根据熊猫数据框python中终端负载的节点/标签分配

Question

Input data frame with requirement for boarding in a transport allocation setting.

import pandas as pd

data1 = {
        'N_Id': [0,1,2,2,2,2,4,5,5,5,10,10,11,11,13,13,13,13],
        'N_Name': ['Destiny','N_Area','Pstation','Pstation','Pstation','Pstation','A_Area','T_depot','T_depot','T_depot','B_colony','B_colony','c_colony','c_colony','Z_colony','Z_colony','Z_colony','Z_colony'],
        'Geocode': ['9.994798,0.206728','2.994798,0.206728','2.989385,0.201941','2.989385,0.201941','2.989385,0.201941','2.989385,0.201941','3.006515,0.247101','3.000568,0.256468','3.000568,0.256468','3.000568,0.256468','3.049031,0.167278','3.049031,0.167278','4.049031,0.167278','4.049031,0.167278','5.2,0.7','5.2,0.7','5.2,0.7','5.2,0.7'],
        'pcode': ['None','M023','L123','M0232','L1234','K0324','M0137','M01368','M01369','K07249','M01375','F04509','F04609','F04610','F1','F2','F3','F4']
    }
    
df1 = pd.DataFrame.from_dict(data1)

output

df1
Out[45]: 
    N_Id    N_Name            Geocode   pcode
0      0   Destiny  9.994798,0.206728    None
1      1    N_Area  2.994798,0.206728    M023
2      2  Pstation  2.989385,0.201941    L123
3      2  Pstation  2.989385,0.201941   M0232
4      2  Pstation  2.989385,0.201941   L1234
5      2  Pstation  2.989385,0.201941   K0324
6      4    A_Area  3.006515,0.247101   M0137
7      5   T_depot  3.000568,0.256468  M01368
8      5   T_depot  3.000568,0.256468  M01369
9      5   T_depot  3.000568,0.256468  K07249
10    10  B_colony  3.049031,0.167278  M01375
11    10  B_colony  3.049031,0.167278  F04509
12    11  c_colony  4.049031,0.167278  F04609
13    11  c_colony  4.049031,0.167278  F04610
14    13  Z_colony            5.2,0.7      F1
15    13  Z_colony            5.2,0.7      F2
16    13  Z_colony            5.2,0.7      F3
17    13  Z_colony            5.2,0.7      F4

second data frame with a route plan/to load according to the Terminal/Nodal load.

data2= {
            'BusID': ['V1','V1','V1','V4','V4','V4','V5','V5','V5','V0','v100'],
            'Tcap': [4,4,4,8,8,8,12,12,12,12,8],
            'Terminal_Load':[1,2,1,2,1,1,2,1,1,0,4],
            'N_Id': [1,2,5,2,5,10,11,4,10,0,13],
            'N_Name': ['N_Area','Pstation','T_depot','Pstation','T_depot','B_colony','c_colony','A_Area','B_colony','Destiny','Z_colony'],
            
        }
        
df2 = pd.DataFrame.from_dict(data2)

output of the second data frame.

df2
Out[46]: 
   BusID  Tcap  Terminal_Load  N_Id    N_Name
0     V1     4              1     1    N_Area
1     V1     4              2     2  Pstation
2     V1     4              1     5   T_depot
3     V4     8              2     2  Pstation
4     V4     8              1     5   T_depot
5     V4     8              1    10  B_colony
6     V5    12              2    11  c_colony
7     V5    12              1     4    A_Area
8     V5    12              1    10  B_colony
9     V0    12              0     0   Destiny
10  v100     8              4    13  Z_colony

required Dataframe format..

data3 = {
            'BusID': ['V1','V1','V1','V4','V4','V4','V5','V5','V5','V0','v100'],
            'Tcap': [4,4,4,8,8,8,12,12,12,12,8],
            'Terminal_Load':[1,2,1,2,1,1,2,1,1,0,4],
            'N_Id': [1,2,5,2,5,10,11,4,10,0,13],
            'N_Name': ['N_Area','Pstation','T_depot','Pstation','T_depot','B_colony','c_colony','A_Area','B_colony','Destiny','Z_colony'],
            'Pcode': ['M023','L123,M0232','M01368','L1234,K0324','M01369','M01375','F04609,F04610','M0137','F04509','','F1,F2,F3,F4'],
        }

df3 = pd.DataFrame.from_dict(data3)

required output...

df3
Out[47]: 
   BusID  Tcap  Terminal_Load  N_Id    N_Name          Pcode
0     V1     4              1     1    N_Area           M023
1     V1     4              2     2  Pstation     L123,M0232
2     V1     4              1     5   T_depot         M01368
3     V4     8              2     2  Pstation    L1234,K0324
4     V4     8              1     5   T_depot         M01369
5     V4     8              1    10  B_colony         M01375
6     V5    12              2    11  c_colony  F04609,F04610
7     V5    12              1     4    A_Area          M0137
8     V5    12              1    10  B_colony         F04509
9     V0    12              0     0   Destiny               
10  v100     8              4    13  Z_colony    F1,F2,F3,F4

The required Dataframe is the combination of both the first to dataframe, I need to assign the PCode based on the requirement Terminal Load. The load at a terminal is on the Terminal_Load value, for example at nodal ID 1 the load is 1 so 1 person is listed in the pcode, 2nd row terminal load is 2 and the nodal ID is 2 hence two person are loaded at Pcode. Terminal load 0 at the 0 node. requesting a pandas dataframe solution or any Non Pandas implementation just guessing any labelled pop up methods..or any algorithm to capacity and allocate.. and after allocating remove the label from the queue.

Answer 1

First aggregate list like previous solution and add new column to df2 with aggregate lists:

s = (df1.groupby(['N_Id','N_Name'])['pcode']
        .agg(list)
        .rename('Pcode'))

df = df2.join(s, on=['N_Id','N_Name'])

Then need split lists per groups (same per groups, so selected first list by iat[0] ) by Terminal_Load values like linked solution - only is necessary cumulative sum and starting by 0 :

def f(x):
    indices = [0] + x['Terminal_Load'].cumsum().tolist()
    s = x['Pcode'].iat[0]
    #https://stackoverflow.com/a/10851479/2901002
    x['Pcode'] = [','.join(s[indices[i]:indices[i+1]]) for i in range(len(indices)-1)]
    return x

df = df.groupby(['N_Id','N_Name']).apply(f)
print (df)
   BusID  Tcap  Terminal_Load  N_Id    N_Name          Pcode
0     V1     4              1     1    N_Area           M023
1     V1     4              2     2  Pstation     L123,M0232
2     V1     4              1     5   T_depot         M01368
3     V4     8              2     2  Pstation    L1234,K0324
4     V4     8              1     5   T_depot         M01369
5     V4     8              1    10  B_colony         M01375
6     V5    12              2    11  c_colony  F04609,F04610
7     V5    12              1     4    A_Area          M0137
8     V5    12              1    10  B_colony         F04509
9     V0    12              0     0   Destiny               
10  v100     8              4    13  Z_colony    F1,F2,F3,F4