Calculate nearest distance to certain points in python - pandas

Question

I have a dataset as shown below, each sample has x and y values and the corresponding result
Sr. X Y Resut
1 2 12 Positive
2 4 3 positive
....
Visualization
Grid size is 12 * 8
How I can calculate the nearest distance for each sample from red points (positive ones)?
Red = Positive,
Blue = Negative
Sr. X Y Result Nearest-distance-red
1 2 23 Positive ?
2 4 3 Negative ?
....
dataset

Answer 1

Its a lot easier when there is sample data, make sure to include that next time.
I generate random data
import numpy as np
import pandas as pd
import sklearn
x = np.linspace(1,50)
y = np.linspace(1,50)
GRID = np.meshgrid(x,y)
grid_colors = 1* ( np.random.random(GRID[0].size) > .8 )
sample_data = pd.DataFrame( {'X': GRID[0].flatten(), 'Y':GRID[1].flatten(), 'grid_color' : grid_colors})
sample_data.plot.scatter(x="X",y='Y', c='grid_color', colormap='bwr', figsize=(10,10))
BallTree (or KDTree) can create a tree to query with
from sklearn.neighbors import BallTree
red_points = sample_data[sample_data.grid_color == 1]
blue_points = sample_data[sample_data.grid_color != 1]
tree = BallTree(red_points[['X','Y']], leaf_size=15, metric='minkowski')
and use it with
distance, index = tree.query(sample_data[['X','Y']], k=1)
now add it to the DataFrame
sample_data['nearest_point_distance'] = distance
sample_data['nearest_point_X'] = red_points.X.values[index]
sample_data['nearest_point_Y'] = red_points.Y.values[index]
which gives
X Y grid_color nearest_point_distance nearest_point_X \
0 1.0 1.0 0 2.0 3.0
1 2.0 1.0 0 1.0 3.0
2 3.0 1.0 1 0.0 3.0
3 4.0 1.0 0 1.0 3.0
4 5.0 1.0 1 0.0 5.0
nearest_point_Y
0 1.0
1 1.0
2 1.0
3 1.0
4 1.0
Modification to have red point not find themself;
Find the nearest k=2 instead of k=1;
distance, index = tree.query(sample_data[['X','Y']], k=2)
And, with help of numpy indexing, make red points use the second instead of the first found;
sample_size = GRID[0].size
sample_data['nearest_point_distance'] = distance[np.arange(sample_size),sample_data.grid_color]
sample_data['nearest_point_X'] = red_points.X.values[index[np.arange(sample_size),sample_data.grid_color]]
sample_data['nearest_point_Y'] = red_points.Y.values[index[np.arange(sample_size),sample_data.grid_color]]
The output type is the same, but due to randomness it won't agree with earlier made picture.

Answer 2

cKDTree for scipy can calculate that distance for you. Something along those lines should work:
df['Distance_To_Red'] = cKDTree(coordinates_of_red_points).query((df['x'], df['y']), k=1)