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 4
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]])
np.concatenate
, np.hstack
and np.vstack
will do what you want. Note however that NumPy arrays are not suitable for use as dynamic arrays. Use Python lists for that purpose instead.
E = np.array([
]).reshape(0, 5)
print("E: \n{}\nShape {}\n".format(E, E.shape))
A = np.vstack([
[1, 2, 3, 4, 5],
[10, 20, 30, 40, 50]]
)
print("A:\n{}\nShape {}\n".format(A, A.shape))
C = np.r_[
E,
A
].astype(np.int32)
print("C:\n{}\nShape {}\n".format(C, C.shape))
E:
[]
Shape (0, 5)
A:
[[ 1 2 3 4 5]
[10 20 30 40 50]]
Shape (2, 5)
C:
[[ 1 2 3 4 5]
[10 20 30 40 50]]
Shape (2, 5)
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]])
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
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