Mapping a pandas dataframe to a n-dimensional array, where each dimension corresponds to one of the x columns

I have a dataframe with columns x0 x1 x2 x3 x4 and y0 y1 y2 y3 y4.

First ten rows:

	Id	x0	x1	x2	x3	x4	y0	y1	y2	y3	y4
0	0	-5.0	-5.0	-5.0	-5.0	-5.0	268035854.2037072	0.94956508069182	3520.7568220782514	-412868933.038522	242572043.87727848
1	1	-5.0	-5.0	-5.0	-5.0	-4.5	268035883.40390667	0.94956508069182	3482.0382462663074	-412868933.038522	242572043.87727848
2	2	-5.0	-5.0	-5.0	-5.0	-4.0	268035901.1170006	0.94956508069182	3443.3196704543634	-412868933.038522	242572043.87727848
3	3	-5.0	-5.0	-5.0	-5.0	-3.5	268035911.8642905	0.94956508069182	3404.6010946424194	-412868933.038522	242572043.87727848
4	4	-5.0	-5.0	-5.0	-5.0	-3.0	268035918.38904288	0.94956508069182	3365.882518830476	-412868933.038522	242572043.87727848
5	5	-5.0	-5.0	-5.0	-5.0	-2.5	268035922.35671327	0.94956508069182	3327.163943018532	-412868933.038522	242572043.87727848
6	6	-5.0	-5.0	-5.0	-5.0	-2.0	268035924.7800574	0.94956508069182	3288.445367206588	-412868933.038522	242572043.87727848
7	7	-5.0	-5.0	-5.0	-5.0	-1.5	268035926.27763835	0.94956508069182	3249.726791394644	-412868933.038522	242572043.87727848
8	8	-5.0	-5.0	-5.0	-5.0	-1.0	268035927.2317166	0.94956508069182	3211.0082155827004	-412868933.038522	242572043.87727848
9	9	-5.0	-5.0	-5.0	-5.0	-0.5	268035927.8858225	0.94956508069182	3172.2896397707564	-412868933.038522	242572043.87727848

I did this:

values = df_train[['y0', 'y1', 'y2', 'y3', 'y4']].values
values.shape

I now have shape (4084101, 5)

I would like to have shape (21, 21, 21, 21, 21, 5) (so that the first shape is x0, the second x1, like if we had a 5D graph). Basically, it should be values[1, 0, 0, 0, 0] to access the tuple (y0, y1, y2, y3, y4) corresponding to x0=-4.5, x1=-5, ..., x4=-5.

21 because values go from -5 to 5 for the x0, ..., x4 with step 0.5
and 5 because y0, y1, y2, y3, y4
I did values = values.reshape(21, 21, 21, 21, 21, 5)
But when I do values[1][0][0][0][0], I expected to have the value corresponding to x1=-4.5, x2=-5, ..., x4=-5 but I don't.

One bad idea that I had (complexity wise) was to make a dictionary in which keys are tuples (x0, x1, x2, x3, x4) and attributes the index where to find the y values.
And then fill a np.zeros((21, 21, 21, 21, 21, 5)) dataframe.

# Get the values
values = df_train[['y0', 'y1', 'y2', 'y3', 'y4']].values

# Create a dictionary to map the x0, x1, x2, x3, x4 values to indices
grid = {}
for i, row in df_train.iterrows():
    x0, x1, x2, x3, x4 = [int((x + 5) / 0.5) for x in [row['x0'], row['x1'], row['x2'], row['x3'], row['x4']]]
    grid[(x0, x1, x2, x3, x4)] = i

# Create the reshaped array
reshaped_values = np.zeros((21, 21, 21, 21, 21, 5))
for key, index in grid.items():
    reshaped_values[key[0]][key[1]][key[2]][key[3]][key[4]] = values[index]

but it takes almost a minute on my computer ... and looks like the worst idea ever.

Your code works but I think your dataframe is not sorted

df_train = df_train.sort_values(['x0', 'x1', 'x2', 'x3', 'x4'])
values = df_train[['y0', 'y1', 'y2', 'y3', 'y4']].values