Combining NumPy Arrays

Question

I have two 20x100x3 NumPy arrays that I want to combine into a 40 x 100 x 3 array, i.e. just add more lines to the array. I am confused by what function I want: is it vstack, hstack, column_stack or maybe something else?

Answer 1

I believe this is vstack you want

p=array_2 q=array_2 p=numpy.vstack([p,q]) 

Answer 2

One of the best ways to learn is to experiment, but I would say you want np.vstack , although there are other ways to do the same thing:

 a = np.ones((20,100,3)) b = np.vstack((a,a)) print b.shape # (40,100,3) 

or

 b = np.concatenate((a,a),axis=0) 

EDIT

As a note, on my machine for arrays with dimensions in the OP question, I found that np.concatenate about 2 times faster than np.vstack

 In [172]: a = np.random.normal(size=(20,100,3)) In [173]: c = np.random.normal(size=(20,100,3)) In [174]: %timeit b = np.concatenate((a,c),axis=0) 100000 loops, best of 3: 13.3 us per loop In [175]: %timeit b = np.vstack((a,c)) 10000 loops, best of 3: 26.1 us per loop 

Answer 3

It may be worth mentioning that

  np.concatenate((a1, a2, ...), axis=0) 

is the general form, and vstack and hstack are specific cases. It’s easiest for me to just find out what measurement I want to take and provide this as an argument to np.concatenate.

Answer 4

I tried a little test between r_ and vstack, and the result is very interesting:

 import numpy as np NCOLS = 10 NROWS = 2 NMATRICES = 10000 def mergeR(matrices): result = np.zeros([0, NCOLS]) for m in matrices: result = np.r_[ result, m] def mergeVstack(matrices): result = np.vstack(matrices) def main(): matrices = tuple( np.random.random([NROWS, NCOLS]) for i in xrange(NMATRICES) ) mergeR(matrices) mergeVstack(matrices) return 0 if __name__ == '__main__': main() 

Then I launched the profiler:

 python -m cProfile -s cumulative np_merge_benchmark.py 

and results:

 ncalls tottime percall cumtime percall filename:lineno(function) ... 1 0.579 0.579 4.139 4.139 np_merge_benchmark.py:21(mergeR) ... 1 0.000 0.000 0.054 0.054 np_merge_benchmark.py:27(mergeVstack) 

So the vstack path is 77 times faster!

Answer 5

By the way, there is r_ :

 >>> from scipy import * >>> a = rand(20,100,3) >>> b = rand(20,100,3) >>> a.shape (20, 100, 3) >>> b.shape (20, 100, 3) >>> r_[a,b].shape (40, 100, 3) >>> (r_[a,b] == vstack([a,b])).all() True