Python language is incredibly easy to use and learn for new beginners and newcomers. The python language is one of the most accessible programming languages available because it has simplified syntax and not complicated, which gives more emphasis on natural language. Due to its ease of learning and usage, python codes can be easily written and executed much faster than other programming languages. When Guido van Rossum was creating python in the s, he made sure to design it to be a general-purpose language.
One of the main reasons for the popularity of python would be its simplicity in syntax so that it could be easily read and understood even by amateur developers also.
One can also quickly experiment by changing the code base of python because it is an interpreted language which makes it even more popular among all kinds of developers. Python was created more than 30 years ago, which is a lot of time for any community of programming language to grow and mature adequately to support developers ranging from beginner to expert levels. There are plenty of documentation, guides and Video Tutorials for Python language are available that learner and developer of any skill level or ages can use and receive the support required to enhance their knowledge in python programming language.
Many students get introduced to computer science only through Python language, which is the same language used for in-depth research projects. The community always guides learners who learn data science. But python has no such kind of problems because it has been here for a very long time. The python developer community is one of the most incredibly active programming language communities. This means that if somebody has an issue with python language, they can get instant support from developers of all levels ranging from beginner to expert in the community.
Getting help on time plays a vital role in the development of the project, which otherwise might cause delays. Programming languages grows faster when a corporate sponsor backs it. Google adopted python language way back in and have used it for many applications and platforms since then.
Lots of Institutional effort and money have been devoted to the training and success of the python language by Google.
They have even created a dedicated portal only for python. Due to its corporate sponsorship and big supportive community of python, python has excellent libraries that you can use to select and save your time and effort on the initial cycle of development. There are also lots of cloud media services that offer cross-platform support through library-like tools, which can be extremely beneficial.
Libraries with specific focus are also available like nltk for natural language processing or scikit-learn for machine learning applications. There are many frameworks and libraries are available for python language, such as:. Ask any python developer, and they will wholeheartedly agree that the python language is efficient, reliable, and much faster than most modern languages.
Python can be used in nearly any kind of environment , and one will not face any kind of performance loss issue irrespective of the platform one is working. One more best thing about versatility of python language is that it can be used in many varieties of environments such as mobile applications, desktop applications, web development, hardware programming, and many more.
The versatility of python makes it more attractive to use due to its high number of applications. Cloud Computing, Machine Learning, and Big Data are some of the hottest trends in the computer science world right now, which helps lots of organizations to transform and improve their processes and workflows. Python language is the second most popular used tool after R language for data science and analytics. Lots of many data processing workloads in the organization are powered by python language only.
Most of the research and development takes place in python language due to its many applications, including ease of analyzing and organizing the usable data. Python is really simple, it's designed expressly to promote readability of code, and allow simple expression of concepts in very few lines. And yeah, for a well rounded education, you do need to get down in the internal bit fields of the CPU, learn about what exactly is happening in terms of the actual digital logic that makes the CPU work, how addition and multiplication works in hex and then how to make that happen in hardware, how memory management works, all that jazz.
Back in the day, the code that I wrote was the tightest of the tight, focused on doing more in a single CPU clock cycle than other people thought was possible. I did that in assembler, and C, and frequently had C urp up assembler so I could see what it was doing with my code which I would then revise until the output aligned with the instructions I knew would minimize cache misses and maximize pipeline performance.
So if my code is a little slower, I can run it on faster hardware, or I can break it up to run on more processors in parallel. This question originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world. This allows more flexible callable types.
The asynq callable in the asynq framework produces a special callable that can either be called directly producing a synchronous call or through the special.
The AsynqCallable special form is similar to Callable , but describes a callable with this extra. For example, this construct can be used to implement the asynq. Because of limitations in the runtime typing library, some generic aliases involving AsynqCallable will not work at runtime. Quoting the type annotation works around this. PEP added support for type guards, a mechanism to narrow the type of a variable.
However, it only supports narrowing the first argument to a function. Pyanalyze supports an extended version that combines with PEP 's Annotated type to support guards on any function parameter. HasAttrGuard is similar to ParameterTypeGuard and TypeGuard , but instead of narrowing a type, it indicates that an object has a particular attribute.
For example, consider this function:. In practice the main use of this type is to implement the type of hasattr itself. In pure Python hasattr could look like this:. As currently implemented, HasAttrGuard does not narrow types; instead it preserves the previous type of a variable and adds the additional attribute. ExternalType is a way to refer to a type that is not imported at runtime.
The type must be fully qualified. Our motivating use case is our database schema definition file: we would like to map each column to the enum it corresponds to, but those enums are defined in code that should not be imported by the schema definition.
Python is sufficiently dynamic that almost any check like the ones run by pyanalyze will inevitably have false positives: cases where the script sees an error, but the code in fact runs fine.
Attributes may be added at runtime in hard-to-detect ways, variables may be created by direct manipulation of the globals dictionary, and the mock module can change anything into anything. Although pyanalyze has a number of whitelists to deal with these false positives, it is usually better to write code in a way that doesn't require use of the whitelist: code that's easier for the script to understand is probably also easier for humans to understand.
Just as the tool inevitably has false positives, it equally inevitably cannot find all code that will throw a runtime error. It is generally impossible to statically determine what a program does or whether it runs successfully without actually running the program. Pyanalyze doesn't check program logic and it cannot always determine exactly what value a variable will have.
It is no substitute for unit tests. Pyanalyze has hundreds of unit tests that check its behavior. To run them, you can just run pytest in the project directory.
Skip to content. Star A static analysis tool for Python Apache Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats commits. Failed to load latest commit information. View code. Some use cases for this tool include: Catching bugs before they reach production. The script will catch accidental mistakes like writing " collections. Other categories of bugs it can find include variables that may be undefined at runtime, duplicate keys in dict literals, and missing await keywords.
Making refactoring easier. When you make a change like removing an object attribute or moving a class from one file to another, pyanalyze will often be able to flag code that you forgot to change.
Finding dead code. It has an option for finding Python objects functions and classes that are not used anywhere in the codebase. Checking type annotations. Type annotations are useful as documentation for readers of code, but only when they are actually correct. Although pyanalyze does not support the full Python type system see below for details , it can often detect incorrect type annotations. About A static analysis tool for Python Resources Readme.
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