It also allows system provisioning or configuration<\/b> through tools such as Ansible or Salt. However, this is far from being its only applications.<\/p>
Another use is application programming<\/b>. It is possible to create all kinds of applications using this language. Although it does not allow generating standard binaries from a script, third-party packages such as cx freeze and PyInstaller compensate for this weakness.<\/p>
In addition, Python is the most used language for Data Science and machine learning<\/b>. The vast majority of libraries used for these two data analysis disciplines have Python interfaces. This explains its popularity as a high-level command interface for machine learning libraries and other numerical algorithms.<\/p>
This language is also used for creating web services<\/b> and RESTful APIs<\/b>. Its various native libraries and third-party web frameworks allow programming data-driven websites with just a few lines of code. Another use case is metaprogramming<\/b> and code generation. Every element of this language is an object, including modules and libraries.<\/p>
This makes Python a very efficient code generator. It is possible to write applications<\/b> manipulating their own functions, much more extensible than with other languages. It is also possible to use it to direct code generation systems<\/b> such as LLVM to create code in other languages.<\/p>https:\/\/youtu.be\/J0Aq44Pze-w\t\t\n\t\t\tWho is using Python ?<\/h3>
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Python is increasingly used in the field of programming, for two reasons. Firstly, as previously mentioned, it is one of the most versatile and general-purpose languages.<\/p>
Furthermore, despite its versatility, Python remains one of the easiest programming<\/b> languages<\/b> to learn. This is because its syntax is similar to English. This allows beginners to understand it easily and therefore start learning it very easily<\/b>.<\/p>
Despite its simplicity, Python can be used for the most complex projects. For example, it is used in the field of AI and machine learning.<\/p>
Therefore, this language is used by a wide variety of profiles. Examples include beginner programmers, web and mobile application developers, software engineers, and data scientists and other data professionals.<\/p>\t\t\n\t\t\t
What are the advantages of Python ?<\/h3>\t\t\n\t\t
Python has many strengths. Due to its minimalism, it takes very little time to start using it. Its syntax is designed to be readable and straightforward<\/b>. Beginners can easily learn to master it. As a result, developers spend more time trying to solve problems than dealing with language complexities.<\/p>
Another advantage is Python’s popularity<\/b>. Widely used, this language is supported by most OSs, and there are a large number of compatible libraries and service APIs.<\/p>
Despite its ease of use, this language can be used for both scripting and automation as well as for the development of professional-quality software. It is therefore extremely versatile<\/b>.<\/p>
Furthermore, each update to Python adds very useful new features that keep it aligned with modern development practices. As a result, it does not become obsolete.<\/p>https:\/\/www.youtube.com\/watch?v=T5pRlIbr6gg\t\t\n\t\t\t
Weaknesses of Python<\/h3>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t
Despite its many strengths, Python is not suitable for all tasks<\/b>. It is a “high-level” language. Therefore, it is not suitable for system-level programming.<\/p>
It is also not ideal for situations requiring cross-platform independent binaries<\/b>. An independent application for Windows, macOS, and Linux will not be easy to code in Python.<\/p>
Finally, it is best to avoid Python in situations where speed is an absolute priority <\/b>for the application. It is better to turn to C and C++ or other languages of the same caliber. Each function and module are considered objects by Python. This simplifies the writing of high-level code, but reduces speed.<\/p>
The dynamism and malleability of objects make optimization difficult, even after compilation. As a result, Python is significantly slower<\/b> than C\/C++ or Java. However, it is possible to accelerate mathematical and statistical operations using libraries such as NumPy<\/b> and Pandas<\/b>.<\/p>
In addition, Python uses a lot of whitespace. This is sometimes considered an advantage, but also a disadvantage. Some reject this language because of this point, but it actually makes the syntax<\/b> more readable.<\/p>\t\t\n\t\t\tDifferences between Python 2 and Python 3<\/h3>\t\t\n\t\t
Two different versions of Python are available. The older version, Python 2, is still widely used even though it has not received official updates since 2020.<\/p>
The current version, Python 3, brings important and practical new features.<\/b> These include new syntax features, better concurrency controls, and a more efficient interpreter.<\/p>
The adoption of Python 3 has been slowed by the lack of compatibility with third-party libraries. Many of them are only supported by Python 2. It has therefore been difficult to make the transition. This problem has been resolved in recent years, and Python 3 is now the best choice for new projects<\/b>.<\/p>https:\/\/www.youtube.com\/watch?v=oVp1vrfL_w4\t\t\n\t\t\tPython libraries<\/h3>\t\t\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t
Python libraries are one of the main reasons for its success. This is a vast ecosystem of third-party software<\/b>. This collection has grown and expanded over the decades. Several standard libraries are offered, offering modules adapted to the most common programming tasks: networking, asynchronous operation, threading, access to files, etc.<\/p>
Some modules also allow you to manage high-level programming tasks<\/b> needed for modern applications. This can include reading and writing structured file formats such as JSON and XML, manipulating compressed files, or working with protocols and web data formats.<\/p>
The default Python distribution also offers a cross-platform GUI library<\/b> with Tkinter, and an integrated copy<\/b> of the SQLite 3 database. In addition to these native libraries, thousands of third-party libraries are available through the Python Package Index<\/b> (PyPI). It is they who offer this language all its versatility.<\/p>
One example is the BeautifulSoup<\/b> library, which provides an all-in-one tool <\/b>for HTML scraping. On the other hand, \u201cRequests\u201d makes it easy to work with HTTP requests.<\/p>
With frameworks like Flask and Django, it is possible to quickly develop web services<\/b>. Many cloud services can be managed via the Python object model with Apache Libcloud.<\/p>
With NumPy, Pandas and Matplotlib, mathematical and statistical operations can be accelerated. They also facilitate the creation of data visualizations.<\/p>\t\t\n\t\t\t
How can I learn Python? What are the best training programs?<\/h3>\t\t\n\t\t
To learn how to use Python, you can consider Liora’s training programs<\/a>. This programming language is at the heart of our various programs: Data Scientist, Data Engineer, Data Analyst…<\/p> Through these different courses, you will not only learn Python, but also all the skills required to work in the field of data science<\/a> and pursue a career in Big Data. In fact, Python is the most widely used language for data science.<\/p> All of our training programs adopt an innovative and original blended learning<\/b> approach, combining in-person and online learning. They can be completed in a few weeks in an intensive bootcamp<\/b> mode or through continuing education<\/b>.<\/p> Designed by professionals, our programs meet the needs of businesses and allow learners to quickly enter the job market. They also allow you to obtain a diploma certified by the University of Sorbonne<\/b>.<\/p>\t\t\n\t\t\t\n