# Multidimensional collections

Categories: magic methods

In the article on magic methods for collections we saw how to implement collection behaviours for our example matrix class. We implemented support of built-in function `len`

, support for for loops and the `in`

operator. We also saw how to might implement `del`

, although our 2 by 2 matrix doesn't allow it.

For getting and setting elements, we implemented `__getitem__`

and `__setitem__`

as if the matrix was a list of 4 elements. In thi sarticle we will see how to improve that.

## Supporting two dimensional selection

Our Matrix is a 2D array, so wouldn't it be nice to support (row, column) style selection? It would be nice if we could use a syntax like:

```
a[0, 1] #row 0, columns 1
```

Well we can! The numpy library does exactly this, and we can easily support this with our Matrix class.

If you are familiar with dictionaries, you will know that a dictionary is indexed by keys rather than integers. A key can be an integer, or a string, but it can also be a tuple:

```
d = dict()
d[0] = 0
d['two'] = 2
d[(0, 1)] = 3
print(d)
```

This gives the result:

```
{0: 0, 'two': 2, (0, 1): 3}
```

Thanks to the magic of tuple packing we can write `d[(0, 1)]`

as `d[0, 1]`

. So we can do this:

```
d[0, 1] = 10
print(d)
```

which gives:

```
{0: 0, 'two': 2, (0, 1): 10}
```

We need to do a similar thing with our matrix class.

## Two dimensional getters and setters

Previously our getter and setter accepted and index of the item in list of length 4:

```
def __getitem__(self, i):
return self.data[i]
```

But now instead we want to pass in a tuple `(row, col)`

. So we need to modify our code like this:

```
def __getitem__(self, t):
row, col = t
return self.data[row*2 + col]
```

We can now do this:

```
m = Matrix(1, 2, 3, 4)
print(m[1, 0])
```

This prints matrix row 1, column 0, which is the value 3.

We can do a similar thing with the setter:

```
def __setitem__(self, t, value):
row, col = t
self.data[row*2 + col] = value
```

which allows us to do this:

```
m[0, 1] = 5
print(m)
```

The matrix has now been updated, with row 0 column 1 set to 5:

```
[1, 5][3, 4]
```

## See also

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