biplot

I saw this tutorial in R w/ autoplot . They plotted the loadings and loading labels: autoplot(prcomp(df), data = iris, colour = 'Species', loadings = TRUE, loadings.colour = 'blue', loadings.label = TRUE, loadings.label.size = 3) https://cran.r-project.org/web/packages/ggfortify/vignettes/plot_pca.html I prefer Python 3 w/ matplotlib, scikit-learn, and pandas for my data analysis. However, I don't know how to add these on? How can you plot these vectors w/ matplotlib ? I've been reading Recovering features names of explained_variance_ratio_ in PCA with sklearn but haven't figured it out yet

I am a newbie with python and found this excellent PCA biplot suggestion ( Plot PCA loadings and loading in biplot in sklearn (like R's autoplot) ). Now I tried to add a legend to the plot for the different targets. But the command plt.legend() doesn't work. Is there an easy way to do it? As an example, the iris data with the biplot code from the link above. import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.decomposition import PCA import pandas as pd from sklearn.preprocessing import StandardScaler iris = datasets.load_iris() X = iris.data y = iris