sklearn.datasets.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.
- Parameters
- return_X_ybool, default=False
If True, returns
(data, target)instead of a Bunch object. See below for more information about thedataandtargetobject.New 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_yis True, then (data,target) will be pandas DataFrames or Series as described below.New in version 0.23.
- Returns
- data
Bunch Dictionary-like object, with the following attributes.
- data{ndarray, dataframe} of shape (150, 4)
The data matrix. If
as_frame=True,datawill be a pandas DataFrame.- target: {ndarray, Series} of shape (150,)
The classification target. If
as_frame=True,targetwill 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 withdataandtarget.New in version 0.23.
- DESCR: str
The full description of the dataset.
- filename: str
The path to the location of the data.
New in version 0.20.
- (data, target)tuple if
return_X_yis True New in version 0.18.
- data
Notes
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.
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']
