sklearn.datasets.load_digits

sklearn.datasets.load_digits(n_class=10, return_X_y=False)[source]

Load and return the digits dataset (classification).

Each datapoint is a 8x8 image of a digit.

Classes 10
Samples per class ~180
Samples total 1797
Dimensionality 64
Features integers 0-16

Read more in the User Guide.

Parameters:

n_class : integer, between 0 and 10, optional (default=10)

The number of classes to return.

return_X_y : boolean, default=False.

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

New in version 0.18.

Returns:

data : Bunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘images’, the images corresponding to each sample, ‘target’, the classification labels for each sample, ‘target_names’, the meaning of the labels, and ‘DESCR’, the full description of the dataset.

(data, target) : tuple if return_X_y is True

New in version 0.18.

This is a copy of the test set of the UCI ML hand-written digits datasets

http://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Examples

To load the data and visualize the images:

>>> from sklearn.datasets import load_digits
>>> digits = load_digits()
>>> print(digits.data.shape)
(1797, 64)
>>> import matplotlib.pyplot as plt 
>>> plt.gray() 
>>> plt.matshow(digits.images[0]) 
>>> plt.show()