Python Libraries

Python’s standard library is rich, but for data science, HTTP clients, and automation you often need third-party libraries. This section introduces the ones we cover: NumPy for numerical arrays and fast operations, Pandas for tables and data analysis, Matplotlib for plotting, and Requests for HTTP. We assume you know Python basics, data types, lists, and dictionaries.

NumPy is the foundation for numerical work: arrays, element-wise math, slicing, and linear algebra. Start with Getting Started and Arrays, then operations and indexing. Pandas builds on NumPy and gives you DataFrames (spreadsheet-like tables) and Series: Getting Started, DataFrames, reading data, filtering, GroupBy. For CSV in Pandas see the How-To parse CSV with Pandas. Matplotlib is for charts: Getting Started, line plots, bar charts. Requests is the standard way to call HTTP APIs: Getting Started, GET and POST, headers and auth; for step-by-step recipes see How to make an HTTP GET request.

We also link to How-To guides (e.g. SQLite, JSON) and Errors when a library raises a common exception (e.g. ModuleNotFoundError if a library is not installed). Use the sidebar to jump to a library or article.