Concatenating empty array in Numpy
68,543
Solution 1
if you know the number of columns before hand:
>>> xs = np.array([[1,2,3,4,5],[10,20,30,40,50]])
>>> ys = np.array([], dtype=np.int64).reshape(0,5)
>>> ys
array([], shape=(0, 5), dtype=int64)
>>> np.vstack([ys, xs])
array([[ 1., 2., 3., 4., 5.],
[ 10., 20., 30., 40., 50.]])
if not:
>>> ys = np.array([])
>>> ys = np.vstack([ys, xs]) if ys.size else xs
array([[ 1, 2, 3, 4, 5],
[10, 20, 30, 40, 50]])
Solution 2
In Python, if possible to work with the individual vectors, to append you should use list.append()
>>> E = []
>>> B = np.array([1,2,3,4,5])
>>> C = np.array([10,20,30,40,50])
>>> E = E.append(B)
>>> E = E.append(C)
[array([1, 2, 3, 4, 5]), array([10, 20, 30, 40, 50])]
and then after all append operations are done, return to np.array thusly
>>> E = np.array(E)
array([[ 1, 2, 3, 4, 5],
[10, 20, 30, 40, 50]])
Solution 3
If you wanna do this just because you cannot concatenate an array with an initialized empty array in a loop, then just use a conditional statement, e.g.
if (i == 0):
do the first assignment
else:
start your contactenate
Solution 4
Something that I've build to deal with this sort of problem. It's also deals with list
input instead of np.array
:
import numpy as np
def cat(tupleOfArrays, axis=0):
# deals with problems of concating empty arrays
# also gives better error massages
# first check that the input is correct
assert isinstance(tupleOfArrays, tuple), 'first var should be tuple of arrays'
firstFlag = True
res = np.array([])
# run over each element in tuple
for i in range(len(tupleOfArrays)):
x = tupleOfArrays[i]
if len(x) > 0: # if an empty array\list - skip
if isinstance(x, list): # all should be ndarray
x = np.array(x)
if x.ndim == 1: # easier to concat 2d arrays
x = x.reshape((1, -1))
if firstFlag: # for the first non empty array, just swich the empty res array with it
res = x
firstFlag = False
else: # actual concatination
# first check that concat dims are good
if axis == 0:
assert res.shape[1] == x.shape[1], "Error concating vertically element index " + str(i) + \
" with prior elements: given mat shapes are " + \
str(res.shape) + " & " + str(x.shape)
else: # axis == 1:
assert res.shape[0] == x.shape[0], "Error concating horizontally element index " + str(i) + \
" with prior elements: given mat shapes are " + \
str(res.shape) + " & " + str(x.shape)
res = np.concatenate((res, x), axis=axis)
return res
if __name__ == "__main__":
print(cat((np.array([]), [])))
print(cat((np.array([1, 2, 3]), np.array([]), [1, 3, 54+1j]), axis=0))
print(cat((np.array([[1, 2, 3]]).T, np.array([]), np.array([[1, 3, 54+1j]]).T), axis=1))
print(cat((np.array([[1, 2, 3]]).T, np.array([]), np.array([[3, 54]]).T), axis=1)) # a bad one
Author by
maxv15
Updated on July 08, 2022Comments
-
maxv15 almost 2 years
in Matlab I do this:
>> E = []; >> A = [1 2 3 4 5; 10 20 30 40 50]; >> E = [E ; A] E = 1 2 3 4 5 10 20 30 40 50
Now I want the same thing in Numpy but I have problems, look at this:
>>> E = array([],dtype=int) >>> E array([], dtype=int64) >>> A = array([[1,2,3,4,5],[10,20,30,40,50]]) >>> E = vstack((E,A)) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/core/shape_base.py", line 226, in vstack return _nx.concatenate(map(atleast_2d,tup),0) ValueError: array dimensions must agree except for d_0
I have a similar situation when I do this with:
>>> E = concatenate((E,A),axis=0) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: arrays must have same number of dimensions
Or:
>>> E = append([E],[A],axis=0) Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python/numpy/lib/function_base.py", line 3577, in append return concatenate((arr, values), axis=axis) ValueError: arrays must have same number of dimensions