load_iris#

sklearn.datasets.load_iris(*, return_X_y=False, as_frame=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

Read more in the User Guide.

Changed in version 0.20: Fixed two wrong data points according to Fisher’s paper. The new version is the same as in R, but not as in the UCI Machine Learning Repository.

Parameters:

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 (150, 4): The data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, Series} of shape (150,): 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.
frame: DataFrame of shape (150, 5): Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.
DESCR: str: The full description of the dataset.
filename: str: The path to the location of the data.

Added in version 0.20.

(data, target)tuple if return_X_y is True

A tuple of two ndarray. The first containing a 2D array of shape (n_samples, n_features) with each row representing one sample and each column representing the features. The second ndarray of shape (n_samples,) containing the target samples.

Added 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)
[np.str_('setosa'), np.str_('versicolor'), np.str_('virginica')]