Is there a more numpythonic way to do this?
#example arrays
arr = np.array([0, 1, 2, 3, 4, 5, 6, 7], dtype=np.float32)
values = np.array([0.2, 3.0, 1.5])
#get the indices where each value falls between values in arr
between = [np.nonzero(i > arr)[0][-1] for i in values]
For sorted arr, we can use np.searchsorted for performance -
In [67]: np.searchsorted(arr,values)-1
Out[67]: array([0, 2, 1])
Timings on large dataset -
In [81]: np.random.seed(0)
...: arr = np.unique(np.random.randint(0,10000, 10000))
...: values = np.random.randint(0,10000, 1000)
# @Andy L.'s soln
In [84]: %timeit np.argmin(values > arr[:,None], axis=0) - 1
10 loops, best of 3: 28.2 ms per loop
# Original soln
In [82]: %timeit [np.nonzero(i > arr)[0][-1] for i in values]
100 loops, best of 3: 8.68 ms per loop
# From this post
In [83]: %timeit np.searchsorted(arr,values)-1
10000 loops, best of 3: 57.8 µs per loop
Use broadcast and argmin
np.argmin(values > arr[:,None], axis=0) - 1
Out[32]: array([0, 2, 1], dtype=int32)
Note: I assume arr is monotonic increasing as in the sample
np.expand_dims(arr, axis=1). Read docs on expand_dims for more info(note: newaxis in the docs of expand_dims is just alias of None): docs.scipy.org/doc/numpy/reference/generated/…