Load and return the iris dataset (classification).

The iris dataset is a classic and very easy multi-class classification dataset.

Classes 3
Samples per class 50
Samples total 150
Dimensionality 4
Features real, positive

Read more in the User Guide.


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.


data : Bunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, ‘target_names’, the meaning of the labels, ‘feature_names’, the meaning of the features, and ‘DESCR’, the full description of the dataset.

(data, target) : tuple if return_X_y is True

New in version 0.18.


Let’s say you are interested in the samples 10, 25, and 50, and want to know their class name.

>>> from sklearn.datasets import load_iris
>>> data = load_iris()
>>>[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']