cjds - 7 months ago 30

Python Question

I have two numpy arrays

`A= np.array([1,1,1,1,0,0,0,0,0,1])`

B= np.array([2,2,2,2,32,1,12,124,1,2)

C= #mean of B's elements where A is 1

D= #mean of B's elements where A is 0

How can I do this? I think it's some combination of

`np.mean`

`np.ma`

Answer

You can use `np.bincount`

for a generic case when you might be dealing with other such IDs/tags in `A`

, like so -

```
np.bincount(A,B)/np.bincount(A)
```

Basically, `np.bincount(A,B)`

gives us the ID based summations of `B`

, where the IDs are from `A`

. Then, we are dividing those summations by the count of each group of IDs to get the average values per ID group.

Sample run -

```
In [12]: A
Out[12]: array([1, 1, 1, 1, 0, 0, 0, 0, 0, 1])
In [13]: B
Out[13]: array([ 2, 2, 2, 2, 32, 1, 12, 124, 1, 2])
In [14]: B[A==0].mean() # Using boolean indexing per ID and getting avg
Out[14]: 34.0
In [15]: B[A==1].mean()
Out[15]: 2.0
In [16]: np.bincount(A,B)/np.bincount(A)
Out[16]: array([ 34., 2.])
```

Source (Stackoverflow)