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>>> print_params(1, 2, 3) (1, 2, 3) The star in front of the parameter puts all the values into the same tuple. It gathers them up, so to speak. I wonder if we can combine this with ordinary parameters . . . Let s write another function and see: def print_params_2(title, *params): print title print params Let s try it: >>> print_params_2('Params:', 1, 2, 3) Params: (1, 2, 3) It works! So the star means Gather up the rest of the positional parameters. I bet if I don t give any parameters to gather, params will be an empty tuple: >>> print_params_2('Nothing:') Nothing: () Indeed. How useful. Does it handle keyword arguments (the same as parameters), too >>> print_params_2('Hmm...', something=42) Traceback (most recent call last): File "<pyshell#60>", line 1, in print_params_2('Hmm...', something=42) TypeError: print_params_2() got an unexpected keyword argument 'something' Doesn t look like it. So we probably need another gathering operator for keyword arguments. What do you think that might be Perhaps ** def print_params_3(**params): print params At least the interpreter doesn t complain about the function. Let s try to call it: >>> print_params_3(x=1, y=2, z=3) {'z': 3, 'x': 1, 'y': 2} Yep. We get a dictionary rather than a tuple. Let s put them all together: def print_params_4(x, y, z=3, *pospar, **keypar): print x, y, z print pospar print keypar This works just like expected: >>> print_params_4(1, 2, 3, 5, 6, 7, foo=1, bar=2) 1 2 3



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s Note Of course, we re using fname and lname simply as rudimentary sources of data. Data most likely comes from some dynamic input source and involves many rows over time, but the technique is nonetheless the same: building a SQL string from a combination of hard-coded SQL keywords and values contained in variables.





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Django has really enabled us to experiment with our Web site s functionality Both as a startup heat-seeking customers and businesses, and now as a part of HP working with a number of partners, we ve had to be very nimble when it comes to adapting the software to new demands The separation of functionality into models, views, and controllers has given us modularity so we can appropriately choose where to extend and modify The underlying Python environment gives us the opportunity to make use of existing libraries to solve problems without reinventing the wheel PIL, PDFlib, ZSI, JSmin, and BeautifulSoup are just a handful of the libraries we ve pulled in to do some heavy lifting for us The most difficult part of our Django use has been the relationship of memory objects to database objects, in a few ways.

A much better way to handle this is with command parameters. A command parameter is a placeholder in the command text where a value will be substituted. In SQL Server, named parameters are used. They begin with @ followed by the parameter name with no intervening space. So, in the following INSERT statement, @MyName and @MyNumber are both parameters:

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First, Django s ORM does not ensure that two references to the same database record are the same Python object, so you can get into situations where two parts of the code are both trying to modify the same record, and one of the copies is stale Second, the Django development model encourages you to base your data objects on database objects We ve found over time more and more uses for data objects that are not tied to the database, and we ve had to migrate away from assuming that data is stored in the database For a large, long-lived code base, it definitely makes sense to spend time up front anticipating the ways your data will be stored and accessed, and building some infrastructure to support those ways.

INSERT INTO MyTable VALUES (@MyName, @MyNumber)

(5, 6, 7) {'foo': 1, 'bar': 2} >>> print_params_4(1, 2) 1 2 3 () {} By combining all these techniques, you can do quite a lot. If you wonder how some combination might work (or whether it s allowed), just try it! (In the next section, you see how * and ** can be used at the point of call as well, regardless of whether they were used in the function definition.) Now, back to the original problem: how you can use this in the name-storing example. The solution is shown here: def store(data, *full_names): for full_name in full_names: names = full_name.split() if len(names) == 2: names.insert(1, '') labels = 'first', 'middle', 'last' for label, name in zip(labels, names): people = lookup(data, label, name) if people: people.append(full_name) else: data[label][name] = [full_name] Using this function is just as easy as using the previous version, which only accepted one name: >>> d = {} >>> init(d) >>> store(d, 'Han Solo') But now you can also do this: >>> store(d, 'Luke Skywalker', 'Anakin Skywalker') >>> lookup(d, 'last', 'Skywalker') ['Luke Skywalker', 'Anakin Skywalker']

We ve also added our own database migration facility so that developers don t have to apply SQL patches to keep their database schemas current Developers who change the schema write a Python function to update the database, and these are applied automatically when the server is started..

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