What is Python?

Python is a versatile interpreted programming language*. It is characterized by a comparatively clear syntax, high modularity, a broad selection of standard libraries and an active community that provides numerous additional modules.

Python is therefore a very flexible platform for various use cases and is popular in areas such as web development (e.g. Django, Flask), automation, scientific data processing (e.g. NumPy, Pandas) and machine learning (e.g. scikit-learn, PyTorch).

The language is imperative and includes the usual elements for representing state and control flow, such as variables, standard data types including lists and mappings, if statements, loops and exception handling. Logic can be organized in standalone functions or in classes; object-oriented programming is supported but not mandatory.

The main difference compared to other widely used languages lies in its syntax, which is lean and therefore quite readable. For example, code blocks are not defined by brackets but solely by indentation. There are also simplified constructs for defining lists and mappings that improve readability (list and dictionary comprehensions). Variable data types may be defined explicitly but do not have to be; in any case, they are checked at runtime (apart from intelligent IDE support).

Python’s strength often becomes apparent in dynamic use cases where heterogeneous data from various sources must be integrated and processed. For example, by combining freely available modules, a social media channel could be scraped, posts analyzed for topic and sentiment, and the resulting data enriched with sales data from a relational database, visualized and provided in multiple data formats.

The downside of Python’s modularity and interpreted nature can be the individual performance of programs. A specifically optimized solution written in a compiled language may outperform a comparable Python solution in terms of execution speed. However, development effort must always be considered, as for many use cases this represents a far greater obstacle than slightly slower runtime performance.

*Python typically translates source code into bytecode at runtime, which is then executed by the Python runtime environment. Some modules are already available as bytecode, which speeds up execution.