# 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.