使用Python使用散点图绘制包括原始数据在内的PCA结果

Question

I have conducted PCA on iris data as an exercise. Here is my code:

#!/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! 

Please advise on how to plot my original iris data and the PCAs derived using a scatter plot.

Answer 1

Here is the way I think you can visualize it. I'll put PC1 on X-Axis and PC2 in Y-Axis and color each point based on its category. Here is the code:

#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()