Add single element to array in numpy
Question:
I have a numpy array containing:
[1, 2, 3]
I want to create an array containing:
[1, 2, 3, 1]
That is, I want to add the first element on to the end of the array.
I have tried the obvious:
np.concatenate((a, a[0]))
But I get an error saying ValueError: arrays must have same number of dimensions
I don’t understand this – the arrays are both just 1d arrays.
Answers:
a[0]
isn’t an array, it’s the first element of a
and therefore has no dimensions.
Try using a[0:1]
instead, which will return the first element of a
inside a single item array.
Try this:
np.concatenate((a, np.array([a[0]])))
http://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html
concatenate needs both elements to be numpy arrays; however, a[0] is not an array. That is why it does not work.
append()
creates a new array which can be the old array with the appended element.
I think it’s more normal to use the proper method for adding an element:
a = numpy.append(a, a[0])
t = np.array([2, 3])
t = np.append(t, [4])
This might be a bit overkill, but I always use the the np.take
function for any wrap-around indexing:
>>> a = np.array([1, 2, 3])
>>> np.take(a, range(0, len(a)+1), mode='wrap')
array([1, 2, 3, 1])
>>> np.take(a, range(-1, len(a)+1), mode='wrap')
array([3, 1, 2, 3, 1])
Let’s say a=[1,2,3]
and you want it to be [1,2,3,1]
.
You may use the built-in append function
np.append(a,1)
Here 1 is an int, it may be a string and it may or may not belong to the elements in the array. Prints: [1,2,3,1]
This command,
numpy.append(a, a[0])
does not alter a
array. However, it returns a new modified array.
So, if a
modification is required, then the following must be used.
a = numpy.append(a, a[0])
When appending only once or once every now and again, using np.append
on your array should be fine. The drawback of this approach is that memory is allocated for a completely new array every time it is called. When growing an array for a significant amount of samples it would be better to either pre-allocate the array (if the total size is known) or to append to a list and convert to an array afterward.
Using np.append
:
b = np.array([0])
for k in range(int(10e4)):
b = np.append(b, k)
1.2 s ± 16.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Using python list converting to array afterward:
d = [0]
for k in range(int(10e4)):
d.append(k)
f = np.array(d)
13.5 ms ± 277 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Pre-allocating numpy array:
e = np.zeros((n,))
for k in range(n):
e[k] = k
9.92 ms ± 752 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
When the final size is unkown pre-allocating is difficult, I tried pre-allocating in chunks of 50 but it did not come close to using a list.
85.1 ms ± 561 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
If you want to add an element use append()
a = numpy.append(a, 1)
in this case add the 1 at the end of the array
If you want to insert an element use insert()
a = numpy.insert(a, index, 1)
in this case you can put the 1 where you desire, using index to set the position in the array.
I have a numpy array containing:
[1, 2, 3]
I want to create an array containing:
[1, 2, 3, 1]
That is, I want to add the first element on to the end of the array.
I have tried the obvious:
np.concatenate((a, a[0]))
But I get an error saying ValueError: arrays must have same number of dimensions
I don’t understand this – the arrays are both just 1d arrays.
a[0]
isn’t an array, it’s the first element of a
and therefore has no dimensions.
Try using a[0:1]
instead, which will return the first element of a
inside a single item array.
Try this:
np.concatenate((a, np.array([a[0]])))
http://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html
concatenate needs both elements to be numpy arrays; however, a[0] is not an array. That is why it does not work.
append()
creates a new array which can be the old array with the appended element.
I think it’s more normal to use the proper method for adding an element:
a = numpy.append(a, a[0])
t = np.array([2, 3])
t = np.append(t, [4])
This might be a bit overkill, but I always use the the np.take
function for any wrap-around indexing:
>>> a = np.array([1, 2, 3])
>>> np.take(a, range(0, len(a)+1), mode='wrap')
array([1, 2, 3, 1])
>>> np.take(a, range(-1, len(a)+1), mode='wrap')
array([3, 1, 2, 3, 1])
Let’s say a=[1,2,3]
and you want it to be [1,2,3,1]
.
You may use the built-in append function
np.append(a,1)
Here 1 is an int, it may be a string and it may or may not belong to the elements in the array. Prints: [1,2,3,1]
This command,
numpy.append(a, a[0])
does not alter a
array. However, it returns a new modified array.
So, if a
modification is required, then the following must be used.
a = numpy.append(a, a[0])
When appending only once or once every now and again, using np.append
on your array should be fine. The drawback of this approach is that memory is allocated for a completely new array every time it is called. When growing an array for a significant amount of samples it would be better to either pre-allocate the array (if the total size is known) or to append to a list and convert to an array afterward.
Using np.append
:
b = np.array([0])
for k in range(int(10e4)):
b = np.append(b, k)
1.2 s ± 16.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
Using python list converting to array afterward:
d = [0]
for k in range(int(10e4)):
d.append(k)
f = np.array(d)
13.5 ms ± 277 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
Pre-allocating numpy array:
e = np.zeros((n,))
for k in range(n):
e[k] = k
9.92 ms ± 752 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
When the final size is unkown pre-allocating is difficult, I tried pre-allocating in chunks of 50 but it did not come close to using a list.
85.1 ms ± 561 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
If you want to add an element use append()
a = numpy.append(a, 1)
in this case add the 1 at the end of the array
If you want to insert an element use insert()
a = numpy.insert(a, index, 1)
in this case you can put the 1 where you desire, using index to set the position in the array.