问题
Introduction: I have a pandas dataframe with people who live in different locations (latitude, longitude, floor number). I want to cluster 3 people each in one group. This means, at the end of this process, every person is assigned to one particular group. My dataframe has the length of multiples of 9 (e.g 18 people).
The tricky part is, people in the same group are not allowed to have same location in terms of latitude and longitude.
What is going wrong? After I apply my function to the pandas dataframe, I get a new dataframe in which the people are assigned to groups. However, there are 2 problems:
1) Each time 3 people are not assigned to a group! I don't know why and I don't know if this is the reason for my second problem.
2) For some reasons, which are not clear to me, people with same location (latitude/longitude) are grouped together! Remember, this should never happen.
Here is what I did:
(link to Google Colab)
Import library:
from math import atan2, cos, radians, sin, sqrt
import math
import pandas as pd
import numpy as np
from random import random
Get the data:
array_data=([[ 50.56419 , 8.67667 , 2. , 160. ],
[ 50.5740356, 8.6718179, 1. , 5. ],
[ 50.5746321, 8.6831284, 3. , 202. ],
[ 50.5747453, 8.6765588, 4. , 119. ],
[ 50.5748992, 8.6611471, 2. , 260. ],
[ 50.5748992, 8.6611471, 3. , 102. ],
[ 50.575 , 8.65985 , 2. , 267. ],
[ 50.5751 , 8.66027 , 2. , 7. ],
[ 50.5751 , 8.66027 , 2. , 56. ],
[ 50.57536 , 8.67741 , 1. , 194. ],
[ 50.57536 , 8.67741 , 1. , 282. ],
[ 50.5755255, 8.6884584, 0. , 276. ],
[ 50.5755273, 8.674282 , 3. , 167. ],
[ 50.57553 , 8.6826 , 2. , 273. ],
[ 50.5755973, 8.6847492, 0. , 168. ],
[ 50.5756757, 8.6846139, 4. , 255. ],
[ 50.57572 , 8.65965 , 0. , 66. ],
[ 50.57591 , 8.68175 , 1. , 187. ]])
all_persons = pd.DataFrame(data=array_data) # convert back to dataframe
all_persons.rename(columns={0: 'latitude', 1: 'longitude', 2:'floor', 3:'id'}, inplace=True) # rename columns
This is the function to calculate the distance between the people. If distance equals 0, people have same location in terms of latitude and longitude.
def calculate_distance(lat1, lon1, lat2, lon2, floor_person_1, floor_person_2):
"""
Calculate the shortest distance between two points given by the latitude and
longitude.
"""
scattering_factor = 0.0001
earth_radius = 6373 # Approximate / in km.
lat1 = radians(lat1)
lon1 = radians(lon1)
lat2 = radians(lat2)
lon2 = radians(lon2)
dlon = lon2 - lon1
dlat = lat2 - lat1
a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2
c = 2 * atan2(sqrt(a), sqrt(1 - a))
distance = earth_radius * c # Unit: km. Parameter does not consider floor number
print(distance)
# people share same location (latitude / longitude) but on different floors
# --> its ok to put them in same group
if (distance==0) and (floor_person_1 != floor_person_2):
distance = distance + scattering_factor
print('Identical location but different floors')
print('lat1:', math.degrees(lat1), 'lon1:', math.degrees(lon1))
print('lat2', math.degrees(lat2), 'lon2:', math.degrees(lon2))
else: # people share different locations (latitude / longitude)
pass
return distance
This is my function that groups the people:
def group_people(all_persons, max_distance_parameter):
assert len(all_persons) % 9 == 0
all_persons.set_index("id", drop=False, inplace=True)
all_persons["host"] = np.nan
all_persons["group"] = np.nan
Streufaktor = 0.0001
max_distance = max_distance_parameter
group_number = 0
group = []
for index, candidate in all_persons.iterrows():
if len(group) == 3:
for person in group:
all_persons.at[person["id"], "group"] = group_number
group_number += 1
group = []
if len(group) == 0:
group.append(candidate)
else:
for person in group:
distance = calculate_distance(
candidate["latitude"],
candidate["longitude"],
person["latitude"],
person["longitude"],
candidate['floor'],
person['floor']
)
distance = distance
if 0 < distance <= max_distance:
group.append(candidate)
break
Next, I apply the function to the dataframe and look at the result:
group_people(all_persons,4)
all_persons
This is what I get:
In yellow you see what was going wrong (see problem definition above).
How can I fix this? (please check the linked Google Colab)
来源：https://stackoverflow.com/questions/60820697/grouping-people-in-pandas-dataframe-with-customized-function