src/mozanalysis/frequentist_stats/bootstrap.py [147:186]:
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):
    """Return ``stat_fn`` evaluated on resampled and original data.

    Do the resampling in parallel over the cluster.

    Args:
        data: The data as a 1D numpy array, pandas series, or pandas dataframe.
        stat_fn: Either a function that aggregates each resampled
            population to a scalar (e.g. the default value ``np.mean``
            lets you bootstrap means), or a function that aggregates
            each resampled population to a dict of scalars. In both
            cases, this function must accept a one-dimensional ndarray
            as its input.
        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:
        ``stat_fn`` evaluated over ``num_samples`` samples.

            * By default, a pandas Series of sampled means
            * if ``stat_fn`` returns a scalar, a pandas Series
            * if ``stat_fn`` returns a dict, a pandas DataFrame
              with columns set to the dict keys.
    """
    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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