watex.datasets.load_iris#

watex.datasets.load_iris(*, return_X_y=False, as_frame=False, tag=None, data_names=None, **kws)[source]#

Load and return the iris dataset (classification). The iris dataset is a classic and very easy multi-class classification dataset.

Parameters:

  • return_X_y (bool, default=False) – If True, returns (data, target) instead of a BowlSpace object. See below for more information about the data and target object. .. versionadded:: 0.1.2

  • 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. .. versionadded:: 0.1.2

  • (tag (None) – tag and data_names do nothing. just for API purpose and to allow fetching the same data uing the func:~watex.data.fetch_data since the latter already holds tag and data_names as parameters.

  • data_names) (None) – tag and data_names do nothing. just for API purpose and to allow fetching the same data uing the func:~watex.data.fetch_data since the latter already holds tag and data_names as parameters.

Returns:

  • data (Boxspace) – 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 and target. .. versionadded:: 0.1.2

    DESCR: str

    The full description of the dataset.

    filename: str

    The path to the location of the data. .. versionadded:: 0.1.2

  • (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. .. versionadded:: 0.1.2

Notes

Changed in version 0.1.1: 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 watex.datasets import load_iris >>> data = load_iris() >>> data.target[[10, 25, 50]] array([0, 0, 1]) >>> list(data.target_names) [‘setosa’, ‘versicolor’, ‘virginica’]

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