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Tuples, lists, and strings are sized , that is, they are data types that have a notion of size, and data items of any such data type can be meaningfully passed to the len() function (An exception is raised if a nonsized data item is passed to len()) All Python data items are objects (also called instances) of a particular data type (also called a class) We will use the terms data type and class interchangeably One key difference between an object, and the plain items of data that some other languages provide (eg, C++ or Java s built-in numeric types), is that an object can have methods Essentially, a method is simply a function that is called for a particular object For example, the list type has an append() method, so we can append an object to a list like this:
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>>> x = ["zebra", 49, -879, "aardvark", 200] >>> xappend("more") >>> x ['zebra', 49, -879, 'aardvark', 200, 'more']
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The x object knows that it is a list (all Python objects know what their own data type is), so we don t need to specify the data type explicitly In the implementation of the append() method the rst argument will be the x object itself this is done automatically by Python as part of its syntactic support for methods The append() method mutates, that is, changes, the original list This is possible because lists are mutable It is also potentially more ef cient than creating a new list with the original items and the extra item and then rebinding the object reference to the new list, particularly for very long lists In a procedural language the same thing could be achieved by using the list s
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append() like this (which is perfectly valid Python syntax): >>> listappend(x, "extra") >>> x ['zebra', 49, -879, 'aardvark', 200, 'more', 'extra']
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Here we specify the data type and the data type s method, and give as the rst argument the data item of the data type we want to call the method on, followed by any additional arguments (In the face of inheritance there is a subtle semantic difference between the two syntaxes; the rst form is the one that is most commonly used in practice Inheritance is covered in 6) If you are unfamiliar with object-oriented programming this may seem a bit strange at rst For now, just accept that Python has conventional functions called like this: functionName(arguments); and methods which are called like this: objectNamemethodName(arguments) (Object-oriented programming is covered in 6)
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The dot ( access attribute ) operator is used to access an object s attributes An attribute can be any kind of object, although so far we have shown only method attributes Since an attribute can be an object that has attributes, which in turn can have attributes, and so on, we can use as many dot operators as necessary to access the particular attribute we want The list type has many other methods, including insert() which is used to insert an item at a given index position, and remove() which removes an item at a given index position As noted earlier, Python indexes are always 0-based We saw before that we can get characters from strings using the square brackets operator, and noted at the time that this operator could be used with any sequence Lists are sequences, so we can do things like this:
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>>> x ['zebra', 49, -879, 'aardvark', 200, 'more', 'extra'] >>> x[0] 'zebra' >>> x[4] 200
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Tuples are also sequences, so if x had been a tuple we could retrieve items using square brackets in exactly the same way as we have done for the x list But since lists are mutable (unlike strings and tuples which are immutable), we can also use the square brackets operator to set list elements For example:
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>>> x[1] = "forty nine" >>> x ['zebra', 'forty nine', -879, 'aardvark', 200, 'more', 'extra']
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If we give an index position that is out of range, an exception will be raised we brie y introduce exception-handling in Piece #5, and fully cover exceptions in 4 We have used the term sequence a few times now, relying on an informal understanding of its meaning, and will continue to do so for the time being However, Python de nes precisely what features a sequence must support, and similarly de nes what features a sized object must support, and so on for various other categories that a data type might belong to, as we will see in 8 Lists, tuples, and Python s other built-in collection data types are covered in 3
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