.. _sphx_glr_auto_examples_decomposition_plot_pca_iris.py:


=========================================================
PCA example with Iris Data-set
=========================================================

Principal Component Analysis applied to the Iris dataset.

See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
information on this dataset.




.. image:: /auto_examples/decomposition/images/sphx_glr_plot_pca_iris_001.png
    :align: center





.. code-block:: python

    print(__doc__)


    # Code source: Gaël Varoquaux
    # License: BSD 3 clause

    import numpy as np
    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D


    from sklearn import decomposition
    from sklearn import datasets

    np.random.seed(5)

    centers = [[1, 1], [-1, -1], [1, -1]]
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    fig = plt.figure(1, figsize=(4, 3))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()
    pca = decomposition.PCA(n_components=3)
    pca.fit(X)
    X = pca.transform(X)

    for name, label in [('Setosa', 0), ('Versicolour', 1), ('Virginica', 2)]:
        ax.text3D(X[y == label, 0].mean(),
                  X[y == label, 1].mean() + 1.5,
                  X[y == label, 2].mean(), name,
                  horizontalalignment='center',
                  bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
    # Reorder the labels to have colors matching the cluster results
    y = np.choose(y, [1, 2, 0]).astype(np.float)
    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.spectral)

    ax.w_xaxis.set_ticklabels([])
    ax.w_yaxis.set_ticklabels([])
    ax.w_zaxis.set_ticklabels([])

    plt.show()

**Total running time of the script:**
(0 minutes 0.115 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_pca_iris.py <plot_pca_iris.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_pca_iris.ipynb <plot_pca_iris.ipynb>`