sklearn.datasets.fetch_openml(name=None, version='active', data_id=None, data_home=None, target_column='default-target', cache=True, return_X_y=False, as_frame=False)[source]

Fetch dataset from openml by name or dataset id.

Datasets are uniquely identified by either an integer ID or by a combination of name and version (i.e. there might be multiple versions of the ‘iris’ dataset). Please give either name or data_id (not both). In case a name is given, a version can also be provided.

Read more in the User Guide.



The API is experimental (particularly the return value structure), and might have small backward-incompatible changes in future releases.

namestr or None

String identifier of the dataset. Note that OpenML can have multiple datasets with the same name.

versioninteger or ‘active’, default=’active’

Version of the dataset. Can only be provided if also name is given. If ‘active’ the oldest version that’s still active is used. Since there may be more than one active version of a dataset, and those versions may fundamentally be different from one another, setting an exact version is highly recommended.

data_idint or None

OpenML ID of the dataset. The most specific way of retrieving a dataset. If data_id is not given, name (and potential version) are used to obtain a dataset.

data_homestring or None, default None

Specify another download and cache folder for the data sets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.

target_columnstring, list or None, default ‘default-target’

Specify the column name in the data to use as target. If ‘default-target’, the standard target column a stored on the server is used. If None, all columns are returned as data and the target is None. If list (of strings), all columns with these names are returned as multi-target (Note: not all scikit-learn classifiers can handle all types of multi-output combinations)

cacheboolean, default=True

Whether to cache downloaded datasets using joblib.

return_X_yboolean, default=False.

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

as_frameboolean, default=False

If True, the data is a pandas DataFrame including columns with appropriate dtypes (numeric, string or categorical). The target is a pandas DataFrame or Series depending on the number of target_columns. The Bunch will contain a frame attribute with the target and the data. If return_X_y is True, then (data, target) will be pandas DataFrames or Series as describe above.


Dictionary-like object, with attributes:

datanp.array, scipy.sparse.csr_matrix of floats, or pandas DataFrame

The feature matrix. Categorical features are encoded as ordinals.

targetnp.array, pandas Series or DataFrame

The regression target or classification labels, if applicable. Dtype is float if numeric, and object if categorical. If as_frame is True, target is a pandas object.


The full description of the dataset


The names of the dataset columns

target_names: list

The names of the target columns

New in version 0.22.

categoriesdict or None

Maps each categorical feature name to a list of values, such that the value encoded as i is ith in the list. If as_frame is True, this is None.


More metadata from OpenML

framepandas DataFrame

Only present when as_frame=True. DataFrame with data and target.

(data, target)tuple if return_X_y is True



This interface is experimental and subsequent releases may change attributes without notice (although there should only be minor changes to data and target).

Missing values in the ‘data’ are represented as NaN’s. Missing values in ‘target’ are represented as NaN’s (numerical target) or None (categorical target)