# zip_longest for numpy arrays

I'm trying to combine different length numpy arrays as one could equivalently do using lists with `itertools.zip_longest`. Say I have:

``````a = np.array([1, 5, 9, 13])
b = np.array([2, 6])
``````

With `itertools` one could interleave these two arrays using `chain` and `zip_longest`, and fill the missing values with say `0`:

``````from itertools import chain, zip_longest
list(chain(*zip_longest(*[a, b], fillvalue=0)))
# [1, 2, 5, 6, 9, 0, 13, 0]
``````

Is there a simple way to do this using `numpy` that I'm missing?

On Best Solutions

Here's an almost vectorized one -

``````# https://stackoverflow.com/a/38619350/3293881 @Divakar
def boolean_indexing(v):
lens = np.array([len(item) for item in v])
out_dtype = np.result_type(*[arr.dtype for arr in v])
return out

v = [a,b] # list of all input arrays
out = boolean_indexing(v).ravel('F')
``````

Sample run -

``````In [23]: a = np.array([1, 5, 9, 13])
...: b = np.array([2, 6])
...: c = np.array([7, 8, 10])
...: v = [a,b,c]

In [24]: boolean_indexing(v).ravel('F')
Out[24]: array([ 1,  2,  7,  5,  6,  8,  9,  0, 10, 13,  0,  0])
``````
On

I think I'd do that like this:

``````import numpy as np

def chain_zip_longest(*arrs, fillvalue=0, dtype=None):
arrs = [np.asarray(arr) for arr in arrs]
if not arrs:
return np.array([])
n = len(arrs)
dtype = dtype or np.find_common_type([arr.dtype for arr in arrs], [])
out = np.full(n * max(len(arr) for arr in arrs), fillvalue, dtype=dtype)
for i, arr in enumerate(arrs):
out[i:i + n * len(arr):len(arrs)] = arr
return out

print(chain_zip_longest([1, 2], [3, 4, 5], [6]))
# [1 3 6 2 4 0 0 5 0]
``````