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Here is how to instantiate it with and without an explicit x argument: > a = A(2) It also has a foo() method that returns the self.x attribute multiplied by 3: class A: Here is a simple class that has an _init_() constructor that takes an optional argument x (defaults to 5) and stores it in a self.x attribute. Python also supports an advanced meta-programming model, which we will not get into in this article. An _init_() function serves as a constructor that creates new instances. Python supports classes and has a very sophisticated object-oriented model including multiple inheritance, mixins, and dynamic overloading.
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Finally, we store the result in a variable called x and print it. The evaluation consists of taking the input and applying all the functions in the pipeline (in this case just the double function). Then a "double" function is added to the pipeline, and finally the cool Ω function terminates the pipeline and causes it to evaluate itself. The integers are fed into an empty pipeline designated by Pipeline(). The first element range(5) creates a list of integers. What's going on here? Let's break it down step by step. Live Exampleīefore diving into all the details, let's see a very simple pipeline in action: x = range(5) | Pipeline() | double | Ω I will take advantage of Python's extensibility and use the pipe character ("|") to construct the pipeline.
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It consists of a list of arbitrary functions that can be applied to a collection of objects and produce a list of results. The pipeline data structure is interesting because it is very flexible. In this tutorial you will implement a custom pipeline data structure that can perform arbitrary operations on its data. Python provides full-fledged support for implementing your own data structure using classes and custom operators.