title: Plotting PCA results including original data with scatter plot using Python
body: <p>I have conducted PCA on iris data as an exercise. Here is my code: </p>
  
  <pre><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! 
  </code></pre>
  
  <p>Please advise on how to plot my original iris data and the PCAs derived using a scatter plot.</p>