What do you know about Python libraries: pandas, NumPy, PySpark, SQLAlchemy, requests, PyYAML?

• pandas — a library for convenient work with tabular data, providing DataFrame and Series structures, allowing easy filtering, grouping, aggregating, and transforming data.

• NumPy — the foundation for scientific computing in Python, offering multi-dimensional arrays and numerous mathematical functions, often used together with pandas.

• PySpark — an interface for working with Apache Spark in Python, enabling processing of large distributed data, performing parallel computations, and building complex ETL processes.

• SQLAlchemy — an ORM library for working with databases, allowing writing queries in Python, abstracting from specific DBMS, supporting declarative model definitions.

• requests — a simple and convenient library for HTTP requests, enabling sending GET, POST, and other requests, working with headers, sessions, and cookies.

• PyYAML — a library for parsing and generating YAML files, often used for configurations.

All these libraries are widely used in analytics, data processing, and development, each solving its own task and often used together.