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Python wrapper

As explained in Fast Python: High performance techniques for large datasets

A Python wrapper is a Python interface that exposes the operations of an underlying library, service, or native implementation.

A Python wrapper exposes an underlying implementation through Python objects and function calls. In Cython, generated code separates a Python-facing wrapper from the function implementation, allowing Python code to call native C logic. section 5.2.3 section 7.4.1

The wrapper preserves the Python-level interface: parameters and return values remain Python objects. It performs packing, unpacking, and conversions before invoking the implementation, so annotations can make the native implementation faster while making wrapper conversion more involved. section 5.2.3

Cython wrapper and typed implementation

A plain Cython function accepts a Python object and performs Python-level addition. An annotated version accepts a native C integer internally. Both remain callable through Python, but the annotated path converts the Python argument before executing native integer addition.

How Fast Python: High performance techniques for large datasets covers it

How deep it goes
Descends into generated signatures, object conversion, and separate wrapper and implementation functions.
Examples
Uses Cython add4 functions and PyArrow’s layered architecture as concrete wrapper examples.
What it assumes
Assumes familiarity with Python and introduces the distinction between Python objects and native C types.
What you can do after
Evaluate wrapper overhead and understand how Python arguments reach typed native implementations.
How it connects
Connects Python wrappers with Cython, C, native types, pandas, and Apache Arrow.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Fast Python: High performance techniques for large datasets. The section links point to the parts of the book it draws on.