What Are the Application Scenarios of Python

Python is a simple, easy-to-learn yet powerful programming language. It has high-level data structures and allows object-oriented programming in a simple and efficient way. This is thanks to Python’s design philosophy: it was Guido’s design that gave it elegant syntax, dynamic data types and a rich set of library components.

To understand Python, we must first be clear that Python is a programming language, one with a rich set of library components. This means that, in principle, it can theoretically do everything Matlab can do—at worst, you write it from scratch. This not only lowers the barrier to learning it, but also means Python can be applied in all kinds of fields, for example: web development, game development, web crawler development, DevOps development, data analysis, data mining, cloud computing, artificial intelligence and so on.

Python is known for its development efficiency, which means it strives to complete tasks with the shortest code. In fact, in the layers in between, Guido did a great deal of encapsulation and created efficient data structures, so using Python is standing on the shoulders of giants. What people often criticize Python for is its execution efficiency, but even so, Python still has the character of a glue language, because whatever people can think of, Guido naturally thought of too: it allows us to write the time-critical parts in more efficient languages such as C/C++, and finally glue them together with Python, which largely solves Python’s execution efficiency problem. In fact, in enterprise applications, as long as it is not an extremely demanding component that interacts directly with users—for example in most data-related tasks—Python’s running efficiency is already fully comparable to C/C++.

Some things about the mindset of programming will be shared with you on our fourth day. In today’s session, we will mainly explain the following 6 fields.

Web development

Django Flask: quickly build core enterprise business logic

Crawler development

Scrapy BS4: easily scrape massive amounts of data for specific needs

DevOps development

Django Ansible: flexibly manage important enterprise asset information

Data analysis and mining

numpy pandas matplotlib scipy sklearn Keras: fine-grained processing of large batches of complex matrix data, artificial neural networks

Cloud computing development

Open-source cloud computing framework, OpenStack

Artificial intelligence development

Deep learning, machine learning TensorFlow PyTorch