.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _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 `_ for more information on this dataset. .. rst-class:: sphx-glr-horizontal * .. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_001.png :class: sphx-glr-multi-img * .. image:: /auto_examples/datasets/images/sphx_glr_plot_iris_dataset_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none | .. code-block:: default print(__doc__) # Code source: Gaƫl 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.Set1, edgecolor='k') 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.Set1, edgecolor='k', s=40) 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.278 seconds) .. _sphx_glr_download_auto_examples_datasets_plot_iris_dataset.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download :download:`Download Python source code: plot_iris_dataset.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_iris_dataset.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_