/110717013https://www.jianshu.com/p/25a66dee6450

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