- numpy.bincount(x, weights=None, minlength=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. If minlength is specified, there will be at least this number of bins in the output array (though it will be longer if necessary, depending on the contents of 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.
x : array_like, 1 dimension, nonnegative ints
weights : array_like, optional
Weights, array of the same shape as x.
minlength : int, optional
New in version 1.6.0.
A minimum number of bins for the output array.
out : ndarray of ints
The result of binning the input array. The length of out is equal to np.amax(x)+1.
If the input is not 1-dimensional, or contains elements with negative values, or if minlength is non-positive.
If the type of the input is float or complex.
>>> 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
The input array needs to be of integer dtype, otherwise a TypeError is raised:
>>> 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])