in this code im trying to add the sum of values and add it to an array named array_values, but it didnt, only prints []
array_values = ([])
value = 0.0
for a in range(0, 8):
for b in range (1, 5):
value = value + float(klines[a][b])
#print(value)
np.append(array_values, value)#FIX array_values.append(value)
print("añadiendo: ",value)
value = 0.0
print(array_values)
Does this solve your problem?
import numpy as np
array_values = ([])
value = 0.0
for a in range(0, 8):
for b in range (1, 5):
value = value + float(klines[a][b])
#print(value)
array_values = np.append(array_values, value)
print("añadiendo: ",value)
value = 0.0
print(array_values)
np.append returns an ndarray.
A copy of arr with values appended to axis. Note that append does not occur in-place: a new array is allocated and filled. If axis is None, out is a flattened array.
Check the return section to understand better https://numpy.org/doc/stable/reference/generated/numpy.append.html
np.append in a loop is in-efficient compared to list append.alist.append(value) works in place, adding a reference to value to the list. np.append is really a call to np.concatenate, and makes a new array, with copying. np.append is not a list append clone, and should not be used in the same way.Assuming klines is a 2d numeric dtype array:
In [231]: klines = np.arange(1,13).reshape(4,3)
In [232]: klines
Out[232]:
array([[ 1, 2, 3],
[ 4, 5, 6],
[ 7, 8, 9],
[10, 11, 12]])
we can simple sum across rows with:
In [233]: klines.sum(axis=1)
Out[233]: array([ 6, 15, 24, 33])
the equivalent using your style of iteration:
In [234]: alist = []
...: value = 0
...: for i in range(4):
...: for j in range(3):
...: value += klines[i,j]
...: alist.append(value)
...: value = 0
...:
In [235]: alist
Out[235]: [6, 15, 24, 33]
Use of np.append is slower and harder to get right.
Even if klines is a list of lists, the sums can be easily done with:
In [236]: [sum(row) for row in klines]
Out[236]: [6, 15, 24, 33]
