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Docstrings can in turn be extracted from program files to generate documentation in other formats such as HTML or PDF. A program file can be made to contain the documentation, tests, as well as the code and the tests easily verified against the code. This allows code, tests, and documentation to evolve together.
The Decorator Pattern (or an implementation of this design pattern in Python - as the above example) should not be confused with Python Decorators, a language feature of Python. They are different things. Second to the Python Wiki: The Decorator Pattern is a pattern described in the Design Patterns Book.
With language-level support for delegation, this is done implicitly by having self in the delegate refer to the original (sending) object, not the delegate (receiving object). In the delegate pattern, this is instead accomplished by explicitly passing the original object to the delegate, as an argument to a method. [ 1 ] "
In object-oriented programming, the factory method pattern is a design pattern that uses factory methods to deal with the problem of creating objects without having to specify their exact classes.
On March 9, 2011, the Python Software Foundation Board awarded a grant of US$840 to the Read the Docs project for one year of hosting fees. [5] On November 13, 2017, the Linux Mint project announced that they were moving their documentation to Read the Docs. [6] In 2020, Read the Docs received a $200,000 grant from the Chan Zuckerberg ...
Decorator can refer to: A house painter and decorator; Interior design; Decorator pattern in object-oriented programming; Function decorators, in Python; The Decorator, a 1920 film starring Oliver Hardy
It is a common pattern in software testing to send values through test functions and check for correct output. In many cases, in order to thoroughly test functionalities, one needs to test multiple sets of input/output, and writing such cases separately would cause duplicate code as most of the actions would remain the same, only differing in input/output values.
Project Jupyter's name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python and R. Its name and logo are an homage to Galileo's discovery of the moons of Jupiter, as documented in notebooks attributed to Galileo. Jupyter is financially sponsored by NumFOCUS. [1]