😎Top Python libraries for optimizing work with data

✅Pony ORM is a convenient and powerful library for working with object-relational databases, which allows you to write SQL queries using Python syntax. It automatically converts Python code into SQL queries, which simplifies interaction with databases, making it more intuitive and concise. Pony ORM supports major DBMSs such as PostgreSQL, MySQL, SQLite and others, providing flexibility and convenience when creating queries and working with data models.

✅Pypika is a library for creating SQL queries programmatically in Python, which allows you to avoid errors in hand-writing SQL code and protects against SQL injections. It is especially useful for building dynamic and parameterized queries, making it an ideal tool for database applications. Pypika allows you to build queries with a high degree of detail and complexity, while maintaining the readability and security of your code.

✅EdgeDB is a modern database and client library for Python that simplifies managing data schemas and writing queries. It offers a more intuitive and convenient approach compared to traditional SQL databases, providing advanced capabilities for working with data. Key features of EdgeDB include automatic schema generation, working with relational data without the need to write complex SQL queries, as well as support for type safety and a more expressive syntax for manipulating data.

✅Tortoise ORM is a modern asynchronous ORM (Object-Relational Mapping) designed for working with databases in asynchronous Python applications. It supports various relational databases such as PostgreSQL, MySQL, SQLite, and is written with an emphasis on simplicity and ease of use. Tortoise ORM allows you to build complex SQL queries using Python code, automatically synchronizing data models with the database. Support for asynchrony makes it especially useful in high-load or web applications where it is important to efficiently manage resources and database queries.

✅Polars is a high-performance data processing and analysis library in Python and Rust, focused on working with large volumes of data. Thanks to multithreading and an optimized architecture, Polars provides significantly higher execution speeds compared to traditional tools such as Pandas. The library supports a wide range of operations on tabular data (dataframes), offering an intuitive interface for filtering, aggregating and transforming data. It is ideal for tasks that require high performance, especially when working with large data sets.