I'm a newbie at Python. I'm trying to organize a CSV file into a readable grid. When I converted my Excel file to CSV, the output became garbled, a mess of commas and scattered values. I tried list, but it still didn't organize the data the way I wanted it to be. I wanted my code to be organized by categories - such as Ethnic and Racial Roots - in a Pandas grid plot.
Here is the some of the file saved as CSV( which will come out garbled unfortunately ):
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
Here are the codes used for this data ( which I want to put in a Pandas grid plot)
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
Here is my code so far :
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
Here is my output :
['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']
pandas.read_csv() may work better if the data were comma separated. You can specifiy the delimiter used in the data with the delimeter (a.k.a sep) option.
Check out the docs
For example:
pandas.read_csv('file.csv', delimiter=',')
Like Peter was saying, just make sure your data is delimited correctly, and then you can specify it there to be sure it is reading it correctly.
Also, that first header line will screw things up in the first data file. It is probably best that you just remove that, but you can ignore it too by using the skiprows option.
pandas.read_csv('file.csv', delimiter=',', skiprows=1)
Update:
Doing some cleanup of the data, the first reads in just fine without using delimiter or skiprows.
data
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
For the codes, I might rather use a dictionary of dictionaries here.
E.g.
codes = {'Generation':{1:'First', 2: second},
'Location':{1:'New York', 2:'Pennsylvania', 3: 'New Jersey'}
}
Then you can reference the values like this:
codes['Generation'][1] # yeilds 'First'