Function histogram_bin_edges

numpy/lib/_histograms_impl.py:474–677

r""" Function to calculate only the edges of the bins used by the `histogram` function. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int

(a, bins=10, range=None, weights=None)

@array_function_dispatch(_histogram_bin_edges_dispatcher)
def histogram_bin_edges(a, bins=10, range=None, weights=None):
    r"""
    Function to calculate only the edges of the bins used by the `histogram`
    function.

    Parameters
    ----------
    a : array_like
        Input data. The histogram is computed over the flattened array.
    bins : int or sequence of scalars or str, optional
        If `bins` is an int, it defines the number of equal-width
        bins in the given range (10, by default). If `bins` is a
        sequence, it defines the bin edges, including the rightmost
        edge, allowing for non-uniform bin widths.

        If `bins` is a string from the list below, `histogram_bin_edges` will
        use the method chosen to calculate the optimal bin width and
        consequently the number of bins (see the Notes section for more detail
        on the estimators) from the data that falls within the requested range.
        While the bin width will be optimal for the actual data
        in the range, the number of bins will be computed to fill the
        entire range, including the empty portions. For visualisation,
        using the 'auto' option is suggested. Weighted data is not
        supported for automated bin size selection.

        'auto'
            Minimum bin width between the 'sturges' and 'fd' estimators.
            Provides good all-around performance.

        'fd' (Freedman Diaconis Estimator)
            Robust (resilient to outliers) estimator that takes into
            account data variability and data size.

        'doane'
            An improved version of Sturges' estimator that works better
            with non-normal datasets.

        'scott'
            Less robust estimator that takes into account data variability
            and data size.

        'stone'
            Estimator based on leave-one-out cross-validation estimate of
            the integrated squared error. Can be regarded as a generalization
            of Scott's rule.

        'rice'
            Estimator does not take variability into account, only data
            size. Commonly overestimates number of bins required.

        'sturges'
            R's default method, only accounts for data size. Only
            optimal for gaussian data and underestimates number of bins
            for large non-gaussian datasets.

        'sqrt'
            Square root (of data size) estimator, used by Excel and
            other programs for its speed and simplicity.