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我是Python的新手。我试图把一个CSV文件组织成一个可读的网格。当我把Excel文件转换成CSV时，输出变得乱七八糟，一团逗号和零散的值。我试过列表，但它仍然没有按照我想要的方式组织数据。我希望我的代码按类别组织——比如种族和种族根源——在熊猫网格图中。在
以下是一些保存为CSV的文件（很遗憾，这会被弄乱）：
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
以下是用于这些数据的代码（我想把它放在Pandas网格图中）
^{pr2}$以下是我目前为止的代码：
import numpy as np
import csv
import pandas as pd
Lat_pro = open('Identity.Codes.Datafile.csv')
Lat_reader = list(pd.read_csv(Lat_pro))
print Lat_reader
以下是我的输出：
['Unnamed: 0', 'Unnamed: 1', 'Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4',
'Unnamed: 5', 'Ethnic and Racial Roots', 'Unnamed: 7', 'Unnamed: 8', 'Unnamed:
9', 'Unnamed: 10', 'Unnamed: 11', 'Unnamed: 12', 'Unnamed: 13', ' Jobs Held',
'Unnamed: 15', 'Unnamed: 16', 'Unnamed: 17', 'Unnamed: 18', 'Unnamed: 19',
'Unnamed: 20', 'Unnamed: 21', 'Unnamed: 22', ' Identity', 'Unnamed: 24',
'Reason for Latino Identity ', 'Unnamed: 26', 'Unnamed: 27', 'Unnamed: 28',
'Unnamed: 29', 'Unnamed: 30', 'Unnamed: 31', 'Latino ID', 'With Whom Gets
Together-Major Group', 'Unnamed: 34', 'Unnamed: 35', 'Unnamed: 36', 'Unnamed:
37', ' With Whom Gets Together---Specific Group', 'Unnamed: 39', 'Unnamed: 40',
'Unnamed: 41', 'Unnamed: 42', 'Unnamed: 43', 'Unnamed: 44', 'Unnamed: 45',
'Unnamed: 46', 'Unnamed: 47', 'Unnamed: 48', 'Unnamed: 49', 'Unnamed: 50',
'Unnamed: 51', 'Unnamed: 52', 'Transnational Behaviors', 'Unnamed: 54',
'Unnamed: 55', 'Unnamed: 56', 'Unnamed: 57', 'Unnamed: 58', 'Unnamed: 59',
'Unnamed: 60', 'Unnamed: 61', 'Unnamed: 62', 'Unnamed: 63', 'Unnamed: 64',
'Unnamed: 65', 'Unnamed: 66', 'Unnamed: 67', 'Perceptions of Opportunity,
Inequality, Discrimination', 'Unnamed: 69', 'Unnamed: 70', 'Unnamed: 71',
'Unnamed: 72']
Tags： cleanwithgroupidentityskillgetstogethercur
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如果数据以逗号分隔，
pandas.read_csv()可能会更好地工作。可以使用delimeter（即sep）选项指定数据中使用的分隔符。在退房the docs
例如：
就像Peter所说的，只要确保数据的分隔正确，然后就可以在那里指定它，以确保它正确地读取数据。在
另外，第一个标题行将把第一个数据文件中的事情搞砸。最好是删除它，但也可以使用
^{pr2}$skiprows选项忽略它。在更新：
在对数据进行一些清理后，第一个文件可以很好地读取，而不使用}。在
delimiter或{数据
对于代码，我宁愿在这里用字典。在
例如
然后可以引用如下值：
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