# correct and efficient way to flatten array in numpy in python?

## Question:

I have:

```
a = array([[1,2,3],[4,5,6]])
```

and I’d like to flatten it, joining the two inner lists into one flat array entry. I can do:

```
array(list(flatten(a)))
```

but that seems inefficient due to the list cast (I want to end up with an array and not a generator.)

Also, how can this be generalized to an array like this:

```
b = array([[[1,2,3],[4,5,6]], [[10,11,12],[13,14,15]]])
```

where the result should be:

```
b = array([[1,2,3,4,5,6],
[10,11,12,13,14,15]])
```

are there builtin/efficient numpy/scipy operators for this? thanks.

## Answers:

You can use the `reshape`

method.

```
>>> import numpy
>>> b = numpy.array([[[1,2,3],[4,5,6]], [[10,11,12],[13,14,15]]])
>>> b.reshape([2, 6])
array([[ 1, 2, 3, 4, 5, 6],
[10, 11, 12, 13, 14, 15]])
```

How about:

```
>>> import numpy as np
>>> a=np.arange(1,7).reshape((2,3))
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> a.flatten()
array([1, 2, 3, 4, 5, 6])
```

and

```
>>> import numpy as np
>>> b=np.arange(1,13).reshape((2,2,3))
>>> b
array([[[ 1, 2, 3],
[ 4, 5, 6]],
[[ 7, 8, 9],
[10, 11, 12]]])
>>> b.reshape((2,6))
array([[ 1, 2, 3, 4, 5, 6],
[ 7, 8, 9, 10, 11, 12]])
```

You might need to check out `numpy.flatten`

and `numpy.ravel`

, both return a 1-d array from an n-d array.

Furthermore, if you’re not going to modify the returned 1-d array, I suggest you use `numpy.ravel`

, since it doesn’t make a copy of the array, but just return a view of the array, which is much faster than `numpy.flatten`

.

```
>>>a = np.arange(10000).reshape((100,100))
>>>%timeit a.flatten()
100000 loops, best of 3: 4.02 µs per loop
>>>%timeit a.ravel()
1000000 loops, best of 3: 412 ns per loop
```

Also check out this post.

```
a = np.arange(10000)
%timeit a.reshape(100,100)
1000000 loops, best of 3: 517 ns per loop
%timeit a.resize(100,100)
1000000 loops, best of 3: 428 ns per loop
```

I wonder reshape should take far lesser time but its almost similar