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