15 Python Libraries for Data Science

If you've read our introduction to Python, you already know that it's one of the most widely used programming languages today, celebrated for its efficiency and code readability. As a programming language for data science, Python represents a compromise between R, which is heavily focused on data analysis and visualization, and Java, which forms the backbone of many large-scale applications. This flexibility means that Python can act as a single tool that brings together your entire workflow. Python is often the choice for developers who need to apply statistical techniques or data analysis in their work, or for data scientists whose tasks need to be integrated with web apps or production environments. Its combination of machine learning libraries and flexibility makes Python uniquely well-suited to developing sophisticated models and prediction engines that plug directly into production systems.