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() 

Examples using sklearn.datasets.load_digits