# jax.numpy.digitizeΒΆ

jax.numpy.digitize(x, bins, right=False)[source]ΒΆ

Return the indices of the bins to which each value in input array belongs.

LAX-backend implementation of digitize(). Original docstring below.

right

order of bins

returned index i satisfies

False

increasing

bins[i-1] <= x < bins[i]

True

increasing

bins[i-1] < x <= bins[i]

False

decreasing

bins[i-1] > x >= bins[i]

True

decreasing

bins[i-1] >= x > bins[i]

If values in x are beyond the bounds of bins, 0 or len(bins) is returned as appropriate.

Parameters
• x (array_like) β Input array to be binned. Prior to NumPy 1.10.0, this array had to be 1-dimensional, but can now have any shape.

• bins (array_like) β Array of bins. It has to be 1-dimensional and monotonic.

• right (bool, optional) β 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[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins.

Returns

indices β Output array of indices, of same shape as x.

Return type

ndarray of ints

Raises
• ValueError β If bins is not monotonic.

• TypeError β If the type of the input is complex.

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 1-dimensional.

For monotonically _increasing_ bins, the following are equivalent:

np.digitize(x, bins, right=True)
np.searchsorted(bins, x, side='left')


Note that as the order of the arguments are reversed, the side must be too. The searchsorted call is marginally faster, as it does not do any monotonicity checks. Perhaps more importantly, it supports all dtypes.

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])