a-pandas-ex-group-coordinates-by-distance 0.10
Group coordinates by euclidean distance
License: MIT
Keywords: opencv,cv2,coordinates,pandas


# Group coordinates by euclidean distance 

```python
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

```