load_wine#

sklearn.datasets.load_wine(*, return_X_y=False, as_frame=False)[source]#

Load and return the wine dataset (classification).

Added in version 0.18.

The wine dataset is a classic and very easy multi-class classification dataset.

Classes	3
Samples per class	[59,71,48]
Samples total	178
Dimensionality	13
Features	real, positive

The copy of UCI ML Wine Data Set dataset is downloaded and modified to fit standard format from: https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

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 the data and target objects.
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 (178, 13): The data matrix. If as_frame=True, data will be a pandas DataFrame.
target: {ndarray, Series} of shape (178,): 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 (178, 14): Only present when as_frame=True. DataFrame with data and target.

Added in version 0.23.
DESCR: str: The full description of the dataset.

(data, target)tuple if return_X_y is True

A tuple of two ndarrays. The first contains a 2D array of shape (178, 13) with each row representing one sample and the columns representing the features. The second array of shape (178,) contains the target samples. If as_frame=True, both arrays are pandas objects, i.e. X a dataframe and y a series.

Examples

Let’s say you are interested in the samples 10, 80, and 140, and want to know their class name.

>>> from sklearn.datasets import load_wine
>>> data = load_wine()
>>> data.target[[10, 80, 140]]
array([0, 1, 2])
>>> list(data.target_names)
[np.str_('class_0'), np.str_('class_1'), np.str_('class_2')]

Gallery examples#

Outlier detection on a real data set

ROC Curve with Visualization API

Importance of Feature Scaling