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# `sklearn.datasets`.fetch_california_housing

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

  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_home str 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_missing bool, 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_y bool, default=False

      If True, returns `(data.data, data.target)` instead of a Bunch object.

      New in version 0.20.

    as_frame bool, 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:
    dataset `Bunch`

      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 using `sklearn.datasets.fetch_california_housing`

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Comparing Random Forests and Histogram Gradient Boosting models

Comparing Random Forests and Histogram Gradient Boosting models

Imputing missing values before building an estimator

Imputing missing values before building an estimator

Imputing missing values with variants of IterativeImputer

Imputing missing values with variants of IterativeImputer

Compare the effect of different scalers on data with outliers

Compare the effect of different scalers on data with outliers