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官网就有,何必问?Python Release Python 3.5.0a3
Python 3.5.0a3本回答至少在很多地方有错误 详情python 3.5 中 PEP0484 新加入的 Type Hints 的使用方法是什么? - 酿泉的回答Python 3.5.0a3 was released on March 30th, 2015.
Major new features of the 3.5 series, compared to 3.4Python 3.5 is still in development, and 3.5.0a1 is the second alpha release. Many new features are still being planned and written. Among the new major new features and changes in the 3.4 release series so far are
- PEP 461, adding support for "%-formatting" for bytes and bytearray objects
- PEP 465, a new operator (@) for matrix multiplication
- PEP 475, adding support for automatic retries of interrupted system calls
- PEP 471, os.scandir()
怒答,今天看到,昨天发布的,Python 3.5b1 有类型检查模块了!
以前有 mypy,现在终于是官方的了!def fuck(x: Fuck) -> Fuck 这种语法以前唯一的意义在能让 PyCharm 补全
Python Release Python 3.5.0b1
PEP 484, the typing module, a new standard for type annotations好爽,好爽,狂喜乱舞中!
这一刻,TypeScript Flow TypedRacket 灵魂附体!现在 Python 某种程度上能同时享受静态类型语言的安全和动态类型语言的灵活性了,某种程度。
正在看 PEP 0484 -- Type Hints 。
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">TypeVar</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Tuple</span>
<span class="n">T</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s">'T'</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">complex</span><span class="p">)</span>
<span class="n">Vector</span> <span class="o">=</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">T</span><span class="p">,</span> <span class="n">T</span><span class="p">]]</span>
<span class="k">def</span> <span class="nf">inproduct</span><span class="p">(</span><span class="n">v</span><span class="p">:</span> <span class="n">Vector</span><span class="p">)</span> <span class="o">-></span> <span class="n">T</span><span class="p">:</span>
<span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">v</span><span class="p">)</span>
Python 3.5将支持Async/Await异步编程

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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