NicolaiF - 10 months ago 51

Python Question

The numpy mean function works perfectly fine when the dimensions are the same.

`a = np.array([[1, 2], [3, 4]])`

a.mean(axis=1)

array([ 1.5, 3.5])

But if I do it with varrying row size it gives an error

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

a.mean(axis=1)

IndexError: tuple index out of range

I cannot find anything on the documentation regarding this problem. I could calculate the mean myself but I would like to use the build in function for this, seeing that it should be possible.

Answer Source

Here's an approach -

```
# Store length of each subarray
lens = np.array(map(len,a))
# Generate IDs based on the lengths
IDs = np.repeat(np.arange(len(lens)),lens)
# Use IDs to do bin-based summing of a elems and divide by subarray lengths
out = np.bincount(IDs,np.concatenate(a))/lens
```

Sample run -

```
In [34]: a # Input array
Out[34]: array([[1, 2], [3, 4, 5]], dtype=object)
In [35]: lens = np.array(map(len,a))
...: IDs = np.repeat(np.arange(len(lens)),lens)
...: out = np.bincount(IDs,np.concatenate(a))/lens
...:
In [36]: out # Average output
Out[36]: array([ 1.5, 4. ])
```

Simpler alternative way using list comprehension -

```
In [38]: [np.mean(i) for i in a]
Out[38]: [1.5, 4.0]
```