Interweaving two numpy arrays

Question

Assume the following arrays are given:

a = array([1,3,5]) b = array([2,4,6]) 

How would one interweave them efficiently so that one gets a third array like this

c = array([1,2,3,4,5,6]) 

It can be assumed that length(a)==length(b).

Answer 1

I like Josh's answer. I just wanted to add a more mundane, usual, and slightly more verbose solution. I don't know which is more efficient. I expect they will have similar performance.

import numpy as np a = np.array([1,3,5]) b = np.array([2,4,6])  c = np.empty((a.size + b.size,), dtype=a.dtype) c[0::2] = a c[1::2] = b 

Answer 2

I thought it might be worthwhile to check how the solutions performed in terms of performance. And this is the result:

This clearly shows that the most upvoted and accepted answer (Pauls answer) is also the fastest option.

The code was taken from the other answers and from another Q&A:

# Setup import numpy as np  def Paul(a, b):     c = np.empty((a.size + b.size,), dtype=a.dtype)     c[0::2] = a     c[1::2] = b     return c  def JoshAdel(a, b):     return np.vstack((a,b)).reshape((-1,),order='F')  def xioxox(a, b):     return np.ravel(np.column_stack((a,b)))  def Benjamin(a, b):     return np.vstack((a,b)).ravel([-1])  def andersonvom(a, b):     return np.hstack( zip(a,b) )  def bhanukiran(a, b):     return np.dstack((a,b)).flatten()  def Tai(a, b):     return np.insert(b, obj=range(a.shape[0]), values=a)  def Will(a, b):     return np.ravel((a,b), order='F')  # Timing setup timings = {Paul: [], JoshAdel: [], xioxox: [], Benjamin: [], andersonvom: [], bhanukiran: [], Tai: [], Will: []} sizes = [2**i for i in range(1, 20, 2)]  # Timing for size in sizes:     func_input1 = np.random.random(size=size)     func_input2 = np.random.random(size=size)     for func in timings:         res = %timeit -o func(func_input1, func_input2)         timings[func].append(res)  %matplotlib notebook  import matplotlib.pyplot as plt import numpy as np  fig = plt.figure(1) ax = plt.subplot(111)  for func in timings:     ax.plot(sizes,              [time.best for time in timings[func]],              label=func.__name__)  # you could also use "func.__name__" here instead ax.set_xscale('log') ax.set_yscale('log') ax.set_xlabel('size') ax.set_ylabel('time [seconds]') ax.grid(which='both') ax.legend() plt.tight_layout() 

Just in case you have numba available you could also use that to create a function:

import numba as nb  @nb.njit def numba_interweave(arr1, arr2):     res = np.empty(arr1.size + arr2.size, dtype=arr1.dtype)     for idx, (item1, item2) in enumerate(zip(arr1, arr2)):         res[idx*2] = item1         res[idx*2+1] = item2     return res 

It could be slightly faster than the other alternatives: