numpy.digitize#

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

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:
xarray_like

Input array to be binned. Prior to NumPy 1.10.0, this array had tobe 1-dimensional, but can now have any shape.

binsarray_like

Array of bins. It has to be 1-dimensional and monotonic.

rightbool, 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:
indicesndarray 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.

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.

numpy.digitize is implemented in terms ofnumpy.searchsorted.This means that a binary search is used to bin the values, which scalesmuch 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 monotonicallyincreasingbins, 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

>>>importnumpyasnp>>>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])
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