I have a dataframe with columns 'x', 'y', 'z', 'v' where x,y and z are the coordinates and v a variable assigned on the point. My dataframe is already regularly spaced in xyz, to be illustrative it's like a big rubix cube but shaped like a 3d rectangle. My spacing between points is set to 1 horizontally and verticaly. I found answers for surfaces but not really for 3d grid data.
I need to convert my dataframe to a 3d (not sure if I can say 4d?) grid in numpy. (I convert to full numpy so I can process my bloc model faster with numba njit)
initial data example :
---------x---y---z---------v
0--------0---0---0--0.375027
1--------0---0---1--0.511405
2--------0---0---2--0.645937
3--------0---0---3--0.229538
4--------0---0---4--0.274867
...------..--..--..------...
160078--26--76--72--0.404251
160079--26--76--73--0.010852
160080--26--76--74--0.048079
160081--26--76--75--0.426528
160082--26--76--76--0.723565
Assuming that there is no missing index and that they are already sorted, it's just a simple reshape:
x, y, z = df[["x", "y", "z"]].max() + 1
v = df["v"].to_numpy().reshape((x, y, z))
If your indices have gaps, you can try this. Any missing values will show up as nan:
indices = df[["x", 'y', "z"]].to_numpy().T
v = np.tile(np.nan, indices.max(axis=1) + 1)
v[tuple(indices)] = df["v"]