How can I extract data from a Python tuple?
Question
I'm a newbie to Python. For this data, I'm working with Jupytier iPython. I'm trying to extract numerical data from a csv file to then run Sklearn on. I have :
Opened and read the CSV file with Pandas
Set the data to a dictionary I set up to organza my data
( set up a Pandas DataFrame to see my codes and values )
Set up my data in a np array to make it accessible for machine learning
Reshaped my data to a 2D array for further accessibility for machine learning
I've tried setting my data to a target attribute , but I realized that my Lat_reader with pandas is a tuple. I would have to extract data out of my tuple before I can conduct any type of ML. Basically, my Lat-reader is now a tuple, and Sklearns can't work with a tuple. There is data inside the tuple that I want to work with, but I want to change it from a tuple.
Here is my code :
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
Here is a sample of the output:
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
1 answers
solution1
1 ACCPTED 2016-05-30 22:57:11
If you mean basic python syntax, you should go there first.
https://docs.python.org/3/tutorial/datastructures.html#tuples-and-sequences
Here are some examples:
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
To convert a tuple to a list (since you want something other than a tuple, I figured maybe a list will work)
print [x for x in data]
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