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CourseCatalog.dat contains records consisting of three tab-delimited fields: a course number, a course title, and the number of credits that the course is worth, represented as a floating-point number. The record format is thus as follows (note that <tab> in this and all of the following examples represents the presence of an otherwise invisible tab character): courseNo <tab> courseTitle <tab> credits In other words, each record in this data file represents the attribute values of a single Course object. Here are the actual contents of the CourseCatalog.dat file that we ll use for all of the work that we do in this chapter and in 17; this data will be used to instantiate Course objects that will in turn be stored within the CourseCatalog collection:

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Note The representation in Listing 8-15 is purely fictitious and only serves educational purposes. Data structures as shown in Listing 8-15 do not occur in reality.

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Limiting access based on certain permissions or some other test, or providing a different location for the login view works essentially the same way. The raw way is to run your test on request.user in the view directly. For example, this view checks to make sure the user is logged in and has the permission polls.can_vote (more about how permissions works follows): def vote(request): if request.user.is_authenticated() and request.user.has_perm('polls.can_vote')): # vote here else: return HttpResponse("You can't vote in this poll.") Again, Django provides a shortcut called user_passes_test. It takes arguments and generates a specialized decorator for your particular situation: def user_can_vote(user): return user.is_authenticated() and user.has_perm("polls.can_vote") @user_passes_text(user_can_vote, login_url="/login/") def vote(request): # Code here can assume a logged-in user with the correct permission. ... user_passes_test takes one required argument: a callable that takes a User object and returns True if the user is allowed to view the page. Note that user_passes_test does not automatically check that the User is authenticated; you should do that yourself. In this example we re also showing the second optional argument, login_url, which lets you specify the URL for your login page (/accounts/login/ by default). Since it s a relatively common task to check whether a user has a particular permission, Django provides a shortcut for that case: the permission_required() decorator. Using this decorator, the earlier example can be written as follows: from django.contrib.auth.decorators import permission_required @permission_required('polls.can_vote', login_url="/login/") def vote(request): # ... Note that permission_required() also takes an optional login_url parameter, which also defaults to '/accounts/login/'.

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Faculty.dat contains records consisting of four tab-delimited fields, representing a professor s name, SSN, title, and the department that the professor works for. The record format is thus as follows: profName <tab> ssn <tab> title <tab> department In other words, each record in this data file represents the attribute values of a single Professor object. Here are the actual contents of the Faculty.dat file that we ll use for all of the work that we will do in this chapter and in 17; this data will be used to instantiate Professor objects that will in turn be stored within the Faculty collection: Jacquie Barker <tab> 123-45-6789 <tab> Adjunct Professor <tab> Info. Technology Claudio Cioffi <tab> 567-81-2345 <tab> Full Professor <tab> Comp. Social Sciences Snidely Whiplash <tab> 987-65-4321 <tab> Full Professor <tab> Phys. Ed.

Listing 8-15. The Effect of GROUP BY e.DEPTNO e.EMPNO e.JOB e.MGR e.MSAL e.COMM e.DEPTNO ======= ============ ====== ====== ====== ======== {7782 {'MANAGER' {7839 {2450 {NULL 10 ,7839 ,'DIRECTOR' ,NULL ,5000 ,NULL ,7934} ,'ADMIN' } ,7782} ,1300} ,NULL} ----------------------------------------------------------------{7369 {'TRAINER' {7902 { 800 {NULL 20 ,7566 ,'MANAGER' ,7839 ,2975 ,NULL ,7788 ,'TRAINER' ,7566 ,3000 ,NULL ,7876 ,'TRAINER' ,7788 ,1100 ,NULL ,7902} ,'TRAINER'} ,7566} ,3000} ,NULL} ----------------------------------------------------------------{7499 {'SALESREP' {7698 {1600 { 300 30 ,7521 ,'SALESREP' ,7698 ,1250 , 500 ,7654 ,'SALESREP' ,7698 ,1250 ,1400 ,7698 ,'MANAGER' ,7839 ,2850 ,NULL ,7844 ,'SALESREP' ,7698 ,1500 , 0 ,7900} ,'ADMIN' } ,7698} , 800} ,NULL} ----------------------------------------------------------------Going back to Listing 8-14, it now becomes clear what the COUNT(e.EMPNO) function does: it returns the number of elements of each e.EMPNO set. You could argue that (as an effect of the GROUP BY e.DEPTNO clause) the last column in Listing 8-15 (e.DEPTNO) contains regular values, and the other five columns become set-valued attributes. You can use only e.DEPTNO in the SELECT clause. If you want to see data from the other columns in your query result, you must use group functions (such as COUNT) to aggregate those sets into a single value. See the next section for a discussion of group functions.

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