Check if X,Y column pair in table B is within delta distance of any X, Y column pair in table A

Question

I have a dataframe named origA :

X, Y
10, 20
11, 2
9, 35
8, 7

And another one named calcB :

Xc, Yc
1, 7
9, 22

I want to check that for every Xc, Yc pair in calcB if there is a X,Y pair in origA that has an euclidean distance to Xc, Yc that is less than delta and if yes, put True in the respective row at a new column Detected in origA .

Answer 1

You can using the method from scipy

import scipy
delta=5
ary = scipy.spatial.distance.cdist(dfa, dfb, metric='euclidean')
ary
Out[189]: 
array([[15.8113883 ,  2.23606798],
       [11.18033989, 20.09975124],
       [29.12043956, 13.        ],
       [ 7.        , 15.03329638]])
dfa['detected']=(ary<delta).any(1)
dfa
Out[191]: 
    X   Y  detected
0  10  20      False
1  11   2      True
2   9  35      True
3   8   7      False

Answer 2

@Wen-Ben's solution might work for small datasets. However, you run quickly into performance problems when you try to compute the distances for many points. Hence, there are already plenty of smart algorithms which reduce the amount of required distance calculations - one of them is BallTree (provided by scikit-learn):

from sklearn.neighbors import BallTree

# Prepare the data and the search radius:
origA = pd.DataFrame()
origA['X'] = [10, 11, 9, 8]
origA['Y'] = [20, 2, 35, 7]

calcB = pd.DataFrame()
calcB['Xc'] = [1, 9]
calcB['Yc'] = [7, 22]

delta = 5

# Stack the coordinates together:
pointsA = np.column_stack([origA.X, origA.Y])
pointsB = np.column_stack([calcB.Xc, calcB.Yc])

# Create the Ball Tree and search for close points:
tree = BallTree(pointsB)
detected = tree.query_radius(pointsA, r=delta, count_only=True)

# Add results as additional column:
origA['Detected'] = detected.astype(bool)

Output

    X   Y   Detected
0   10  20  True
1   11  2   False
2   9   35  False
3   8   7   False