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Fonction histogram_bin_edges - module numpy

Signature de la fonction histogram_bin_edges

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

Description

histogram_bin_edges.__doc__

    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 `Notes` 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'
            Maximum of 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 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.

    range : (float, float), optional
        The lower and upper range of the bins.  If not provided, range
        is simply ``(a.min(), a.max())``.  Values outside the range are
        ignored. The first element of the range must be less than or
        equal to the second. `range` affects the automatic bin
        computation as well. While bin width is computed to be optimal
        based on the actual data within `range`, the bin count will fill
        the entire range including portions containing no data.

    weights : array_like, optional
        An array of weights, of the same shape as `a`.  Each value in
        `a` only contributes its associated weight towards the bin count
        (instead of 1). This is currently not used by any of the bin estimators,
        but may be in the future.

    Returns
    -------
    bin_edges : array of dtype float
        The edges to pass into `histogram`

    See Also
    --------
    histogram

    Notes
    -----
    The methods to estimate the optimal number of bins are well founded
    in literature, and are inspired by the choices R provides for
    histogram visualisation. Note that having the number of bins
    proportional to :math:`n^{1/3}` is asymptotically optimal, which is
    why it appears in most estimators. These are simply plug-in methods
    that give good starting points for number of bins. In the equations
    below, :math:`h` is the binwidth and :math:`n_h` is the number of
    bins. All estimators that compute bin counts are recast to bin width
    using the `ptp` of the data. The final bin count is obtained from
    ``np.round(np.ceil(range / h))``.

    'auto' (maximum of the 'sturges' and 'fd' estimators)
        A compromise to get a good value. For small datasets the Sturges
        value will usually be chosen, while larger datasets will usually
        default to FD.  Avoids the overly conservative behaviour of FD
        and Sturges for small and large datasets respectively.
        Switchover point is usually :math:`a.size \approx 1000`.

    'fd' (Freedman Diaconis Estimator)
        .. math:: h = 2 \frac{IQR}{n^{1/3}}

        The binwidth is proportional to the interquartile range (IQR)
        and inversely proportional to cube root of a.size. Can be too
        conservative for small datasets, but is quite good for large
        datasets. The IQR is very robust to outliers.

    'scott'
        .. math:: h = \sigma \sqrt[3]{\frac{24 * \sqrt{\pi}}{n}}

        The binwidth is proportional to the standard deviation of the
        data and inversely proportional to cube root of ``x.size``. Can
        be too conservative for small datasets, but is quite good for
        large datasets. The standard deviation is not very robust to
        outliers. Values are very similar to the Freedman-Diaconis
        estimator in the absence of outliers.

    'rice'
        .. math:: n_h = 2n^{1/3}

        The number of bins is only proportional to cube root of
        ``a.size``. It tends to overestimate the number of bins and it
        does not take into account data variability.

    'sturges'
        .. math:: n_h = \log _{2}n+1

        The number of bins is the base 2 log of ``a.size``.  This
        estimator assumes normality of data and is too conservative for
        larger, non-normal datasets. This is the default method in R's
        ``hist`` method.

    'doane'
        .. math:: n_h = 1 + \log_{2}(n) +
                        \log_{2}(1 + \frac{|g_1|}{\sigma_{g_1}})

            g_1 = mean[(\frac{x - \mu}{\sigma})^3]

            \sigma_{g_1} = \sqrt{\frac{6(n - 2)}{(n + 1)(n + 3)}}

        An improved version of Sturges' formula that produces better
        estimates for non-normal datasets. This estimator attempts to
        account for the skew of the data.

    'sqrt'
        .. math:: n_h = \sqrt n

        The simplest and fastest estimator. Only takes into account the
        data size.

    Examples
    --------
    >>> arr = np.array([0, 0, 0, 1, 2, 3, 3, 4, 5])
    >>> np.histogram_bin_edges(arr, bins='auto', range=(0, 1))
    array([0.  , 0.25, 0.5 , 0.75, 1.  ])
    >>> np.histogram_bin_edges(arr, bins=2)
    array([0. , 2.5, 5. ])

    For consistency with histogram, an array of pre-computed bins is
    passed through unmodified:

    >>> np.histogram_bin_edges(arr, [1, 2])
    array([1, 2])

    This function allows one set of bins to be computed, and reused across
    multiple histograms:

    >>> shared_bins = np.histogram_bin_edges(arr, bins='auto')
    >>> shared_bins
    array([0., 1., 2., 3., 4., 5.])

    >>> group_id = np.array([0, 1, 1, 0, 1, 1, 0, 1, 1])
    >>> hist_0, _ = np.histogram(arr[group_id == 0], bins=shared_bins)
    >>> hist_1, _ = np.histogram(arr[group_id == 1], bins=shared_bins)

    >>> hist_0; hist_1
    array([1, 1, 0, 1, 0])
    array([2, 0, 1, 1, 2])

    Which gives more easily comparable results than using separate bins for
    each histogram:

    >>> hist_0, bins_0 = np.histogram(arr[group_id == 0], bins='auto')
    >>> hist_1, bins_1 = np.histogram(arr[group_id == 1], bins='auto')
    >>> hist_0; hist_1
    array([1, 1, 1])
    array([2, 1, 1, 2])
    >>> bins_0; bins_1
    array([0., 1., 2., 3.])
    array([0.  , 1.25, 2.5 , 3.75, 5.  ])