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 objects.

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: ndarray of shape (3, )

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 ndarrays. The first contains a 2D array of shape (150, 4) with each row representing one sample and the columns representing the features. The second ndarray of shape (150,) 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

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()
>>> samples = [10, 25, 50]
>>> data.target[samples]
array([0, 0, 1])
>>> data.target_names[data.target[samples]]
array(['setosa', 'setosa', 'versicolor'], dtype='<U10')

See Principal Component Analysis (PCA) on Iris Dataset for a more detailed example of how to work with the iris dataset.

Gallery examples

Plot classification probability

Plot Hierarchical Clustering Dendrogram

Concatenating multiple feature extraction methods

Incremental PCA

Principal Component Analysis (PCA) on Iris Dataset

Comparison of LDA and PCA 2D projection of Iris dataset

Factor Analysis (with rotation) to visualize patterns

Plot the decision surfaces of ensembles of trees on the iris dataset

Univariate Feature Selection

Gaussian process classification (GPC) on iris dataset

Regularization path of L1- Logistic Regression

Plot multi-class SGD on the iris dataset

Introducing the set_output API

GMM covariances

Evaluate the performance of a classifier with Confusion Matrix

Nested versus non-nested cross-validation

Test with permutations the significance of a classification score

Precision-Recall

Multiclass Receiver Operating Characteristic (ROC)

Receiver Operating Characteristic (ROC) with cross validation

Nearest Neighbors Classification

Comparing Nearest Neighbors with and without Neighborhood Components Analysis

Nearest Centroid Classification

Compare Stochastic learning strategies for MLPClassifier

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 1.2

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

RBF SVM parameters

SVM-Anova: SVM with univariate feature selection

Plot the decision surface of decision trees trained on the iris dataset

Understanding the decision tree structure