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To use the template system in Python code, just follow these two steps: 1. Create a Template object by providing the raw template code as a string. Django also offers a way to create Template objects by designating the path to a template file on the filesystem; we ll examine that in a bit. 2. Call the render() method of the Template object with a given set of variables (i.e., the context). This returns a fully rendered template as a string, with all of the variables and block tags evaluated according to the context. The following sections describe each step in more detail.



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In many presentations of object-oriented programming, the order of these concepts is different. Encapsulation and inheritance are presented first, and then they are used to model real-world objects. That s all fine and dandy, but in my opinion, the most interesting feature of object-oriented programming is polymorphism. It is also the feature most people (in my experience) get confused by. Therefore I ll start with polymorphism, and show that this concept alone should be enough to make you like object-oriented programming.





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Let s look at an example. Imagine you re trying to connect to a remote database server over the Internet for detailed information about some business transactions. You search on a particular date for all available transactions, and the results are displayed. Behind the scenes, your application creates a connection with the data source, joins a couple of tables, and retrieves the results. Suppose you now want to edit this information and add or remove details. Whatever the reason, your application will go through the same cycle over and over again: creating a new connection, joining tables, and retrieving data. Not only is there overhead in creating a new connection each time, but you may be doing a lot of other redundant work, especially if you re dealing with the same data. Wouldn t it be better if you could connect to the data source once, store the data locally in a structure that resembles a relational database, close the connection, modify the local data, and then propagate the changes to the data source when the time is right This is exactly what the dataset is designed to do. A dataset stores relational data as collections of data tables. You met data tables briefly in the previous chapter when a System.Data.DataTable object was to hold schema information. In that instance, however, the data table contained only schema information, but in a dataset, the data tables contain both metadata describing the structure of the data and the data itself. Figure 8-1 shows the dataset architecture.

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The easiest way to create a Template object is to instantiate it directly. The Template class lives in the django.template module, and the constructor takes one argument, the raw template code. Let s dip into the Python interactive interpreter to see how this works in code.

The architecture mirrors the logical design of a relational database. You ll see how to use data tables, data rows, and data columns in this chapter, but we won t cover constraints and will leave relationships until 11.

Throughout this book, we feature example Python interactive interpreter sessions. You can recognize these examples by the triple >> greater-than signs (Python prompt)>>>)>greater-than signs (>>>), which designate the interpreter s prompt. If you re copying examples from this book, don t copy those greater-than signs. Multiline statements in the interactive interpreter are padded with three dots (...), for example: >>> print """This is a ... string that spans ... three lines.""" This is a string that spans three lines. >>> def my_function(value): ... print value >>> my_function('hello') hello Those three dots at the start of the additional lines are inserted by the Python shell they re not part of our input. We include them here to be faithful to the actual output of the interpreter. If you copy our examples to follow along, don t copy those dots.

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