CODEWITHSUNDEEP
pythonpandasdistanceminimum
Kirill Kondratenko
Kirill Kondratenko

Reputation: 377

How do I find the minimum distance for each point in a dataframe relative to all points in another dataframe?

I have two data frames with coordinates of attractions and exists.

import pandas as pd
import geopy
from geopy.distance import geodesic
attr = pd.DataFrame(
    {'attraction':['circuit', 'roller coaster'],
    'latitude':[53.35923, 53.35958],
    'longitude':[83.71394, 83.71256]})
exits = pd.DataFrame(
    {'exits':['exit','exit2','exit3', 'exit4'],
    'latitude':[53.35911, 53.3606, 53.35953, 53.3603],
    'longitude':[83.71503, 83.71407, 83.71154, 83.71216]})

  attraction        latitude    longitude
0 circuit           53.35923    83.71394
1 roller coaster    53.35958    83.71256

  exits     latitude    longitude
0 exit      53.35911    83.71503
1 exit2     53.36060    83.71407
2 exit3     53.35953    83.71154
3 exit4     53.36030    83.71216

I want to add a column with the distance to the nearest exit (minimum distance) in the first dataframe. It has to look like:

  attraction        min_distance
0 circuit           73.789480
1 roller coaster    68.137324

I have a code for this, but it is very far from ideal, I want to find out how to make it easier

def distance(row, name, i):
    return exits[name][[i]]
attr['d_to_e0_1'] = attr.apply(lambda r: distance(r, 'latitude', 0), axis=1)
attr['d_to_e0_2'] = attr.apply(lambda r: distance(r, 'longitude', 0), axis=1)
attr['d_to_e1_1'] = attr.apply(lambda r: distance(r, 'latitude', 1), axis=1)
attr['d_to_e1_2'] = attr.apply(lambda r: distance(r, 'longitude', 1), axis=1)
attr['d_to_e2_1'] = attr.apply(lambda r: distance(r, 'latitude', 2), axis=1)
attr['d_to_e2_2'] = attr.apply(lambda r: distance(r, 'longitude', 2), axis=1)
attr['d_to_e3_1'] = attr.apply(lambda r: distance(r, 'latitude', 3), axis=1)
attr['d_to_e3_2'] = attr.apply(lambda r: distance(r, 'longitude', 3), axis=1)


def distance(row):
    return geopy.distance.distance((row.latitude, row.longitude), (row.d_to_e0_1, row.d_to_e0_2)).m
attr['dist_to_e0'] = attr.apply(lambda r: distance(r), axis=1)

def distance(row):
    return geopy.distance.distance((row.latitude, row.longitude), (row.d_to_e1_1, row.d_to_e1_2)).m
attr['dist_to_e1'] = attr.apply(lambda r: distance(r), axis=1)

def distance(row):
    return geopy.distance.distance((row.latitude, row.longitude), (row.d_to_e2_1, row.d_to_e2_2)).m
attr['dist_to_e2'] = attr.apply(lambda r: distance(r), axis=1)

def distance(row):
    return geopy.distance.distance((row.latitude, row.longitude), (row.d_to_e3_1, row.d_to_e3_2)).m
attr['dist_to_e3'] = attr.apply(lambda r: distance(r), axis=1)

def min_distance(row):
    return min (row.dist_to_e0, row.dist_to_e1, row.dist_to_e2, row.dist_to_e3)
attr['min_distance'] = attr.apply(lambda r: min_distance(r), axis=1)

attr[['attraction', 'min_distance']].copy()

Upvotes: 0

Views: 585

Answers (1)

jlb_gouveia
jlb_gouveia

Reputation: 603

I did the following:

for att in attr.index:
    distances = []
    for ex in exits.index:
        distances.append(geopy.distance.distance(attr.loc[att, ['latitude','longitude']], exits.loc[ex,['latitude','longitude']]))
    min_dist = min(distances)
    attr.loc[att, 'min_distance'] = min_dist

attr

    attraction      latitude    longitude   min_distance
0   circuit         53.35923    83.71394    0.07378947966924111 km
1   roller coaster  53.35958    83.71256    0.06813732373534863 km

Upvotes: 1

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