# Importing idca Data¶

In this example we will import the MTC example dataset, starting from a csv text file in idca format. Suppose that data file is gzipped, named “MTCwork.csv.gz” and is located in the current directory (use os.getcwd() to see what is the current directory).

Tip

If you want to practice with this example, you can put this file into the current directory by using the command:

larch.DB.Example().export_idca("MTCwork.csv.gz", exclude={'caseid','altid'})


We can take a peek at the contents of the file, examining the first 10 lines:

>>> import gzip
>>> with gzip.open("MTCwork.csv.gz", 'rt') as previewfile:
...     print(*(next(previewfile) for x in range(10)))
1,1,1,13.38,2,15.38,70.63,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
1,2,0,18.38,2,20.38,35.32,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
1,3,0,20.38,2,22.38,20.18,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
1,4,0,25.9,15.2,41.1,115.64,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
1,5,0,40.5,2,42.5,0,2,1,2,7.69,664,726,15.52,9.96,37.26,3.48,1,0,35,1,0,4,1,42.5,7,0,1,1,0,0,0,0,0,0,4,1,1
2,1,0,29.92,10,39.92,390.81,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
2,2,0,34.92,10,44.92,195.4,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
2,3,0,21.92,10,31.92,97.97,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1
2,4,1,22.96,14.2,37.16,185,3,1,2,11.62,738,9,35.81,53.33,32.91,764.19,1,0,40,1,0,1,1,17.5,7,0,1,1,0,0,0,1,0,1,1,0,1


The first line of the file contains column headers. After that, each line represents an alternative available to a decision maker. In our sample data, we see the first 5 lines of data share a caseid of 1, indicating that they are 5 different alternatives available to the first decision maker. The identity of the alternatives is given by the number in the column altid. The observed choice of the decision maker is indicated in the column chose with a 1 in the appropriate row. We can import this data easily:

>>> d = larch.DB.CSV_idca("MTCwork.csv.gz", caseid="casenum", altid="altnum", choice="chose")


We can then look at some of the attibutes of the imported data:

>>> d.variables_ca()
('caseid', 'altid', 'casenum', 'altnum', 'chose', 'ivtt', 'ovtt', 'tottime', 'totcost')
>>> d.variables_co()
('caseid', 'casenum', 'hhid', 'perid', 'numalts', 'dist', 'wkzone', 'hmzone', 'rspopden', 'rsempden', 'wkpopden', 'wkempden', 'vehavdum', 'femdum', 'age', 'drlicdum', 'noncadum', 'numveh', 'hhsize', 'hhinc', 'famtype', 'hhowndum', 'numemphh', 'numadlt', 'nmlt5', 'nm5to11', 'nm12to16', 'wkccbd', 'wknccbd', 'corredis', 'vehbywrk', 'vocc', 'wgt')
>>> d.alternative_codes()
(1, 2, 3, 4, 5, 6)
>>> d.alternative_names()
('1', '2', '3', '4', '5', '6')


Larch automatically analyzed the data file to find variables that do not vary within cases, and transformed those into idco format variables. If you would prefer that Larch not do this (there are a variety of reasons why you might not want this) you can set the keyword argument tablename_co to none:

>>> d1 = larch.DB.CSV_idca("MTCwork.csv.gz", tablename_co=None, caseid="casenum", altid="altnum", choice="chose")
>>> d1.variables_ca()
('caseid', 'altid', 'casenum', 'altnum', 'chose', 'ivtt', 'ovtt', 'tottime', 'totcost', 'hhid', 'perid', 'numalts', 'dist', 'wkzone', 'hmzone', 'rspopden', 'rsempden', 'wkpopden', 'wkempden', 'vehavdum', 'femdum', 'age', 'drlicdum', 'noncadum', 'numveh', 'hhsize', 'hhinc', 'famtype', 'hhowndum', 'numemphh', 'numadlt', 'nmlt5', 'nm5to11', 'nm12to16', 'wkccbd', 'wknccbd', 'corredis', 'vehbywrk', 'vocc', 'wgt')
>>> d1.variables_co()
('caseid',)
>>> d1.alternative_codes()
(1, 2, 3, 4, 5, 6)
>>> d1.alternative_names()
('1', '2', '3', '4', '5', '6')


In this case the set of variables in the idco table isn’t actually empty, because that table is actually now expressed as a special view of the single idca table:

>>> d1.queries.qry_idco()
'SELECT DISTINCT caseid AS caseid FROM (SELECT casenum AS caseid, altnum AS altid, * FROM data)'


In either case, the set of all possible alternatives is deduced automatically from all the values in the altid column. However, the alterative names are not very descriptive when they are set automatically, as the csv data file does not have enough information to tell what each alternative code number means.