Python is the most popular and widely used computer programming language, particularly in the fields of Data Science and Machine Learning. What's more, Python is a cross-platform language that runs on a variety of operating systems, such as Windows, macOS and Linux, making it an ideal choice for developers working on different environments. Find out all you need to know about the Python language in our Python Crash Course: origins, usage, tools, advantages, disadvantages, training.
Python Crash Course: What is Python?
It is commonly used for backend development of web or mobile applications, as well as for the development of software and PC applications. Python also allows you to write system scripts to create instructions for a computer system.
Python Crash Course: Who invented the Python language?
The Python language was created in 1989 by Guido van Rossum, a Dutch computer scientist. Originally, he developed Python to improve the programming language ABC, which was primarily used for education.
Guido van Rossum’s goal was to create a programming language that was easy to read, write, and maintain while also being powerful and flexible. He named his new programming language “Python” as a tribute to the British comedy troupe Monty Python, which he admired.
Python has experienced exponential growth in popularity since its creation and has become the programming language of choice for many businesses and projects, particularly in the fields of data science, artificial intelligence, and web development. Today, Python is an open-source programming language supported by a large community of developers worldwide.
What is the purpose of the Python language?
One of the primary use cases for Python is scripting and automation. This language can replace shell scripts and also automate interactions with web browsers or application graphical interfaces. Here are some other key uses of the Python language:
It is also used for system provisioning and configuration through tools like Ansible or Salt. However, these are far from its only applications.
Another use is application programming. You can create all kinds of applications using this language. Even though it doesn’t allow you to generate standard binaries from a script, third-party packages like cx Freeze and PyInstaller compensate for this weakness.
Furthermore, 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 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 built-in libraries and third-party web frameworks allow you to develop 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.
You can write applications that manipulate their own functions, which are much more extensible than with other languages. You can also use it to control code generation systems like LLVM to create code in other languages.
Who uses Python?
Python is increasingly used in the field of programming for two main reasons. First, as mentioned earlier, 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 closely resembles everyday English, making it easy for beginners to understand and start learning.
Despite its simplicity, Python can be used for highly complex projects. It is extensively used in fields such as AI and Machine Learning.
As a result, Python is used by a wide variety of profiles. Examples include novice programmers, web and mobile app developers, software engineers, as well as Data Scientists and other data professionals.
Python Crash Course: What are the advantages of Python?
The Python language has many strengths. Due to its minimalism, it requires very little time to start using it and a Python Crash Course may be sufficient. Its syntax is designed to be readable and straightforward. Beginners can learn to master it easily. As a result, developers spend more time trying to solve problems rather than getting bogged down in language complexities.
Despite its ease of use, this language can be used for both scripting and automation and for the development of high-quality software. It is extremely versatile.
Furthermore, each update of the Python language adds new and very useful features, allowing it to remain aligned with modern development practices. As a result, it does not become obsolete.
What are the disadvantages of Python?
Despite its many strengths, Python is not suitable for all tasks. It is a “high-level” language and is not well-suited for low-level system programming.
It is also not ideal for situations requiring cross-platform independent binaries. Developing an independent application for Windows, macOS, and Linux would not be straightforward in Python.
Finally, Python is not the best choice when speed is an absolute priority for the application. It’s better to turn to C and C++ or another similarly low-level language.
Python treats every function and module as objects. This simplifies high-level code writing but affects speed.
The dynamism and flexibility of objects make optimization difficult, even after compilation. Thus, Python is significantly slower than C/C++ or Java. However, you can speed up mathematical and statistical operations using libraries like NumPy and Pandas.
Moreover, Python uses significant whitespace, which some consider an advantage and others a disadvantage. Some people dislike Python because of this, but in reality, it makes the syntax more readable.
Python Crash Course: The differences between Python 2 and Python 3
Two different versions of Python are available. The older version, Python 2, continues to be widely used even though it no longer receives official updates since 2020.
The current version, Python 3, brings new and important features. This includes new syntax features, better concurrency controls, and a more efficient interpreter.
The adoption of Python 3 was slowed down by the lack of compatibility with third-party libraries. Many of them were only supported by Python 2, making it difficult to transition. This issue has been resolved in recent years, and Python 3 is now the best choice for new projects.
Which are the Python libraries?
Python libraries are one of the main reasons for its success. It’s a vast ecosystem of software developed by third parties. This collection has grown and expanded over the decades.
Several standard libraries are provided, offering modules tailored to the most common programming tasks: networking, asynchronous operations, threading, file access, and more.
Some modules also allow for high-level programming tasks required for modern applications. This can include reading and writing structured file formats like JSON and XML, handling compressed files, or working with web protocols and data formats.
The default Python distribution also offers a cross-platform graphical interface library with Tkinter and a built-in copy of the SQLite 3 database.
In addition to these built-in libraries, thousands of third-party libraries are available through the Python Package Index (PyPI). It’s these libraries that provide Python with its versatility.
What Python libraries do you need to know?
There are many Python libraries that can be useful depending on the domain of application and specific needs. However, here are some of the main Python libraries that are recommended to learn in a Python Crash Course:
- NumPy: a library for mathematical and numerical operations on arrays and matrices.
- Pandas: a library for data manipulation and analysis in tabular form.
- Matplotlib: a library for creating graphs and data visualizations.
- Scikit-learn: a library for machine learning and data exploration.
- TensorFlow: a library for deep learning and building neural network models.
- PyTorch: a library for deep learning and creating neural network models.
Beautiful Soup: a library for parsing HTML and XML data.
- Requests: a library for sending HTTP requests.
- Flask and Django: frameworks for web service development. These libraries are very popular and widely used in the fields of data science, machine learning, data analysis, and web development.
How do I learn Python? What are the best courses?
To learn how to use Python effectively, consider enrolling in DataScientest’s training programs. This programming language is at the core 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. Indeed, Python is the most widely used language for data science.
All our training programs adopt an innovative and original approach called Blended Learning, combining in-person and remote learning. You can complete them in just a few weeks in an intensive BootCamp (Python Crash Course) mode or through Continuous Training.
Our programs are designed by professionals and meet the needs of companies and enable learners to quickly enter the job market.