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# `sklearn.datasets`.load_wine

sklearn.datasets.load_wine(*, return_X_y=False, as_frame=False)

  Load and return the wine dataset (classification).

  New 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_y bool, default=False

      If True, returns `(data, target)` instead of a Bunch object. See below for more information about the `data` and `target` object.

    as_frame bool, 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.

      New in version 0.23.

  Returns:
    data `Bunch`

      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`.

        New 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 by default. The first contains a 2D array of shape (178, 13) with each row representing one sample and each column representing the features. The second array of shape (178,) contains the target samples.

  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)
      ['class_0', 'class_1', 'class_2']

## Examples using `sklearn.datasets.load_wine`

Outlier detection on a real data set

Outlier detection on a real data set

ROC Curve with Visualization API

ROC Curve with Visualization API

Importance of Feature Scaling

Importance of Feature Scaling