mindspore.numpy.histogram(a, bins=10, range=None, weights=None, density=False)[source]

Computes the histogram of a dataset.


String values for bins is not supported. Deprecated numpy argument normed is not supported.

  • a (Union[int, float, bool, list, tuple, Tensor]) – Input data. The histogram is computed over the flattened array.

  • bins (Union[int, tuple, list, Tensor], 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.

  • 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.

  • weights (Union[int, float, bool, list, tuple, Tensor], 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). If density is True, the weights are normalized, so that the integral of the density over the range remains 1.

  • density (boolean, 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.


(Tensor, Tensor), the values of the histogram and the bin edges.


ValueError – if x and weights do not have the same size.

Supported Platforms:

Ascend GPU CPU


>>> from mindspore import numpy as np
>>> print(np.histogram([1, 2, 1], bins=[0, 1, 2, 3]))
(Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00,  2.00000000e+00,  1.00000000e+00]),
Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))
>>> print(np.histogram(np.arange(4), bins=np.arange(5), density=True))
(Tensor(shape=[4], dtype=Float32, value=
[ 2.50000000e-01,  2.50000000e-01,  2.50000000e-01,  2.50000000e-01]),
Tensor(shape=[5], dtype=Int32, value= [0, 1, 2, 3, 4]))
>>> print(np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]))
(Tensor(shape=[3], dtype=Float32, value= [ 1.00000000e+00,  4.00000000e+00,  1.00000000e+00]),
Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))