def f_classif(X, y):
    """Compute the ANOVA F-value for the provided sample.

    Read more in the :ref:`User Guide <univariate_feature_selection>`.

    Parameters
    ----------
    X : {array-like, sparse matrix} shape = [n_samples, n_features]
        The set of regressors that will be tested sequentially.

    y : array of shape(n_samples)
        The data matrix.

    Returns
    -------
    F : array, shape = [n_features,]
        The set of F values.

    pval : array, shape = [n_features,]
        The set of p-values.

    See also
    --------
    chi2: Chi-squared stats of non-negative features for classification tasks.
    f_regression: F-value between label/feature for regression tasks.
    """
    X, y = check_X_y(X, y, ['csr', 'csc', 'coo'])
    args = [X[safe_mask(X, y is k)] for k in np.unique(y)]
    return f_oneway(*args)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/feature_selection/univariate_selection.py/f_classif/operator `==` -> `is`