Implementation:Scikit learn Scikit learn Load Iris

Field	Value
source	scikit-learn|https://github.com/scikit-learn/scikit-learn
domains	Data_Science
last_updated	2026-02-08 15:00 GMT

Overview

Concrete tool for loading the Iris flower dataset provided by scikit-learn.

Description

The load_iris function returns the classic Iris flower dataset, originally collected by R.A. Fisher in 1936. The dataset contains:

• 150 samples (50 per class)
• 4 numeric features: sepal length (cm), sepal width (cm), petal length (cm), petal width (cm)
• 3 target classes: setosa, versicolor, virginica

All features are real-valued and positive. The dataset is bundled with scikit-learn and requires no network access.

Usage

• Quick classification benchmarks to verify that a new algorithm works correctly on a well-understood problem.
• Tutorials and teaching examples where a small, clean dataset is needed.
• Unit tests and integration tests for scikit-learn-based pipelines.
• Exploratory data analysis and visualization demonstrations.

Code Reference

Source Location

sklearn/datasets/_base.py, function load_iris

Signature

def load_iris(*, return_X_y=False, as_frame=False):

Import

from sklearn.datasets import load_iris

I/O Contract

Inputs

Parameter	Type	Default	Description
return_X_y	bool	False	If True, returns a (data, target) tuple instead of a Bunch object.
as_frame	bool	False	If True, returns data as a pandas DataFrame and target as a pandas Series.

Outputs

Condition	Return Type	Description
return_X_y=False (default)	sklearn.utils.Bunch	Dictionary-like object with keys: data (ndarray of shape (150, 4)), target (ndarray of shape (150,)), feature_names (list of 4 strings), target_names (ndarray of shape (3,)), DESCR (str), filename (str), frame (DataFrame or None).
return_X_y=True	tuple(ndarray, ndarray)	A tuple (data, target) where data has shape (150, 4) and target has shape (150,).
as_frame=True	DataFrame / Series	When combined with return_X_y=True, both elements are pandas objects. When combined with return_X_y=False, the Bunch's data and target attributes are pandas objects and frame is a full DataFrame of shape (150, 5).

Usage Examples

Basic loading as a Bunch:

from sklearn.datasets import load_iris

iris = load_iris()
print(iris.data.shape)        # (150, 4)
print(iris.target.shape)      # (150,)
print(iris.target_names)      # ['setosa' 'versicolor' 'virginica']
print(iris.feature_names)     # ['sepal length (cm)', 'sepal width (cm)', ...]

Loading as X, y arrays directly:

from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
print(X.shape)  # (150, 4)
print(y.shape)  # (150,)

Loading as a pandas DataFrame:

from sklearn.datasets import load_iris

iris = load_iris(as_frame=True)
print(type(iris.data))    # <class 'pandas.core.frame.DataFrame'>
print(iris.data.head())

Related Pages

• Principle:Scikit_learn_Scikit_learn_Dataset_Loading