a = np.array([1,2,3])
b = np.array([4,5])
l = [a,b]
I want a function stack_padding such that:
assert(stack_padding(l) == np.array([[1,2,3],[4,5,0]])
Is there a standard way in numpy of achieving
EDIT: l could have potentially many more elements
I think itertools.zip_longest with fill_value=0 can work for you:
import itertools
a = np.array([1,2,3])
b = np.array([4,5])
l = [a,b]
def stack_padding(l):
return np.column_stack((itertools.zip_longest(*l, fillvalue=0)))
>>> stack_padding(l)
array([[1, 2, 3],
[4, 5, 0]])
squeeze or something like that : np.dstack((itertools.zip_longest(*l, fillvalue=0))).squeeze()
column_stack is dstack without adding the dimension
np.stack(list(zip_longest...)), axis=1) gives more control over the orientation.With numpy.pad:
a = np.array([1,2,3])
b = np.array([4,5])
l = [a,b]
max_len = max([len(arr) for arr in l])
padded = np.array([np.lib.pad(arr, (0, max_len - len(arr)), 'constant', constant_values=0) for arr in l])
If you don't want to use itertools and column_stack, numpy.ndarray.resize will also do the job perfectly. As mentioned by jtweeder, you just need to know to resulting size of each rows. The advantage to use resize is that numpy.ndarray is contiguous in memory. Resizing is faster when each row differs alot in size. The performance difference is observable between the two approaches.
import numpy as np
import timeit
import itertools
def stack_padding(it):
def resize(row, size):
new = np.array(row)
new.resize(size)
return new
# find longest row length
row_length = max(it, key=len).__len__()
mat = np.array( [resize(row, row_length) for row in it] )
return mat
def stack_padding1(l):
return np.column_stack((itertools.zip_longest(*l, fillvalue=0)))
if __name__ == "__main__":
n_rows = 200
row_lengths = np.random.randint(30, 50, size=n_rows)
mat = [np.random.randint(0, 100, size=s) for s in row_lengths]
def test_stack_padding():
global mat
stack_padding(mat)
def test_itertools():
global mat
stack_padding1(mat)
t1 = timeit.timeit(test_stack_padding, number=1000)
t2 = timeit.timeit(test_itertools, number=1000)
print('With ndarray.resize: ', t1)
print('With itertool and vstack: ', t2)
The resize method wins in the above comparison:
>>> With ndarray.resize: 0.30080295499647036
>>> With itertool and vstack: 1.0151802329928614
max(it, key=len).__len__() always returns len(it)
it. I ran several tests on my machine and it seemed to work correctly. What is it in your situation? A python list or numpy.ndarray? p.s. It should work with both the two types though..
b.resize(a.shape, refcheck=False)That will resize b to[4,5,0].