[Python] Convert Regularly Geographic Points into a matrix (keeping the correct order)

Considering the following dataset:

freguesia Datetime C1 latitude longitude geometry
0 Parque das Nações 2022-09-07 09:30:00+00:00 72.37 38.753769 -9.095670 POINT (-9.09567 38.75377)
1 Parque das Nações 2022-09-07 09:30:00+00:00 4.65 38.753769 -9.093873 POINT (-9.09387 38.75377)
2 Parque das Nações 2022-09-07 09:30:00+00:00 433.18 38.755170 -9.101060 POINT (-9.10106 38.75517)
3 Parque das Nações 2022-09-07 09:30:00+00:00 274.43 38.755170 -9.099263 POINT (-9.09926 38.75517)
4 Parque das Nações 2022-09-07 09:30:00+00:00 212.09 38.755170 -9.097466 POINT (-9.09747 38.75517)
5 Parque das Nações 2022-09-07 09:30:00+00:00 49.86 38.755170 -9.095670 POINT (-9.09567 38.75517)


You can reproduce this entire dataframe loading the following dict:

{'freguesia': {0: 'Parque das Nações', 1: 'Parque das Nações', 2: 'Parque das Nações', 3: 'Parque das Nações', 4: 'Parque das Nações', 5: 'Parque das Nações', 6: 'Parque das Nações', 7: 'Parque das Nações', 8: 'Parque das Nações', 9: 'Parque das Nações', 10: 'Parque das Nações', 11: 'Parque das Nações', 12: 'Parque das Nações', 13: 'Parque das Nações', 14: 'Parque das Nações'}, 'Datetime': {0: '2022-09-07 09:30:00+00:00', 1: '2022-09-07 09:30:00+00:00', 2: '2022-09-07 09:30:00+00:00', 3: '2022-09-07 09:30:00+00:00', 4: '2022-09-07 09:30:00+00:00', 5: '2022-09-07 09:30:00+00:00', 6: '2022-09-07 09:30:00+00:00', 7: '2022-09-07 09:30:00+00:00', 8: '2022-09-07 09:30:00+00:00', 9: '2022-09-07 09:30:00+00:00', 10: '2022-09-07 09:30:00+00:00', 11: '2022-09-07 09:30:00+00:00', 12: '2022-09-07 09:30:00+00:00', 13: '2022-09-07 09:30:00+00:00', 14: '2022-09-07 09:30:00+00:00'}, 'C1': {0: 72.37, 1: 4.65, 2: 433.18, 3: 274.43, 4: 212.09, 5: 49.86, 6: 3.82, 7: 173.22, 8: 75.16, 9: 506.67, 10: 433.19, 11: 136.86, 12: 2.24, 13: 0.0, 14: 0.0}, 'latitude': {0: 38.7537686909, 1: 38.7537686909, 2: 38.7551697675, 3: 38.7551697675, 4: 38.7551697675, 5: 38.7551697675, 6: 38.7551697675, 7: 38.7565708166, 8: 38.7565708166, 9: 38.7565708166, 10: 38.7565708166, 11: 38.7565708166, 12: 38.7565708166, 13: 38.7565708166, 14: 38.7565708166}, 'longitude': {0: -9.09566985629, 1: -9.09387322572, 2: -9.10105974799, 3: -9.09926311742, 4: -9.09746648685, 5: -9.09566985629, 6: -9.09387322572, 7: -9.10285637856, 8: -9.10105974799, 9: -9.09926311742, 10: -9.09746648685, 11: -9.09566985629, 12: -9.09387322572, 13: -9.09207659515, 14: -9.09027996458}}


If I load into geopandas, I get these points regularly divided:

import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

crs={'init':'epsg:4326'}
gps_data = gpd.GeoDataFrame(
test, geometry=gpd.points_from_xy(test.longitude, test.latitude, crs=crs))

gps_data.plot()




My question is:

How to convert these points into a matrix, with C1 column values, respecting the points location of the plot ?

Expected output:

[
[173.22, 75.16, 506.67, 433.19, 136.86, 2.24, 0 , 0],
[0, 433.18, 274.43, 212.09, 49.86, 3.82, 0, 0],
[0, 0, 0, 0, 72.37, 4.65, 0, 0]]

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