how to read iris data in python

1print(__doc__)
2
3
4# Code source: Gaël Varoquaux
5# Modified for documentation by Jaques Grobler
6# License: BSD 3 clause
7
8import matplotlib.pyplot as plt
9from mpl_toolkits.mplot3d import Axes3D
10from sklearn import datasets
11from sklearn.decomposition import PCA
12
13# import some data to play with
14iris = datasets.load_iris()
15X = iris.data[:, :2]  # we only take the first two features.
16y = iris.target
17
18x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
19y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
20
21plt.figure(2, figsize=(8, 6))
22plt.clf()
23
24# Plot the training points
25plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
26            edgecolor='k')
27plt.xlabel('Sepal length')
28plt.ylabel('Sepal width')
29
30plt.xlim(x_min, x_max)
31plt.ylim(y_min, y_max)
32plt.xticks(())
33plt.yticks(())
34
35# To getter a better understanding of interaction of the dimensions
36# plot the first three PCA dimensions
37fig = plt.figure(1, figsize=(8, 6))
38ax = Axes3D(fig, elev=-150, azim=110)
39X_reduced = PCA(n_components=3).fit_transform(iris.data)
40ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=y,
41           cmap=plt.cm.Set1, edgecolor='k', s=40)
42ax.set_title("First three PCA directions")
43ax.set_xlabel("1st eigenvector")
44ax.w_xaxis.set_ticklabels([])
45ax.set_ylabel("2nd eigenvector")
46ax.w_yaxis.set_ticklabels([])
47ax.set_zlabel("3rd eigenvector")
48ax.w_zaxis.set_ticklabels([])
49
50plt.show()
51