如何使用 Pandas 组织数据?

How can I organize data using Pandas?

我是 Python 的新手。我正在尝试将 CSV 文件组织成可读的网格。当我将我的 Excel 文件转换为 CSV 时,输出变得乱码,一堆乱七八糟的逗号和分散的值。我试过列表,但它仍然没有按照我想要的方式组织数据。我希望我的代码在 Pandas 网格图中按类别(例如民族和种族根源)进行组织。

这里是保存为CSV格式的部分文件(不幸的是会出现乱码):

Ethnic and Racial Roots                                                                             Jobs Held                                              Identity                         Reason for Latino Identity                          Latino ID              With Whom Gets Together-Major Group                                 With Whom Gets Together---Specific Group                                                                                                             Transnational Behaviors                                                         Perceptions of Opportunity, Inequality, Discrimination              
Subject Code    Gen Place   Age Male    Country African European    Indian  Other   Color   Docs    Reason  Return  1st Occup   1st Oc Code 1st Wage    Cur Occup   Cur Oc Code Cur Wage    Cur Hours/Day   Father Occ  Mother Occ  Identity    ID as Latino    Ethnicity   Culture Language    Politics    Values  Emotions    Everything  Among Imms  Mexican Cen Amer    Caribbean   South Amer  Latinos-Gen Mex Gua Nic SS  Hon CR  PR  DR  Ecu Col Ven Bra Per Arg USYrs   Contact R-Remits    P-Remits    Quantity    Freq Sent   How Sent    Use 1   Use 2   US Bank OS Bank Type Com 1  Type Com 2  Presents    Educ    EngAbil EconOpps    OthOpps Ineqaulity  Discrim Context
F-001   1   1   28  1   2   0   1   1   0   1   2   3   4   serv sk park    8   7.5 serv sk park    8   14  10  99  99  1   1   1   0   0   0   0   0   0   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   2   1   1   8   1   1   1   1   2   1   1   1   0   9   13  1   1   0   1   1   3
F-002   1   2   35  1   15  1   1   1   0   3   9   6   4   sales work uns  7   7   music artist    10  7   99  9   9   1   1   0   0   0   0   0   1   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   4   1   1   8   3   2   5   3   2   1   1   1   0   1   13  2   2   0   1   9   9
F-003   1   1   30  0   10  0   1   1   0   1   2   1   1   restfood unsk   7   2.9 inspect arq skill   8   2.9 10  99  99  2   1   1   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   2   1   1   8   3   1   1   2   0   2   2   1   0   2   6   0   1   0   3   1   2
F-007   1   3   19  1   10  0   0   1   0   3   2   1   4   cleanserv unsk  7   8   restfood unsk   7   8   10  3   3   1   1   1   0   0   0   0   0   0   1   9   9   9   9   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   3   1   1   8   1   1   1   5   1   2   1   1   0   1   6   1   1   0   3   1   1
F-008   1   3   20  1   10  0   0   1   0   3   2   1   1   professional    10  8.75    restfood skill  8   8.75    10  3   3   1   1   0   0   0   0   1   0   0   1   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   9   4   1   1   8   1   1   1   4   5   2   1   1   0   2   11  1   1   0   1   1   8
F-010   1   2   21  0   5   0   1   1   0   1   1   5   1   serv sk cashier 8   6.75    serv skill libra    8   10  10  8   1   1   1   0   1   0   0   0   0   0   3   0   0   1   1   0   0   0   0   0   0   0   1   1   1   0   1   0   0   0   3   1   1   8   1   1   1   2   3   1   2   4   0   1   13  2   1   0   1   0   3
F-013   1   3   29  1   5   1   1   0   0   1   2   2   4   manufa unsk 4   4   manufa unsk 4   4   8   10  10  2   1   0   1   0   0   0   0   0   1   0   0   1   1   0   0   0   0   0   0   0   1   1   0   0   1   0   0   0   8   1   2   8   9   9   9   9   9   9   9   1   4   1   18  2   2   0   3   1   4
F-014   1   1   25  1   10  0   1   1   0   3   2   1   4   restfood unsk   7   3.5 restfood unsk   7   3.5 9   6   1   1   1   1   0   0   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   5   1   3   8   2   4   1   2   0   2   1   1   0   1   6   0   1   0   3   0   0
F-015   1   3   23  1   5   1   1   0   0   3   9   6   4   unknown 99  99  unknwon 99  99  99  99  99  9   9   9   9   9   9   9   9   9   9   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0       0   9   9   9   9   9   9   9   9   9   9   9   9   99  9   9   9   9   9   9
F-016   1   3   30  0   5   1   1   1   0   2   3   3   2   clean serv unsk 7   7   clean serv unsk 7   7   10  5   1   1   1   0   0   0   0   1   0   0   1   0   1   1   1   0   0   0   0   0   0   0   1   1   0   1   0   0   0   0   4   1   1   8   2   1   1   4   2   1   2   3   0   1   9   1   1   0   1   1   3
F-017   1   3   21  0   10  0   1   1   0   3   2   1   1   domest garden   7   5   homekeeper  1   5   8   6   1   1   1   1   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   2   1   2   8   3   2   1   3   2   2   2   4   0   1   9   0   1   0   2   1   5
F-018   1   3   23  1   10  1   1   1   0   3   2   3   2   ambulant unsk   7       restfood unsk   7       99  9   1   1   1   0   1   0   0   0   0   0   2   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   4   1   1   8   3   2   1   2   0   2   2   3   0   1   12  2   9   9   2   1   4
F-019   1   3   34  1   4   0   1   1   0   1   1   2   4   domest garden   7   3   professional    10  3   99  10  9   1   1   0   1   0   0   0   0   0   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   6   1   2   8   9   9   9   9   9   9   9   1   0   2   20  1   1   0   1   1   8
F-020   1   3   33  1   3   1   1   0   0   1   2   1   4   domestic serv   7   1.25    sales work unsk 7   1.25    12  5   1   1   1   0   0   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   4   1   1   8   1   1   1   4   0   1   1   1   4   1   14  1   1   0   1   1   4
F-021   1   3   33  0   5   1   0   1   1   4   3   2   2   clean serv unsk 7   9   clean serv unsk 7   9   10  3   1   1   1   0   1   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   10  1   3   8   3   4   1   2   3   2   2   1   1   1   14  1   1   0   2   1   3
F-022   1   3   33  1   3   1   1   1   0   1   2   2   1   sales work uns  7   99  clean serv unsk 7   99  8   99  1   1   1   1   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   2   1   1   8   1   1   1   1   5   1   1   2   3   2   12  1   1   0   1   1   8
F-024   1   3   26  1   15  1   1   1   0   3   2   2   4   restfood unsk   7   8.75    sales work unsk 7   8.75    99  5   7   1   1   0   0   1   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   2   1   1   8   3   1   1   2   0   2   1   1   0   2   13  1   1   0   9   0   0
F-025   1   2   31  1   6   0   1   1   0   1   3   5   2   serv rest skill 8   7.5 restfood unsk   7   7.5 12  9   1   2   1   1   0   0   0   0   0   0   1   0   1   0   1   0   0   1   0   0   0   0   0   0   0   1   0   0   1   0   13  0   3   8   3   4   3   2   0   2   2   1   0   2   12  2   1   0   1   1   1
F-026   1   3   31  0   6   0   1   1   0   3   3   5   4   serv hotel skill    8   8   manager proffes 10  8   8   5   1   1   1   0   0   1   0   0   0   0   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   11  1   3   8   3   5   5   2   0   1   2   1   0   1   13  2   1   0   3   1   3
F-027   1   3   20  1   14  0   1   1   0   1   1   3   4   adm asist NGO   10  3.75    superv rest skill\  8   3.75    8   8   8   1   1   0   1   0   0   0   0   0   9   1   0   1   1   0   1   0   0   0   0   0   1   1   0   0   1   0   0   0   3   1   2   8   9   9   9   9   9   1   1   1   0   1   12  2   1   0   9   1   4
F-028   1   1   20  0   10  0   1   1   0   3   1   5   1   manufcloth unsk 7   2.5 adm asist NGO   10  2.5 8   7   1   2   1   1   0   0   0   0   0   0   4   1   1   1   0   0   1   0   0   1   1   0   0   1   0   0   0   0   0   0   1   1   1   8   3   1   1   2   0   2   2   1   0   1   12  0   1   0   3   1   4
F-032   1   3   22  1   6   0   1   1   0   1   2   2   1   restfood unsk   7   6.25    restfood unsk   7   6.25    12  9   1   2   1   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   4   1   1   8   2   1   1   1   2   2   2   1   0   9   9   1   1   0   1   0   0
F-033   1   1   20  1   10  0   1   1   0   1   2   3   1   restfood unsk   7   12  servworker skil;    8   12  10  6   1   2   1   0   0   1   0   0   0   0   1   1   1   0   1   0   1   0   0   1   1   0   0   0   1   1   0   0   0   0   2   1   2   8   1   1   1   2   0   2   2   1   0   2   12  1   1   0   1   1   3
F-034   1   3   30  0   4   1   1   1   0   1   3   2   3   manufa unsk 4   99  domestic serv   7   99  5   11  1   1   1   1   0   0   0   0   0   0   1   0   0   0   1   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0   2   1   2   8   9   9   9   9   9   1   2   2   0   1   16  2   1   0   2   1   4
F-035   1   3   22  1   10  0   1   1   0   1   2   5   1   cleanserv unsk  7   10  restfood unsk   7   10  10  9   9   1   1   1   0   0   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   2   1   1   8   1   2   7   4   0   2   2   1   0   1   7   1   1   0   1   1   6
F-036   1   3   26  0   3   0   1   1   0   2   2   1   1   salesfood unsk  7   6   domerstserv uns 7   6   99  99  99  1   1   0   0   0   0   1   0   0   2   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   1   0   0   0   5   1   1   8   3   1   1   1   2   1   1   9   9   9   12  1   9   9   9   9   9
F-037   1   3   25  1   10  0   0   1   0   3   2   5   1   restfood unsk   7   99  restfood unsk   7   99  4   3   1   1   1   0   1   0   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   1   1   1   8   2   1   2   1   2   2   2   1   0   2   7   1   1   0   1   0   0
F-038   1   1   19  0   5   1   1   1   0   5   1   5   2   salespharm uns  7   7.5 restfood unsk   7   7.5 5   6   8   1   1   0   0   0   0   0   0   1   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   9   1   1   8   3   4   1   3   2   2   2   1   0   1   13  1   1   0   3   1   8
F-039   1   3   21  1   13  0   1   1   1   3   2   5   4   manufac unskil  4   5.25    salespharm uns  7   5.25    99  9   1   1   1   0   0   0   0   0   0   1   1   0   0   0   1   0   0   0   0   0   0   0   0   0   0   1   1   0   0   1   4   1   3   8   3   4   7   1   0   1   2   1   0   1   12  2   1   0   2   1   3
F-040   1   3   20  0   5   1   1   0   0   4   1   5   1   manufac unskill 4   5.5 clean serv unsk 7   5.5 8   5   9   1   1   0   0   0   0   1   0   0   1   0   0   1   1   0   0   0   0   0   0   0   1   1   0   1   1   0   0   0   2   1   2   8   3   2   1   3   0   1   2   1   0   1   12  0   1   0   2   1   8
F-041   1   2   25  0   6   0   1   1   0   3   2   5   1   manufac unskill 4   3   restfood unsk   7   3   8   99  99  1   1   0   0   0   0   1   0   0   1

这是用于此数据的代码(我想将其放入 Pandas 网格图中)

Codes                                                                                                                                                                                                                                                                                               
Generation      1= First  2=Second                                                                                                                                                                                                                                                                                      
Location        1=New York  2=New Jersey  3=Pennsylvania                                                                                                                                                                                                                                                                                        
Age     Age at Last Birthday                                                                                                                                                                                                                                                                                        
Gender      0=Female   1=Male                                                                                                                                                                                                                                                                                       
Country     1=Arg 2=Bol  3=Bra  4=Col  5=DR  6=Ecu  7= El Sal 8=Gua  9=Hon 10=Mex 11=Nic  12=Pan 13=Peru  14=PR  15=Ven                                                                                                                                                                                                                                                                                     
African Roots       0=No   1=Yes                                                                                                                                                                                                                                                                                        
European Roots      0=No   1=Yes                                                                                                                                                                                                                                                                                        
Indian Roots        0=No   1=Yes                                                                                                                                                                                                                                                                                        
Other Roots     0=No   1=Yes                                                                                                                                                                                                                                                                                        
Skin Color      1=Light   2=Medium Light   3=Medium   4=Mediium Dark   5=Dark                                                                                                                                                                                                                                                                                       
Legal Status        1=Documents   2=No Documents   3=Questionable Documents   9=Missing                                                                                                                                                                                                                                                                                     
Reason for Migration        1=supply-side economics   2=demand-side economics   3=network links   4=violence at origin   5=family reasons   6=other                                                                                                                                                                                                                                                                                     
Return Plans        1=Yes   2=No   3=Don't Know   4=No Answer   9=Not Asked                                                                                                                                                                                                                                                                                     
Occupation      1=Unpaid  2=Student  3=Agrigulture  4=Unskilled Operative  5=Skilled Operative   6=Transport Worker                                                                                                                                                                                                                                                                                     
        7=Unsilled Services  8=Skilled Services  9=Small Business  10=Professional  11=Retired  99=Unknown                                                                                                                                                                                                                                                                                      
Wage        Wage in U.S. Dollars;  88=Not applicable;  99=Unknown                                                                                                                                                                                                                                                                                       
Hours Worked        Hours Worked; 88=Not Applicable;  99=Unknown                                                                                                                                                                                                                                                                                        
Identity        1=Latino  2=American  3=Both   9=Unknown                                                                                                                                                                                                                                                                                        
Latino Identity Among Immigrants        1=Yes  2=No  3=Yes-No  4=Don't Know  9=Missing                                                                                                                                                                                                                                                                                      
Reasons for Latino Identity     1=Yes   0=No   9=Unknown                                                                                                                                                                                                                                                                                        
With Whom Gets Together     1=Yes   0=No   9=Unknown                                                                                                                                                                                                                                                                                        
USYrs       Number of Years in US; 88=Not Applicable; 99 Missing                                                                                                                                                                                                                                                                                        
In Contact with Home Community      1=Yes   0=No  9=Unknown                                                                                                                                                                                                                                                                                         
R Sends Money Home      1=Yes   2=No   3=Send Other  9=Unknown                                                                                                                                                                                                                                                                                      
Parent Sends Money Home (Second Generation Only)        1=Yes   2=No   8=Not Applicable  9=Unknown                                                                                                                                                                                                                                                                                      
Quantity Sent by Respondent or Parent       1=Half of Paycheck  2=20% of Paycheck  3=Varies Month to Month                                                                                                                                                                                                                                                                                      
How Money Sent      1=Moneygram   2=Paisano   3=Friend   4=Self   5=Bank  6=Moneygram and Paisano  7=Moneygram and Friend                                                                                                                                                                                                                                                                                       
Frequency Money Sent        1=Once a Month   2=Twice a Year   3=Once a Year   4=Once in a While   5=Holidays                                                                                                                                                                                                                                                                                        
How Money Used      0=No Use  1=Buy House   2=Family Expenses   3=Health   4=Education   5=Savings   6=Pay a Debt                                                                                                                                                                                                                                                                                           
Bank in US      1=Yes   2=No   9=Unknown                                                                                                                                                                                                                                                                                        
Bank Overseas       1=Yes   2=No   9=Unknown                                                                                                                                                                                                                                                                                        
Type of Communication       1=Land Phone   2=Cell Phone   3=Calling Card   4=Email   5=Regular Mail   6=No Communication   9=Unknwn                                                                                                                                                                                                                                                                                     
Presents Sent       1=Yes   2=No   9=Unknown                                                                                                                                                                                                                                                                                        
Education       In Years                                                                                                                                                                                                                                                                                        
EngAbil     0=None  1=Some English   2=Good English   9=Missing                                                                                                                                                                                                                                                                                     
EconOpps        1=More in US   2=More at Origin  3=Same at Both   9=Missing                                                                                                                                                                                                                                                                                     
OthOpps     0=Just Earnings  1=Personal  2=Work   3=Study   4=Political   9=Missing                                                                                                                                                                                                                                                                                     
Inequality      1=More at Origin   2=More in US  3=Same in Both  9=Missing                                                                                                                                                                                                                                                                                      
Discrim     1=Yes   0=No  9=Missing                                                                                                                                                                                                                                                                                     
Context     1=Work/School   2=On Street  3=Language  4=Race/Ethnicity  5=Medical  6=Violence  7=Poverty 8=Other  9=Missing                                                                                                                                                                                                                                                                                      

到目前为止,这是我的代码:

import numpy as np

import csv

import pandas as pd



Lat_pro =  open('Identity.Codes.Datafile.csv')

Lat_reader = list(pd.read_csv(Lat_pro))



print Lat_reader

这是我的输出:

['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4',
'Unnamed: 5', 'Ethnic and Racial Roots', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed:
9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', ' Jobs Held',
'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',
'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', ' Identity', 'Unnamed: 24',
'Reason for Latino Identity ', 'Unnamed: 26', 'Unnamed: 27', 'Unnamed: 28',
'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Latino ID', 'With Whom Gets
Together-Major Group', 'Unnamed: 34', 'Unnamed: 35', 'Unnamed: 36', 'Unnamed:
37', ' With Whom Gets Together---Specific Group', 'Unnamed: 39', 'Unnamed: 40',
'Unnamed: 41', 'Unnamed: 42', 'Unnamed: 43', 'Unnamed: 44', 'Unnamed: 45',
'Unnamed: 46', 'Unnamed: 47', 'Unnamed: 48', 'Unnamed: 49', 'Unnamed: 50',
'Unnamed: 51', 'Unnamed: 52', 'Transnational Behaviors', 'Unnamed: 54',
'Unnamed: 55', 'Unnamed: 56', 'Unnamed: 57', 'Unnamed: 58', 'Unnamed: 59',
'Unnamed: 60', 'Unnamed: 61', 'Unnamed: 62', 'Unnamed: 63', 'Unnamed: 64',
'Unnamed: 65', 'Unnamed: 66', 'Unnamed: 67', 'Perceptions of Opportunity,
Inequality, Discrimination', 'Unnamed: 69', 'Unnamed: 70', 'Unnamed: 71',
'Unnamed: 72']
如果数据以逗号分隔,

pandas.read_csv() 可能会更好。您可以使用 delimeter (a.k.a sep) 选项指定数据中使用的分隔符。

查看 the docs

例如:

pandas.read_csv('file.csv', delimiter=',')

就像 Peter 所说的那样,只需确保您的数据被正确分隔,然后您可以在那里指定它以确保它正确读取它。

此外,第一个 header 行会在第一个数据文件中搞砸。最好只删除它,但您也可以使用 skiprows 选项忽略它。

pandas.read_csv('file.csv', delimiter=',', skiprows=1)

更新:

对数据进行一些清理,第一次读取时没有使用 delimiterskiprows

数据

Ethnic,and,Racial,Roots,Jobs,Held,Identity,Reason,for,Latino,Identity,Latino,ID,With,Whom,Gets,Together-Major,Group,With,Whom,Gets,Together---Specific,Group,Transnational,Behaviors,Perceptions,of,Opportunity,,Inequality,,Discrimination,
Subject,Code,Gen,Place,Age,Male,Country,African,European,Indian,Other,Color,Docs,Reason,Return,1st,Occup,1st,Oc,Code,1st,Wage,Cur,Occup,Cur,Oc,Code,Cur,Wage,Cur,Hours/Day,Father,Occ,Mother,Occ,Identity,ID,as,Latino,Ethnicity,Culture,Language,Politics,Values,Emotions,Everything,Among,Imms,Mexican,Cen,Amer,Caribbean,South,Amer,Latinos-Gen,Mex,Gua,Nic,SS,Hon,CR,PR,DR,Ecu,Col,Ven,Bra,Per,Arg,USYrs,Contact,R-Remits,P-Remits,Quantity,Freq,Sent,How,Sent,Use,1,Use,2,US,Bank,OS,Bank,Type,Com,1,Type,Com,2,Presents,Educ,EngAbil,EconOpps,OthOpps,Ineqaulity,Discrim,Context
F-001,1,1,28,1,2,0,1,1,0,1,2,3,4,serv,sk,park,8,7.5,serv,sk,park,8,14,10,99,99,1,1,1,0,0,0,0,0,0,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,2,1,1,8,1,1,1,1,2,1,1,1,0,9,13,1,1,0,1,1,3
F-002,1,2,35,1,15,1,1,1,0,3,9,6,4,sales,work,uns,7,7,music,artist,10,7,99,9,9,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,8,3,2,5,3,2,1,1,1,0,1,13,2,2,0,1,9,9
F-003,1,1,30,0,10,0,1,1,0,1,2,1,1,restfood,unsk,7,2.9,inspect,arq,skill,8,2.9,10,99,99,2,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,8,3,1,1,2,0,2,2,1,0,2,6,0,1,0,3,1,2
F-007,1,3,19,1,10,0,0,1,0,3,2,1,4,cleanserv,unsk,7,8,restfood,unsk,7,8,10,3,3,1,1,1,0,0,0,0,0,0,1,9,9,9,9,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,1,1,8,1,1,1,5,1,2,1,1,0,1,6,1,1,0,3,1,1
F-008,1,3,20,1,10,0,0,1,0,3,2,1,1,professional,10,8.75,restfood,skill,8,8.75,10,3,3,1,1,0,0,0,0,1,0,0,1,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,9,4,1,1,8,1,1,1,4,5,2,1,1,0,2,11,1,1,0,1,1,8
F-010,1,2,21,0,5,0,1,1,0,1,1,5,1,serv,sk,cashier,8,6.75,serv,skill,libra,8,10,10,8,1,1,1,0,1,0,0,0,0,0,3,0,0,1,1,0,0,0,0,0,0,0,1,1,1,0,1,0,0,0,3,1,1,8,1,1,1,2,3,1,2,4,0,1,13,2,1,0,1,0,3
F-013,1,3,29,1,5,1,1,0,0,1,2,2,4,manufa,unsk,4,4,manufa,unsk,4,4,8,10,10,2,1,0,1,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,8,1,2,8,9,9,9,9,9,9,9,1,4,1,18,2,2,0,3,1,4
F-014,1,1,25,1,10,0,1,1,0,3,2,1,4,restfood,unsk,7,3.5,restfood,unsk,7,3.5,9,6,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,5,1,3,8,2,4,1,2,0,2,1,1,0,1,6,0,1,0,3,0,0
F-015,1,3,23,1,5,1,1,0,0,3,9,6,4,unknown,99,99,unknwon,99,99,99,99,99,9,9,9,9,9,9,9,9,9,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,9,9,9,9,9,9,9,9,9,9,9,99,9,9,9,9,9,9
F-016,1,3,30,0,5,1,1,1,0,2,3,3,2,clean,serv,unsk,7,7,clean,serv,unsk,7,7,10,5,1,1,1,0,0,0,0,1,0,0,1,0,1,1,1,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,4,1,1,8,2,1,1,4,2,1,2,3,0,1,9,1,1,0,1,1,3
F-017,1,3,21,0,10,0,1,1,0,3,2,1,1,domest,garden,7,5,homekeeper,1,5,8,6,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,2,8,3,2,1,3,2,2,2,4,0,1,9,0,1,0,2,1,5
F-018,1,3,23,1,10,1,1,1,0,3,2,3,2,ambulant,unsk,7,restfood,unsk,7,99,9,1,1,1,0,1,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,8,3,2,1,2,0,2,2,3,0,1,12,2,9,9,2,1,4
F-019,1,3,34,1,4,0,1,1,0,1,1,2,4,domest,garden,7,3,professional,10,3,99,10,9,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,1,2,8,9,9,9,9,9,9,9,1,0,2,20,1,1,0,1,1,8
F-020,1,3,33,1,3,1,1,0,0,1,2,1,4,domestic,serv,7,1.25,sales,work,unsk,7,1.25,12,5,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,8,1,1,1,4,0,1,1,1,4,1,14,1,1,0,1,1,4
F-021,1,3,33,0,5,1,0,1,1,4,3,2,2,clean,serv,unsk,7,9,clean,serv,unsk,7,9,10,3,1,1,1,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,1,3,8,3,4,1,2,3,2,2,1,1,1,14,1,1,0,2,1,3
F-022,1,3,33,1,3,1,1,1,0,1,2,2,1,sales,work,uns,7,99,clean,serv,unsk,7,99,8,99,1,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,8,1,1,1,1,5,1,1,2,3,2,12,1,1,0,1,1,8
F-024,1,3,26,1,15,1,1,1,0,3,2,2,4,restfood,unsk,7,8.75,sales,work,unsk,7,8.75,99,5,7,1,1,0,0,1,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,8,3,1,1,2,0,2,1,1,0,2,13,1,1,0,9,0,0
F-025,1,2,31,1,6,0,1,1,0,1,3,5,2,serv,rest,skill,8,7.5,restfood,unsk,7,7.5,12,9,1,2,1,1,0,0,0,0,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,13,0,3,8,3,4,3,2,0,2,2,1,0,2,12,2,1,0,1,1,1
F-026,1,3,31,0,6,0,1,1,0,3,3,5,4,serv,hotel,skill,8,8,manager,proffes,10,8,8,5,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,1,3,8,3,5,5,2,0,1,2,1,0,1,13,2,1,0,3,1,3
F-027,1,3,20,1,14,0,1,1,0,1,1,3,4,adm,asist,NGO,10,3.75,superv,rest,skill\,8,3.75,8,8,8,1,1,0,1,0,0,0,0,0,9,1,0,1,1,0,1,0,0,0,0,0,1,1,0,0,1,0,0,0,3,1,2,8,9,9,9,9,9,1,1,1,0,1,12,2,1,0,9,1,4
F-028,1,1,20,0,10,0,1,1,0,3,1,5,1,manufcloth,unsk,7,2.5,adm,asist,NGO,10,2.5,8,7,1,2,1,1,0,0,0,0,0,0,4,1,1,1,0,0,1,0,0,1,1,0,0,1,0,0,0,0,0,0,1,1,1,8,3,1,1,2,0,2,2,1,0,1,12,0,1,0,3,1,4
F-032,1,3,22,1,6,0,1,1,0,1,2,2,1,restfood,unsk,7,6.25,restfood,unsk,7,6.25,12,9,1,2,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,1,1,8,2,1,1,1,2,2,2,1,0,9,9,1,1,0,1,0,0
F-033,1,1,20,1,10,0,1,1,0,1,2,3,1,restfood,unsk,7,12,servworker,skil;,8,12,10,6,1,2,1,0,0,1,0,0,0,0,1,1,1,0,1,0,1,0,0,1,1,0,0,0,1,1,0,0,0,0,2,1,2,8,1,1,1,2,0,2,2,1,0,2,12,1,1,0,1,1,3
F-034,1,3,30,0,4,1,1,1,0,1,3,2,3,manufa,unsk,4,99,domestic,serv,7,99,5,11,1,1,1,1,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,2,1,2,8,9,9,9,9,9,1,2,2,0,1,16,2,1,0,2,1,4
F-035,1,3,22,1,10,0,1,1,0,1,2,5,1,cleanserv,unsk,7,10,restfood,unsk,7,10,10,9,9,1,1,1,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,8,1,2,7,4,0,2,2,1,0,1,7,1,1,0,1,1,6
F-036,1,3,26,0,3,0,1,1,0,2,2,1,1,salesfood,unsk,7,6,domerstserv,uns,7,6,99,99,99,1,1,0,0,0,0,1,0,0,2,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,5,1,1,8,3,1,1,1,2,1,1,9,9,9,12,1,9,9,9,9,9
F-037,1,3,25,1,10,0,0,1,0,3,2,5,1,restfood,unsk,7,99,restfood,unsk,7,99,4,3,1,1,1,0,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,8,2,1,2,1,2,2,2,1,0,2,7,1,1,0,1,0,0
F-038,1,1,19,0,5,1,1,1,0,5,1,5,2,salespharm,uns,7,7.5,restfood,unsk,7,7.5,5,6,8,1,1,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,9,1,1,8,3,4,1,3,2,2,2,1,0,1,13,1,1,0,3,1,8
F-039,1,3,21,1,13,0,1,1,1,3,2,5,4,manufac,unskil,4,5.25,salespharm,uns,7,5.25,99,9,1,1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,4,1,3,8,3,4,7,1,0,1,2,1,0,1,12,2,1,0,2,1,3
F-040,1,3,20,0,5,1,1,0,0,4,1,5,1,manufac,unskill,4,5.5,clean,serv,unsk,7,5.5,8,5,9,1,1,0,0,0,0,1,0,0,1,0,0,1,1,0,0,0,0,0,0,0,1,1,0,1,1,0,0,0,2,1,2,8,3,2,1,3,0,1,2,1,0,1,12,0,1,0,2,1,8
F-041,1,2,25,0,6,0,1,1,0,3,2,5,1,manufac,unskill,4,3,restfood,unsk,7,3,8,99,99,1,1,0,0,0,0,1,0,0,1

对于代码,我宁愿在这里使用字典。

例如

codes = {'Generation':{1:'First', 2: second},
         'Location':{1:'New York', 2:'Pennsylvania', 3: 'New Jersey'}
         }

然后你可以像这样引用值:

codes['Generation'][1] # yeilds 'First'