I have following dataframe df(sample):
lat lon crs Band1 x y
0 41.855584 20.619156 b'' 1568.0 468388.198606 4.633812e+06
1 41.855584 20.622590 b'' 1562.0 468673.173031 4.633811e+06
2 41.855584 20.626023 b'' 1605.0 468958.147443 4.633810e+06
3 41.859017 20.612290 b'' 1598.0 467819.970900 4.634196e+06
4 41.859017 20.615723 b'' 1593.0 468104.930108 4.634195e+06
5 41.859017 20.619156 b'' 1600.0 468389.889303 4.634193e+06
6 41.859017 20.622590 b'' 1586.0 468674.848486 4.634192e+06
7 41.859017 20.626023 b'' 1577.0 468959.807656 4.634191e+06
8 41.859017 20.629456 b'' 1584.0 469244.766814 4.634190e+06
9 41.859017 20.632889 b'' 1598.0 469529.725959 4.634188e+06
fields x and y are coordinates in xy plane, and Band1 is point elevation ( in essence it is z coordinate ). Dataframe is rectangle grid with x and yas center grid coordinate and Band1 as grid elevation.
How can I detect which of grid cells is highest in Band1 against neighboring cells?
Expected output in this case is additional column in dataframe with boolean value defining that cell is highest in elevation Band1 prior to neighboring 4 cells.
I can easily get neigbouring grid distances and indices with:
X=df[['x','y']].to_numpy()
nbrs = NearestNeighbors(n_neighbors=5, algorithm='ball_tree').fit(X)
distances, indices = nbrs.kneighbors(X)
With Indices output:
array([[0, 1, 5, 6, 4],
[1, 2, 0, 6, 7],
[2, 1, 7, 8, 6],
[3, 4, 5, 0, 6],
[4, 5, 3, 0, 6],
[5, 6, 4, 0, 1],
[6, 7, 5, 1, 2],
[7, 8, 6, 2, 1],
[8, 9, 7, 2, 6],
[9, 8, 7, 2, 6]], dtype=int64)
I can loop though dataframe and compare all members, but its resource consuming since i have 1M rows. Any help is appreciated.
IIUC, you can use indices to get the corresponding value in the column Band1, then use np.argmax with the parameter axis set to 1 to get the position of the highest value per row. If the value is 0, then it means that the Band1 of this row is higher than the ones of the neighbors like:
df['local_high'] = np.argmax(df['Band1'].to_numpy()[indices], axis=1)==0
and you get
lat lon crs Band1 x y local_high
0 41.855584 20.619156 b'' 1568.0 468388.198606 4633812.0 False
1 41.855584 20.622590 b'' 1562.0 468673.173031 4633811.0 False
2 41.855584 20.626023 b'' 1605.0 468958.147443 4633810.0 True
3 41.859017 20.612290 b'' 1598.0 467819.970900 4634196.0 False
4 41.859017 20.615723 b'' 1593.0 468104.930108 4634195.0 False
5 41.859017 20.619156 b'' 1600.0 468389.889303 4634193.0 True
6 41.859017 20.622590 b'' 1586.0 468674.848486 4634192.0 False
7 41.859017 20.626023 b'' 1577.0 468959.807656 4634191.0 False
8 41.859017 20.629456 b'' 1584.0 469244.766814 4634190.0 False
9 41.859017 20.632889 b'' 1598.0 469529.725959 4634188.0 False