Build a Python list and convert that to a Numpy array. That takes amortized O(1) time per append + O(n) for the conversion to array, for a total of O(n).
Use with AI a = []
for x in y:
a.append(x)
a = np.array(a)
Answer from Fred Foo on Stack OverflowBuild a Python list and convert that to a Numpy array. That takes amortized O(1) time per append + O(n) for the conversion to array, for a total of O(n).
Use with AI a = []
for x in y:
a.append(x)
a = np.array(a)
You can do this:
Use with AIa = np.array([])
for x in y:
a = np.append(a, x)
import numpy as np
myArrayShape = 2
myArray = np.empty(shape=2)
Note that this generates random values for each element in the array.
I think numpy array is just like array in clang or c++, I mean when you make numpy array you allocate memory depend on your request(size and dtype). So it is better to make array after the size of array is determinated.
Or you can try numpy.append https://numpy.org/doc/stable/reference/generated/numpy.append.html But I don't think it is preferable way because it keeps generate new arrays.
You can use empty or zeros.
For example, to create a new array of 2x3, filled with zeros, use: numpy.zeros(shape=(2,3))
You can do something like np.empty(shape = [1] * (dimensions - 1) + [0]).
Example:
>>> a = np.array([[[[[[]]]]]])
>>> b = np.empty(shape = [1] * 5 + [0])
>>> a.shape == b.shape
True
That is the wrong mental model for using NumPy efficiently. NumPy arrays are stored in contiguous blocks of memory. To append rows or columns to an existing array, the entire array needs to be copied to a new block of memory, creating gaps for the new elements to be stored. This is very inefficient if done repeatedly.
Instead of appending rows, allocate a suitably sized array, and then assign to it row-by-row:
>>> import numpy as np
>>> a = np.zeros(shape=(3, 2))
>>> a
array([[ 0., 0.],
[ 0., 0.],
[ 0., 0.]])
>>> a[0] = [1, 2]
>>> a[1] = [3, 4]
>>> a[2] = [5, 6]
>>> a
array([[ 1., 2.],
[ 3., 4.],
[ 5., 6.]])
A NumPy array is a very different data structure from a list and is designed to be used in different ways. Your use of hstack is potentially very inefficient... every time you call it, all the data in the existing array is copied into a new one. (The append function will have the same issue.) If you want to build up your matrix one column at a time, you might be best off to keep it in a list until it is finished, and only then convert it into an array.
e.g.
mylist = []
for item in data:
mylist.append(item)
mat = numpy.array(mylist)
item can be a list, an array or any iterable, as long
as each item has the same number of elements.
In this particular case (data is some iterable holding the matrix columns) you can simply use
mat = numpy.array(data)
(Also note that using list as a variable name is probably not good practice since it masks the built-in type by that name, which can lead to bugs.)
EDIT:
If for some reason you really do want to create an empty array, you can just use numpy.array([]), but this is rarely useful!
If by empty array, you mean an array with 0 dimensions, you can simply convert to a NumPy array an empty list:
import numpy as np
np.array([])
# array([], dtype=float64)
or use any of the NumPy's initialization functions with a (0,) or 0 shape, e.g.:
np.zeros((0,))
# array([], dtype=float64)
np.ones((0,))
# array([], dtype=float64)
np.empty((0,))
# array([], dtype=float64)
np.full((0,), 0.0)
# array([], dtype=float64)
etc.
If by empty you mean an array of given size but not initialized (i.e. you only ask the OS for the required memory), you can use
np.empty() specifying the size you need, e.g.:
np.empty((2, 3))
# array([[1.46643506e-316, 0.00000000e+000, 0.00000000e+000],
# [0.00000000e+000, 0.00000000e+000, 0.00000000e+000]])
There are two ways an empty NumPy array can be created: numpy.zeros and numpy.empty.
The syntax for using numpy.zeros and numpy.empty is shown below:
numpy.zeros(shape, dtype=float, order='C')
numpy.empty(shape, dtype=float, order='C')
# Shape -> Shape of the new array, e.g., (2, 3) or 2.
# dtype -> The desired data-type for the array,e.g., numpy.int8. Default is numpy.float64. This parameter is optional.
# order -> Indicates whether multi-dimensional data should be stored in row-major (C-style) or column-major (Fortran-style) order in memory. This parameter is optional.
how to use numpy.zeros to create an empty NumPy array:
import numpy as np
myArr = np.zeros((2,3))
print(myArr)
how to use numpy.empty to create an empty NumPy array:
import numpy as np
myArr = np.empty((2,3))
print(myArr)
numpy.empty, unlike numpy.zeros, does not set the array values to zero and, therefore, may be marginally faster. On the other hand, it requires the user to manually set all the values in the array and should be used with caution.
If by "array" you actually mean a Python list, you can use
Use with AIa = [0] * 10
or
Use with AIa = [None] * 10
You can't do exactly what you want in Python (if I read you correctly). You need to put values in for each element of the list (or as you called it, array).
But, try this:
Use with AIa = [0 for x in range(N)] # N = size of list you want
a[i] = 5 # as long as i < N, you're okay
For lists of other types, use something besides 0. None is often a good choice as well.