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Given an index split list T of length M + 1, where the first element is 0 and the last element is N, generate an array D of length N such that D[T[i]:T[i+1]] = i.

For example, given T = [0, 2, 5, 7], then return D = [0, 0, 1, 1, 1, 2, 2].

I'm trying to avoid a for loop, but the best I can do is:

def expand_split_list(split_list):
    return np.concatenate(
        [
            np.full(split_list[i + 1] - split_list[i], i)
            for i in range(len(split_list) - 1)
        ]
    )

Is there a built-in function for that purpose?

Given an index split list T of length M + 1, where the first element is 0 and the last element is N, generate an array D of length N such that D[T[i]:T[i+1]] = i.

For example, given T = [0, 2, 5, 7], then return D = [0, 0, 1, 1, 1, 2, 2].

I'm trying to avoid a for loop, but the best I can do is:

def expand_split_list(split_list):
    return np.concatenate(
        [
            np.full(split_list[i + 1] - split_list[i], i)
            for i in range(len(split_list) - 1)
        ]
    )

Is there a built-in function for that purpose?

• python
• algorithm
• numpy

Share Improve this question Follow asked Mar 12 at 9:58 LeoLeo 7111 silver badge66 bronze badges

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3 Answers 3

Sorted by: Reset to default 7

You could combine diff, arange, and repeat:

n = np.diff(T)
out = np.repeat(np.arange(len(n)), n)

As a one-liner (python ≥3.8):

out = np.repeat(np.arange(len(n:=np.diff(T))), n)

Another option with assigning ones to an array of zeros, then cumsum:

out = np.zeros(T[-1], dtype=int)
out[T[1:-1]] = 1
out = np.cumsum(out)

Output:

array([0, 0, 1, 1, 1, 2, 2])

A numpy option is np.searchsorted

np.searchsorted(T, np.arange(max(T)), side='right')-1

which gives

array([0, 0, 1, 1, 1, 2, 2])

---

Another option (but seems clumsy) is using itertools.accumulate if you don't want to load numpy

from itertools import accumulate
list(accumulate([1 if i in T else 0 for i in range(max(T))], initial=-1))[1:]

and you will obtain a list

[0, 0, 1, 1, 1, 2, 2]

If you want to leverage broadcasting, a different (but not the fastest) numpy approach could be using np.meshgrid.

def expand_split_list(T):
    grid, _ = np.meshgrid(np.arange(T[-1]), T[:-1], indexing="ij") 
    # Creates a grid of indices and boundaries
    return (grid >= T[:-1]).sum(axis=1) - 1 
    # Boolean mask to check segment membership, then sum to assign group   indices

---

Another numpy approach could be using np.digitize if you want direct binning approach, but it is slightly slower than np.searchsorted() due to monotonicity checks

np.digitize(np.arange(T[-1]), bins=T) - 1

If you're working with Pandas, pd.cut() is another easy way to segment values:

pd.cut(range(T[-1]), bins=T, labels=False, right=False).tolist()

For a pure Python approach, you can use bisect_right(), which performs binary search over T for each element:

from bisect import bisect_right

def expand_split_list(T):
    return [bisect_right(T, i) - 1 for i in range(T[-1])]

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