What is docstring
In software engineering, coding actually plays a very small part, mostly other things, such as writing documents. Documents are tools for communication.
In Python, it is highly recommended to write documents in the code. The code is the document, which is more convenient, easy to maintain, intuitive and consistent.
After the code is written, the documentation is also out. In fact, Markdown has similar ideas. After the text is written, the typesetting is also completed.
Look at the definition of docstring in PEP 0257:
A docstring is a string literal that occurs as the first statement in
a module, function, class, or method definition. Such a docstring
becomes the __doc__ special attribute of that object.
To put it simply, the first statement that appears in a module, function, class, or method is the docstring. It will automatically become the attribute __doc__.
def foo(): """ This is function foo"""
Can be accessed through foo.__doc__ to get 'This is function foo'.
Various docstring styles:
Epytext
This was once a popular style similar to javadoc.
""" This is a javadoc style. @param param1: this is a first param @param param2: this is a second param @return: this is a description of what is returned @raise keyError: raises an exception """
reST
This is a popular style now, reST style, the royal format of Sphinx. I personally also like to use this style, which is more compact.
""" This is a reST style. :param param1: this is a first param :param param2: this is a second param :returns: this is a description of what is returned :raises keyError: raises an exception """
Google Style
""" This is a groups style docs. Parameters: param1 - this is the first param param2 - this is a second param Returns: This is a description of what is returned Raises: KeyError - raises an exception """
Numpydoc (Numpy style)
""" My numpydoc description of a kind of very exhautive numpydoc format docstring. Parameters ---------- first : array_like the 1st param name `first` second : the 2nd param third : {'value', 'other'}, optional the 3rd param, by default 'value' Returns ------- string a value in a string Raises ------ KeyError when a key error OtherError when an other error """
docstring tool third-party library pyment
is used to create and convert docstring.
The method of use is to use pyment to generate a patch and then apply the patch.
$ pyment test.py #生成patch $ patch -p1 < test.py.patch #打patch
Details: https://github.com/dadadel/pyment
Use sphinx's autodoc to automatically produce api documents from docstring, no need Write it again by hand
I have already written the docstring in the code. The content of writing the API document is similar to this. Do I need to copy it one by one to rst? Of course not. sphinx has autodoc function.
First edit the conf.py file,
1. There must be the 'sphinx.ext.autodoc' extensions
2. Make sure that the module that needs to automatically generate documentation can be imported, that is, it is in the path. For example, you may need sys.path.insert(0, os.path.abspath('../..'))
Then, write the rst file,
xxx_api module --------------------- .. automodule:: xxx_api :members: :undoc-members: :show-inheritance:
Type the make html command to generate relevant documents from the docstring without having to write rst by hand.
See the effect:
For more articles related to the multi-line comment document writing style in Python, please pay attention to the PHP Chinese website!

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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