# dask.array.histogram

dask.array.histogram(a, bins=None, range=None, normed=False, weights=None, density=None)

  Blocked variant of `numpy.histogram()`.

  Parameters:
    a dask.array.Array

      Input data; the histogram is computed over the flattened array. If the `weights` argument is used, the chunks of `a` are accessed to check chunking compatibility between `a` and `weights`. If `weights` is `None`, a `dask.dataframe.Series` object can be passed as input data.

    bins int or sequence of scalars, optional

      Either an iterable specifying the `bins` or the number of `bins` and a `range` argument is required as computing `min` and `max` over blocked arrays is an expensive operation that must be performed explicitly. 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 a monotonically increasing array of bin edges, including the rightmost edge, allowing for non-uniform bin widths.

    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.

    normed bool, optional

      This is equivalent to the `density` argument, but produces incorrect results for unequal bin widths. It should not be used.

    weights dask.array.Array, optional

      A dask.array.Array of weights, of the same block structure as `a`. Each value in `a` only contributes its associated weight towards the bin count (instead of 1). If `density` is True, the weights are normalized, so that the integral of the density over the range remains 1.

    density bool, optional

      If `False`, the result will contain the number of samples in each bin. If `True`, the result is the value of the probability density function at the bin, normalized such that the integral over the range is 1. Note that the sum of the histogram values will not be equal to 1 unless bins of unity width are chosen; it is not a probability mass function. Overrides the `normed` keyword if given. If `density` is True, `bins` cannot be a single-number delayed value. It must be a concrete number, or a (possibly-delayed) array/sequence of the bin edges.

  Returns:
    hist dask Array

      The values of the histogram. See density and weights for a description of the possible semantics.

    bin_edges dask Array of dtype float

      Return the bin edges `(length(hist)+1)`.

  Examples

  Using number of bins and range:

      >>> import dask.array as da
      >>> import numpy as np
      >>> x = da.from_array(np.arange(10000), chunks=10)
      >>> h, bins = da.histogram(x, bins=10, range=[0, 10000])
      >>> bins
      array([    0.,  1000.,  2000.,  3000.,  4000.,  5000.,  6000.,  7000.,
              8000.,  9000., 10000.])
      >>> h.compute()
      array([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000])

  Explicitly specifying the bins:

      >>> h, bins = da.histogram(x, bins=np.array([0, 5000, 10000]))
      >>> bins
      array([    0,  5000, 10000])
      >>> h.compute()
      array([5000, 5000])