numpy.digitize¶

numpy.
digitize
(x, bins, right=False)¶ Return the indices of the bins to which each value in input array belongs.
Each index
i
returned is such thatbins[i1] <= x < bins[i]
if bins is monotonically increasing, orbins[i1] > x >= bins[i]
if bins is monotonically decreasing. If values in x are beyond the bounds of bins, 0 orlen(bins)
is returned as appropriate. If right is True, then the right bin is closed so that the indexi
is such thatbins[i1] < x <= bins[i]
orbins[i1] >= x > bins[i]
if bins is monotonically increasing or decreasing, respectively.Parameters: x : array_like
Input array to be binned. Prior to NumPy 1.10.0, this array had to be 1dimensional, but can now have any shape.
bins : array_like
Array of bins. It has to be 1dimensional and monotonic.
right : bool, optional
Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval does not include the right edge. The left bin end is open in this case, i.e., bins[i1] <= x < bins[i] is the default behavior for monotonically increasing bins.
Returns: out : ndarray of ints
Output array of indices, of same shape as x.
Raises: ValueError
If bins is not monotonic.
TypeError
If the type of the input is complex.
See also
Notes
If values in x are such that they fall outside the bin range, attempting to index bins with the indices that
digitize
returns will result in an IndexError.New in version 1.10.0.
np.digitize is implemented in terms of np.searchsorted. This means that a binary search is used to bin the values, which scales much better for larger number of bins than the previous linear search. It also removes the requirement for the input array to be 1dimensional.
Examples
>>> x = np.array([0.2, 6.4, 3.0, 1.6]) >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0]) >>> inds = np.digitize(x, bins) >>> inds array([1, 4, 3, 2]) >>> for n in range(x.size): ... print(bins[inds[n]1], "<=", x[n], "<", bins[inds[n]]) ... 0.0 <= 0.2 < 1.0 4.0 <= 6.4 < 10.0 2.5 <= 3.0 < 4.0 1.0 <= 1.6 < 2.5
>>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.]) >>> bins = np.array([0, 5, 10, 15, 20]) >>> np.digitize(x,bins,right=True) array([1, 2, 3, 4, 4]) >>> np.digitize(x,bins,right=False) array([1, 3, 3, 4, 5])