JPO : Webinar d'information sur nos formations → RDV mardi à 17h30.

Python : Focus on the most popular programming language

python

Python is the most popular and widely used programming language, particularly in the fields of data science and machine learning. Learn everything you need to know about it: definition, how it works, use cases, advantages, training...

What is Python used for ?

One of the main use cases of Python is scripting and automation. This language can, for example, replace shell scripts, but also automate interactions with web browsers or the graphical interfaces of applications.

It also allows system provisioning or configuration through tools such as Ansible or Salt. However, this is far from being its only applications.

Another use is application programming. 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.

In addition, Python is the most used language for Data Science and machine learning. 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.

This language is also used for creating web services and RESTful APIs. 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 and code generation. Every element of this language is an object, including modules and libraries.

This makes Python a very efficient code generator. It is possible to write applications manipulating their own functions, much more extensible than with other languages. It is also possible to use it to direct code generation systems such as LLVM to create code in other languages.

Who is using Python ?

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.

Furthermore, despite its versatility, Python remains one of the easiest programming languages 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.

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.

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.

What are the advantages of Python ?

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. Beginners can easily learn to master it. As a result, developers spend more time trying to solve problems than dealing with language complexities.

Another advantage is Python’s popularity. Widely used, this language is supported by most OSs, and there are a large number of compatible libraries and service APIs.

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.

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.

Weaknesses of Python

Despite its many strengths, Python is not suitable for all tasks. It is a “high-level” language. Therefore, it is not suitable for system-level programming.

It is also not ideal for situations requiring cross-platform independent binaries. An independent application for Windows, macOS, and Linux will not be easy to code in Python.

JUMPSTART YOUR CAREER
IN A DATA SCIENCE

Are you interested in a career change into Big Data, but don’t know where to start? Then you should take a look at our Data Science training course

JUMPSTART YOUR CAREER
IN A DATA SCIENCE

Are you interested in a career change into Big Data, but don’t know where to start? 

Then you should take a look at our Data Science training course

Finally, it is best to avoid Python in situations where speed is an absolute priority 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.

The dynamism and malleability of objects make optimization difficult, even after compilation. As a result, Python is significantly slower than C/C++ or Java. However, it is possible to accelerate mathematical and statistical operations using libraries such as NumPy and Pandas.

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 more readable.

Differences between Python 2 and Python 3

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.

The current version, Python 3, brings important and practical new features. These include new syntax features, better concurrency controls, and a more efficient interpreter.

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.

Python libraries

Python libraries are one of the main reasons for its success. This is a vast ecosystem of third-party software. 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.

Some modules also allow you to manage high-level programming tasks 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.

The default Python distribution also offers a cross-platform GUI library with Tkinter, and an integrated copy of the SQLite 3 database. In addition to these native libraries, thousands of third-party libraries are available through the Python Package Index (PyPI). It is they who offer this language all its versatility.

One example is the BeautifulSoup library, which provides an all-in-one tool for HTML scraping. On the other hand, “Requests” makes it easy to work with HTTP requests.

With frameworks like Flask and Django, it is possible to quickly develop web services. Many cloud services can be managed via the Python object model with Apache Libcloud.

With NumPy, Pandas and Matplotlib, mathematical and statistical operations can be accelerated. They also facilitate the creation of data visualizations.

How can I learn Python? What are the best training programs?

To learn how to use Python, you can consider DataScientest’s training programs. This programming language is at the heart of our various programs: Data Scientist, Data Engineer, Data Analyst…

Through these different courses, you will not only learn Python, but also all the skills required to work in the field of data science and pursue a career in Big Data. In fact, Python is the most widely used language for data science.

All of our training programs adopt an innovative and original blended learning approach, combining in-person and online learning. They can be completed in a few weeks in an intensive bootcamp mode or through continuing education.

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.

Related articles

You are not available?

Leave us your e-mail, so that we can send you your new articles when they are published!

Would you like to receive our data newsletter 💌 weekly?