numpy.histogram(a, bins=10, range=None, normed=False, weights=None, new=None)

Compute the histogram of a set of data.


a : array_like

Input data. The histogram is computed over the flattened array.

bins : int or sequence of scalars, 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. Note that with new set to False, values below the range are ignored, while those above the range are tallied in the rightmost bin.

normed : 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.

weights : array_like, 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 normed is True, the weights are normalized, so that the integral of the density over the range remains 1. The weights keyword is only available with new set to True.

new : {None, True, False}, optional

Whether to use the new semantics for histogram:
  • None : the new behaviour is used, no warning is printed.
  • True : the new behaviour is used and a warning is raised about the future removal of the new keyword.
  • False : the old behaviour is used and a DeprecationWarning is raised.

As of NumPy 1.3, this keyword should not be used explicitly since it will disappear in NumPy 2.0.


hist : array

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

bin_edges : array of dtype float

Return the bin edges (length(hist)+1). With new=False, return the left bin edges (length(hist)).


All but the last (righthand-most) bin is half-open. In other words, if bins is:

[1, 2, 3, 4]

then the first bin is [1, 2) (including 1, but excluding 2) and the second [2, 3). The last bin, however, is [3, 4], which includes 4.


>>> np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
(array([0, 2, 1]), array([0, 1, 2, 3]))
>>> np.histogram(np.arange(4), bins=np.arange(5), normed=True)
(array([ 0.25,  0.25,  0.25,  0.25]), array([0, 1, 2, 3, 4]))
>>> np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3])
(array([1, 4, 1]), array([0, 1, 2, 3]))
]), array([0, 1, 2, 3]))
>>> a = np.arange(5)
>>> hist, bin_edges = np.histogram(a, normed=True)
>>> hist
array([ 0.5,  0. ,  0.5,  0. ,  0. ,  0.5,  0. ,  0.5,  0. ,  0.5])
>>> hist.sum()
>>> np.sum(hist*np.diff(bin_edges))

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