Mathews24 - 2 months ago 6x

Python Question

I have a dictionary with entries labelled as

`{(k,i): value, ...}`

`[k,i]`

`(k,i)`

`k = 4`

`i = 60`

`k = 24`

`i = 31`

Answer

Here's an approach -

```
# Get keys (as indices for output) and values as arrays
idx = np.array(d.keys())
vals = np.array(d.values())
# Get dimensions of output array based on max extents of indices
dims = idx.max(0)+1
# Setup output array and assign values into it indexed by those indices
out = np.zeros(dims,dtype=vals.dtype)
out[idx[:,0],idx[:,1]] = vals
```

We could also use sparse matrices to get the final output. e.g. with `coordinate format sparse matrices`

. This would be memory efficient when kept as sparse matrices. So, the last step could be replaced by something like this -

```
from scipy.sparse import coo_matrix
out = coo_matrix((vals, (idx[:,0], idx[:,1])), dims).toarray()
```

Sample run -

```
In [70]: d
Out[70]: {(1, 4): 120, (2, 2): 72, (2, 3): 100, (5, 2): 88}
In [71]: out
Out[71]:
array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 120],
[ 0, 0, 72, 100, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 88, 0, 0]])
```

To make it generic for ndarrays of any number of dimensions, we can use linear-indexing and use `np.put`

to assign values into the output array. Thus, in our first approach, just replace the last step of assigning values with something like this -

```
np.put(out,np.ravel_multi_index(idx.T,dims),vals)
```

Sample run -

```
In [106]: d
Out[106]: {(1,0,0): 99, (1,0,4): 120, (2,0,2): 72, (2,1,3): 100, (3,0,2): 88}
In [107]: out
Out[107]:
array([[[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]],
[[ 99, 0, 0, 0, 120],
[ 0, 0, 0, 0, 0]],
[[ 0, 0, 72, 0, 0],
[ 0, 0, 0, 100, 0]],
[[ 0, 0, 88, 0, 0],
[ 0, 0, 0, 0, 0]]])
```

Source (Stackoverflow)

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