load_digits (original) (raw)
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.
This is a copy of the test set of the UCI ML hand-written digits datasetshttps://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 andtarget.
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.gray() plt.matshow(digits.images[0]) <...> plt.show()
