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Python has a similar approach to document its in-built methods, however mimics the language's lack of fixation on scope and data types. [5] This documentation has the syntax of each method, along with a short description and an example of the typical use of the method or function.
[1] Many languages that apply this style attempt to minimize or eliminate side effects by describing what the program must accomplish in terms of the problem domain, rather than describing how to accomplish it as a sequence of the programming language primitives [2] (the how being left up to the language's implementation).
In computer science, divide and conquer is an algorithm design paradigm.A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly.
Introduced in Python 2.2 as an optional feature and finalized in version 2.3, generators are Python's mechanism for lazy evaluation of a function that would otherwise return a space-prohibitive or computationally intensive list. This is an example to lazily generate the prime numbers:
The Zen of Python is a collection of 19 "guiding principles" for writing computer programs that influence the design of the Python programming language. [1] Python code that aligns with these principles is often referred to as "Pythonic". [2] Software engineer Tim Peters wrote this set of principles and posted it on the Python mailing list in ...
Refactoring is usually motivated by noticing a code smell. [2] For example, the method at hand may be very long, or it may be a near duplicate of another nearby method. Once recognized, such problems can be addressed by refactoring the source code, or transforming it into a new form that behaves the same as before but that no longer "smells".
In the AFC, the two-time reigning champion Kansas City Chiefs have continued their quest to become the first team to win three Super Bowls in a row as the current No. 1 seed.
One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large risks overfitting the training data and poorly generalizing to new samples.