Thus, the bar argument is initialized to its default (i.e., an empty list) only when foo() is first defined, but then calls to foo() (i.e., without a bar argument specified) will continue to use the same list to which bar was originally initialized.įYI, a common workaround for this is as follows: > def foo(bar=None):Ĭommon Mistake #2: Using class variables incorrectlyĬonsider the following example: > class A(object): The more advanced Python programming answer is that the default value for a function argument is only evaluated once, at the time that the function is defined. Huh? Why did it keep appending the default value of "baz" to an existing list each time foo() was called, rather than creating a new list each time? In the above code, for example, one might expect that calling foo() repeatedly (i.e., without specifying a bar argument) would always return 'baz', since the assumption would be that each time foo() is called (without a bar argument specified) bar is set to (i.e., a new empty list).īut let’s look at what actually happens when you do this: > foo() bar.append("baz") # but this line could be problematic, as we'll see.Ī common mistake is to think that the optional argument will be set to the specified default expression each time the function is called without supplying a value for the optional argument. For example, consider this Python function definition: > def foo(bar=): # bar is optional and defaults to if not specified While this is a great feature of the language, it can lead to some confusion when the default value is mutable. Python allows you to specify that a function argument is optional by providing a default value for it. (Note: This article is intended for a more advanced audience than Common Mistakes of Python Programmers, which is geared more toward those who are newer to the language.) Common Mistake #1: Misusing expressions as defaults for function arguments With that in mind, this article presents a “top 10” list of somewhat subtle, harder-to-catch mistakes that can bite even some more advanced Python developers in the rear. Python’s simple, easy-to-learn syntax can mislead Python developers – especially those who are newer to the language – into missing some of its subtleties and underestimating the power of the diverse Python language. Python supports modules and packages, thereby encouraging program modularity and code reuse. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components or services.
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics.
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