Cython’s pure Python mode lets you keep a module in familiar .py syntax, add Cython type information selectively, and compile it into a native extension. That can speed up bottlenecks without rewriting the whole module in C—but compiling alone is no guarantee of a major improvement. Profile first, then focus type declarations on the work that actually limits performance.
What Cython’s pure Python mode does
Pure Python mode is a way to write Cython-compatible code using Python-style source files and syntax. You can add Cython-specific declarations and decorators, use annotations where appropriate, or put extra declarations in an augmenting .pxd file. Cython then translates the code and builds a native extension. In supported cases, the original .py source can still run with the Python interpreter.
The approach is useful when you want to make targeted changes to an existing Python program rather than maintain a separate implementation in Cython’s traditional syntax. It is not a promise that every Python feature can be compiled unchanged: some Cython-only constructs, including cython.cimports, are not ordinary Python and cannot be executed directly by the interpreter. Cython recommends using a recent Cython 3 release for pure Python mode. See the official Pure Python Mode documentation.
What speedup should you expect?
Cython’s documentation characterizes compiling pure Python scripts as typically producing about a 20–50% speed gain. That is a general estimate from the Cython project, not a guarantee for a particular program, machine, or workload. If most of a program’s time is spent in libraries that already run compiled code, or in work that remains highly dynamic, compiling the surrounding Python may do little.
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Adding C-level types to the hot path can make a much larger difference when it removes repeated Python-level operations. In Cython’s quickstart, compiling an untyped integration example yields a documented 35% speedup; after adding types to that example, the documented result is four times the speed of its pure Python version. Those figures belong to that tutorial example, not a prediction for other applications. The quickstart’s practical recommendation is to profile and type only where there is a reason. Read Cython’s static-typing quickstart for its example and explanation.
Find the code worth changing
1. Profile the real workload
Use a representative run of your application to identify which functions consume the most time. Optimizing a function that is rarely called will not materially improve the whole program. Start with Cython’s profiling guidance and choose a specific bottleneck before editing declarations.
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2. Inspect Cython’s annotation output
Generate an annotation report with cython -a your_module.py (or the equivalent annotation option in your build). The report helps show where generated code still interacts with Python’s C API. White lines indicate code translated to pure C; yellow lines indicate Python interaction, with darker shading representing more interaction. This is a diagnostic guide, not a performance measurement: benchmark the compiled program to determine whether a change helped.
3. Add types to measured hot operations
In numerical code, repeated arithmetic and loop variables are common places where Python’s dynamic behavior can add overhead. When the annotation report and profile point to such work, add a fitting Cython type—for example, cython.int for a C integer or cython.double for a C double. Keep declarations limited to code where they can reduce meaningful overhead; unnecessary types can make code less flexible, harder to maintain, or even slower.
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4. Rebuild, benchmark, and check correctness
After changing declarations, rebuild the extension and benchmark the same representative workload under comparable conditions. Check outputs and edge cases as well as runtime. In particular, confirm that values stay within the range of any chosen C type and that conversions behave as expected.
Choose annotations carefully: Python types are not C types
A common mistake is assuming that a Python annotation such as int necessarily asks Cython for a C integer. In Cython 3, ordinary int annotation refers to Python’s integer type; use cython.int when C integer behavior is intended. The distinction matters because Python integers can grow to arbitrary precision, while C integer types have a fixed range.
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C integer arithmetic does not check for overflow. Cython documents that converting an out-of-range Python value to a C type raises OverflowError; arithmetic performed as C arithmetic should not be assumed to preserve Python’s arbitrary-precision behavior. Review the quickstart’s static typing details before applying C types to values whose range or semantics are uncertain.
Compilation changes how you build and distribute the module
Although the source may remain a .py file, Cython compilation produces generated C or C++ source and a platform-specific native extension, such as a .so or .pyd. Installing or distributing that extension therefore requires a compatible build workflow and artifacts for the relevant platform and Python environment. Pure Python mode preserves a Python-like source style; it does not make the compiled result a pure-Python package. See Cython’s source-file and compilation guide.
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A practical decision rule
- Try compilation first if you want to see whether a modest gain is available without changing much code, but measure rather than assume an improvement.
- Add selective Cython types when profiling identifies a hot function and annotation output shows costly Python interaction in work that can use C-level types.
- Keep Python semantics where they matter if arbitrary-precision integers, dynamic values, or interpreter execution are important; type only the operations where fixed C behavior is suitable.
- Plan for native builds if you need to distribute the compiled module across platforms or environments.
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