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Using a Python Dictionary to Represent a 2D Grid

4 December 2025 #python #TIL

It’s Advent of Code time! Problems featuring a two dimensional grid are not uncommon, so developing a strategy to deal with this is useful.

In the past I would use a pair of nested arrays to represent this grid. This was a bit messy, you would have to reference grid coordinates “backwards” as Y, X, and it would require all sorts of additional out-of-bounds checking.

An improvement is to use a dictionary where the key is a tuple of “X,Y” coordinates.

Sample Input

Just a simple 3x3 grid of sequential whole numbers. I’m not going to actually bother to convert these to integers for our example, because the value actually doesn’t matter for this strategy.

>>> print(input)
012
345
678

Populating the Dictionary

grid = {}
for y, line in enumerate(input.split('\n')):
    for x, val in enumerate(line):
        grid[(x, y)] = val

Accessing the Data

Use a tuple for the dictionary key. x=1, y=2

>>> grid[(1, 2)]
'7'

Searching the Grid

Dictionary comprehension can be used to search the grid for keys that hold the desired value.

>>> [key for key, val in grid.items() if val == '7']
[(1, 2)]

Finding Neighbors

In the Advent of Code problems, finding the contents of neighboring locations is also very common. This function will return a list of all neighboring locations, for a given position.

def get_neighbors(grid: dict, pos: tuple) -> list[tuple]:
    x0, y0 = pos
    candidates = [
        (x0 - 1, y0 + 1),(x0, y0 - 1),(x0 + 1, y0 - 1),
        (x0 - 1, y0),                 (x0 + 1, y0),
        (x0 - 1, y0 - 1),(x0, y0 + 1),(x0 + 1, y0 + 1),
    ]
    return [p for p in candidates if p in grid]

We can see that the 1,2 coordinate location (value: 7) has five valid neighboring locations:

>>> get_neighbors(grid, (1,2))
[(1, 1), (2, 1), (0, 2), (2, 2), (0, 1)]

Future Learning - Complex Numbers

I’ve seen people discuss using complex numbers to represent the grids. I honestly haven’t wrapped my head around the math for that, but I would like to learn.

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