numpy.digitize¶

numpy.digitize(x,bins,right=False)¶

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

right	order of bins	returned indexi 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 inx are beyond the bounds ofbins, 0 orlen(bins) isreturned as appropriate.

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:	indices: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.

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.Thesearchsorted call is marginally faster, as it does not do anymonotonicity 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)>>>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])