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@property def radius(self): """The circle's radius >>> circle = Circle(-2) Traceback (most recent call AssertionError: radius must >>> circle = Circle(4) >>> circleradius = -1 Traceback (most recent call AssertionError: radius must >>> circleradius = 6 """ return self__radius

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@radiussetter def radius(self, radius): assert radius > 0, "radius must be nonzero and non-negative" self__radius = radius

We use an assert to ensure a nonzero and non-negative radius and store the radius s value in the private attribute self__radius Notice that the getter and setter (and deleter if we needed one) all have the same name it is the decorators that distinguish them, and the decorators rename them appropriately so that no name con icts occur The decorator for the setter may look strange at rst sight Every property that is created has a getter, setter, and deleter attribute, so once the radius property is created using @property, the radiusgetter, radiussetter, and radiusdeleter attributes become available The radiusgetter is set to the

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The secure networking functionality described in this chapter is new in MIDP 20 The secure networking operations ensure the integrity and privacy of communications using standard secure network protocols Interface HttpsConnection provides the application developer with the familiar popular network programming protocol HTTPS that integrates easily with the existing network servers and services The SecureConnection interface extends the flexibility of sockets to the network infrastructure built on the reliable and robust SSL and TLS protocols Both secure connection types make it easy to write robust and secure network applications [ Team LiB ]

been created (An implementation of a real sorted dictionary that automatically maintains its keys in sorted order is presented in 6; 276)

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Once we have collections of data items, it is natural to want to iterate over all the items they contain In this section s rst subsection we will introduce some of Python s iterators and the operators and functions that involve iterators Another common requirement is to copy a collection There are some subtleties involved here because of Python s use of object references (for the sake of ef ciency), so in this section s second subsection, we will examine how to copy collections and get the behavior we want

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[ Team LiB ]

An iterable data type is one that can return each of its items one at a time Any object that has an __iter__() method, or any sequence (ie, an object that has a __getitem__() method taking integer arguments starting from 0) is an iterable and can provide an iterator An iterator is an object that provides a __next__() method which returns each successive item in turn, and raises a StopIteration exception when there are no more items Table 34 lists the operators and functions that can be used with iterables The order in which items are returned depends on the underlying iterable In the case of lists and tuples, items are normally returned in sequential order starting from the rst item (index position 0), but some iterators return the items in an arbitrary order for example, dictionary and set iterators The built-in iter() function has two quite different behaviors When given a collection data type or a sequence it returns an iterator for the object it is passed or raises a TypeError if the object cannot be iterated This use arises when creating custom collection data types, but is rarely needed in other contexts The second iter() behavior occurs when the function is passed a callable (a function or method), and a sentinel value In this case the function passed in is called once at each iteration, returning the function s return value each time, or raising a StopIteration exception if the return value equals the sentinel When we use a for item in iterable loop, Python in effect calls iter(iterable) to get an iterator This iterator s __next__() method is then called at each loop iteration to get the next item, and when the StopIteration exception is raised, it is caught and the loop is terminated Another way to get an iterator s next item is to call the built-in next() function Here are two equivalent pieces of code (multiplying the values in a list), one using a for in loop and the other using an explicit iterator:.

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__iter__()

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