numpy.digitize¶

numpy.digitize(x,bins,right=False)¶

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

Each indexi returned is such thatbins[i-1]<=x<bins[i] ifbins is monotonically increasing, orbins[i-1]>x>=bins[i] ifbins is monotonically decreasing. If values inx are beyond thebounds ofbins, 0 orlen(bins) is returned as appropriate. If rightis True, then the right bin is closed so that the indexi is suchthatbins[i-1]<x<=bins[i] orbins[i-1]>=x>bins[i] ifbinsis monotonically increasing or decreasing, respectively.

Parameters:	x : array_like Input array to be binned. Prior to NumPy 1.10.0, this array had tobe 1-dimensional, but can now have any shape. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. right : bool, optional Indicating whether the intervals include the right or the left binedge. Default behavior is (right==False) indicating that the intervaldoes not include the right edge. The left bin end is open in thiscase, i.e., bins[i-1] <= x < bins[i] is the default behavior formonotonically increasing bins.
Returns:	out : ndarray of ints Output array of indices, of same shape asx.
Raises:	ValueError Ifbins is not monotonic. TypeError If the type of the input is complex.

See also

bincount,histogram,unique,searchsorted

Notes

If values inx are such that they fall outside the bin range,attempting to indexbins with the indices thatdigitize returnswill result in an IndexError.

New in version 1.10.0.

np.digitize is implemented in terms ofnp.searchsorted. This meansthat a binary search is used to bin the values, which scales much betterfor larger number of bins than the previous linear search. It also removesthe requirement for the input array to be 1-dimensional.

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)>>>indsarray([1, 4, 3, 2])>>>forninrange(x.size):...print(bins[inds[n]-1],"<=",x[n],"<",bins[inds[n]])...0.0 <= 0.2 < 1.04.0 <= 6.4 < 10.02.5 <= 3.0 < 4.01.0 <= 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])