Python3.x 使用 pandas 数据帧映射到中转( node )点的跨容量请求

发布于05月17日

Input Dataframe With TerminalID,TName,XY cordinate, PeopleID

import pandas as pd

data = {
        'TerminalID': ['5','21','21','2','21','2','5','22','22','22','2','32','41','41','42','50','50'],
        'TName': ['AD','AMBO','AMBO','PS','AMBO','PS','AD','AM','AM','AM','PS','BO','BA','BA','BB','AZ','AZ'],
        'xy': ['1.12731,1.153756','0.12731,0.153757','0.12731,0.153757','1.989385,1.201941','0.12731,0.153757','1.989385,1.201941','1.12731,1.153756','2.12731,1.153756','2.12731,1.153756','2.12731,1.153756','1.989385,1.201941','1.989385,1.201941','2.989385,1.201941','2.989385,1.201941','2.989385,3.201941','3.989385,3.201941','3.989385,3.201941'],
        'Pcode': [ 'None','Z014','Z015','Z016','Z017','Z018','None','Z020','Z021','Z022','Z023','Z024','Z025','Z026','Z027','Z028','Z029']
    }

df = pd.DataFrame.from_dict(data)

Out[55]:

output of DF1

   TerminalID TName                 xy Pcode
0           5    AD   1.12731,1.153756  None
1          21  AMBO   0.12731,0.153757  Z014
2          21  AMBO   0.12731,0.153757  Z015
3           2    PS  1.989385,1.201941  Z016
4          21  AMBO   0.12731,0.153757  Z017
5           2    PS  1.989385,1.201941  Z018
6           5    AD   1.12731,1.153756  None
7          22    AM   2.12731,1.153756  Z020
8          22    AM   2.12731,1.153756  Z021
9          22    AM   2.12731,1.153756  Z022
10          2    PS  1.989385,1.201941  Z023
11         32    BO  1.989385,1.201941  Z024
12         41    BA  2.989385,1.201941  Z025
13         41    BA  2.989385,1.201941  Z026
14         42    BB  2.989385,3.201941  Z027
15         50    AZ  3.989385,3.201941  Z028
16         50    AZ  3.989385,3.201941  Z029

DF2,

T\u cap是终端ID的容量要求，T\u load是负载细节，Tcap是运行计数增量，T\u load是终端的实际请求，

data2= {
        'BusID': ['18','18','18','18','18','18','18','18','18'],
        'Tcap': ['0','2','3','6','7','8','10','12','12'],
        'T_Load': ['0','2','1','2','2','1','2','2','0'],
        'TerminalID': [ '5','21','33','2','32','42','41','50','5'],
        
        'TName':['AD','AMBO','AM','PS','BO','BB','BA','AZ','AD']
    }

df2 = pd.DataFrame.from_dict(data2)

Out[59]:

  BusID Tcap T_Load TerminalID TName
0    18    0      0          5    AD
1    18    2      2         21  AMBO
2    18    3      1         33    AM
3    18    6      2          2    PS
4    18    7      2         32    BO
5    18    8      1         42    BB
6    18   10      2         41    BA
7    18   12      2         50    AZ
8    18   12      0          5    AD

Data Frame # the Final output requested

输出基于T\U负载连续应变.

data3 = {
        'BusID': ['18','18','18','18','18','18','18','18','18'],
        'Tcap': ['0','2','3','6','7','8','10','12','12'],
        'T_Load': ['0','2','1','3','1','1','2','2','0'],
        'TerminalID': [ '5','21','33','2','32','42','41','50','5'],
        
        'TName':['AD','AMBO','AM','PS','BO','BB','BA','AZ','AD'],
        'Pcode':['None','Z013,Z019','Z020','Z016,Z018,Z023','Z024','Z027','Z025,Z026','Z028,Z029','None']
    }
    
    df3 = pd.DataFrame.from_dict(data3)

Out[61]:

  BusID Tcap T_Load TerminalID TName           Pcode
0    18    0      0          5    AD            None
1    18    2      2         21  AMBO       Z013,Z019
2    18    3      1         33    AM            Z020
3    18    6      3          2    PS  Z016,Z018,Z023
4    18    7      1         32    BO            Z024
5    18    8      1         42    BB            Z027
6    18   10      2         41    BA       Z025,Z026
7    18   12      2         50    AZ       Z028,Z029
8    18   12      0          5    AD            None

谢谢你

df2['Plist'] = df2['TName'].map(df.groupby('TName')['Pcode'] .agg(lambda x: ','.join(e for e in x if e != 'None')) .replace('', 'None') )

BusID Tcap T_Load TerminalID TName Plist 0 18 0 0 5 AD None 1 18 2 2 21 AMBO Z014,Z015,Z017 2 18 3 1 22 AM Z020,Z021,Z022 3 18 6 3 2 PS Z016,Z018,Z023 4 18 7 1 32 BO Z024 5 18 8 1 42 BB Z027 6 18 10 2 41 BA Z025,Z026 7 18 12 2 50 AZ Z028,Z029 8 18 12 0 5 AD None

df2['P_list'] = (df2.groupby('T_Load')['P_list'] .apply(lambda c: c.str.extract(rf'((?:[^,]+,?){{,{str(c.name)}}})', expand=False) .str.strip(',') ) .replace('', 'None') )

BusID Tcap T_Load TerminalID TName P_list 0 18 0 0 5 AD None 1 18 2 2 21 AMBO Z014,Z015 2 18 3 1 22 AM Z020 3 18 6 3 2 PS Z016,Z018,Z023 4 18 7 1 32 BO Z024 5 18 8 1 42 BB Z027 6 18 10 2 41 BA Z025,Z026 7 18 12 2 50 AZ Z028,Z029 8 18 12 0 5 AD None

Python3.x 使用 pandas 数据帧映射到中转( node )点的跨容量请求

Input Dataframe With TerminalID,TName,XY cordinate, PeopleID

Out[55]:

output of DF1

DF2,

Out[59]:

Data Frame # the Final output requested

Out[61]:

推荐答案

update: limiting the 输出:

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