numpy.bincount

numpy.bincount(x, weights=None)

Count number of occurrences of each value in array of non-negative ints.

The number of bins (of size 1) is one larger than the largest value in x. Each bin gives the number of occurrences of its index value in x. If weights is specified the input array is weighted by it, i.e. if a value n is found at position i, out[n] += weight[i] instead of out[n] += 1.

Parameters:

x : array_like, 1 dimension, nonnegative ints

Input array.

weights : array_like, optional

Weights, array of the same shape as x.

Returns:

out : ndarray of ints

The result of binning the input array. The length of out is equal to np.amax(x)+1.

Raises:

ValueError :

If the input is not 1-dimensional, or contains elements with negative values.

TypeError :

If the type of the input is float or complex.

See also

histogram, digitize, unique

Examples

>>> np.bincount(np.arange(5))
array([1, 1, 1, 1, 1])
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
array([1, 3, 1, 1, 0, 0, 0, 1])

>>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
>>> np.bincount(x).size == np.amax(x)+1
True

>>> np.bincount(np.arange(5, dtype=np.float))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: array cannot be safely cast to required type

A possible use of bincount is to perform sums over variable-size chunks of an array, using the weights keyword.

>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
>>> x = np.array([0, 1, 1, 2, 2, 2])
>>> np.bincount(x,  weights=w)
array([ 0.3,  0.7,  1.1])