Tom V - 2 years ago 157

Python Question

The motivation here is to take a time series and get the average activity throughout a sub-period (day, week).

It is possible to reshape an array and take the mean over the y axis to achieve this, similar to this answer (but using axis=2):

Averaging over every n elements of a numpy array

but I'm looking for something which can handle arrays of length N%k != 0 and does not solve the issue by reshaping and padding with ones or zeros (e.g numpy.resize), i.e takes the average over the existing data only.

E.g Start with a sequence

`[2,2,3,2,2,3,2,2,3,6]`

`In: [[2,2,3],`

[2,2,3],

[2,2,3],

[6]], k =3

`Out: [3,2,3]`

Instead of:

`In: [[2,2,3],`

[2,2,3],

[2,2,3],

[6,0,0]], k =3

`Out: [3,1.5,2.25]`

Thank you.

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

You can *easily* do it by padding, reshaping and calculating by how many elements to divide each row:

```
>>> import numpy as np
>>> a = np.array([2,2,3,2,2,3,2,2,3,6])
>>> k = 3
```

Pad data

```
>>> b = np.pad(a, (0, k - a.size%k), mode='constant').reshape(-1, k)
>>> b
array([[2, 2, 3],
[2, 2, 3],
[2, 2, 3],
[6, 0, 0]])
```

Then create a mask:

```
>>> c = a.size // k # 3
>>> d = (np.arange(k) + c * k) < a.size # [True, False, False]
```

The first part of `d`

will create an array that contains `[9, 10, 11]`

, and compare it to the size of `a`

(10), generating the mentioned boolean mask.

And divide it:

```
>>> b.sum(0) / (c + 1.0 * d)
array([ 3., 2., 3.])
```

The above will divide the first column by 4 (`c + 1 * True`

) and the rest by 3. This is vectorized numpy, thus, it scales very well to large arrays.

Everything can be written shorter, I just show all the steps to make it more clear.

Recommended from our users: **Dynamic Network Monitoring from WhatsUp Gold from IPSwitch**. ** Free Download**