Calculate nearest distance to certain points in python

问题

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

回答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))

grid

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.

回答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)

来源：https://stackoverflow.com/questions/65399711/calculate-nearest-distance-to-certain-points-in-python

标签

pandas

dataframe

numpy

distance