src/mozanalysis/bayesian_stats/bayesian_bootstrap.py [238:284]:
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):
    """Return ``stat_fn`` evaluated on resampled data.

    Args:
        data: The data as a list, 1D numpy array, or pandas series
        stat_fn (callable, optional): A function that either:

            * Aggregates each resampled population to a scalar (e.g.
              the default, ``bb_mean``), or
            * Aggregates each resampled population to a dict of
              scalars (e.g. the func returned by
              ``make_bb_quantile_closure`` when given multiple
              quantiles.

            In both cases, this function must accept two parameters:

            * a one-dimensional ndarray or pandas Series of values,
            * an identically shaped object of weights for these values

        num_samples: The number of samples to return
        seed_start: A seed for the random number generator; this
            function will use seeds in the range::

                [seed_start, seed_start + num_samples)

            and these particular seeds must not be used elsewhere
            in this calculation. By default, use a random seed.

        threshold_quantile (float, optional): An optional threshold
            quantile, above which to discard outliers. E.g. ``0.9999``.

    Returns:
        A Series or DataFrame with one row per sample and one column
        per output of ``stat_fn``.

    References:
        Rubin, Donald B. The Bayesian Bootstrap. Ann. Statist. 9 (1981),
            no. 1, 130--134. https://dx.doi.org/10.1214/aos/1176345338
    """
    if type(data) is not np.ndarray:
        data = np.array(data.to_numpy(dtype="float", na_value=np.nan))

    if np.isnan(data).any():
        raise ValueError("'data' contains null values")

    if threshold_quantile:
        data = filter_outliers(data, threshold_quantile)
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src/mozanalysis/frequentist_stats/bootstrap.py [271:280]:
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):
    """Params are similar to `get_bootstrap_samples`"""
    if type(data) is not np.ndarray:
        data = np.array(data.to_numpy(dtype="float", na_value=np.nan))

    if np.isnan(data).any():
        raise ValueError("'data' contains null values")

    if threshold_quantile:
        data = filter_outliers(data, threshold_quantile)
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