SQL (Structured Query Language) and Python are two different tools with distinct purposes, and they excel in different areas. While both SQL and Python are capable of performing various data manipulation tasks, there are certain operations that SQL is specifically designed for and that may be more challenging or less efficient to achieve in Python. Here are a few examples:

1. Querying databases: SQL is specifically designed for querying and manipulating data stored in relational databases. It provides a standardized syntax for retrieving data using powerful querying capabilities, such as filtering, joining, aggregating, and sorting data across multiple tables. While Python can interact with databases using libraries like SQLAlchemy, SQL provides a more streamlined and optimized approach for database operations.
2. Optimized database operations: SQL databases are highly optimized for handling large datasets efficiently. They employ indexing, caching, and query optimization techniques to deliver fast query results even on massive datasets. Python, on the other hand, is a general-purpose programming language and may require additional coding and optimization techniques to achieve similar performance levels when working with large datasets.
3. Declarative programming: SQL is a declarative language, meaning you specify what data you want to retrieve or modify, and the database engine determines the most efficient way to execute the query. In Python, on the other hand, you often need to write imperative code, specifying how operations should be performed step by step. Declarative programming in SQL can lead to more concise and readable code.
4. Schema definition and data integrity: SQL enables you to define the structure of your data using tables, columns, constraints, and relationships. It ensures data integrity through features like primary keys, foreign keys, unique constraints, and check constraints. While Python can work with structured data, it requires additional effort to enforce such constraints and ensure data consistency.
5. Transaction management: SQL provides built-in mechanisms for managing transactions, which allow you to group multiple database operations into a single atomic unit. You can ensure that either all the operations within a transaction are executed successfully, or none of them are executed at all. Python does not have built-in transaction management features, although various libraries and frameworks can help achieve similar functionality.

It's worth noting that Python has a rich ecosystem of libraries and frameworks for data manipulation, analysis, and visualization, such as Pandas, NumPy, and matplotlib, which provide extensive capabilities for working with data. However, when it comes to tasks specifically related to querying and manipulating data in relational databases, SQL is often a more suitable and efficient choice.