Group coordinates by euclidean distance

from a_pandas_ex_group_coordinates_by_distance import pd_add_group_coordinates_by_distance

pd_add_group_coordinates_by_distance()

import pandas as pd



#first way, from list/tuple



coordinates = [(745.8010864257812, 519.8585205078125),

 (747.8574829101562, 522.5038452148438),

 (747.9273071289062, 517.1298828125),

 (747.9273071289062, 517.1298828125),

 (750.921142578125, 522.3074951171875),

 (756.1781005859375, 449.8744812011719),

 (757.0703125, 461.237548828125),

 (757.0703125, 461.237548828125),

 (757.1057739257812, 438.6798095703125),

 (830.8739624023438, 144.21884155273438),

 (759.8501586914062, 435.39776611328125),

 (759.8501586914062, 435.39776611328125),

 (761.2493896484375, 468.02178955078125),

 (761.2493896484375, 468.02178955078125),

 (764.5658569335938, 521.395263671875),

 (1079.3170166015625, 199.76937866210938),

 (770.1127319335938, 474.63946533203125),

 (770.3933715820312, 425.3490295410156),

 (773.7312622070312, 516.6536254882812),

 (776.908447265625, 515.5355224609375),

 (776.908447265625, 515.5355224609375),

 (778.0835571289062, 520.68896484375),

 (779.8836059570312, 519.2072143554688),

 (780.3491821289062, 420.33465576171875),

 (780.3491821289062, 420.33465576171875),

 (782.48388671875, 478.8080139160156),

 (782.48388671875, 478.8080139160156),

 (1083.74462890625, 151.22621154785156),

 (1083.74462890625, 151.22621154785156),

 (1083.74462890625, 151.22621154785156),

 (1083.74462890625, 151.22621154785156),

 (784.2761840820312, 478.5111083984375),

 (759.8501586914062, 435.39776611328125),

 (784.2761840820312, 478.5111083984375),

 (819.1412353515625, 137.67359924316406),

 (819.1412353515625, 137.67359924316406),

 (819.1412353515625, 137.67359924316406),

 (797.492919921875, 524.4356079101562),

 (825.904541015625, 125.7273941040039),

 (826.0745849609375, 149.3106231689453),

 (800.8538818359375, 446.9717102050781),

 (800.8538818359375, 446.9717102050781),

 (801.9922485351562, 517.8736572265625),

 (801.9922485351562, 517.8736572265625),

 (802.3947143554688, 520.4193725585938),

 (802.3947143554688, 520.4193725585938),

 (804.0225830078125, 519.9164428710938),

 (804.0225830078125, 519.9164428710938),

 (808.3038940429688, 431.790771484375),

 (808.3038940429688, 431.790771484375),

 (809.5233154296875, 464.2477722167969),

 (809.5233154296875, 464.2477722167969),

 (812.5013427734375, 438.7483825683594),

 (813.3584594726562, 449.6587829589844)]



df=pd.Q_group_coordinates_by_distance_df(coordinates=coordinates,max_euclidean_distance=100)

print(df)



              x           y  item

0    745.801086  519.858521     0

1    747.857483  522.503845     0

2    747.927307  517.129883     0

3    750.921143  522.307495     0

4    756.178101  449.874481     0

5    757.070312  461.237549     0

6    757.105774  438.679810     0

7    759.850159  435.397766     0

8    761.249390  468.021790     0

9    764.565857  521.395264     0

10   770.112732  474.639465     0

11   770.393372  425.349030     0

12   773.731262  516.653625     0

13   776.908447  515.535522     0

14   778.083557  520.688965     0

15   779.883606  519.207214     0

16   782.483887  478.808014     0

17   784.276184  478.511108     0

18   797.492920  524.435608     0

19   800.853882  446.971710     0

20   801.992249  517.873657     0

21   802.394714  520.419373     0

22   804.022583  519.916443     0

23   809.523315  464.247772     0

24   813.358459  449.658783     0

25   830.873962  144.218842     1

26   819.141235  137.673599     1

27   825.904541  125.727394     1

28   826.074585  149.310623     1

29  1079.317017  199.769379     2

30  1083.744629  151.226212     2





#second way, directly from DataFrame with 2 columns (column names don't matter, just the right order (x,y))

df2=pd.DataFrame(coordinates)

df3=df2.d_group_coordinates_by_distance_df(max_euclidean_distance=100)

print(df3)



              x           y  item

0    745.801086  519.858521     0

1    747.857483  522.503845     0

2    747.927307  517.129883     0

3    750.921143  522.307495     0

4    756.178101  449.874481     0

5    757.070312  461.237549     0

6    757.105774  438.679810     0

7    759.850159  435.397766     0

8    761.249390  468.021790     0

9    764.565857  521.395264     0

10   770.112732  474.639465     0

11   770.393372  425.349030     0

12   773.731262  516.653625     0

13   776.908447  515.535522     0

14   778.083557  520.688965     0

15   779.883606  519.207214     0

16   782.483887  478.808014     0

17   784.276184  478.511108     0

18   797.492920  524.435608     0

19   800.853882  446.971710     0

20   801.992249  517.873657     0

21   802.394714  520.419373     0

22   804.022583  519.916443     0

23   809.523315  464.247772     0

24   813.358459  449.658783     0

25   830.873962  144.218842     1

26   819.141235  137.673599     1

27   825.904541  125.727394     1

28   826.074585  149.310623     1

29  1079.317017  199.769379     2

30  1083.744629  151.226212     2