我是Python的新手。对于这些数据，我正在使用Jupytier iPython。我正在尝试从csv文件中提取数字数据，然后运行Sklearn。我有：
打开并使用熊猫读取CSV文件
将数据设置为我为整理我的数据而设置的字典
（ 设置一个熊猫数据帧来查看我的代码和值）
在 np 数组中设置我的数据，使其可供机器学习访问
将数据重塑为 2D 数组，以便进一步访问机器学习
我尝试将数据设置为目标属性，但我意识到我与熊猫的Lat_reader是一个元组。我必须先从元组中提取数据，然后才能执行任何类型的 ML。 基本上，我的 Lat-reader 现在是一个元组，而 Sklearns 不能使用元组。元组内有我想处理的数据，但我想从元组更改它。
这是我的代码：
import numpy as np
import pandas as pd
codes = {'Generation':{1:'First', 2: 'Second'},
'Location':{1:'New York', 2:'Pennsylvania', 3: 'New Jersey'},
'Age at Last Birthday':{},
'Gender':{1:'Male', 2:'Female'},
'Country':{1: 'Arg', 2:'Bol', 3:'Bra', 4:'Col', 5:'DR', 6:'Edu', 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:'Dont Know', 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 in US Dollars':{88:'Not Appliable',99:'Unknown'},
'Hours Worked':{ 88:'Not Appliable', 99:'Unknown'},
'Identity':{1:'Latino', 2:'American', 3:'Both',99:'Unknown'},
'Latino Identity Among Immigrants':{1:'Yes', 2:'No', 3:'Yes-No', 4: 'Dont Know', 9:'Missing'},
'Reasons for Latino Identity':{1:'Yes',2:'No', 9:'Unknown'},
'With Whom Gets Together':{1:'Yes', 2:'No', 9:'Unknown'},
'USYrs':{88:'Not Appliable',99:'Mising'},
'In Contact With Home':{1:'Yes',2:'No',9:'Unknown'},
'R Send Money Home':{1:'Yes',2:'No',3:'Send Other', 9:'Unknown'},
'Parent Send Money':{1:'Yes',2:'No',3:'Not Appliable',4:'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',3:'Unknown'},
'Bank Overseas':{1:'Yes',2:'No',3:'Unknown'},
'Type of Communication':{1:'Land Phone',2:'Cell Phone',3:'Calling Card',4:'Email',5:'Regular Mail',
6:'No Communication', 9:'Unknown'},
'Presents Sent':{1:'Yes',2:'No', 3:'Unknown'},
'Education':{},
'EngAbli':{0:'None',1:'Some English',2:'Good English',9:'Missing'},
'EconOpp':{1: 'More in the US',2:'More at Origin',3:'Same at Both', 9:'Missing'},
'OthOpps':{0:'Just Earnings',1:'Personal',2:'Work',3:'Study'},
'Inequality':{1:'More at Origin',2:'More in the US',3:'Same in Both', 9:'Missing'},
'Discrim':{1:'Yes',0:'No',9:'Missing'},
'Context':{1:'Work/School',2:'On The Street',3:'Language',4:'Race/Ethnicity',5:'Medical',6:'Violence',
7:'Poverty',8:'Other',9:'Missing'}}
pd.DataFrame(codes.items(), columns =['Codes', 'Values'])
Lat_pro = open('Identity.Codes.Datafile.csv')
Lat_reader = (pd.read_csv(Lat_pro), ',' )
np.array(Lat_reader)
newLat_reader = np.reshape(A, (202,73))
print newLat_reader
下面是输出的示例：
Unnamed: 0 Unnamed: 1
0 Subject Code Gen
1 F-001 1
2 F-002 1
3 F-003 1
4 F-007 1
5 F-008 1
6 F-010 1
7 F-013 1
8 F-014 1
9 F-015 1
10 F-016 1
11 F-017 1
12 F-018 1
13 F-019 1
14 F-020 1
15 F-021 1
16 F-022 1
17 F-024 1
18 F-025 1
19 F-026 1
20 F-027 1
21 F-028 1
22 F-032 1
23 F-033 1
24 F-034 1
25 F-035 1
26 F-036 1
27 F-037 1
28 F-038 1
29 F-039 1
.. ... ...
172 Legal Status NaN
173 Reason for Migration NaN
174 Return Plans NaN
175 Occupation NaN
176 NaN NaN
177 Wage NaN
178 Hours Worked NaN
179 Identity NaN
180 Latino Identity Among Immigrants NaN
181 Reasons for Latino Identity NaN
182 With Whom Gets Together NaN
183 USYrs NaN
184 In Contact with Home Community NaN
185 R Sends Money Home NaN
186 Parent Sends Money Home (Second Generation Only) NaN
187 Quantity Sent by Respondent or Parent NaN
188 How Money Sent NaN
189 Frequency Money Sent NaN
190 How Money Used NaN
191 Bank in US NaN
192 Bank Overseas NaN
193 Type of Communication NaN
194 Presents Sent NaN
195 Education NaN
196 EngAbil NaN
197 EconOpps NaN
198 OthOpps NaN
199 Inequality NaN
200 Discrim NaN
201 Context NaN
Unnamed: 2 Unnamed: 3 Unnamed: 4
0 Place Age Male
1 1 28 1
2 2 35 1
3 1 30 0
4 3 19 1
5 3 20 1
6 2 21 0
7 3 29 1
8 1 25 1
9 3 23 1
10 3 30 0
11 3 21 0
12 3 23 1
13 3 34 1
14 3 33 1
15 3 33 0
16 3 33 1
17 3 26 1
18 2 31 1
19 3 31 0
20 3 20 1
21 1 20 0
22 3 22 1
23 1 20 1
24 3 30 0
25 3 22 1
26 3 26 0
27 3 25 1
28 1 19 0
29 3 21 1
.. ... ... ...
172 1=Documents 2=No Documents 3=Questionable ... NaN NaN
173 1=supply-side economics 2=demand-side econom... NaN NaN
174 1=Yes 2=No 3=Don't Know 4=No Answer 9=... NaN NaN
175 1=Unpaid 2=Student 3=Agrigulture 4=Unskille... NaN NaN
176 7=Unsilled Services 8=Skilled Services 9=Sma... NaN NaN
177 Wage in U.S. Dollars; 88=Not applicable; 99=... NaN NaN
178 Hours Worked; 88=Not Applicable; 99=Unknown NaN NaN
179 1=Latino 2=American 3=Both 9=Unknown NaN NaN
180 1=Yes 2=No 3=Yes-No 4=Don't Know 9=Missing NaN NaN
181 1=Yes 0=No 9=Unknown NaN NaN
182 1=Yes 0=No 9=Unknown NaN NaN
183 Number of Years in US; 88=Not Applicable; 99 M... NaN NaN
184 1=Yes 0=No 9=Unknown NaN NaN
185 1=Yes 2=No 3=Send Other 9=Unknown NaN NaN
186 1=Yes 2=No 8=Not Applicable 9=Unknown NaN NaN
187 1=Half of Paycheck 2=20% of Paycheck 3=Varie... NaN NaN
188 1=Moneygram 2=Paisano 3=Friend 4=Self ... NaN NaN
189 1=Once a Month 2=Twice a Year 3=Once a Yea... NaN NaN
190 0=No Use 1=Buy House 2=Family Expenses 3=... NaN NaN
191 1=Yes 2=No 9=Unknown NaN NaN
192 1=Yes 2=No 9=Unknown NaN NaN
193 1=Land Phone 2=Cell Phone 3=Calling Card ... NaN NaN
194 1=Yes 2=No 9=Unknown NaN NaN
195 In Years NaN NaN
196 0=None 1=Some English 2=Good English 9=Mi... NaN NaN
197 1=More in US 2=More at Origin 3=Same at Bot... NaN NaN
198 0=Just Earnings 1=Personal 2=Work 3=Study ... NaN NaN
199 1=More at Origin 2=More in US 3=Same in Bot... NaN NaN
200 1=Yes 0=No 9=Missing NaN NaN
201 1=Work/School 2=On Street 3=Language 4=Rac... NaN NaN
如果你的意思是基本的python语法，你应该先去那里。
https://docs.python.org/3/tutorial/datastructures.html#tuples-and-sequences
以下是一些示例：
data = ('first', 'second', 'third', 'fourth')
print "count is ", len(data)
print "accessing by array notation: ", data[0]
print "for loop:"
for x in data:
print x
将元组转换为列表（因为您想要元组以外的其他东西，我想也许列表会起作用）
print [x for x in data]