.. _sphx_glr_auto_examples_datasets_plot_iris_dataset.py:


=========================================================
The Iris Dataset
=========================================================
This data sets consists of 3 different types of irises'
(Setosa, Versicolour, and Virginica) petal and sepal
length, stored in a 150x4 numpy.ndarray

The rows being the samples and the columns being:
Sepal Length, Sepal Width, Petal Length	and Petal Width.

The below plot uses the first two features.
See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
information on this dataset.



.. rst-class:: sphx-glr-horizontal


    *

      .. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png
            :scale: 47

    *

      .. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_002.png
            :scale: 47





.. code-block:: python

    print(__doc__)


    # Code source: Gaël Varoquaux
    # Modified for documentation by Jaques Grobler
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    from mpl_toolkits.mplot3d import Axes3D
    from sklearn import datasets
    from sklearn.decomposition import PCA

    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    Y = iris.target

    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5

    plt.figure(2, figsize=(8, 6))
    plt.clf()

    # Plot the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
    plt.xlabel('Sepal length')
    plt.ylabel('Sepal width')

    plt.xlim(x_min, x_max)
    plt.ylim(y_min, y_max)
    plt.xticks(())
    plt.yticks(())

    # To getter a better understanding of interaction of the dimensions
    # plot the first three PCA dimensions
    fig = plt.figure(1, figsize=(8, 6))
    ax = Axes3D(fig, elev=-150, azim=110)
    X_reduced = PCA(n_components=3).fit_transform(iris.data)
    ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
               cmap=plt.cm.Paired)
    ax.set_title("First three PCA directions")
    ax.set_xlabel("1st eigenvector")
    ax.w_xaxis.set_ticklabels([])
    ax.set_ylabel("2nd eigenvector")
    ax.w_yaxis.set_ticklabels([])
    ax.set_zlabel("3rd eigenvector")
    ax.w_zaxis.set_ticklabels([])

    plt.show()

**Total running time of the script:**
(0 minutes 0.158 seconds)



.. container:: sphx-glr-download

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


.. container:: sphx-glr-download

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