Group people which have different locations - pandas

Question

I need help. My function to group people does not really work. The problem is quite simple:
I have a dataframe with following columns:
latitude
longitude
floor
These columns are random locations of people.
The dataframe has a length of multiple of 9 (in this example the length is 36. I reality it is much longer).
I want to group people by the following idea:
First I create a new column with the name "group".
People who live close together (according to coordinates) are assigned to a specific group number (1, 2 3,...). Each group contains 3 people.
Now the tricky part: People with identical coordinates can not be in the same group!
This is how I did it:
Here is the data:
array_data=([[ 50.56419 , 8.67667 , 2. , 160. ],
[ 50.5643136, 8.6772816, 3. , 89. ],
[ 50.5646274, 8.6763909, 0. , 259. ],
[ 50.5661047, 8.6765931, 1. , 100. ],
[ 50.5663442, 8.6575205, 1. , 117. ],
[ 50.56686 , 8.67598 , 1. , 95. ],
[ 50.56747 , 8.67604 , 2. , 199. ],
[ 50.56762 , 8.6702799, 0. , 148. ],
[ 50.5693473, 8.6640855, -1. , 50. ],
[ 50.5693473, 8.6640855, 0. , 111. ],
[ 50.5705819, 8.6597279, 2. , 183. ],
[ 50.57067 , 8.65694 , 2. , 257. ],
[ 50.57075 , 8.65748 , 1. , 211. ],
[ 50.57075 , 8.65748 , 1. , 292. ],
[ 50.5722461, 8.6598248, 2. , 142. ],
[ 50.57254 , 8.65895 , 1. , 116. ],
[ 50.57259 , 8.6592 , 2. , 228. ],
[ 50.5731636, 8.667609 , 1. , 181. ],
[ 50.5737814, 8.6720067, 0. , 173. ],
[ 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. ]])
Convert array to dataframe and rename the columns:
df = pd.DataFrame(data=array_data) # convert back to dataframe
df.rename(columns={0: 'latitude', 1: 'longitude', 2:'floor', 3:'id'}, inplace=True) # rename columns
Now we have the dataframe. With the following functions I was trying to group the people:
First we need to find a way, to get the distances to the people:
def calculate_distance(lat1, lon1, lat2, lon2):
"""
Calculate the shortest distance between two points given by the latitude and
longitude.
"""
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))
return earth_radius * c # in km.
And here is the main function. I try to group people if they have NOT the same location:
def sort_people(all_persons, max_distance_parameter):
'''
People in the same group have different location!
'''
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
scattering_factor= 0.0001 # to seperate same floor numbers
max_distance = max_distance_parameter
group_number = 0
group = []
for _, 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"],
)
distance = distance
if candidate['floor'] == -1: # consider the floor when calculating the distance
distance = distance + scattering_factor + 0.001
elif candidate['floor'] == 0:
distance = distance + scattering_factor + 0.002
elif candidate['floor'] == 1:
distance = distance + scattering_factor + 0.003
elif candidate['floor'] == 2:
distance = distance + scattering_factor + 0.004
elif candidate['floor'] == 3:
distance = distance + scattering_factor + 0.005
elif candidate['floor'] == 4:
distance = distance + scattering_factor + 0.006
elif candidate['floor'] == 5:
distance = distance + scattering_factor + 0.007
elif candidate['floor'] == 6:
distance = distance + scattering_factor + 0.008
elif candidate['floor'] == 7:
distance = distance + scattering_factor + 0.009
elif candidate['floor'] == 8:
distance = distance + scattering_factor + 0.010
elif candidate['floor'] == 9:
distance = distance + scattering_factor + 0.011
elif candidate['floor'] == 10:
distance = distance + scattering_factor + 0.012
elif candidate['floor'] == 11:
distance = distance + scattering_factor + 0.013
elif candidate['floor'] == 12:
distance = distance + scattering_factor + 0.014
elif candidate['floor'] == 13:
distance = distance + scattering_factor + 0.015
elif candidate['floor'] == 14:
distance = distance + scattering_factor + 0.016
else:
distance = distance + scattering_factor + 0.017
if 0 < distance <= max_distance:
group.append(candidate)
break
Long story short: this doesn't really work. At the end, I get a dataframe and I find people in the same locations which are assigned to the same group. How would you do it?

Answer 1

Using your solution, add logic to see if the person with same coordinates is already in the group. When you check if the group has no people, add. But if there is a person in the group, for each person in the group calculate the distance between that person and the candidate. If it is 0, then break(exit and don't add the person to that group). Then go to next group and do the same.
Btw. I'm not familiar with this programming language so I might've written bad syntax, so please use the above pseudocode to be a guide. Cheers!
if len(group) == 0:
group.append(candidate)
else:
for person in group:
distance = calculate_distance(
candidate["latitude"],
candidate["longitude"],
person["latitude"],
person["longitude"],
)
distance = distance
**if distance == 0 : break;**
if candidate['floor'] == -1: # consider the floor when calculating the distance
distance = distance + scattering_factor + 0.001
elif candidate['floor'] == 0:
distance = distance + scattering_factor + 0.002
elif candidate['floor'] == 1:
distance = distance + scattering_factor + 0.003
elif candidate['floor'] == 2:
distance = distance + scattering_factor + 0.004
elif candidate['floor'] == 3:
distance = distance + scattering_factor + 0.005
elif candidate['floor'] == 4:
distance = distance + scattering_factor + 0.006
elif candidate['floor'] == 5:
distance = distance + scattering_factor + 0.007
elif candidate['floor'] == 6:
distance = distance + scattering_factor + 0.008
elif candidate['floor'] == 7:
distance = distance + scattering_factor + 0.009
elif candidate['floor'] == 8:
distance = distance + scattering_factor + 0.010
elif candidate['floor'] == 9:
distance = distance + scattering_factor + 0.011
elif candidate['floor'] == 10:
distance = distance + scattering_factor + 0.012
elif candidate['floor'] == 11:
distance = distance + scattering_factor + 0.013
elif candidate['floor'] == 12:
distance = distance + scattering_factor + 0.014
elif candidate['floor'] == 13:
distance = distance + scattering_factor + 0.015
elif candidate['floor'] == 14:
distance = distance + scattering_factor + 0.016
else:
distance = distance + scattering_factor + 0.017
if 0 < distance <= max_distance:
group.append(candidate)
break