dask.array.histogram2d
dask.array.histogram2d¶
- dask.array.histogram2d(x, y, bins=10, range=None, normed=None, weights=None, density=None)[source]¶
Blocked variant of
numpy.histogram2d().- Parameters
- xdask.array.Array
An array containing the x-coordinates of the points to be histogrammed.
- ydask.array.Array
An array containing the y-coordinates of the points to be histogrammed.
- binssequence 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).- rangetuple 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)).
- normedbool, 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.
- weightsdask.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.
- densitybool, 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
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.]])