this code demonstrates and describes basic visualization(2 out of four features) of the Iris Dataset, provided in the sklearn package.

iris_plot.py

from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
import numpy as np

# load the dataset to be used
iris = load_iris()

# get the 150*4 features into varibale features
features = iris.data

# optional : useful to give labels to the axes
feature_names = iris.feature_names

# holds an array of nSamples(150 in this case), with class labels(Iris Setose=0, Iris Versicolor=1, Iris Virginica=2)
target = iris.target

print feature_names # prints ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

# zip function returns tuples, in i-th iteration, gives tuples containing i-th element of each argument passes
for t, marker, c in zip(xrange(3), ">oo", "rgb"):
    plt.scatter(features[target == t, 1],
                features[target == t, 2],
                marker = marker,
                c=c)