使用Python用散点图绘制PCA结果，包括原始数据

Question

作为练习，我对虹膜数据进行了主成分分析。这是我的代码：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import style
style.use("ggplot")
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA # as sklearnPCA
import pandas as pd
#=================
df = pd.read_csv('iris.csv');
# Split the 1st 4 columns comprising values
# and the last column that has species
X = df.ix[:,0:4].values
y = df.ix[:,4].values
X_std = StandardScaler().fit_transform(X);  # standardization of data
# Fit the model with X_std and apply the dimensionality reduction on X_std.
pca = PCA(n_components=2) # 2 PCA components;
Y_pca = pca.fit_transform(X_std)
# How to plot my results???? I am struck here! 

请告知如何绘制我的原始虹膜数据和使用散点图得出的PCA。

Answer 1

这是我认为你可以可视化它的方法。我将PC1放在X轴上，PC2放在Y轴上，并根据其类别为每个点着色。这是代码：

#first we need to map colors on labels
dfcolor = pd.DataFrame([['setosa','red'],['versicolor','blue'],['virginica','yellow']],columns=['Species','Color'])
mergeddf = pd.merge(df,dfcolor)
#Then we do the graph
plt.scatter(Y_pca[:,0],Y_pca[:,1],color=mergeddf['Color'])
plt.show()