Apply PCA to a CSV file and plot its datapoints (one per line).Usage: pca.py <csv_file>

pca.py

"""Apply PCA to a CSV file and plot its datapoints (one per line).

The first column should be a category (determines the color of each datapoint),
the second a label (shown alongside each datapoint)."""
import sys
import pandas
import pylab as pl
from sklearn import preprocessing
from sklearn.decomposition import PCA


def main():
	"""Load data."""
	try:
		csvfile = sys.argv[1]
	except IndexError:
		print '%s\n\nUsage: %s [--3d] <csv_file>' % (__doc__, sys.argv[0])
		return
	data = pandas.read_csv(csvfile, index_col=(0, 1))

	# first column provides labels
	ylabels = [a for a, _ in data.index]
	labels = [text for _, text in data.index]
	encoder = preprocessing.LabelEncoder().fit(ylabels)

	xdata = data.as_matrix(data.columns)
	ydata = encoder.transform(ylabels)
	target_names = encoder.classes_
	plotpca(xdata, ydata, target_names, labels, csvfile)


def plotpca(xdata, ydata, target_names, items, filename):
	"""Make plot."""
	pca = PCA(n_components=2)
	components = pca.fit(xdata).transform(xdata)

	# Percentage of variance explained for each components
	print('explained variance ratio (first two components):',
			pca.explained_variance_ratio_)

	pl.figure()  # Make a plotting figure
	pl.subplots_adjust(bottom=0.1)

	# NB: a maximum of 7 targets will be plotted
	for i, (c, m, target_name) in enumerate(zip(
			'rbmkycg', 'o^s*v+x', target_names)):
		pl.scatter(components[ydata == i, 0], components[ydata == i, 1],
				color=c, marker=m, label=target_name)
		for n, x, y in zip(
				(ydata == i).nonzero()[0],
				components[ydata == i, 0],
				components[ydata == i, 1]):
			pl.annotate(
					items[n],
					xy=(x, y),
					xytext=(5, 5),
					textcoords='offset points',
					color=c,
					fontsize='small',
					ha='left',
					va='top')
	pl.legend()
	pl.title('PCA of %s' % filename)
	pl.show()


if __name__ == '__main__':
	main()

AttributeError: 'DataFrame' object has no attribute 'as_matrix'
Getting this error when I tried to run for my own csv file. The code is as follows

import sys
import pandas
import pylab as pl
from sklearn import preprocessing
from tkinter.filedialog import asksaveasfilename, askopenfilename
from sklearn.decomposition import PCA

def main():

data = pandas.read_csv(askopenfilename(), index_col=(0, 1))

# first column provides labels
ylabels = [a for a, _ in data.index]
labels = [text for _, text in data.index]
encoder = preprocessing.LabelEncoder().fit(ylabels)

xdata = data.as_matrix(data.columns)
ydata = encoder.transform(ylabels)
target_names = encoder.classes_
plotpca(xdata, ydata, target_names, labels, csvfile)

def plotpca(xdata, ydata, target_names, items, filename):
"""Make plot."""
pca = PCA(n_components=2)
components = pca.fit(xdata).transform(xdata)

# Percentage of variance explained for each components
print('explained variance ratio (first two components):',
		pca.explained_variance_ratio_)

pl.figure()  # Make a plotting figure
pl.subplots_adjust(bottom=0.1)

# NB: a maximum of 7 targets will be plotted
for i, (c, m, target_name) in enumerate(zip(
		'rbmkycg', 'o^s*v+x', target_names)):
	pl.scatter(components[ydata == i, 0], components[ydata == i, 1],
			color=c, marker=m, label=target_name)
	for n, x, y in zip(
			(ydata == i).nonzero()[0],
			components[ydata == i, 0],
			components[ydata == i, 1]):
		pl.annotate(
				items[n],
				xy=(x, y),
				xytext=(5, 5),
				textcoords='offset points',
				color=c,
				fontsize='small',
				ha='left',
				va='top')
pl.legend()
pl.title('PCA of %s' % filename)
pl.show()

if name == 'main':
main()

replace the line "xdata = data.as_matrix(data.columns)" with "xdata = data.values"