`sklearn.datasets`.fetch_california_housing#

sklearn.datasets.fetch_california_housing(*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False)[source]#

Load the California housing dataset (regression).

Samples total	20640
Dimensionality	8
Features	real
Target	real 0.15 - 5.

Read more in the User Guide.

Parameters:

data_homestr or path-like, default=None: Specify another download and cache folder for the datasets. By default all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.
download_if_missingbool, default=True: If False, raise an OSError if the data is not locally available instead of trying to download the data from the source site.
return_X_ybool, default=False: If True, returns (data.data, data.target) instead of a Bunch object.

New in version 0.20.
as_framebool, 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.

New in version 0.23.

Returns:

datasetBunch

Dictionary-like object, with the following attributes.

datandarray, shape (20640, 8): Each row corresponding to the 8 feature values in order. If as_frame is True, data is a pandas object.
targetnumpy array of shape (20640,): Each value corresponds to the average house value in units of 100,000. If as_frame is True, target is a pandas object.
feature_nameslist of length 8: Array of ordered feature names used in the dataset.
DESCRstr: Description of the California housing dataset.
framepandas DataFrame: Only present when as_frame=True. DataFrame with data and target.

New in version 0.23.

(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.

New in version 0.20.

Notes

This dataset consists of 20,640 samples and 9 features.

Examples

>>> from sklearn.datasets import fetch_california_housing
>>> housing = fetch_california_housing()
>>> print(housing.data.shape, housing.target.shape)
(20640, 8) (20640,)
>>> print(housing.feature_names[0:6])
['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup']