Python is the most popular and widely used computer programming language, especially in the field of Data Science and Machine Learning. Discover everything you need to know: definition, operation, use cases, benefits, training...
Python is a generalist computer programming language. Unlike HTML, CSS or JavaScript, its use is not limited to web development.
It can be used for any type of programming and software development.
It is used in particular for the back-end development of web or mobile applications, and for the development of software and applications for PC.
It also allows you to write system scripts to create instructions for a computer system.
In addition, Python is the most popular computer language for big data processing, performing mathematical calculations or machine learning. In general, this is the language of choice for Data Science.
What is Python for?
One of the main use cases of Python is script and automation. For example, this language can replace shell scripts, but it can also automate interactions with web browsers or application graphical interfaces.
It also allows system supply or configuration through tools like Ansible or Salt. However, it is far from being its only applications.
Another use is application programming.
You can create all kinds of applications using that language. Even if it does not allow to generate standard binaries from a script, third-party packages like cx Freeze and PyInstaller compensate for this weakness.
In addition, Python is the most widely 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 why it is its popularity as a high-level control interface for Machine Learning libraries and other digital algorithms.
This language is also used to create RESTful web services and APIs. Its various native libraries and third-party web frameworks allow you to program data-drivent websites with only a few lines of code.
Another use case is metaprogramming and code generation. Each element of this language is an object, including modules and libraries.
This is what makes Python a very effective code generator. It is possible to write applications manipulating their own functions, much more extensible than with other languages. It can also be used to run code generation systems such as LLVM to create code in other languages.
Who uses Python?
Python is increasingly used in programming, for two main reasons. First, as mentioned earlier, it is one of the most versatile and generalist languages.
Moreover, despite its versatility, Python remains one of the easiest programming languages to learn. For good reason, its syntax is similar to the current English.
This is what allows a beginner to understand it and therefore to start learning it very easily.
Despite its simplicity, Python can be used for complex projects. It is used for example in the field of AI and Machine Learning.
Become a Data Scientist:
Therefore, this language is used by a wide variety of profiles. For example, we can mention beginner programmers, developers of web and mobile applications, software engineers, but also data scientists and other data professionals.
Become a Data Scientist
What are the advantages of Python?
The Python language has many strengths. Because of its minimalism, it takes very little time to start using it. Its syntax is designed to be readable and direct. Beginners can learn to master it easily. Thus, developers spend more time trying to solve problems than focusing on language complexities.
Another advantage is the popularity of Python. Widely used, this language is supported by most OS, and there are a large number of libraries and APIs of compatible services.
Despite its simplicity of use, this language can be used for scripting and automation as well as for the development of professional quality software. It is therefore extremely versatile.
In addition, each update of the Python language adds new useful features allowing it to remain aligned with modern development practices. In fact, it is not sinking into obsolescence.
Python’s weaknesses
Despite its many strengths, Python is not suitable for all tasks. It is a high-level language. It is therefore not adequate for system-level programming.
It is also not ideal for situations requiring independent cross-platform binaries. An independent application for Windows, macOS and Linux will not be easy to code in Python.
A CAREER IN DATA STRETCHES YOUR ARMS!
Are you interested in converting to big data, but don’t know where to start? Discover our Data Science trainings.
I discover the formations
Finally, it is best to avoid Python for situations where speed is a top priority for the application. It is better to turn to C and C++ or other similar language.
Each function and module are considered objects by Python. This simplifies the writing of high-level code, but reduces the speed.
The dynamism and malleability of objects make optimization difficult, even after compilation. Thus, 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 many white spaces. This is sometimes seen as an advantage, but also as a disadvantage. Some people reject this language because of this point, but it actually makes the syntax more readable.
Training in Python for Data Science
Differences between Python 2 and Python 3
Two different versions of Python are available. The old version, Python 2, continues to be massively used even though it has not received an official update since 2020.
The current version, Python 3, brings important and practical new features. These include new syntax features, better competition 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. So it is difficult to make the transition. This problem has been solved in recent years, and Python 3 is now the best choice for new projects.
Python libraries
Python libraries ( » libraries «) are one of the main reasons for its success. This is a vast ecosystem of software developed by third parties. This collection has grown and expanded over the years.
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 Matplotlbi, mathematical and statistical operations can be accelerated. They also facilitate the creation of data visualizations.
How to learn Python? What are the best trainings?
To learn how to use Python, you can turn to DataScientest trainings. This programming language is at the heart of our various programs: Data Scientist, Data Engineer, Data Analyst, etc.
the skills required:
Through these different courses, you will learn not only Python, but also all the skills required to work in the field of data science and practice one of the professions of Big Data. Indeed, Python is the most used language for Data Science.
All our trainings adopt an innovative and original Blended Learning approach, combining face-to-face with distance. It is possible to complete them in a few weeks in intensive BootCamp mode, or in Continuing Education.
Designed by professionals, our programs meet the needs of businesses and allow learners to enter the labour market very quickly. They also lead to a diploma certified by the University of the Sorbonne.
Learn how to use Python
You know all about the Python language. Discover other essential Data Science resources, such as the GitHub code hosting service or the Docker containerization platform.
keywords: machine learning, machine learning is, python machine learning,machine learning modeling, andrew ng machine learning ,
ai learning , aws machine learning, supervised learning ,unsupervised learning, ai ml, deep learning ai, tensorflow, data analytics, master's in data science, online master's data science, data analytics degrees, data science degrees, certified data scientist, master's in data analytics online , ms in data science, datascience berkeley ,uc berkeley data science, data science for managers, data science for beginners, certified data scientist, data science for all, big data analyst, r for data science, pandas, keras,tensorflowjs,hands on machine learning.
DOWNLOAD THIS EBOOK FREE PDF!
