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value or the most expensive order. Aggregate functions, as their name indicates, work on a set of records and then calculate the appropriate aggregated value. SUM, MIN, MAX, AVG, and COUNT are frequently used aggregate functions.

Example: {{ number|stringformat:"02i" }} Formats the variable according to the argument, a string formatting specifier. This specifier uses Python string-formatting syntax, with the exception that the leading % is dropped. See http://docs.python.org/lib/typesseq-strings.html for documentation of Python string formatting.



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There is one problem with this, however. If nested is a string-like object (string, Unicode, UserString, and so on), it is a sequence and will not raise TypeError, yet you do not want to iterate over it.

Let s find the minimum, maximum, and average number of items of each product from the Order Details table. Enter the following query into SSMSE and execute it. You should see the results in Figure 11-7.

Example: {{ value|time:"P" }} Formats a time according to the given format (same as the now tag).

select productid 'Product ID', min(quantity) 'Minimum', max(quantity) 'Maximum', avg(quantity) 'Average' from "order details" group by productid order by productid





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Examples: {{ datetime|timesince }} {{ datetime|timesince:"other_datetime" }} Formats a date as the time since that date (e.g., 4 days, 6 hours ). Takes an optional argument that is a variable containing the date to use as the comparison point (without the argument, the comparison point is now). For example, if blog_date is a date instance representing midnight on 1 June 2006, and comment_date is a date instance for 08:00 on 1 June 2006, then {{ comment_date|timesince:blog_date }} would return 8 hours .

You used the MIN and MAX functions to find the minimum and maximum values and the AVG function to calculate the average value:

Note There are two main reasons why you shouldn t iterate over string-like objects in the flatten function. First, you want to treat string-like objects as atomic values, not as sequences that should be flattened. Second, iterating over them would actually lead to infinite recursion because the first element of a string is another string of length one, and the first element of that string is the string itself(!).

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Method calls are slightly more complex than the other lookup types. Here are some things to keep in mind: If, during the method lookup, a method raises an exception, the exception will be propagated, unless the exception has a silent_variable_failure attribute whose value is True. If the exception does have a silent_variable_failure attribute, the variable will render as an empty string, for example: >>> t = Template("My name is {{ person.first_name }}.") >>> class PersonClass3: ... def first_name(self): ... raise AssertionError, "foo" >>> p = PersonClass3() >>> t.render(Context({"person": p})) Traceback (most recent call last): ... AssertionError: foo >>> ... >>> ... ... >>> >>> "My class SilentAssertionError(AssertionError): silent_variable_failure = True class PersonClass4: def first_name(self): raise SilentAssertionError p = PersonClass4() t.render(Context({"person": p})) name is ."

min(quantity) 'Minimum', max(quantity) 'Maximum', avg(quantity) 'Average'

A method call will work only if the method has no required arguments. Otherwise, the system will move to the next lookup type (list-index lookup). Obviously, some methods have side effects, and it would be foolish at best, and possibly even a security hole, to allow the template system to access them. Say, for instance, you have a BankAccount object that has a delete() method. A template shouldn t be allowed to include something like {{ account.delete }}. To prevent this, set the function attribute alters_data on the method: def delete(self): # Delete the account delete.alters_data = True The template system won t execute any method marked in this way. In other words, if a template includes {{ account.delete }}, that tag will not execute the delete() method. It will fail silently.

Because you wanted the results listed by product, you used the GROUP BY clause. From the result set you see that product 1 had a minimum order quantity of 2, a maximum order quantity of 80, and an average order quantity of 21.

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