histogramdd#

named_arrays.histogramdd(*sample, bins, axis=None, min=None, max=None, density=False, weights=None)#

A thin wrapper around numpy.histogramdd() which adds an axis argument.

Parameters:

  • sample (AbstractScalar) – The data to be histrogrammed. Note the difference in signature compared to numpy.histogramdd(), each component must be a separate argument, instead of a single argument containing a sequence of arrays. This is done so that multiple dispatch works better for this function.

  • bins (dict[str, int] | AbstractScalar | Sequence[AbstractScalar]) –

    The bin specification of the histogram:

    • If bins is a dictionary, the keys are interpreted as the axis names and the values are the number of bins along each axis. This dictionary must have the same number of elements as sample.

    • If bins is an array or a sequence of arrays, it describes the monotonically-increasing bin edges along each dimension

  • axis (None | str | Sequence[str]) – The logical axes along which to histogram the data points. If None (the default), the histogram will be computed along all the axes of sample.

  • min (None | AbstractScalar | Sequence[AbstractScalar]) – The lower boundary of the histogram along each dimension. If None (the default), the minimum of each element of sample is used.

  • max (None | AbstractScalar | Sequence[AbstractScalar]) – The upper boundary of the histogram along each dimension. If None (the default), the maximum of each elemennt of sample is used.

  • density (bool) – If False (the default), returns the number of samples in each bin. If True, returns the probability density in each bin.

  • weights (None | AbstractScalar) – An optional array weighting each sample.

Return type:

tuple[AbstractScalar, tuple[AbstractScalar, …]]