# dask.array.histogram2d

dask.array.histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None)

  Blocked variant of `numpy.histogram2d()`.

  Parameters:
    x dask.array.Array

      An array containing the x-coordinates of the points to be histogrammed.

    y dask.array.Array

      An array containing the y-coordinates of the points to be histogrammed.

    bins sequence of arrays describing bin edges, int, or sequence of ints

      The bin specification. See the bins argument description for `histogramdd()` for a complete description of all possible bin configurations (this function is a 2D specific version of histogramdd).

    range tuple of pairs, optional.

      The leftmost and rightmost edges of the bins along each dimension when integers are passed to bins; of the form: ((xmin, xmax), (ymin, ymax)).

    normed bool, optional

      An alias for the density argument that behaves identically. To avoid confusion with the broken argument in the histogram function, density should be preferred.

    weights dask.array.Array, optional

      An array of values weighing each sample in the input data. The chunks of the weights must be identical to the chunking along the 0th (row) axis of the data sample.

    density bool, optional

      If False (the default) return the number of samples in each bin. If True, the returned array represents the probability density function at each bin.

  Returns:
    dask.array.Array

      The values of the histogram.

    dask.array.Array

      The edges along the x-dimension.

    dask.array.Array

      The edges along the y-dimension.

  See also

  `histogram`
  `histogramdd`

  Examples

      >>> import dask.array as da
      >>> x = da.array([2, 4, 2, 4, 2, 4])
      >>> y = da.array([2, 2, 4, 4, 2, 4])
      >>> bins = 2
      >>> range = ((0, 6), (0, 6))
      >>> h, xedges, yedges = da.histogram2d(x, y, bins=bins, range=range)
      >>> h
      dask.array<sum-aggregate, shape=(2, 2), dtype=float64, chunksize=(2, 2), chunktype=numpy.ndarray>
      >>> xedges
      dask.array<array, shape=(3,), dtype=float64, chunksize=(3,), chunktype=numpy.ndarray>
      >>> h.compute()
      array([[2., 1.],
             [1., 2.]])