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In Python I can append to an empty array like:
>>> a = []
>>> a.append([1,2,3])
>>> a.append([1,2,3])
>>> a
[[1, 2, 3], [1, 2, 3]]
How can I do the same in NumPy? np.append flattens the array, unfortunately (and I need to have an empty array at the beginning).
506
numpy.append() in Python. About : numpy.append(array, values, axis = None) : appends values along the mentioned axis at the end of the array. Parameters : array : [array_like]Input array. values : [array_like]values to be added in the arr.
If axis is None, out is a flattened array. Here array a is created and then two arrays are appended to a with the help of np.append (). The resulting array of append function is a copy of the original array with other arrays added to it.
In this article, we are going to discuss the difference between append (), insert (), and, extend () method in Python lists. It adds an element at the end of the list. The argument passed in the append function is added as a single element at end of the list and the length of the list is increased by 1.
NumPy Arrays Are NOT Always Faster Than Lists If lists had been useless compared to NumPy arrays, they would have probably been dumped by the Python community. An example where lists rise and shine in comparison with NumPy arrays is the append () function. " append () " adds values to the end of both lists and NumPy arrays.
OP intended to start with empty array. So, here's one approach using NumPy
In [2]: a = np.empty((0,3), int)
In [3]: a
Out[3]: array([], shape=(0L, 3L), dtype=int32)
In [4]: a = np.append(a, [[1,2,3]], axis=0)
In [5]: a
Out[5]: array([[1, 2, 3]])
In [6]: a = np.append(a, [[1,2,3]], axis=0)
In [7]: a
Out[7]:
array([[1, 2, 3],
[1, 2, 3]])
BUT, if you're appending in a large number of loops. It's faster to append list first and convert to array than appending NumPy arrays.
In [8]: %%timeit
...: list_a = []
...: for _ in xrange(10000):
...: list_a.append([1, 2, 3])
...: list_a = np.asarray(list_a)
...:
100 loops, best of 3: 5.95 ms per loop
In [9]: %%timeit
....: arr_a = np.empty((0, 3), int)
....: for _ in xrange(10000):
....: arr_a = np.append(arr_a, np.array([[1,2,3]]), 0)
....:
10 loops, best of 3: 110 ms per loop
I think you're looking for vstack:
>>> import numpy as np
>>> a = [1, 2, 3]
>>> b = [1, 2, 3]
>>> np.vstack((a, b))
array([[1, 2, 3],
[1, 2, 3]])
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