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The main content of this article is "example Analysis of Python feature problems". Interested friends may wish to have a look. The method introduced in this paper is simple, fast and practical. Next let the editor to take you to learn "Python feature problem example analysis" it!

If you take a closer look, one of the two parameters, one Python feature is unchanged for the lifetime of the request, and the other is the value used for filtering. So I finally used lambda to pass the parameters of a function in stages. The example is as follows: filter function:

# Normal statement-based flow control if: func1 () elif: func2 () else: func3 () # Equivalent "short circuit" _ expression (and func1 ()) Or (and func2 ()) or (func3 ()) # Example "short circuit" expression > > x = 3 > >

Python is a dynamic language and often claims to be the slowest of the dynamic languages. The recent advent of Ruby has only slightly changed that. Hehe, I heard that Ruby is more "dynamic" than Python. An alternative way to improve efficiency in Python is to avoid using dynamic features, such as excessive "." while ensuring readability. Operators are inefficient.

In addition, you can use some functional programming methods, such as many built-in functions, such as map (), filter (), zip (), etc., using lambda is also a good way. Since Python 1. 0, Python has had most of the FP characteristics listed above. But for most Python features, they are presented in a very mixed language.

Largely because of the OOP feature of the Python feature, you can use what you want and ignore the rest (until you need it later). With Python 2. 0, list connotations add some great "syntactic whitewash". Although no new capabilities are added to the list connotations, they make many of the old capabilities look much better.

The basic elements of FP in Python are the functions map (), reduce (), and filter (), and the operator lambda. In Python 1.x, the apply () function is also convenient for applying the list return value of one function directly to another function.

Python 2. 0 provides improved syntax for this purpose. It may be surprising, but a few of these functions (and basic operators) are enough to write almost any Python program; in particular, all flow control statements (if, elif, else, assert, try, except, finally, for, break, continue, while, def) can be handled in a functional style using only FP functions and operators.

While actually eliminating all flow control commands in your program may only be useful for joining "messy Python" competition (with code that looks very much like Lisp), it is worth understanding how FP uses functions and recursion to represent flow control.

At this point, I believe that you have a deeper understanding of the "Python feature example analysis", might as well come to the actual operation of it! Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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