sklearn.datasets.load_iris()

sklearn.datasets.load_iris(return_X_y=False) [source]

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

Parameters:	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, ?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.

Examples

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()
>>> data.target[[10, 25, 50]]
array([0, 0, 1])
>>> list(data.target_names)
['setosa', 'versicolor', 'virginica']