load_digits#

sklearn.datasets.load_digits(*, n_class=10, return_X_y=False, as_frame=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

This is a copy of the test set of the UCI ML hand-written digits datasets https://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits

Read more in the User Guide.

Parameters:

n_classint, default=10: The number of classes to return. Between 0 and 10.
return_X_ybool, default=False: If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

Added in version 0.18.
as_framebool, default=False: If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric). The target is a pandas DataFrame or Series depending on the number of target columns. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as described below.

Added in version 0.23.

Returns:

dataBunch

Dictionary-like object, with the following attributes.

data{ndarray, dataframe} of shape (1797, 64): The flattened data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, Series} of shape (1797,): The classification target. If as_frame=True, target will be a pandas Series.
feature_names: list: The names of the dataset columns.
target_names: list: The names of target classes.

Added in version 0.20.
frame: DataFrame of shape (1797, 65): Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.
images: {ndarray} of shape (1797, 8, 8): The raw image data.
DESCR: str: The full description of the dataset.

(data, target)tuple if return_X_y is True

A tuple of two ndarrays by default. The first contains a 2D ndarray of shape (1797, 64) with each row representing one sample and each column representing the features. The second ndarray of shape (1797) contains the target samples. If as_frame=True, both arrays are pandas objects, i.e. X a dataframe and y a series.

Added in version 0.18.

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.matshow(digits.images[0], cmap="gray")
<...>
>>> plt.show()